added mltiple networks

app.py

@@ -10,10 +10,17 @@ Original file is located at
 # Commented out IPython magic to ensure Python compatibility.
 # %%capture
 # !pip install gradio
+# # !pip install gradio==3.50.2
+
+
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%capture
 # 
 # !pip install cmocean
 # !pip install mesa
-##
+# 
+# !pip install opinionated
 
 import random
 import pandas as pd
@@ -32,9 +39,14 @@ import cmocean
 import tqdm
 
 import scipy as sp
-from scipy.stats import beta
 
+# from compress_pickle import dump, load
+
+from scipy.stats import beta
 
+# # %%capture
+# !pip install git+https://github.com/MNoichl/opinionated.git#egg=opinionated
+# # import opinionated
 
 import opinionated
 import matplotlib.pyplot as plt
@@ -44,7 +56,6 @@ from opinionated.core import download_googlefont
 download_googlefont('Quicksand', add_to_cache=True)
 plt.rc('font', family='Quicksand')
 
-
 experiences = {
             'dissident_experiences': [1,0,0],
             'supporter_experiences': [1,1,1],
@@ -533,97 +544,94 @@ class PoliticalModel(Model):
 
 
 
-def run_simulation(n_agents=300, share_regime_supporters=0.4, threshold=0.5, social_learning_factor=1, simulation_steps=400, half_life=20):
-    # Helper functions like graph_from_coordinates, ensure_neighbors should be defined outside this function
-
-    # Complete graph
-    G = nx.complete_graph(n_agents)
+# def run_simulation(n_agents=300, share_regime_supporters=0.4, threshold=0.5, social_learning_factor=1, simulation_steps=400, half_life=20):
+#     # Helper functions like graph_from_coordinates, ensure_neighbors should be defined outside this function
 
-    # Networks dictionary
-    networks = {
-        "physical": {"network": G, "type": "physical", "positions": nx.circular_layout(G)}#kamada_kawai
-    }
+#     # Complete graph
+#     G = nx.complete_graph(n_agents)
 
-    # Intervention list
-    intervention_list = [    ]
+#     # Networks dictionary
+#     networks = {
+#         "physical": {"network": G, "type": "physical", "positions": nx.circular_layout(G)}#kamada_kawai
+#     }
 
-    # Initialize the model
-    model = PoliticalModel(n_agents, networks, share_regime_supporters, threshold,
-                           social_learning_factor, half_life=half_life, print_agents=False, print_frequency=50, agent_reporters=True, intervention_list=intervention_list)
+#     # Intervention list
+#     intervention_list = [    ]
 
-    # Run the model
-    for _ in tqdm.tqdm_notebook(range(simulation_steps)):  # Run for specified number of steps
-        model.step()
-    return model
+#     # Initialize the model
+#     model = PoliticalModel(n_agents, networks, share_regime_supporters, threshold,
+#                            social_learning_factor, half_life=half_life, print_agents=False, print_frequency=50, agent_reporters=True, intervention_list=intervention_list)
 
-# Example usage
+#     # Run the model
+#     for _ in tqdm.tqdm_notebook(range(simulation_steps)):  # Run for specified number of steps
+#         model.step()
+#     return model
 
-def run_and_plot_simulation(n_agents=300, share_regime_supporters=0.4, threshold=0.5, social_learning_factor=1, simulation_steps=40, half_life=20):
-    model =run_simulation(n_agents=n_agents, share_regime_supporters=share_regime_supporters, threshold=threshold, social_learning_factor=social_learning_factor, simulation_steps=simulation_steps, half_life=half_life)
-    # Get data and reset index
-    agent_df = model.datacollector.get_agent_vars_dataframe().reset_index()
+# # Example usage
 
-    # Pivot the dataframe
-    agent_df_pivot = agent_df.pivot(index='Step', columns='AgentID', values='Estimation')
+radius=.09
+physical_graph_points = np.random.rand(100, 2)
+physical_graph = graph_from_coordinates(physical_graph_points, radius)
+physical_graph = nx.convert_node_labels_to_integers(ensure_neighbors(physical_graph))
 
-    # Create the plot
-    fig, ax = plt.subplots(figsize=(12, 8))
-    for column in agent_df_pivot.columns:
-        plt.plot(agent_df_pivot.index, agent_df_pivot[column], color='gray', alpha=0.1)
+# unconnected nodes: link or drop?
+networks = {
+    "physical": {"network": physical_graph, "type": "physical", "positions": physical_graph_points, 'network_data_to_keep':{'radius':radius},'homophily':0. }}
 
-    # Compute and plot the mean estimation
-    mean_estimation = agent_df_pivot.mean(axis=1)
-    plt.plot(mean_estimation.index, mean_estimation, color='black', linewidth=2)
 
