BMPlot API Reference

This page provides API reference documentation for the BMPlot module which contains functions for plotting and visualizing BMTK network models and simulation results.

Core Plotting Functions

bmtool.bmplot.is_notebook()

Detect if code is running in a Jupyter notebook environment.

Returns:

bool True if running in a Jupyter notebook, False otherwise.

Notes:

This is used to determine whether to call plt.show() explicitly or rely on Jupyter's automatic display functionality.

Source code in bmtool/bmplot.py

def is_notebook() -> bool:
    """
    Detect if code is running in a Jupyter notebook environment.

    Returns:
    --------
    bool
        True if running in a Jupyter notebook, False otherwise.

    Notes:
    ------
    This is used to determine whether to call plt.show() explicitly or
    rely on Jupyter's automatic display functionality.
    """
    try:
        shell = get_ipython().__class__.__name__
        if shell == 'ZMQInteractiveShell':
            return True   # Jupyter notebook or qtconsole
        elif shell == 'TerminalInteractiveShell':
            return False  # Terminal running IPython
        else:
            return False  # Other type (?)
    except NameError:
        return False      # Probably standard Python interpreter

bmtool.bmplot.total_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, synaptic_info='0', include_gap=True)

Generate a plot displaying total connections or other synaptic statistics.

Parameters:

config : str Path to a BMTK simulation config file. title : str, optional Title for the plot. If None, a default title will be used. sources : str Comma-separated string of network names to use as sources. targets : str Comma-separated string of network names to use as targets. sids : str, optional Comma-separated string of source node identifiers to filter. tids : str, optional Comma-separated string of target node identifiers to filter. no_prepend_pop : bool, optional If True, don't display population name before sid or tid in the plot. save_file : str, optional Path to save the plot. If None, plot is not saved. synaptic_info : str, optional Type of information to display: - '0': Total connections (default) - '1': Mean and standard deviation of connections - '2': All synapse .mod files used - '3': All synapse .json files used include_gap : bool, optional If True, include gap junctions and chemical synapses in the analysis. If False, only include chemical synapses.

Returns:

None The function generates and displays a plot.

Source code in bmtool/bmplot.py

def total_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, synaptic_info='0', include_gap=True):
    """
    Generate a plot displaying total connections or other synaptic statistics.

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation config file.
    title : str, optional
        Title for the plot. If None, a default title will be used.
    sources : str
        Comma-separated string of network names to use as sources.
    targets : str
        Comma-separated string of network names to use as targets.
    sids : str, optional
        Comma-separated string of source node identifiers to filter.
    tids : str, optional
        Comma-separated string of target node identifiers to filter.
    no_prepend_pop : bool, optional
        If True, don't display population name before sid or tid in the plot.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    synaptic_info : str, optional
        Type of information to display:
        - '0': Total connections (default)
        - '1': Mean and standard deviation of connections
        - '2': All synapse .mod files used
        - '3': All synapse .json files used
    include_gap : bool, optional
        If True, include gap junctions and chemical synapses in the analysis.
        If False, only include chemical synapses.

    Returns:
    --------
    None
        The function generates and displays a plot.
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    text,num, source_labels, target_labels = util.connection_totals(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,synaptic_info=synaptic_info,include_gap=include_gap)

    if title == None or title=="":
        title = "Total Connections"
    if synaptic_info=='1':
        title = "Mean and Stdev # of Conn on Target"    
    if synaptic_info=='2':
        title = "All Synapse .mod Files Used"
    if synaptic_info=='3':
        title = "All Synapse .json Files Used"
    plot_connection_info(text,num,source_labels,target_labels,title, syn_info=synaptic_info, save_file=save_file)
    return

bmtool.bmplot.percent_connection_matrix(config=None, nodes=None, edges=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, method='total', include_gap=True)

Generates a plot showing the percent connectivity of a network config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph method: what percent to displace on the graph 'total','uni',or 'bi' for total connections, unidirectional connections or bidirectional connections save_file: If plot should be saved include_gap: Determines if connectivity shown should include gap junctions + chemical synapses. False will only include chemical

Source code in bmtool/bmplot.py

def percent_connection_matrix(config=None,nodes=None,edges=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,method = 'total',include_gap=True):
    """
    Generates a plot showing the percent connectivity of a network
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    method: what percent to displace on the graph 'total','uni',or 'bi' for total connections, unidirectional connections or bidirectional connections
    save_file: If plot should be saved
    include_gap: Determines if connectivity shown should include gap junctions + chemical synapses. False will only include chemical
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")

    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    text,num, source_labels, target_labels = util.percent_connections(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,method=method,include_gap=include_gap)
    if title == None or title=="":
        title = "Percent Connectivity"


    plot_connection_info(text,num,source_labels,target_labels,title, save_file=save_file)
    return

bmtool.bmplot.probability_connection_matrix(config=None, nodes=None, edges=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, dist_X=True, dist_Y=True, dist_Z=True, bins=8, line_plot=False, verbose=False, include_gap=True)

Generates probability graphs need to look into this more to see what it does needs model_template to be defined to work

Source code in bmtool/bmplot.py

def probability_connection_matrix(config=None,nodes=None,edges=None,title=None,sources=None, targets=None, sids=None, tids=None, 
                            no_prepend_pop=False,save_file=None, dist_X=True,dist_Y=True,dist_Z=True,bins=8,line_plot=False,verbose=False,include_gap=True):
    """
    Generates probability graphs
    need to look into this more to see what it does
    needs model_template to be defined to work
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    throwaway, data, source_labels, target_labels = util.connection_probabilities(config=config,nodes=None,
        edges=None,sources=sources,targets=targets,sids=sids,tids=tids,
        prepend_pop=not no_prepend_pop,dist_X=dist_X,dist_Y=dist_Y,dist_Z=dist_Z,num_bins=bins,include_gap=include_gap)
    if not data.any():
        return
    if data[0][0]==-1:
        return
    #plot_connection_info(data,source_labels,target_labels,title, save_file=save_file)

