Merge branch 'release-v0.2'

Updated model performing closer to the desired outcomes.

Author: anthonyylee

src/model/lifNetwork.py

@@ -0,0 +1,98 @@
+## Testing script
+
+import src.model.copied_from_ipynb_discussion_with_jesse as lif
+from src.plotting import plotStructure as lifplot
+import matplotlib.pyplot as plt
+import matplotlib
+import numpy as np
+import os
+from tqdm import tqdm
+from datetime import datetime
+import numpy.typing as npt
+
+
+
+
+################################################################################
+### PLOTTING FUNCTIONS, DON'T TOUCH!!!
+################################################################################
+
+def plot_helper(ax:plt.Axes, y:list, label:str,
+                n_sim:int, sim_duration:float, n_neurons:int, proba_conn:float,):
+  """Plot the plot."""
+  ax.plot(y, label=label, marker='+', linestyle='-')
+  ax.set_title(f"<w(t)>\n\
+                  | n_iterations: {n_sim} | sim_duration: {sim_duration} ms |\n\
+                  | n_neurons: {n_neurons} | proba_of_connection: {proba_conn} |")
+  ax.set_xlabel(f"n-th iteration | each iteration = {sim_duration} ms")
+  ax.set_ylabel("mean network weight")
+  ax.legend()
+
+  # ax.vlines(x=iteration*sim_duration, ymin=0, ymax=1, 
+  #           label="Simulation Iteration",
+  #           color="red", alpha=0.3)
+  # ax.set_ylim(bottom=0, top=1)
+  # ax.set_xlim(left=0, right=n_sim*sim_duration)
+  # plt.legend()
+
+  return ax
+
+def save_helper(fig:matplotlib.figure.Figure, iteration:int, export_directory:str) -> None: 
+  """Save the plot in current working directory."""
+  # Export plot
+  os.makedirs(export_directory, exist_ok=True)
+  fig.savefig(f"{export_directory}/iteration_{iteration}.png", 
+              facecolor="white")
+  return
+
+def plot_network_mean_weight_over_time(n_neurons:int=300,
+                                       n_sim:int=10000,
+                                       sim_duration:float=100,
+                                       proba_conn:float=0.08,
+                                       mean_w:list=[0.1, 0.2, 0.3, 0.5],
+                                       export_iteration_skip:int=10,
+                                       plot_dim:tuple=(25, 10)):
+  """Plot the mean network weight over time."""
+
+  # Set variables
+  export_directory = "export_"+datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+  holder_mean_network_w = {}
+  holder_LIF = {}
+  
+  # Instantiate a model for each mean_w item
+  for each_mean_w in mean_w:
+    name = f"mean_w={each_mean_w}"
+    holder_LIF[name] = lif.LIF_Network(n_neurons=n_neurons)
+    holder_LIF[name].p_conn = proba_conn
+    holder_LIF[name].mean_w = each_mean_w
+    holder_LIF[name].random_conn()
+    # lifplot.plot_structure(holder_LIF[name])
+    holder_mean_network_w[name] = []
+
+  # Run each of the n_sim
+  for iteration in tqdm(np.arange(n_sim), 
+                        desc=f"Simulation progress - total of {n_sim} simulations"):
+    # Simulate for each model
+    for nn_name, nn in holder_LIF.items():
+      nn.simulate(timesteps=sim_duration)
+      mean_network_w = np.mean(nn.network_W[nn.network_conn.astype(bool)])
+      holder_mean_network_w[nn_name].append(mean_network_w)
+
+    # Plot every `export_iteration_skip`-th simulation iteration
+    if (iteration % export_iteration_skip == 0):
+      fig, ax = plt.subplots(figsize=plot_dim)
+
+      # Plot for each of the mean_w
+      for nn_name, mean_network_w in holder_mean_network_w.items():
+        plot_helper(ax=ax, y=mean_network_w, label=nn_name,
+                    n_sim=n_sim, sim_duration=sim_duration, n_neurons=n_neurons, 
+                    proba_conn=proba_conn)
+      
+      # Save figure
+      save_helper(fig=fig, iteration=iteration, export_directory=export_directory)
+      
+      # Display inline (JupyterNotebook)
+      # display(fig)
+            
+if __name__ == "__main__":
+   plot_network_mean_weight_over_time()

