This is an experiment in which I did no python coding at all. I started with a prompt for Gemini AI and then Gemini and I have gone back and forth for a couple of days.
I am dreaming up a hands-on museum exhibit on whale echolocation. I want, at first, a one person hunting game. It shouldn't take more than 5-10 minutes for the visitor to 'find the prey item'. The visitor will have some kind of a clicker but I want to start with a mouse and screen cursor.
This is what I know about echolocation:
- Whales, such as orcas, have as much as 10 times the frequency resolution as humans. This suggests they can get much more from listening to a sound than we can.
- Bottlenose dolphins have demonstrated extraordinary skill at discriminating the physical material and size of targets presented to them.
- Some echolocators, e. g. bats, time ther clicks so that the echo they are interested in comes to them halfway through the interckick time interval. Assuming this for whales implies that a target that a whale is interested in is at a distance no more that the speed of sound times half the interckick interval. Separate clicks become a buzz as an attacker converges on its prey. Orca that are tagged with suction-cup hydrophones record the accelerating click rate I have described above and some of those recordings end in a satisfying 'crunch' as the orca bites this fish.
- Whale echolocation clicks are 'focussed' by their fat/oil filled forehead melon into a +- 10 degree cone directed ahead of the whale.
- Since whales don't have opposable thumbs and can't use their eyes for seeing in the dark of the depths of the ocean, much of their brain has evolved to make and process sounds.
- Whales can get the direction of returning echos similar to the way we can tell if a sound is from the right or left and above or below.
I want to create a python app that utilizes these 6 assumptions to create a 'foraging' experience for humans that uses our eyes, hands and ears to experience what whales experience moving, swimming in various directions and speeds while echolocating in their hunt for prey.
In the museum, I envision, at first, a single person with some kind of 'clicker' standing in front of a large LED screen. Later there may be up to 3 people and there could be some sort of collaboration scenario, (orcas are known to share prey with other orca) or some sort of competitive mode. That is all 'then'.
For now, a 'forager' will have a mouse to click and a cursor to guide around a PC screen. Let's start with a deep diving whale, say a sperm whale, that is hunting for giant squid. On the screen, the visitor sees the 'blank' mind of the sperm whale, the blackness of the aybiss. The whale is pointing toward the direction in 3D space that the cursor points to and the 'forager' clicks the mouse. A flash illuminates the screen (likely the whale can't hear much at the moment that it is clicking). Then appearing on the screen and quickly fading out are none to some points of light. These are locations from which the whale has heard an echo. The brightness of the echo depends on the back-scatter coefficient of the target and its range from the whale. In this world the 'target' is the swim bladder of a fish, the lungs of a porpoise, the bit of reflectivity from waving kelp, and the beak of a squid etc.
a. From a single click, a whale can likely tell the type of animal or thing that is returning the echo. The whale may do this by the way the target changes and transforms the frequency character of the outgoing click so that the returning echo carries with it information about the target that whales have learned to decode. We actually don't know if whales do this.
b. When a whale swims toward a target, and then emits another click, the intensity of the echo will be higher because the range is smaller. Echo Received Level, RL_echo Click Source Level, SL, RL_target, outgoing click at the target, backscatter alpha in dB, Range, Rin meters RL_target = SL - 10 * log10(R) 10 is a spreading factor for the outgoing echo. RL_echo = RL_target - alpha - 20*log10(R) In our simulation, the visitor needs to know generally about this distance-echo intensity relationship and after multiple clicks, the visitor will know a bit about how far away the target. In the simulation, this is given by the size of the prey echo on the screen. The whale's current swimming speed should be displayed on the screen of the whales mind as the whale surely knows a lot about how fast it is going. The range also should be displayed on the screen. At the start, we could keep the speed constant or make it some function of the distance. Slow regular speed when far away and then speeding up to 'catch ' the target.
c. For a sperm whale, for example, its target is a squid. So, if the reflection comes from some not-a-squid, the whale will know this. For the human 'forager' here, the spots on the screen after a click should be colored, say, white for not-a-squids and red for a squid.
d. As the human 'forager' continues to click on the red (squid) the whale should speed up toward that spot and the squid should become a bit larger form. We could make it look like a squid, as we are imagining and describing in a human visual way what is developing in the whales brain.
e. As not-a-squid targets pass the whale they need to move by. The whale is moving through 3 D space.
f. The squid should be able to make some moves to get away from the whale and the 'forager' should keep clicking on that moving target
g. If the target 'gets away', the 'forager' will have to look around (using the mouse) to try to find the target again. I don't want this to be too hard. Remember, finishing the game shouldn't take too long.
h. This comes to an end when the whale gets to the squid and catches it or the squid, using its superior maneuverability, disappears. Maybe catching the squid produces a loud 'crunch' of some type.