    #plt.clf()# clears previous plots
    np.seterr(divide='ignore', invalid='ignore')
    num_src, num_tar = data.shape
    fig, axes = plt.subplots(nrows=num_src, ncols=num_tar, figsize=(12,12))
    fig.subplots_adjust(hspace=0.5, wspace=0.5)

    for x in range(num_src):
        for y in range(num_tar):
            ns = data[x][y]["ns"]
            bins = data[x][y]["bins"]

            XX = bins[:-1]
            YY = ns[0]/ns[1]

            if line_plot:
                axes[x,y].plot(XX,YY)
            else:
                axes[x,y].bar(XX,YY)

            if x == num_src-1:
                axes[x,y].set_xlabel(target_labels[y])
            if y == 0:
                axes[x,y].set_ylabel(source_labels[x])

            if verbose:
                print("Source: [" + source_labels[x] + "] | Target: ["+ target_labels[y] +"]")
                print("X:")
                print(XX)
                print("Y:")
                print(YY)

    tt = "Distance Probability Matrix"
    if title:
        tt = title
    st = fig.suptitle(tt, fontsize=14)
    fig.text(0.5, 0.04, 'Target', ha='center')
    fig.text(0.04, 0.5, 'Source', va='center', rotation='vertical')
    notebook = is_notebook
    if notebook == False:
        fig.show()

    return

bmtool.bmplot.convergence_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, convergence=True, method='mean+std', include_gap=True, return_dict=None)

Generates connection plot displaying convergence data config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved method: 'mean','min','max','stdev' or 'mean+std' connvergence plot

Source code in bmtool/bmplot.py

def convergence_connection_matrix(config=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,convergence=True,method='mean+std',include_gap=True,return_dict=None):
    """
    Generates connection plot displaying convergence data
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    method: 'mean','min','max','stdev' or 'mean+std' connvergence plot 
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    return divergence_connection_matrix(config,title ,sources, targets, sids, tids, no_prepend_pop, save_file ,convergence, method,include_gap=include_gap,return_dict=return_dict)

bmtool.bmplot.divergence_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, convergence=False, method='mean+std', include_gap=True, return_dict=None)

Generates connection plot displaying divergence data config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved method: 'mean','min','max','stdev', and 'mean+std' for divergence plot

Source code in bmtool/bmplot.py

def divergence_connection_matrix(config=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,convergence=False,method='mean+std',include_gap=True,return_dict=None):
    """
    Generates connection plot displaying divergence data
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    method: 'mean','min','max','stdev', and 'mean+std' for divergence plot
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    syn_info, data, source_labels, target_labels = util.connection_divergence(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,convergence=convergence,method=method,include_gap=include_gap)


    #data, labels = util.connection_divergence_average(config=config,nodes=nodes,edges=edges,populations=populations)

    if title == None or title=="":

        if method == 'min':
            title = "Minimum "
        elif method == 'max':
            title = "Maximum "
        elif method == 'std':
            title = "Standard Deviation "
        elif method == 'mean':
            title = "Mean "
        else: 
            title = 'Mean + Std '

        if convergence:
            title = title + "Synaptic Convergence"
        else:
            title = title + "Synaptic Divergence"
    if return_dict:
        dict = plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file,return_dict=return_dict)
        return dict
    else:
        plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
        return

bmtool.bmplot.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 sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved type:'convergence' or 'percent' connections

Source code in bmtool/bmplot.py

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 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    type:'convergence' or 'percent' connections
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    if method !='convergence' and method!='percent':
        raise Exception("type must be 'convergence' or 'percent'")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        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,method=method)


    def filter_rows(syn_info, data, source_labels, target_labels):
        """
        Filters out rows in a connectivity matrix that contain only NaN or zero values.

        This function is used to clean up connection matrices by removing rows that have no meaningful data,
        which helps create more informative visualizations of network connectivity.

        Parameters:
        -----------
        syn_info : numpy.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : numpy.ndarray
            2D matrix containing connectivity data with rows representing sources and columns representing targets.
        source_labels : list
            List of labels for the source populations corresponding to rows in the data matrix.
        target_labels : list
            List of labels for the target populations corresponding to columns in the data matrix.

        Returns:
        --------
        tuple
            A tuple containing the filtered (syn_info, data, source_labels, target_labels) with invalid rows removed.
        """
        # Identify rows with all NaN or all zeros
        valid_rows = ~np.all(np.isnan(data), axis=1) & ~np.all(data == 0, axis=1)

        # Filter rows based on valid_rows mask
        new_syn_info = syn_info[valid_rows]
        new_data = data[valid_rows]
        new_source_labels = np.array(source_labels)[valid_rows]

        return new_syn_info, new_data, new_source_labels, target_labels

    def filter_rows_and_columns(syn_info, data, source_labels, target_labels):
        """
        Filters out both rows and columns in a connectivity matrix that contain only NaN or zero values.

        This function performs a two-step filtering process: first removing rows with no data,
        then transposing the matrix and removing columns with no data (by treating them as rows).
        This creates a cleaner, more informative connectivity matrix visualization.

        Parameters:
        -----------
        syn_info : numpy.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : numpy.ndarray
            2D matrix containing connectivity data with rows representing sources and columns representing targets.
        source_labels : list
            List of labels for the source populations corresponding to rows in the data matrix.
        target_labels : list
            List of labels for the target populations corresponding to columns in the data matrix.