src/modelTestingScripts/28_TestingJesseNotebookModel.py

@@ -0,0 +1,96 @@
+## Testing script
+
+from src.model import lifNetwork as lif
+from src.plotting import plotStructure as lifplot
+import matplotlib.pyplot as plt
+import matplotlib
+import numpy as np
+import os
+from tqdm import tqdm
+from datetime import datetime
+import numpy.typing as npt
+
+
+
+
+################################################################################
+### PLOTTING FUNCTIONS, DON'T TOUCH!!!
+################################################################################
+
+def plot_helper(ax:plt.Axes, y:list, label:str,
+                n_sim:int, sim_duration:float, n_neurons:int, proba_conn:float,):
+  """Plot the plot."""
+  ax.plot(y, label=label, marker='+', linestyle='-')
+  ax.set_title(f"<w(t)>\n\
+                  | n_iterations: {n_sim} | sim_duration: {sim_duration} ms |\n\
+                  | n_neurons: {n_neurons} | proba_of_connection: {proba_conn} |")
+  ax.set_xlabel(f"n-th iteration | each iteration = {sim_duration} ms")
+  ax.set_ylabel("mean network weight")
+  ax.legend()
+
+  # ax.vlines(x=iteration*sim_duration, ymin=0, ymax=1, 
+  #           label="Simulation Iteration",
+  #           color="red", alpha=0.3)
+  # ax.set_ylim(bottom=0, top=1)
+  # ax.set_xlim(left=0, right=n_sim*sim_duration)
+  # plt.legend()
+
+  return ax
+
+def save_helper(fig:matplotlib.figure.Figure, iteration:int, export_directory:str) -> None: 
+  """Save the plot in current working directory."""
+  # Export plot
+  os.makedirs(export_directory, exist_ok=True)
+  fig.savefig(f"{export_directory}/iteration_{iteration}.png", 
+              facecolor="white")
+  return
+
+def plot_network_mean_weight_over_time(n_neurons:int=300,
+                                       n_sim:int=10000,
+                                       sim_duration:float=100,
+                                       proba_conn:float=0.08,
+                                       mean_w:list=[0.1, 0.2, 0.3, 0.5],
+                                       export_iteration_skip:int=10,
+                                       plot_dim:tuple=(25, 10)):
+  """Plot the mean network weight over time."""
+
+  # Set variables
+  export_directory = "export_"+datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+  holder_mean_network_w = {}
+  holder_LIF = {}
+  
+  # Instantiate a model for each mean_w item
+  for each_mean_w in mean_w:
+    name = f"mean_w={each_mean_w}"
+    holder_LIF[name] = lif.LIF_Network(n_neurons=n_neurons)
+    holder_LIF[name].random_conn(proba_conn=proba_conn, mean_w=each_mean_w)
+    # lifplot.plot_structure(holder_LIF[name])
+    holder_mean_network_w[name] = []
+
+  # Run each of the n_sim
+  for iteration in tqdm(np.arange(n_sim), 
+                        desc=f"Simulation progress - total of {n_sim} simulations"):
+    # Simulate for each model
+    for nn_name, nn in holder_LIF.items():
+      nn.simulate(sim_duration=sim_duration)
+      mean_network_w = nn.calc_nn_mean_w()
+      holder_mean_network_w[nn_name].append(mean_network_w)
+
+    # Plot every `export_iteration_skip`-th simulation iteration
+    if (iteration % export_iteration_skip == 0):
+      fig, ax = plt.subplots(figsize=plot_dim)
+
+      # Plot for each of the mean_w
+      for nn_name, mean_network_w in holder_mean_network_w.items():
+        plot_helper(ax=ax, y=mean_network_w, label=nn_name,
+                    n_sim=n_sim, sim_duration=sim_duration, n_neurons=n_neurons, 
+                    proba_conn=proba_conn)
+      
+      # Save figure
+      save_helper(fig=fig, iteration=iteration, export_directory=export_directory)
+      
+      # Display inline (JupyterNotebook)
+      # display(fig)
+            
+if __name__ == "__main__":
+   plot_network_mean_weight_over_time()

