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import streamlit as st
import numpy as np
from scipy.ndimage import gaussian_filter
import plotly.graph_objects as go
from time import sleep
class NeuralFieldExplorer:
def __init__(self, size=100, time_depth=50):
self.size = size
self.time_depth = time_depth
self.energy_flow_history = np.zeros((time_depth, size, size))
# Field parameters
self.u = np.zeros((size, size))
self.v = np.zeros((size, size))
self.phi = np.zeros((size, size))
# Initialize central disturbance
self.u[size//2, size//2] = 2.0
# Physics parameters
self.dt = 0.1
self.dx = 1.0
self.dy = 1.0
self.c = 1.0
self.alpha = 0.05
self.beta = 0.02
def update_fields(self):
laplacian = (
-4 * self.u +
np.roll(self.u, 1, axis=0) +
np.roll(self.u, -1, axis=0) +
np.roll(self.u, 1, axis=1) +
np.roll(self.u, -1, axis=1)
) / (self.dx * self.dy)
quantum_input = np.random.normal(0, 0.1, (self.size, self.size))
classical_input = np.zeros((self.size, self.size))
a = self.c**2 * laplacian - self.beta * self.v - self.alpha * (self.u**3) + quantum_input + classical_input
v_new = self.v + a * self.dt
u_new = self.u + v_new * self.dt
phi_new = self.phi + (v_new * self.dt)
self.u, self.v, self.phi = u_new, v_new, phi_new
def calculate_energy_flow(self):
grad_x = np.gradient(self.u, axis=0)
grad_y = np.gradient(self.u, axis=1)
energy_flow = np.sqrt(grad_x**2 + grad_y**2)
energy_flow = gaussian_filter(energy_flow, sigma=1)
return (energy_flow - energy_flow.min()) / (energy_flow.max() - energy_flow.min() + 1e-8)
def update_history(self, energy_flow):
self.energy_flow_history = np.roll(self.energy_flow_history, -1, axis=0)
self.energy_flow_history[-1] = energy_flow
def create_3d_visualization(self):
x, y = np.meshgrid(np.arange(self.size), np.arange(self.size))
# Create empty lists for our surface plots
surfaces = []
# Create a surface for each time slice
for i in range(0, self.time_depth, 2):
z = i * np.ones_like(x)
# Create surface with custom coloring
surfaces.append(
go.Surface(
x=x,
y=y,
z=z,
surfacecolor=self.energy_flow_history[i],
showscale=False,
opacity=0.3,
colorscale='Magma'
)
)
return surfaces
def main():
st.title("🧠 Neural Field Pattern Explorer")
st.write("Exploring the 3D structure of neural field patterns in real-time!")
# Initialize session state
if 'explorer' not in st.session_state:
st.session_state.explorer = NeuralFieldExplorer()
st.session_state.frame_count = 0
# Control panel
col1, col2, col3 = st.columns(3)
with col1:
running = st.checkbox('Run Simulation', value=True)
with col2:
speed = st.slider('Animation Speed', 1, 10, 5)
with col3:
transparency = st.slider('Layer Transparency', 0.1, 1.0, 0.3)
# Create placeholders for our visualizations
plot3d = st.empty()
plot2d = st.empty()
# Main simulation loop
while running:
# Update fields
st.session_state.explorer.update_fields()
energy_flow = st.session_state.explorer.calculate_energy_flow()
st.session_state.explorer.update_history(energy_flow)
# Create 3D visualization
surfaces = st.session_state.explorer.create_3d_visualization()
# Update 3D plot
fig3d = go.Figure(data=surfaces)
fig3d.update_layout(
title='3D Neural Field Patterns',
scene=dict(
xaxis_title='X',
yaxis_title='Y',
zaxis_title='Time',
camera=dict(
up=dict(x=0, y=0, z=1),
center=dict(x=0, y=0, z=0),
eye=dict(x=1.5, y=1.5, z=1.5)
)
),
width=800,
height=600
)
# Update 2D plot
fig2d = go.Figure(data=go.Heatmap(
z=energy_flow,
colorscale='Magma'
))
fig2d.update_layout(
title='Current Energy Flow',
width=400,
height=400
)
# Display plots
plot3d.plotly_chart(fig3d, use_container_width=True)
plot2d.plotly_chart(fig2d, use_container_width=True)
# Control animation speed
sleep(1.0 / speed)
st.session_state.frame_count += 1
# Break if checkbox is unchecked
if not running:
break
if __name__ == "__main__":
st.set_page_config(page_title="Neural Field Explorer", layout="wide")
main() |