I want you to think hard about all that I have written here and begin to layout the design of this educational game. There will be a number of python classes and some kind of looping and mouse reading algorithm that advances the on screen display moment by moment depending upon the 'forager's actions.
Show me an outline of your recommendations.
We can work on actual python code later.
p.s. I don't yet have a name for this effort to help a human see and feel what a whale feels using echolocation in pursuit of prey.
This is a brilliantly conceived project. It bridges the gap between hard biology/physics and intuitive human experience. You are effectively asking to translate Umwelt (the self-centered world of an organism) from an acoustic 3D map into a visual/auditory human interface. Here is a design outline for your project. I have taken the liberty of suggesting a working title. Working Title: Deep Echo: The Leviathan’s Lens
Since humans are visual creatures and whales are acoustic, we must create a synesthetic interface. We are not simulating "what the ocean looks like"; we are simulating "the mental map inside the whale's brain."
• The Visuals (The "Mind's Eye"):
◦ The Abyss: The screen is strictly black (RGB:0,0,0).
◦ The "Flash": When the user clicks, a faint, translucent cone projects briefly from the center, fading instantly (simulating the outgoing pulse).
◦ The Return: Objects do not persist. They appear as points of light (Gaussian blobs) only when the echo returns, then fade to black. The user must "paint" the dark with sound to maintain a mental image of the environment.
• The Audio:
◦ Binaural Panning: If the target is to the left of the cursor, the echo sounds in the left ear/speaker.
◦ The "Buzz": As the target gets closer, the allowable click rate increases, creating a sonic crescendo.
II. The User Experience (The 5-Minute Arc)
1. Phase 1: The Descent (Tutorial)
◦ Screen is black. Text prompt: "You are a Sperm Whale. You see with sound. Click to search."
◦ User clicks. They hear the Click... (wait)... Echo.
◦ They see white static dots (biological noise: shrimp, currents). This teaches the basic mechanic.
2. Phase 2: The Search (Foraging)
◦ The user scans the mouse (the Melon).
◦ They encounter "distractors" (White dots).
▪ School of fish: A diffuse cloud of weak white dots.
▪ Submarine terrain: A solid wall of white if they look down.
◦ They must find the Red Signal (The Squid).
3. Phase 3: The Approach (Convergence)
◦ User centers on the Red Signal.
◦ Physics Trigger: As Range (R) decreases, the echo returns faster. The app allows the user to click again sooner.
◦ Visual Feedback: The red dot becomes brighter (less transmission loss) and larger (subtends a larger angle).
4. Phase 4: The Terminal Buzz (The Chase)
◦ The Squid (AI) detects the ping rate increase and attempts to flee (lateral movement).
◦ The user must track the moving red target aggressively.
◦ The click rate becomes a continuous "buzz" (like a chainsaw or creaking door).
5. Phase 5: Contact
◦ Range < 2 meters.
◦ Screen flashes. Audio: CRUNCH.
◦ Victory screen with stats: Time to catch, clicks used, depth achieved.
III. Python Architecture Recommendations
To build this, we need an Object-Oriented structure that separates the Simulation World (3D math) from the Presentation Layer (2D Screen).
1. The SonarPhysics Class
Handles the math of sound propagation (Your assumptions b, 3).
• Input: Source Level (SL), Distance (R), Target Strength (TS/Backscatter).
• Method calculate_latency(R): Returns time (t) for echo return based on speed of sound in water (c≈1500m/s).
• Method calculate_intensity(R):
◦ Applies your formula: RL=SL−40log10(R)+TS−2αR (Combined 2-way transmission loss).
◦ Returns a brightness value (0.0 to 1.0).
2. The Target Class
Represents objects in the deep.
• Attributes:
◦ position_3d: (x,y,z) coordinates relative to the whale.
◦ type: "Squid" (Prey) or "Flora/Fauna" (Distractor).
◦ backscatter_coeff: How reflective is it?
◦ velocity_vector: For the moving squid.
• Method ping(whale_vector): Returns True if the target falls within the whale's ±10∘ cone.