src/modelTestingScripts/29_TestingPostFixing.py

@@ -0,0 +1,97 @@
+## Testing script
+
+from src.model import lifNetwork as lif
+from src.plotting import plotStructure as lifplot
+import matplotlib.pyplot as plt
+import matplotlib
+import numpy as np
+import os
+from tqdm import tqdm
+from datetime import datetime
+import numpy.typing as npt
+
+
+
+
+################################################################################
+### PLOTTING FUNCTIONS, DON'T TOUCH!!!
+################################################################################
+
+def plot_helper(ax:plt.Axes, y:list, label:str,
+                n_sim:int, sim_duration:float, n_neurons:int, proba_conn:float,):
+  """Plot the plot."""
+  ax.plot(y, label=label, marker='+', linestyle='-')
+  ax.set_title(f"<w(t)>\n\
+                  | n_iterations: {n_sim} | sim_duration: {sim_duration} ms |\n\
+                  | n_neurons: {n_neurons} | proba_of_connection: {proba_conn} |")
+  ax.set_xlabel(f"n-th iteration | each iteration = {sim_duration} ms")
+  ax.set_ylabel("mean network weight")
+  ax.legend()
+
+  # ax.vlines(x=iteration*sim_duration, ymin=0, ymax=1, 
+  #           label="Simulation Iteration",
+  #           color="red", alpha=0.3)
+  # ax.set_ylim(bottom=0, top=1)
+  # ax.set_xlim(left=0, right=n_sim*sim_duration)
+  # plt.legend()
+
+  return ax
+
+def save_helper(fig:matplotlib.figure.Figure, iteration:int, export_directory:str) -> None: 
+  """Save the plot in current working directory."""
+  # Export plot
+  os.makedirs(export_directory, exist_ok=True)
+  plt.grid(True)
+  fig.savefig(f"{export_directory}/iteration_{iteration}.png", 
+              facecolor="white")
+  return
+
+def plot_network_mean_weight_over_time(n_neurons:int=500,
+                                       n_sim:int=50000,
+                                       sim_duration:float=1000,
+                                       proba_conn:float=0.08,
+                                       mean_w:list=[0.1, 0.2, 0.3, 0.5],
+                                       export_iteration_skip:int=10,
+                                       plot_dim:tuple=(25, 10)):
+  """Plot the mean network weight over time."""
+
+  # Set variables
+  export_directory = "export_"+datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+  holder_mean_network_w = {}
+  holder_LIF = {}
+  
+  # Instantiate a model for each mean_w item
+  for each_mean_w in mean_w:
+    name = f"mean_w={each_mean_w}"
+    holder_LIF[name] = lif.LIF_Network(n_neurons=n_neurons)
+    holder_LIF[name].random_conn(proba_conn=proba_conn, mean_w=each_mean_w)
+    # lifplot.plot_structure(holder_LIF[name])
+    holder_mean_network_w[name] = []
+
+  # Run each of the n_sim
+  for iteration in tqdm(np.arange(n_sim), 
+                        desc=f"Simulation progress - total of {n_sim} simulations"):
+    # Simulate for each model
+    for nn_name, nn in holder_LIF.items():
+      nn.simulate(sim_duration=sim_duration)
+      mean_network_w = nn.calc_nn_mean_w()
+      holder_mean_network_w[nn_name].append(mean_network_w)
+
+    # Plot every `export_iteration_skip`-th simulation iteration
+    if (iteration % export_iteration_skip == 0):
+      fig, ax = plt.subplots(figsize=plot_dim)
+
+      # Plot for each of the mean_w
+      for nn_name, mean_network_w in holder_mean_network_w.items():
+        plot_helper(ax=ax, y=mean_network_w, label=nn_name,
+                    n_sim=n_sim, sim_duration=sim_duration, n_neurons=n_neurons, 
+                    proba_conn=proba_conn)
+      
+      # Save figure
+      save_helper(fig=fig, iteration=iteration, export_directory=export_directory)
+      
+      # Display inline (JupyterNotebook)
+      # display(fig)
+            
+if __name__ == "__main__":
+   plot_network_mean_weight_over_time()

src/modelTestingScripts/30_PostFixing_PlotWithGrid.py

@@ -1,6 +1,6 @@
 ## Testing script
 
-from src.model import lifNetwork_tonyTEST as lif
+from src.model import lifNetwork_test_tony as lif
 from src.plotting import plotStructure as lifplot
 import matplotlib.pyplot as plt
 import numpy as np

src/modelTestingScripts/5_modelTest_tony_aliCapacitance_kappa8.py

@@ -1,6 +1,6 @@
 ## Testing script
 
-from src.model import lifNetwork_tonyTEST as lif
+from src.model import lifNetwork_test_tony as lif
 from src.plotting import plotStructure as lifplot
 import matplotlib.pyplot as plt
 import numpy as np

src/modelTestingScripts/6_modelTest_tony_aliCapacitance_kappa400.py

@@ -1,6 +1,6 @@
 ## Testing script
 
-from src.model import lifNetwork_tonyTEST as lif
+from src.model import lifNetwork_test_tony as lif
 from src.plotting import plotStructure as lifplot
 import matplotlib.pyplot as plt
 import numpy as np

src/modelTestingScripts/7_modelTest_tony_paperCapacitance_kappa8.py

@@ -1,6 +1,6 @@
 ## Testing script
 
-from src.model import lifNetwork_tonyTEST as lif
+from src.model import lifNetwork_test_tony as lif
 from src.plotting import plotStructure as lifplot
 import matplotlib.pyplot as plt
 import numpy as np