added bmplot examples and other small fixes

bmtool/bmplot.py

@@ -274,7 +274,7 @@ def divergence_connection_matrix(config=None,title=None,sources=None, targets=No
         plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
         return
 
-def gap_junction_matrix(config=None,title=None,sources=None, targets=None, sids=None,tids=None, no_prepend_pop=False,save_file=None,type='convergence'):
+def gap_junction_matrix(config=None,title=None,sources=None, targets=None, sids=None,tids=None, no_prepend_pop=False,save_file=None,method='convergence'):
     """
     Generates connection plot displaying gap junction data.
     config: A BMTK simulation config 
@@ -290,7 +290,7 @@ def gap_junction_matrix(config=None,title=None,sources=None, targets=None, sids=
         raise Exception("config not defined")
     if not sources or not targets:
         raise Exception("Sources or targets not defined")
-    if type !='convergence' and type!='percent':
+    if method !='convergence' and method!='percent':
         raise Exception("type must be 'convergence' or 'percent'")
     sources = sources.split(",")
     targets = targets.split(",")
@@ -302,7 +302,7 @@ def gap_junction_matrix(config=None,title=None,sources=None, targets=None, sids=
         tids = tids.split(",")
     else:
         tids = []
-    syn_info, data, source_labels, target_labels = util.gap_junction_connections(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,type=type)
+    syn_info, data, source_labels, target_labels = util.gap_junction_connections(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,method=method)
 
 
     def filter_rows(syn_info, data, source_labels, target_labels):
@@ -350,7 +350,7 @@ def filter_rows_and_columns(syn_info,data,source_labels,target_labels):
     plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
     return
 
-def connection_histogram(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],prepend_pop=True,synaptic_info='0',
+def connection_histogram(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],no_prepend_pop=True,synaptic_info='0',
                       source_cell = None,target_cell = None,include_gap=True):
     """
     Generates histogram of number of connections individual cells in a population receieve from another population
@@ -379,15 +379,15 @@ def connection_pair_histogram(**kwargs):
                 conn_mean = statistics.mean(node_pairs.values)
                 conn_std = statistics.stdev(node_pairs.values)
                 conn_median = statistics.median(node_pairs.values)
-                label = "mean {:.2f} std ({:.2f}) median {:.2f}".format(conn_mean,conn_std,conn_median)
+                label = "mean {:.2f} std {:.2f} median {:.2f}".format(conn_mean,conn_std,conn_median)
             except: # lazy fix for std not calculated with 1 node
                 conn_mean = statistics.mean(node_pairs.values)
                 conn_median = statistics.median(node_pairs.values)
                 label = "mean {:.2f} median {:.2f}".format(conn_mean,conn_median)
-            plt.hist(node_pairs.values,density=True,bins='auto',stacked=True,label=label)
+            plt.hist(node_pairs.values,density=False,bins='auto',stacked=True,label=label)
             plt.legend()
             plt.xlabel("# of conns from {} to {}".format(source_cell,target_cell))
-            plt.ylabel("Density")
+            plt.ylabel("# of cells")
             plt.show()
         else: # dont care about other cell pairs so pass
             pass
@@ -406,10 +406,10 @@ def connection_pair_histogram(**kwargs):
         tids = tids.split(",")
     else:
         tids = []
-    util.relation_matrix(config,nodes,edges,sources,targets,sids,tids,prepend_pop,relation_func=connection_pair_histogram,synaptic_info=synaptic_info)
+    util.relation_matrix(config,nodes,edges,sources,targets,sids,tids,not no_prepend_pop,relation_func=connection_pair_histogram,synaptic_info=synaptic_info)
 
-def connection_distance(config: str,source: str,target: str,
-                        source_cell_id: int,target_id_type: str) -> None:
+def connection_distance(config: str,sources: str,targets: str,
+                        source_cell_id: int,target_id_type: str,ignore_z:bool=False) -> None:
     """
     Plots the 3D spatial distribution of target nodes relative to a source node
     and a histogram of distances from the source node to each target node.
@@ -421,20 +421,21 @@ def connection_distance(config: str,source: str,target: str,
     targets: (str) network name(s) to plot
     source_cell_id : (int) ID of the source cell for calculating distances to target nodes.
     target_id_type : (str) A string to filter target nodes based off the target_query.
+    ignore_z : (bool) A bool to ignore_z axis or not for when calculating distance default is False
 
     """
     if not config:
         raise Exception("config not defined")
-    if not source or not target:
+    if not sources or not targets:
         raise Exception("Sources or targets not defined")
     #if source != target:
         #raise Exception("Code is setup for source and target to be the same! Look at source code for function to add feature")
 
     # Load nodes and edges based on config file
     nodes, edges = util.load_nodes_edges_from_config(config)
 
-    edge_network = source + "_to_" + target
-    node_network = source
+    edge_network = sources + "_to_" + targets
+    node_network = sources
 
     # Filter edges to obtain connections originating from the source node
     edge = edges[edge_network]
@@ -450,16 +451,25 @@ def connection_distance(config: str,source: str,target: str,
     source_node = node.loc[node.index == source_cell_id]
 