        Returns:
        --------
        tuple
            A tuple containing the filtered (syn_info, data, source_labels, target_labels) with both
            invalid rows and columns removed.
        """
        # Filter rows first
        syn_info, data, source_labels, target_labels = filter_rows(syn_info, data, source_labels, target_labels)

        # Transpose data to filter columns
        transposed_syn_info = np.transpose(syn_info)
        transposed_data = np.transpose(data)
        transposed_source_labels = target_labels
        transposed_target_labels = source_labels

        # Filter columns (by treating them as rows in transposed data)
        transposed_syn_info, transposed_data, transposed_source_labels, transposed_target_labels = filter_rows(
            transposed_syn_info, transposed_data, transposed_source_labels, transposed_target_labels
        )

        # Transpose back to original orientation
        filtered_syn_info = np.transpose(transposed_syn_info)
        filtered_data = np.transpose(transposed_data)
        filtered_source_labels = transposed_target_labels  # Back to original source_labels
        filtered_target_labels = transposed_source_labels  # Back to original target_labels

        return filtered_syn_info, filtered_data, filtered_source_labels, filtered_target_labels


    syn_info, data, source_labels, target_labels = filter_rows_and_columns(syn_info, data, source_labels, target_labels)

    if title == None or title=="":
        title = 'Gap Junction'
        if method == 'convergence':
            title+=' Syn Convergence'
        elif method == 'percent':
            title+=' Percent Connectivity'
    plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
    return

bmtool.bmplot.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 config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph source_cell: where connections are coming from target_cell: where connections on coming onto save_file: If plot should be saved

Source code in bmtool/bmplot.py

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
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    source_cell: where connections are coming from
    target_cell: where connections on coming onto
    save_file: If plot should be saved
    """
    def connection_pair_histogram(**kwargs):
        """
        Creates a histogram showing the distribution of connection counts between a specific source and target cell type.

        This function is designed to be used with the relation_matrix utility and will only create histograms
        for the specified source and target cell types, ignoring all other combinations.

        Parameters:
        -----------
        kwargs : dict
            Dictionary containing the following keys:
            - edges: DataFrame containing edge information
            - sid: Column name for source ID type in the edges DataFrame
            - tid: Column name for target ID type in the edges DataFrame
            - source_id: Value to filter edges by source ID type
            - target_id: Value to filter edges by target ID type

        Global parameters used:
        ---------------------
        source_cell : str
            The source cell type to plot.
        target_cell : str
            The target cell type to plot.
        include_gap : bool
            Whether to include gap junctions in the analysis. If False, gap junctions are excluded.

        Returns:
        --------
        None
            Displays a histogram showing the distribution of connection counts.
        """
        edges = kwargs["edges"] 
        source_id_type = kwargs["sid"]
        target_id_type = kwargs["tid"]
        source_id = kwargs["source_id"]
        target_id = kwargs["target_id"]
        if source_id == source_cell and target_id == target_cell:
            temp = edges[(edges[source_id_type] == source_id) & (edges[target_id_type]==target_id)]
            if include_gap == False:
                temp = temp[temp['is_gap_junction'] != True]
            node_pairs = temp.groupby('target_node_id')['source_node_id'].count()
            try:
                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)
            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=False,bins='auto',stacked=True,label=label)
            plt.legend()
            plt.xlabel("# of conns from {} to {}".format(source_cell,target_cell))
            plt.ylabel("# of cells")
            plt.show()
        else: # dont care about other cell pairs so pass
            pass

    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    util.relation_matrix(config,nodes,edges,sources,targets,sids,tids,not no_prepend_pop,relation_func=connection_pair_histogram,synaptic_info=synaptic_info)

bmtool.bmplot.connection_distance(config, sources, targets, source_cell_id, target_id_type, ignore_z=False)

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.

Parameters:

config: (str) A BMTK simulation config sources: (str) network name(s) to plot 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

Source code in bmtool/bmplot.py

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.

    Parameters:
    ----------
    config: (str) A BMTK simulation config 
    sources: (str) network name(s) to plot
    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 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 = sources + "_to_" + targets
    node_network = sources

    # Filter edges to obtain connections originating from the source node
    edge = edges[edge_network]
    edge = edge[edge['source_node_id'] == source_cell_id]
    if target_id_type:
        edge = edge[edge['target_query'].str.contains(target_id_type, na=False)]

    target_node_ids = edge['target_node_id']

    # Filter nodes to obtain only the target and source nodes
    node = nodes[node_network]
    target_nodes = node.loc[node.index.isin(target_node_ids)]
    source_node = node.loc[node.index == source_cell_id]

    # Calculate distances between source node and each target node
    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 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):
    #     ax.text(target_nodes['pos_x'].iloc[i], target_nodes['pos_y'].iloc[i], target_nodes['pos_z'].iloc[i],
    #             f'{distance:.2f}', color='black', fontsize=8, ha='center')

    plt.legend()
    plt.show()

    # Plot distances in a separate 2D plot
    plt.figure(figsize=(8, 6)) 
    plt.hist(distances, bins=20, color='blue', edgecolor='black')
    plt.xlabel("Distance")
    plt.ylabel("Count")
    plt.title(f"Distance from Source Node to Each Target Node")
    plt.grid(True)
    plt.show()

bmtool.bmplot.edge_histogram_matrix(config=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=None, edge_property=None, time=None, time_compare=None, report=None, title=None, save_file=None)

Generates a matrix of histograms showing the distribution of edge properties between different populations.

This function creates a grid of histograms where each cell in the grid represents the distribution of a specific edge property (e.g., synaptic weights, delays) between a source population (row) and target population (column).

Parameters:

config : str Path to a BMTK simulation config file. sources : str Comma-separated list of source network names. targets : str Comma-separated list of target network names. sids : str, optional Comma-separated list of source node identifiers to filter by. tids : str, optional Comma-separated list of target node identifiers to filter by. no_prepend_pop : bool, optional If True, population names are not prepended to node identifiers in the display. edge_property : str The edge property to analyze and display in the histograms (e.g., 'syn_weight', 'delay'). time : int, optional Time point to analyze from a time series report. time_compare : int, optional Second time point for comparison with 'time'. report : str, optional Name of the report to analyze. title : str, optional Custom title for the plot. save_file : str, optional Path to save the generated plot.

Returns:

None Displays a matrix of histograms.

Source code in bmtool/bmplot.py

def edge_histogram_matrix(config=None,sources = None,targets=None,sids=None,tids=None,no_prepend_pop=None,edge_property = None,time = None,time_compare = None,report=None,title=None,save_file=None):
    """
    Generates a matrix of histograms showing the distribution of edge properties between different populations.

