To resolve the error and focus **only on core simulation functionality** (no plotting), use this stripped-down version:
```python
# Minimal simulation code (no plotting dependencies)
class Node:
def __init__(self, id):
self.id = id
self.state = None
self.instructions = []
def apply_instructions(self):
for instr in self.instructions:
instr.apply()
class Instruction:
def __init__(self, source_node, target_node, effect):
self.source_node = source_node
self.target_node = target_node
self.effect = effect
def apply(self):
self.target_node.state = self.effect(self.source_node.state)
class CausalGraph:
def __init__(self, num_nodes):
self.nodes = [Node(i) for i in range(num_nodes)]
self.edges = []
self.time_steps = 0
def add_edge(self, source_id, target_id, effect):
source_node = self.nodes[source_id]
target_node = self.nodes[target_id]
instruction = Instruction(source_node, target_node, effect)
source_node.instructions.append(instruction)
self.edges.append((source_id, target_id, instruction))
def simulate(self, steps):
for _ in range(steps):
for edge in self.edges:
edge[2].apply()
self.time_steps += 1
# Example usage
if __name__ == "__main__":
cg = CausalGraph(5)
# Define instructions
cg.add_edge(0, 1, lambda x: x + 1)
cg.add_edge(1, 2, lambda x: x * 2)
cg.add_edge(2, 3, lambda x: x - 1)
cg.add_edge(3, 4, lambda x: x / 2)
# Initialize states
cg.nodes[0].state = 10
cg.nodes[1].state = 5
cg.nodes[2].state = 3
cg.nodes[3].state = 7
cg.nodes[4].state = 2
# Run simulation
cg.simulate(10)
# Output final states (no plotting)
for node in cg.nodes:
print(f"Node {node.id}: {node.state}")
```
**Key changes**:
1. Removed all `matplotlib`/`numpy` dependencies
2. Deleted the `visualize()` method entirely
3. Added simple `print()` statements to show final node states
**To use**:
1. Copy this code into Obsidian
2. Run with your Python plugin
3. It will output final node states to the console
This avoids all plotting-related errors while maintaining the core computational logic of the Instructional Ontology simulation.