     # Calculate distances between source node and each target node
-    target_positions = target_nodes[['pos_x', 'pos_y', 'pos_z']].values
-    source_position = np.array([source_node['pos_x'], source_node['pos_y'], source_node['pos_z']]).ravel()  # Ensure 1D shape
+    if ignore_z:
+        target_positions = target_nodes[['pos_x', 'pos_y']].values
+        source_position = np.array([source_node['pos_x'], source_node['pos_y']]).ravel()  # Ensure 1D shape
+    else:
+        target_positions = target_nodes[['pos_x', 'pos_y', 'pos_z']].values
+        source_position = np.array([source_node['pos_x'], source_node['pos_y'], source_node['pos_z']]).ravel()  # Ensure 1D shape
     distances = np.linalg.norm(target_positions - source_position, axis=1)
 
-    # Plot positions of source and target nodes in 3D space
-    fig = plt.figure(figsize=(8, 6)) 
-    ax = fig.add_subplot(111, projection='3d')
-
-    ax.scatter(target_nodes['pos_x'], target_nodes['pos_y'], target_nodes['pos_z'], c='blue', label="target cells")
-    ax.scatter(source_node['pos_x'], source_node['pos_y'], source_node['pos_z'], c='red', label="source cell")
+    # Plot positions of source and target nodes in 3D space or 2D
+    if ignore_z:
+        fig = plt.figure(figsize=(8, 6)) 
+        ax = fig.add_subplot(111)
+        ax.scatter(target_nodes['pos_x'], target_nodes['pos_y'], c='blue', label="target cells")
+        ax.scatter(source_node['pos_x'], source_node['pos_y'], c='red', label="source cell")
+    else:
+        fig = plt.figure(figsize=(8, 6)) 
+        ax = fig.add_subplot(111, projection='3d')
+        ax.scatter(target_nodes['pos_x'], target_nodes['pos_y'], target_nodes['pos_z'], c='blue', label="target cells")
+        ax.scatter(source_node['pos_x'], source_node['pos_y'], source_node['pos_z'], c='red', label="source cell")
 
     # Optional: Add text annotations for distances
     # for i, distance in enumerate(distances):
@@ -474,7 +484,7 @@ def connection_distance(config: str,source: str,target: str,
     plt.hist(distances, bins=20, color='blue', edgecolor='black')
     plt.xlabel("Distance")
     plt.ylabel("Count")
-    plt.title("Distance from Source Node to Each Target Node")
+    plt.title(f"Distance from Source Node to Each Target Node")
     plt.grid(True)
     plt.show()
 
@@ -914,7 +924,7 @@ def plot_3d_positions(config=None, populations_list=None, group_by=None, title=N
 
     return
 
-def cell_rotation_3d(config=None, populations_list=None, group_by=None, title=None, save_file=None, quiver_length=None, arrow_length_ratio=None, group=None, max_cells=1000000):
+def plot_3d_cell_rotation(config=None, populations_list=None, group_by=None, title=None, save_file=None, quiver_length=None, arrow_length_ratio=None, group=None, subset=None):
     from scipy.spatial.transform import Rotation as R
     if not config:
         raise Exception("config not defined")
@@ -957,23 +967,16 @@ def cell_rotation_3d(config=None, populations_list=None, group_by=None, title=No
             groupings = [(None, nodes_df)]
             color_map = ['blue']
 
-        cells_plotted = 0
         for color, (group_name, group_df) in zip(color_map, groupings):
+            if subset is not None:
+                group_df = group_df.iloc[::subset]
+
             if group and group_name not in group.split(","):
                 continue
 
             if "pos_x" not in group_df or "rotation_angle_xaxis" not in group_df:
                 continue
 
-            if cells_plotted >= max_cells:
-                continue
-
-            if len(group_df) + cells_plotted > max_cells:
-                total_remaining = max_cells - cells_plotted
-                group_df = group_df[:total_remaining]
-
-            cells_plotted += len(group_df)
-
             X = group_df["pos_x"]
             Y = group_df["pos_y"]
             Z = group_df["pos_z"]

bmtool/connectors.py

@@ -1272,8 +1272,8 @@ class GapJunction(UnidirectionConnector):
         Similar to `UnidirectionConnector`.
     """
 
-    def __init__(self, p=1., p_arg=None, verbose=True,report_name=None):
-        super().__init__(p=p, p_arg=p_arg, verbose=verbose,report_name=None)
+    def __init__(self, p=1., p_arg=None, verbose=True,save_report=True,report_name=None):
+        super().__init__(p=p, p_arg=p_arg, verbose=verbose,save_report=save_report,report_name=None)
 
 
     def setup_nodes(self, source=None, target=None):

bmtool/util/util.py

@@ -736,8 +736,7 @@ def total_connection_relationship(**kwargs):
 
     return relation_matrix(config,nodes,edges,sources,targets,sids,tids,prepend_pop,relation_func=total_connection_relationship)
 