    This function creates a grid of histograms where each cell in the grid represents the distribution of a 
    specific edge property (e.g., synaptic weights, delays) between a source population (row) and 
    target population (column).

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation config file.
    sources : str
        Comma-separated list of source network names.
    targets : str
        Comma-separated list of target network names.
    sids : str, optional
        Comma-separated list of source node identifiers to filter by.
    tids : str, optional
        Comma-separated list of target node identifiers to filter by.
    no_prepend_pop : bool, optional
        If True, population names are not prepended to node identifiers in the display.
    edge_property : str
        The edge property to analyze and display in the histograms (e.g., 'syn_weight', 'delay').
    time : int, optional
        Time point to analyze from a time series report.
    time_compare : int, optional
        Second time point for comparison with 'time'.
    report : str, optional
        Name of the report to analyze.
    title : str, optional
        Custom title for the plot.
    save_file : str, optional
        Path to save the generated plot.

    Returns:
    --------
    None
        Displays a matrix of histograms.
    """

    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    if time_compare:
        time_compare = int(time_compare)

    data, source_labels, target_labels = util.edge_property_matrix(edge_property,nodes=None,edges=None,config=config,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,report=report,time=time,time_compare=time_compare)

    # Fantastic resource
    # https://stackoverflow.com/questions/7941207/is-there-a-function-to-make-scatterplot-matrices-in-matplotlib 
    num_src, num_tar = data.shape
    fig, axes = plt.subplots(nrows=num_src, ncols=num_tar, figsize=(12,12))
    fig.subplots_adjust(hspace=0.5, wspace=0.5)

    for x in range(num_src):
        for y in range(num_tar):
            axes[x,y].hist(data[x][y])

            if x == num_src-1:
                axes[x,y].set_xlabel(target_labels[y])
            if y == 0:
                axes[x,y].set_ylabel(source_labels[x])

    tt = edge_property + " Histogram Matrix"
    if title:
        tt = title
    st = fig.suptitle(tt, fontsize=14)
    fig.text(0.5, 0.04, 'Target', ha='center')
    fig.text(0.04, 0.5, 'Source', va='center', rotation='vertical')
    plt.draw()

bmtool.bmplot.plot_connection_info(text, num, source_labels, target_labels, title, syn_info='0', save_file=None, return_dict=None)

Function to plot connection information as a heatmap, including handling missing source and target values. If there is no source or target, set the value to 0.

Source code in bmtool/bmplot.py

def plot_connection_info(text, num, source_labels, target_labels, title, syn_info='0', save_file=None, return_dict=None):
    """
    Function to plot connection information as a heatmap, including handling missing source and target values.
    If there is no source or target, set the value to 0.
    """

    # Ensure text dimensions match num dimensions
    num_source = len(source_labels)
    num_target = len(target_labels)

    # Set color map
    matplotlib.rc('image', cmap='viridis')

    # Create figure and axis for the plot
    fig1, ax1 = plt.subplots(figsize=(num_source, num_target))
    num = np.nan_to_num(num, nan=0) # replace NaN with 0
    im1 = ax1.imshow(num)

    # Set ticks and labels for source and target
    ax1.set_xticks(list(np.arange(len(target_labels))))
    ax1.set_yticks(list(np.arange(len(source_labels))))
    ax1.set_xticklabels(target_labels)
    ax1.set_yticklabels(source_labels, size=12, weight='semibold')

    # Rotate the tick labels for better visibility
    plt.setp(ax1.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor", size=12, weight='semibold')

    # Dictionary to store connection information
    graph_dict = {}

    # Loop over data dimensions and create text annotations
    for i in range(num_source):
        for j in range(num_target):
            # Get the edge info, or set it to '0' if it's missing
            edge_info = text[i, j] if text[i, j] is not None else 0

            # Initialize the dictionary for the source node if not already done
            if source_labels[i] not in graph_dict:
                graph_dict[source_labels[i]] = {}

            # Add edge info for the target node
            graph_dict[source_labels[i]][target_labels[j]] = edge_info

            # Set text annotations based on syn_info type
            if syn_info == '2' or syn_info == '3':
                if num_source > 8 and num_source < 20:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=8, weight='semibold')
                elif num_source > 20:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=7, weight='semibold')
                else:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=11, weight='semibold')
            else:
                fig_text = ax1.text(j, i, edge_info,
                                    ha="center", va="center", color="w", size=11, weight='semibold')

    # Set labels and title for the plot
    ax1.set_ylabel('Source', size=11, weight='semibold')
    ax1.set_xlabel('Target', size=11, weight='semibold')
    ax1.set_title(title, size=20, weight='semibold')

    # Display the plot or save it based on the environment and arguments
    notebook = is_notebook()  # Check if running in a Jupyter notebook
    if notebook == False:
        fig1.show()

    if save_file:
        plt.savefig(save_file)

    if return_dict:
        return graph_dict
    else:
        return

bmtool.bmplot.connector_percent_matrix(csv_path=None, exclude_strings=None, assemb_key=None, title='Percent connection matrix', pop_order=None)

Generates and plots a connection matrix based on connection probabilities from a CSV file produced by bmtool.connector.

This function is useful for visualizing percent connectivity while factoring in population distance and other parameters. It processes the connection data by filtering the 'Source' and 'Target' columns in the CSV, and displays the percentage of connected pairs for each population combination in a matrix.

Parameters:

csv_path : str Path to the CSV file containing the connection data. The CSV should be an output from the bmtool.connector classes, specifically generated by the save_connection_report() function. exclude_strings : list of str, optional List of strings to exclude rows where 'Source' or 'Target' contain these strings. title : str, optional, default='Percent connection matrix' Title for the generated plot. pop_order : list of str, optional List of population labels to specify the order for the x- and y-ticks in the plot.

Returns:

None Displays a heatmap plot of the connection matrix, showing the percentage of connected pairs between populations.

Source code in bmtool/bmplot.py

def connector_percent_matrix(csv_path: str = None, exclude_strings=None, assemb_key=None, title: str = 'Percent connection matrix', pop_order=None) -> None:
    """
    Generates and plots a connection matrix based on connection probabilities from a CSV file produced by bmtool.connector.