3. The Whale Class ( The Player)
• Attributes:
◦ heading: Vector determined by Mouse Position.
◦ speed: Variable. Speed=f(Click_Rate).
◦ cooldown: Determined by the distance to the last focused target (Assumption 3).
• Method emit_click(): Triggers the raycasting check against all Targets.
4. The DisplayEngine Class
• Function: Projects 3D world coordinates onto a 2D screen (Perspective Projection).
• Visuals:
◦ Instead of rendering 3D models, it renders Sprites (Dots).
◦ Size of Dot = Inverse of Distance.
◦ Alpha (Transparency) of Dot = Time since last echo (fades out over 1-2 seconds).
◦ Color = Derived from Target.type.
IV. The "Foraging" Algorithm (The Main Loop)
This is the logic flow that will run every frame (e.g., 60 times a second).
While Game_Is_Running:
1. READ INPUT:
- Get Mouse (x, y). Convert to 3D Heading Vector (yaw, pitch).
- Check for Mouse Click.
2. UPDATE WHALE:
- If Clicked and Cooldown == 0:
- Play Sound "Click"
- Calculate Distance to closest object in cone.
- Set Cooldown = (2 * Distance) / Speed_of_Sound
- Trigger "Flash" visual.
- Move Whale forward in 3D space (Speed based on recent click frequency).
3. UPDATE TARGETS:
- Move Squid (AI Logic: If Whale is close/buzzing, add random lateral velocity).
- Move "background" objects relative to Whale speed (creating flow).
- Remove objects that pass behind the whale.
4. HANDLE ECHOES (The "Seeing"):
- For every target in the "Cone of Sound":
- Calculate Delay based on distance.
- Schedule an "Echo Event" to appear on screen after Delay.
5. DRAW FRAME:
- Clear Screen to Black.
- For each scheduled Echo Event that has "arrived":
- Draw Dot at projected (x,y).
- Calculate Brightness (Sonar Equation).
- Color: Red if Squid, White otherwise.
- Fade dot out as time passes.
- Draw UI (Depth, Speed, Inter-click Interval).
6. CHECK WIN CONDITION:
- If Distance to Squid < 2m: TRIGGER END SEQUENCE.
V. Specific Features to Implement (Based on your notes)
• The "Squid Filter" (Assumption a/c): The Python code will have a simple dictionary lookup. When a raycast hits an object, it checks obj.type.
◦ if type == 'squid': return COLOR_RED
◦ else: return COLOR_WHITE
◦ Note: To make this more realistic/educational, the "Red" shouldn't be instant. Maybe it flickers white/red until the whale is within 50m, simulating that frequency discrimination gets better at higher intensities.
• The Speed Mechanic (Assumption b): We will tie the whale's velocity to the Click Rate.
◦ Whale Idle Speed: 1 m/s.
◦ Every time the user clicks, add momentary acceleration.
◦ Therefore, as the user enters the "Buzz" phase (rapid clicking because the target is close), the whale naturally accelerates to "Ramming Speed." This creates a satisfying gameplay loop where Listening = Moving.
• The "Cone" (Assumption 4): We will use a dot product calculation between the Whale's Looking Vector and the Vector to the Target. If the angle is >10∘, the target remains silent (invisible). This forces the user to "sweep" the mouse to find targets.
VI. Next Step
I have the logic ready to translate this into a Python prototype.
Would you like me to generate a basic Python script (using the pygame library) that sets up the "Black Screen," the "Mouse Cone," and a single stationary target that "pings" when you click on it?
This would give you a visual proof-of-concept for the "Fading Echo" mechanic immediately.
It incorporates the 6 whale behavior points I stated above and the physics of motion and clicks and echos.
It has two modes. One is ‘learning’ and the other is ‘foraging in darkness’ (Button at top right). In the learning mode there are two x’s. The large one is where the whale is pointing and the small one is where the prey is. The whale moves in the direction of the cursor with some inertia.
This model assumes several important things.
One is that a whale can tell a prey echo from the echo from some other object. Here the prey shows up red and non-prey items are white and the synthesized echos sound different for prey and non-prey.
Another is that the whale can tell what direction to turn when a prey is heard within the whale echolocation cone. For us humans, this is shown by the locations in the cone of the prey and non-prey items.
A third is that the faster the user clicks the faster the whale swims.
Take it on a test run and let me know what you think?