-    # Set the plot title and labels
-    plt.title('Agent Estimation Over Time', loc='right')
-    plt.xlabel('Time step')
-    plt.ylabel('Estimation')
-    return fig
+model = PoliticalModel(100, networks, .5, .5,.5, half_life=20, print_agents=False, print_frequency=50, agent_reporters=True, intervention_list=[])
 
 
-# run_and_plot_simulation(n_agents=300, share_regime_supporters=0.4, threshold=0.5, social_learning_factor=1, simulation_steps=40, half_life=20)
+for _ in tqdm.tqdm_notebook(range(40)):  # Run for specified number of steps
+    model.step()
 
-import gradio as gr
 import matplotlib.pyplot as plt
+import pandas as pd
+
+# Assuming 'model' is defined and has a datacollector with the necessary data
+agent_df = model.datacollector.get_agent_vars_dataframe().reset_index()
+
+# Pivot the dataframe for Estimation
+agent_df_pivot = agent_df.pivot(index='Step', columns='AgentID', values='Estimation')
+
+# Create the result plot
+run_plot, ax = plt.subplots(figsize=(12, 8))
+
+# Define colors for Dissident and Supporter
+colors = {1: '#d6a44b', 0: '#1b4968'}  # 1 for Dissident, 0 for Supporter
+labels = {1: 'Dissident', 0: 'Supporter'}
+legend_handles = []
+
+# Plot each agent's data
+for agent_id in agent_df_pivot.columns:
+    # Get the agent type (Dissident or Supporter)
+    agent_type = agent_df[agent_df['AgentID'] == agent_id]['Dissident'].iloc[0]
+
+    # Plot
+    line, = plt.plot(agent_df_pivot.index, agent_df_pivot[agent_id], color=colors[agent_type], alpha=0.1)
+
+
+# Compute and plot the mean estimation for each group
+for agent_type, color in colors.items():
+    mean_estimation = agent_df_pivot.loc[:, agent_df[agent_df['Dissident'] == agent_type]['AgentID']].mean(axis=1)
+    plt.plot(mean_estimation.index, mean_estimation, color=color, linewidth=2, label=f'{labels[agent_type]}')
+
+# Set the plot title and labels
+plt.title('Agent Estimation Over Time', loc='right')
+plt.xlabel('Time step')
+plt.ylabel('Estimation')
+
+# Add legend
+plt.legend(loc='lower right')
+
+
+plt.show()
 
+def run_and_plot_simulation(separate_agent_types=False,n_agents=300, share_regime_supporters=0.4, threshold=0.5, social_learning_factor=1, simulation_steps=40, half_life=20,
+                            phys_network_radius=.06, powerlaw_exponent=3,physical_network_type='physical_network_type_fully_connected',
+                            introduce_physical_homophily_true_false=False,physical_homophily=.5,
+                            introduce_social_media_homophily_true_false=False,social_media_homophily=5,social_media_network_type_random_geometric_radius=.07,social_media_network_type_powerlaw_exponent=3,
+                            social_media_network_type='Powerlaw',use_social_media_network=False):
 
-# Gradio interface
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-  with gr.Column():
-    gr.Markdown("""# Simulate Revolutions
-                    Agents are placed on a fully connected graph. Vary the parameters below, and click 'Run Simulation' to run.
-                """)
-    with gr.Row():
-      with gr.Column():
-
-
-          # Sliders for each parameter
-          n_agents_slider = gr.Slider(minimum=100, maximum=500, step=10, label="Number of Agents", value=150)
-          share_regime_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Share of Regime Supporters", value=0.4)
-          threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Threshold", value=0.5)
-          social_learning_slider = gr.Slider(minimum=0.0, maximum=2.0, step=0.1, label="Social Learning Factor", value=1.0)
-          steps_slider = gr.Slider(minimum=10, maximum=100, step=5, label="Simulation Steps", value=40)
-          half_life_slider = gr.Slider(minimum=5, maximum=50, step=5, label="Half-Life", value=20)
-
-      with gr.Column():
-          # Button to trigger the simulation
-          button = gr.Button("Run Simulation")
-          plot_output = gr.Plot(label="Simulation Result")
-
-    # Function to call when button is clicked
-    def run_simulation_and_plot(*args):
-        fig = run_and_plot_simulation(*args)
-        return fig
-
-    # Setting up the button click event
-    button.click(
-        run_simulation_and_plot,
-        inputs=[n_agents_slider, share_regime_slider, threshold_slider, social_learning_slider, steps_slider, half_life_slider],
-        outputs=[plot_output]
-    )
-
-# Launch the interface
-if __name__ == "__main__":
-    demo.launch(debug=True)
+    print(physical_network_type)