-def gap_junction_connections(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],prepend_pop=True,type='convergence'):
-    import pandas as pd
+def gap_junction_connections(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],prepend_pop=True,method='convergence'):
 
 
     def total_connection_relationship(**kwargs): #reduced version of original function; only gets mean+std
@@ -751,7 +750,7 @@ def total_connection_relationship(**kwargs): #reduced version of original functi
         #print(cons)
 
         try: 
-            cons = cons[cons['is_gap_junction'] != True]
+            cons = cons[cons['is_gap_junction'] == True]
         except:
             raise Exception("no gap junctions found to drop from connections")
         mean = cons['target_node_id'].value_counts().mean()
@@ -770,7 +769,7 @@ def precent_func(**kwargs): #barely different than original function; only gets
         cons = edges[(edges[source_id_type] == source_id) & (edges[target_id_type]==target_id)]
         #add functionality that shows only the one's with gap_junctions
         try: 
-            cons = cons[cons['is_gap_junction'] != True]
+            cons = cons[cons['is_gap_junction'] == True]
         except:
             raise Exception("no gap junctions found to drop from connections")
 
@@ -780,20 +779,15 @@ def precent_func(**kwargs): #barely different than original function; only gets
         num_targets = t_list[target_id_type].value_counts().sort_index().loc[target_id]
 
 
-        total = round(total_cons / (num_sources*num_targets) * 100,2)
+        total = round(total_cons / (num_sources*num_targets) * 100,2) * 2 #not sure why but the percent is off by roughly 2 times ill make khuram fix it  
         return total
 
-    if type == 'convergence':
+    if method == 'convergence':
         return relation_matrix(config,nodes,edges,sources,targets,sids,tids,prepend_pop,relation_func=total_connection_relationship)
-    elif type == 'percent':
+    elif method == 'percent':
         return relation_matrix(config,nodes,edges,sources,targets,sids,tids,prepend_pop,relation_func=precent_func)
 
 
-def gap_junction_percent_connections(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],prepend_pop=True,method=None):
-    import pandas as pd
-
-
-
 def connection_probabilities(config=None,nodes=None,edges=None,sources=[],
     targets=[],sids=[],tids=[],prepend_pop=True,dist_X=True,dist_Y=True,dist_Z=True,num_bins=10,include_gap=True):
 

examples/bmplot/circuit_config.json

@@ -5,20 +5,20 @@
     "$COMPONENTS_DIR": "$BASE_DIR/components"
   },
   "components": {
-    "mechanisms_dir": "$COMPONENTS_DIR/mechanisms",
+    "__pycache___dir": "$COMPONENTS_DIR/__pycache__",
+    "biophysical_neuron_models_dir": "$COMPONENTS_DIR/biophysical_neuron_models",
     "filter_models_dir": "$COMPONENTS_DIR/filter_models",
+    "mechanisms_dir": "$COMPONENTS_DIR/mechanisms",
     "morphologies_dir": "$COMPONENTS_DIR/morphologies",
-    "__pycache___dir": "$COMPONENTS_DIR/__pycache__",
+    "point_neuron_models_dir": "$COMPONENTS_DIR/point_neuron_models",
     "synaptic_models_dir": "$COMPONENTS_DIR/synaptic_models",
-    "biophysical_neuron_models_dir": "$COMPONENTS_DIR/biophysical_neuron_models",
-    "templates_dir": "$COMPONENTS_DIR/templates",
-    "point_neuron_models_dir": "$COMPONENTS_DIR/point_neuron_models"
+    "templates_dir": "$COMPONENTS_DIR/templates"
   },
   "networks": {
     "nodes": [
       {
-        "node_types_file": "$NETWORK_DIR/example_net_node_types.csv",
-        "nodes_file": "$NETWORK_DIR/example_net_nodes.h5"
+        "nodes_file": "$NETWORK_DIR/bio_net_nodes.h5",
+        "node_types_file": "$NETWORK_DIR/bio_net_node_types.csv"
       },
       {
         "nodes_file": "$NETWORK_DIR/background_nodes.h5",
@@ -27,14 +27,18 @@
     ],
     "edges": [
       {
-        "edges_file": "$NETWORK_DIR/background_example_net_edges.h5",
-        "edge_types_file": "$NETWORK_DIR/background_example_net_edge_types.csv"
+        "edge_types_file": "$NETWORK_DIR/bio_net_bio_net_edge_types.csv",
+        "edges_file": "$NETWORK_DIR/bio_net_bio_net_edges.h5"
       },
       {
-        "edge_types_file": "$NETWORK_DIR/example_net_example_net_edge_types.csv",
-        "edges_file": "$NETWORK_DIR/example_net_example_net_edges.h5"
+        "edge_types_file": "$NETWORK_DIR/background_bio_net_edge_types.csv",
+        "edges_file": "$NETWORK_DIR/background_bio_net_edges.h5"
       }
     ],
-    "gap_juncs": []
+    "gap_juncs": [
+      {
+        "gap_juncs_file": "/home/gjgpb9/cortex_modeling/bmtool/examples/bmplot/network/bio_net_gap_juncs.h5"
+      }
+    ]
   }
 }