    This function is useful for visualizing percent connectivity while factoring in population distance and other parameters. 
    It processes the connection data by filtering the 'Source' and 'Target' columns in the CSV, and displays the percentage of 
    connected pairs for each population combination in a matrix.

    Parameters:
    -----------
    csv_path : str
        Path to the CSV file containing the connection data. The CSV should be an output from the bmtool.connector 
        classes, specifically generated by the `save_connection_report()` function.
    exclude_strings : list of str, optional
        List of strings to exclude rows where 'Source' or 'Target' contain these strings.
    title : str, optional, default='Percent connection matrix'
        Title for the generated plot.
    pop_order : list of str, optional
        List of population labels to specify the order for the x- and y-ticks in the plot.

    Returns:
    --------
    None
        Displays a heatmap plot of the connection matrix, showing the percentage of connected pairs between populations.
    """
    # Read the CSV data
    df = pd.read_csv(csv_path)

    # Choose the column to display
    selected_column = "Percent connectionivity within possible connections"

    # Filter the DataFrame based on exclude_strings
    def filter_dataframe(df, column_name, exclude_strings):
        def process_string(string):

            match = re.search(r"\[\'(.*?)\'\]", string)
            if exclude_strings and any(ex_string in string for ex_string in exclude_strings):
                return None
            elif match:
                filtered_string = match.group(1)
                if 'Gap' in string:
                        filtered_string = filtered_string + "-Gap"

                if assemb_key:
                    if assemb_key in string:
                        filtered_string = filtered_string + assemb_key

                return filtered_string  # Return matched string

            return string  # If no match, return the original string

        df[column_name] = df[column_name].apply(process_string)
        df = df.dropna(subset=[column_name])

        return df

    df = filter_dataframe(df, 'Source', exclude_strings)
    df = filter_dataframe(df, 'Target', exclude_strings)

    #process assem rows and combine them into one prob per assem type
    if assemb_key:
        assems = df[df['Source'].str.contains(assemb_key)]
        unique_sources = assems['Source'].unique()

        for source in unique_sources:
            source_assems = assems[assems['Source'] == source]
            unique_targets = source_assems['Target'].unique()  # Filter targets for the current source

            for target in unique_targets:
                # Filter the assemblies with the current source and target
                unique_assems = source_assems[source_assems['Target'] == target]

                # find the prob of a conn
                forward_probs = []
                for _,row in unique_assems.iterrows():
                    selected_percentage = row[selected_column]
                    selected_percentage = [float(p) for p in selected_percentage.strip('[]').split()]
                    if len(selected_percentage) == 1 or len(selected_percentage) == 2:
                        forward_probs.append(selected_percentage[0])
                    if len(selected_percentage) == 3:
                        forward_probs.append(selected_percentage[0])
                        forward_probs.append(selected_percentage[1])

                mean_probs = np.mean(forward_probs)
                source = source.replace(assemb_key, "")
                target = target.replace(assemb_key, "")
                new_row = pd.DataFrame({
                    'Source': [source],
                    'Target': [target],
                    'Percent connectionivity within possible connections': [mean_probs],
                    'Percent connectionivity within all connections': [0]
                })

                df = pd.concat([df, new_row], ignore_index=False)

    # Prepare connection data
    connection_data = {}
    for _, row in df.iterrows():
        source, target, selected_percentage = row['Source'], row['Target'], row[selected_column]
        if isinstance(selected_percentage, str):
            selected_percentage = [float(p) for p in selected_percentage.strip('[]').split()]
        connection_data[(source, target)] = selected_percentage

    # Determine population order
    populations = sorted(list(set(df['Source'].unique()) | set(df['Target'].unique())))
    if pop_order:
        populations = [pop for pop in pop_order if pop in populations]  # Order according to pop_order, if provided
    num_populations = len(populations)

    # Create an empty matrix and populate it
    connection_matrix = np.zeros((num_populations, num_populations), dtype=float)
    for (source, target), probabilities in connection_data.items():
        if source in populations and target in populations:
            source_idx = populations.index(source)
            target_idx = populations.index(target)

            if type(probabilities) == float:
                connection_matrix[source_idx][target_idx] = probabilities
            elif len(probabilities) == 1:
                connection_matrix[source_idx][target_idx] = probabilities[0]
            elif len(probabilities) == 2:
                connection_matrix[source_idx][target_idx] = probabilities[0]
            elif len(probabilities) == 3:
                connection_matrix[source_idx][target_idx] = probabilities[0] 
                connection_matrix[target_idx][source_idx] = probabilities[1]
            else:
                raise Exception("unsupported format")

    # Plotting
    fig, ax = plt.subplots(figsize=(10, 8))
    im = ax.imshow(connection_matrix, cmap='viridis', interpolation='nearest')

    # Add annotations
    for i in range(num_populations):
        for j in range(num_populations):
            text = ax.text(j, i, f"{connection_matrix[i, j]:.2f}%", ha="center", va="center", color="w", size=10, weight='semibold')

    # Add colorbar
    plt.colorbar(im, label=f'{selected_column}')

    # Set title and axis labels
    ax.set_title(title)
    ax.set_xlabel('Target Population')
    ax.set_ylabel('Source Population')

    # Set ticks and labels based on populations in specified order
    ax.set_xticks(np.arange(num_populations))
    ax.set_yticks(np.arange(num_populations))
    ax.set_xticklabels(populations, rotation=45, ha="right", size=12, weight='semibold')
    ax.set_yticklabels(populations, size=12, weight='semibold')

    plt.tight_layout()
    plt.show()

Spike and Activity Visualization

bmtool.bmplot.raster(spikes_df=None, config=None, network_name=None, groupby='pop_name', ax=None, tstart=None, tstop=None, color_map=None)

Plots a raster plot of neural spikes, with different colors for each population.

