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import random
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import streamlit as st
class Organelle:
def __init__(self, type):
self.type = type # e.g., "nucleus", "mitochondria", "chloroplast"
class Cell:
def __init__(self, x, y, cell_type="prokaryote"):
self.x = x
self.y = y
self.energy = 100
self.cell_type = cell_type
self.organelles = []
self.size = 1
self.color = "blue"
self.division_threshold = 150
if cell_type == "prokaryote":
self.color = "lightblue"
elif cell_type == "early_eukaryote":
self.organelles.append(Organelle("nucleus"))
self.color = "green"
self.size = 2
elif cell_type == "advanced_eukaryote":
self.organelles.extend([Organelle("nucleus"), Organelle("mitochondria")])
self.color = "red"
self.size = 3
elif cell_type == "plant_like":
self.organelles.extend([Organelle("nucleus"), Organelle("mitochondria"), Organelle("chloroplast")])
self.color = "darkgreen"
self.size = 4
def move(self, environment):
dx = random.uniform(-1, 1)
dy = random.uniform(-1, 1)
self.x = max(0, min(environment.width - 1, self.x + dx))
self.y = max(0, min(environment.height - 1, self.y + dy))
self.energy -= 0.5 * self.size
def feed(self, environment):
if "chloroplast" in [org.type for org in self.organelles]:
# Photosynthesis
self.energy += environment.light_level * 2
else:
# Consume environmental nutrients
self.energy += environment.grid[int(self.y)][int(self.x)] * 0.1
environment.grid[int(self.y)][int(self.x)] *= 0.9
def can_divide(self):
return self.energy > self.division_threshold
def divide(self):
if self.can_divide():
self.energy /= 2
return Cell(self.x, self.y, self.cell_type)
return None
def can_fuse(self, other):
return (self.cell_type == "prokaryote" and other.cell_type == "prokaryote" and
random.random() < 0.001) # 0.1% chance of fusion
def fuse(self, other):
new_cell = Cell(
(self.x + other.x) / 2,
(self.y + other.y) / 2,
"early_eukaryote"
)
new_cell.energy = self.energy + other.energy
return new_cell
class Environment:
def __init__(self, width, height):
self.width = width
self.height = height
self.grid = np.random.rand(height, width) * 10 # Nutrient distribution
self.light_level = 5 # Ambient light level
self.cells = []
self.time = 0
def add_cell(self, cell):
self.cells.append(cell)
def update(self):
self.time += 1
# Update environment
self.grid += np.random.rand(self.height, self.width) * 0.1
self.light_level = 5 + np.sin(self.time / 100) * 2 # Fluctuating light levels
new_cells = []
cells_to_remove = []
for cell in self.cells:
cell.move(self)
cell.feed(self)
if cell.energy <= 0:
cells_to_remove.append(cell)
elif cell.can_divide():
new_cell = cell.divide()
if new_cell:
new_cells.append(new_cell)
# Handle cell fusion
for i, cell1 in enumerate(self.cells):
for cell2 in self.cells[i+1:]:
if cell1.can_fuse(cell2):
new_cell = cell1.fuse(cell2)
new_cells.append(new_cell)
cells_to_remove.extend([cell1, cell2])
# Add new cells and remove dead/fused cells
self.cells.extend(new_cells)
self.cells = [cell for cell in self.cells if cell not in cells_to_remove]
# Introduce mutations
for cell in self.cells:
if random.random() < 0.0001: # 0.01% chance of mutation
if cell.cell_type == "early_eukaryote":
cell.cell_type = "advanced_eukaryote"
cell.organelles.append(Organelle("mitochondria"))
cell.color = "red"
cell.size = 3
elif cell.cell_type == "advanced_eukaryote" and random.random() < 0.5:
cell.cell_type = "plant_like"
cell.organelles.append(Organelle("chloroplast"))
cell.color = "darkgreen"
cell.size = 4
def visualize(self):
fig = make_subplots(rows=1, cols=2, subplot_titles=("Cell Distribution", "Population Over Time"))
def setup_figure(env):
fig = make_subplots(rows=1, cols=2, subplot_titles=("Cell Distribution", "Population Over Time"))
# Cell distribution
for cell_type, data in env.get_visualization_data()[0].items():
fig.add_trace(go.Scatter(
x=data["x"], y=data["y"], mode='markers',
marker=dict(color=data["color"], size=data["size"]),
name=cell_type
), row=1, col=1)
# Population over time
for cell_type, counts in env.population_history.items():
fig.add_trace(go.Scatter(y=counts, mode='lines', name=cell_type), row=1, col=2)
fig.update_xaxes(title_text="X", row=1, col=1)
fig.update_yaxes(title_text="Y", row=1, col=1)
fig.update_xaxes(title_text="Time", row=1, col=2)
fig.update_yaxes(title_text="Population", row=1, col=2)
fig.update_layout(height=600, width=1200, title_text="Cell Evolution Simulation")
return fig
# Streamlit app
st.title("Cell Evolution Simulation")
num_steps = st.slider("Number of simulation steps", 100, 1000, 500)
initial_cells = st.slider("Initial number of cells", 10, 100, 50)
update_interval = st.slider("Update interval (milliseconds)", 100, 1000, 200)
if st.button("Run Simulation"):
env = Environment(100, 100)
# Add initial cells
for _ in range(initial_cells):
cell = Cell(random.uniform(0, env.width), random.uniform(0, env.height))
env.add_cell(cell)
# Set up the figure
fig = setup_figure(env)
chart = st.plotly_chart(fig, use_container_width=True)
# Run simulation
for step in range(num_steps):
env.update()
# Update the figure data
with fig.batch_update():
cell_data, population_history = env.get_visualization_data()
for i, (cell_type, data) in enumerate(cell_data.items()):
fig.data[i].x = data["x"]
fig.data[i].y = data["y"]
fig.data[i].marker.size = data["size"]
for i, (cell_type, counts) in enumerate(population_history.items()):
fig.data[i+4].y = counts # +4 because we have 4 cell types in the first subplot
fig.layout.title.text = f"Cell Evolution Simulation (Time: {env.time})"
# Update the chart
chart.plotly_chart(fig, use_container_width=True)
time.sleep(update_interval / 1000) # Convert milliseconds to seconds
st.write("Simulation complete!") |