Parameters:

spikes_df : pd.DataFrame, optional DataFrame containing spike data with columns 'timestamps', 'node_ids', and optional 'pop_name'. config : str, optional Path to the configuration file used to load node data. network_name : str, optional Specific network name to select from the configuration; if not provided, uses the first network. ax : matplotlib.axes.Axes, optional Axes on which to plot the raster; if None, a new figure and axes are created. tstart : float, optional Start time for filtering spikes; only spikes with timestamps greater than tstart will be plotted. tstop : float, optional Stop time for filtering spikes; only spikes with timestamps less than tstop will be plotted. color_map : dict, optional Dictionary specifying colors for each population. Keys should be population names, and values should be color values.

Returns:

matplotlib.axes.Axes Axes with the raster plot.

Notes:

• If config is provided, the function merges population names from the node data with spikes_df.
• Each unique population from groupby in spikes_df will be represented by a different color if color_map is not specified.
• If color_map is provided, it should contain colors for all unique pop_name values in spikes_df.

Source code in bmtool/bmplot.py

def raster(spikes_df: Optional[pd.DataFrame] = None, config: Optional[str] = None, network_name: Optional[str] = None, groupby:Optional[str] = 'pop_name',
           ax: Optional[Axes] = None,tstart: Optional[float] = None,tstop: Optional[float] = None,
           color_map: Optional[Dict[str, str]] = None) -> Axes:
    """
    Plots a raster plot of neural spikes, with different colors for each population.

    Parameters:
    ----------
    spikes_df : pd.DataFrame, optional
        DataFrame containing spike data with columns 'timestamps', 'node_ids', and optional 'pop_name'.
    config : str, optional
        Path to the configuration file used to load node data.
    network_name : str, optional
        Specific network name to select from the configuration; if not provided, uses the first network.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the raster; if None, a new figure and axes are created.
    tstart : float, optional
        Start time for filtering spikes; only spikes with timestamps greater than `tstart` will be plotted.
    tstop : float, optional
        Stop time for filtering spikes; only spikes with timestamps less than `tstop` will be plotted.
    color_map : dict, optional
        Dictionary specifying colors for each population. Keys should be population names, and values should be color values.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the raster plot.

    Notes:
    -----
    - If `config` is provided, the function merges population names from the node data with `spikes_df`.
    - Each unique population from groupby in `spikes_df` will be represented by a different color if `color_map` is not specified.
    - If `color_map` is provided, it should contain colors for all unique `pop_name` values in `spikes_df`.
    """
    # Initialize axes if none provided
    if ax is None:
        _, ax = plt.subplots(1, 1)

    # Filter spikes by time range if specified
    if tstart is not None:
        spikes_df = spikes_df[spikes_df['timestamps'] > tstart]
    if tstop is not None:
        spikes_df = spikes_df[spikes_df['timestamps'] < tstop]

    # Load and merge node population data if config is provided
    if config:
        nodes = load_nodes_from_config(config)
        if network_name:
            nodes = nodes.get(network_name, {})
        else:
            nodes = list(nodes.values())[0] if nodes else {}
            print("Grabbing first network; specify a network name to ensure correct node population is selected.")

        # Find common columns, but exclude the join key from the list
        common_columns = spikes_df.columns.intersection(nodes.columns).tolist()
        common_columns = [col for col in common_columns if col != 'node_ids']  # Remove our join key from the common list

        # Drop all intersecting columns except the join key column from df2
        spikes_df = spikes_df.drop(columns=common_columns)
        # merge nodes and spikes df
        spikes_df = spikes_df.merge(nodes[groupby], left_on='node_ids', right_index=True, how='left')


    # Get unique population names
    unique_pop_names = spikes_df[groupby].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('tab10')  # Default colormap
        color_map = {pop_name: cmap(i / len(unique_pop_names)) for i, pop_name in enumerate(unique_pop_names)}
    else:
        # Ensure color_map contains all population names
        missing_colors = [pop for pop in unique_pop_names if pop not in color_map]
        if missing_colors:
            raise ValueError(f"color_map is missing colors for populations: {missing_colors}")

    # Plot each population with its specified or generated color
    for pop_name, group in spikes_df.groupby(groupby):
        ax.scatter(group['timestamps'], group['node_ids'], label=pop_name, color=color_map[pop_name], s=0.5)

    # Label axes
    ax.set_xlabel("Time")
    ax.set_ylabel("Node ID")
    ax.legend(title="Population", loc='upper right', framealpha=0.9, markerfirst=False)

    return ax

bmtool.bmplot.plot_firing_rate_pop_stats(firing_stats, groupby, ax=None, color_map=None)

Plots a bar graph of mean firing rates with error bars (standard deviation).

Parameters:

firing_stats : pd.DataFrame Dataframe containing 'firing_rate_mean' and 'firing_rate_std'. groupby : str or list of str Column(s) used for grouping. ax : matplotlib.axes.Axes, optional Axes on which to plot the bar chart; if None, a new figure and axes are created. color_map : dict, optional Dictionary specifying colors for each group. Keys should be group names, and values should be color values.

Returns:

matplotlib.axes.Axes Axes with the bar plot.

Source code in bmtool/bmplot.py

def plot_firing_rate_pop_stats(firing_stats: pd.DataFrame, groupby: Union[str, List[str]], ax: Optional[Axes] = None, 
                               color_map: Optional[Dict[str, str]] = None) -> Axes:
    """
    Plots a bar graph of mean firing rates with error bars (standard deviation).

    Parameters:
    ----------
    firing_stats : pd.DataFrame
        Dataframe containing 'firing_rate_mean' and 'firing_rate_std'.
    groupby : str or list of str
        Column(s) used for grouping.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the bar chart; if None, a new figure and axes are created.
    color_map : dict, optional
        Dictionary specifying colors for each group. Keys should be group names, and values should be color values.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the bar plot.
    """
    # Ensure groupby is a list for consistent handling
    if isinstance(groupby, str):
        groupby = [groupby]

    # Create a categorical column for grouping
    firing_stats["group"] = firing_stats[groupby].astype(str).agg("_".join, axis=1)

    # Get unique group names
    unique_groups = firing_stats["group"].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('viridis')
        color_map = {group: cmap(i / len(unique_groups)) for i, group in enumerate(unique_groups)}
    else:
        # Ensure color_map contains all groups
        missing_colors = [group for group in unique_groups if group not in color_map]
        if missing_colors:
            raise ValueError(f"color_map is missing colors for groups: {missing_colors}")

    # Create new figure and axes if ax is not provided
    if ax is None:
        fig, ax = plt.subplots(figsize=(10, 6))

    # Sort data for consistent plotting
    firing_stats = firing_stats.sort_values(by="group")

    # Extract values for plotting
    x_labels = firing_stats["group"]
    means = firing_stats["firing_rate_mean"]
    std_devs = firing_stats["firing_rate_std"]

    # Get colors for each group
    colors = [color_map[group] for group in x_labels]

    # Create bar plot
    bars = ax.bar(x_labels, means, yerr=std_devs, capsize=5, color=colors, edgecolor="black")

    # Add error bars manually with caps
    _, caps, _ = ax.errorbar(
        x=np.arange(len(x_labels)), 
        y=means, 
        yerr=std_devs, 
        fmt='none', 
        capsize=5, 
        capthick=2, 
        color="black"
    )

    # Formatting
    ax.set_xticks(np.arange(len(x_labels)))
    ax.set_xticklabels(x_labels, rotation=45, ha="right")
    ax.set_xlabel("Population Group")
    ax.set_ylabel("Mean Firing Rate (spikes/s)")
    ax.set_title("Firing Rate Statistics by Population")
    ax.grid(axis='y', linestyle='--', alpha=0.7)

    return ax

bmtool.bmplot.plot_firing_rate_distribution(individual_stats, groupby, ax=None, color_map=None, plot_type='box', swarm_alpha=0.6)

Plots a distribution of individual firing rates using one or more plot types (box plot, violin plot, or swarm plot), overlaying them on top of each other.

Parameters:

individual_stats : pd.DataFrame Dataframe containing individual firing rates and corresponding group labels. groupby : str or list of str Column(s) used for grouping. ax : matplotlib.axes.Axes, optional Axes on which to plot the graph; if None, a new figure and axes are created. color_map : dict, optional Dictionary specifying colors for each group. Keys should be group names, and values should be color values. plot_type : str or list of str, optional List of plot types to generate. Options: "box", "violin", "swarm". Default is "box". swarm_alpha : float, optional Transparency of swarm plot points. Default is 0.6.

Returns:

matplotlib.axes.Axes Axes with the selected plot type(s) overlayed.

Source code in bmtool/bmplot.py

def plot_firing_rate_distribution(individual_stats: pd.DataFrame, groupby: Union[str, list], ax: Optional[Axes] = None, 
                                  color_map: Optional[Dict[str, str]] = None, 
                                  plot_type: Union[str, list] = "box", swarm_alpha: float = 0.6) -> Axes:
    """
    Plots a distribution of individual firing rates using one or more plot types
    (box plot, violin plot, or swarm plot), overlaying them on top of each other.

    Parameters:
    ----------
    individual_stats : pd.DataFrame
        Dataframe containing individual firing rates and corresponding group labels.
    groupby : str or list of str
        Column(s) used for grouping.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the graph; if None, a new figure and axes are created.
    color_map : dict, optional
        Dictionary specifying colors for each group. Keys should be group names, and values should be color values.
    plot_type : str or list of str, optional
        List of plot types to generate. Options: "box", "violin", "swarm". Default is "box".
    swarm_alpha : float, optional
        Transparency of swarm plot points. Default is 0.6.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the selected plot type(s) overlayed.
    """
    # Ensure groupby is a list for consistent handling
    if isinstance(groupby, str):
        groupby = [groupby]

    # Create a categorical column for grouping
    individual_stats["group"] = individual_stats[groupby].astype(str).agg("_".join, axis=1)

    # Validate plot_type (it can be a list or a single type)
    if isinstance(plot_type, str):
        plot_type = [plot_type]

    for pt in plot_type:
        if pt not in ["box", "violin", "swarm"]:
            raise ValueError("plot_type must be one of: 'box', 'violin', 'swarm'.")

    # Get unique groups for coloring
    unique_groups = individual_stats["group"].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('viridis')
        color_map = {group: cmap(i / len(unique_groups)) for i, group in enumerate(unique_groups)}

    # Ensure color_map contains all groups
    missing_colors = [group for group in unique_groups if group not in color_map]
    if missing_colors:
        raise ValueError(f"color_map is missing colors for groups: {missing_colors}")

    # Create new figure and axes if ax is not provided
    if ax is None:
        fig, ax = plt.subplots(figsize=(10, 6))

    # Sort data for consistent plotting
    individual_stats = individual_stats.sort_values(by="group")

    # Loop over each plot type and overlay them
    for pt in plot_type:
        if pt == "box":
            sns.boxplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, width=0.5)
        elif pt == "violin":
            sns.violinplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, inner="quartile", alpha=0.4)
        elif pt == "swarm":
            sns.swarmplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, alpha=swarm_alpha)

    # Formatting
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
    ax.set_xlabel("Population Group")
    ax.set_ylabel("Firing Rate (spikes/s)")
    ax.set_title("Firing Rate Distribution for individual cells")
    ax.grid(axis='y', linestyle='--', alpha=0.7)

    return ax

bmtool.bmplot.plot_entrainment()

Plots entrainment analysis for oscillatory network activity.

This function analyzes and visualizes how well neural populations entrain to rhythmic input or how synchronized they become during oscillatory activity. It can show phase locking, coherence, or other entrainment metrics.

Note: This is currently a placeholder function and not yet implemented.

Parameters:

None

Returns:

None

Source code in bmtool/bmplot.py

def plot_entrainment():
    """
    Plots entrainment analysis for oscillatory network activity.

    This function analyzes and visualizes how well neural populations entrain to rhythmic 
    input or how synchronized they become during oscillatory activity. It can show phase 
    locking, coherence, or other entrainment metrics.

    Note: This is currently a placeholder function and not yet implemented.

    Parameters:
    -----------
    None

    Returns:
    --------
    None
    """
    pass

3D Visualization

bmtool.bmplot.plot_3d_positions(config=None, sources=None, sid=None, title=None, save_file=None, subset=None)

Plots a 3D graph of all cells with x, y, z location.

Parameters: - config: A BMTK simulation config - sources: Which network(s) to plot - sid: How to name cell groups - title: Plot title - save_file: If plot should be saved - subset: Take every Nth row. This will make plotting large network graphs easier to see.

Source code in bmtool/bmplot.py

def plot_3d_positions(config=None, sources=None, sid=None, title=None, save_file=None, subset=None):
    """
    Plots a 3D graph of all cells with x, y, z location.

    Parameters:
    - config: A BMTK simulation config 
    - sources: Which network(s) to plot 
    - sid: How to name cell groups
    - title: Plot title
    - save_file: If plot should be saved
    - subset: Take every Nth row. This will make plotting large network graphs easier to see.
    """

    if not config:
        raise Exception("config not defined")

    if sources is None:
        sources = "all"

    # Set group keys (e.g., node types)
    group_keys = sid
    if title is None:
        title = "3D positions"

    # Load nodes from the configuration
    nodes = util.load_nodes_from_config(config)

    # Get the list of populations to plot
    if 'all' in sources:
        populations = list(nodes)
    else:
        populations = sources.split(",")

    # Split group_by into list 
    group_keys = group_keys.split(",")
    group_keys += (len(populations) - len(group_keys)) * ["node_type_id"]  # Extend the array to default values if not enough given
    if len(group_keys) > 1:
        raise Exception("Only one group by is supported currently!")

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(projection='3d')
    handles = []

    for pop in (list(nodes)):

        if 'all' not in populations and pop not in populations:
            continue

        nodes_df = nodes[pop]
        group_key = group_keys[0] 

        # If group_key is provided, ensure the column exists in the dataframe
        if group_key is not None:
            if group_key not in nodes_df:
                raise Exception(f"Could not find column '{group_key}' in {pop}")

            groupings = nodes_df.groupby(group_key)
            n_colors = nodes_df[group_key].nunique()
            color_norm = colors.Normalize(vmin=0, vmax=(n_colors - 1))
            scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv')
            color_map = [scalar_map.to_rgba(i) for i in range(n_colors)]
        else:
            groupings = [(None, nodes_df)]
            color_map = ['blue']

        # Loop over groupings and plot
        for color, (group_name, group_df) in zip(color_map, groupings):
            if "pos_x" not in group_df or "pos_y" not in group_df or "pos_z" not in group_df:
                print(f"Warning: Missing position columns in group '{group_name}' for {pop}. Skipping this group.")
                continue  # Skip if position columns are missing

            # Subset the dataframe by taking every Nth row if subset is provided
            if subset is not None:
                group_df = group_df.iloc[::subset]

            h = ax.scatter(group_df["pos_x"], group_df["pos_y"], group_df["pos_z"], color=color, label=group_name)
            handles.append(h)

    if not handles:
        print("No data to plot.")
        return

    # Set plot title and legend
    plt.title(title)
    plt.legend(handles=handles)

    # Draw the plot
    plt.draw()

    # Save the plot if save_file is provided
    if save_file:
        plt.savefig(save_file)

    # Show the plot if running outside of a notebook
    if not is_notebook:
        plt.show()

    return ax

bmtool.bmplot.plot_3d_cell_rotation(config=None, sources=None, sids=None, title=None, save_file=None, quiver_length=None, arrow_length_ratio=None, group=None, subset=None)

Source code in bmtool/bmplot.py

def plot_3d_cell_rotation(config=None, sources=None, sids=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")

    if sources is None:
        sources = ["all"]

    group_keys = sids.split(",") if sids else []

    if title is None:
        title = "Cell rotations"

    nodes = util.load_nodes_from_config(config)

    if 'all' in sources:
        populations = list(nodes)
    else:
        populations = sources

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection='3d')
    handles = []

    for nodes_key, group_key in zip(list(nodes), group_keys):
        if 'all' not in populations and nodes_key not in populations:
            continue

        nodes_df = nodes[nodes_key]

        if group_key is not None:
            if group_key not in nodes_df.columns:
                raise Exception(f'Could not find column {group_key}')
            groupings = nodes_df.groupby(group_key)

            n_colors = nodes_df[group_key].nunique()
            color_norm = colors.Normalize(vmin=0, vmax=(n_colors - 1))
            scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv')
            color_map = [scalar_map.to_rgba(i) for i in range(n_colors)]
        else:
            groupings = [(None, nodes_df)]
            color_map = ['blue']

        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

            X = group_df["pos_x"]
            Y = group_df["pos_y"]
            Z = group_df["pos_z"]
            U = group_df["rotation_angle_xaxis"].values
            V = group_df["rotation_angle_yaxis"].values
            W = group_df["rotation_angle_zaxis"].values

            if U is None:
                U = np.zeros(len(X))
            if V is None:
                V = np.zeros(len(Y))
            if W is None:
                W = np.zeros(len(Z))

            # Create rotation matrices from Euler angles
            rotations = R.from_euler('xyz', np.column_stack((U, V, W)), degrees=False)

            # Define initial vectors 
            init_vectors = np.column_stack((np.ones(len(X)), np.zeros(len(Y)), np.zeros(len(Z))))

            # Apply rotations to initial vectors
            rots = np.dot(rotations.as_matrix(), init_vectors.T).T

            # Extract x, y, and z components of the rotated vectors
            rot_x = rots[:, 0]
            rot_y = rots[:, 1]
            rot_z = rots[:, 2]

            h = ax.quiver(X, Y, Z, rot_x, rot_y, rot_z, color=color, label=group_name, arrow_length_ratio=arrow_length_ratio, length=quiver_length)
            ax.scatter(X, Y, Z, color=color, label=group_name)
            ax.set_xlim([min(X), max(X)])
            ax.set_ylim([min(Y), max(Y)])
            ax.set_zlim([min(Z), max(Z)])
            handles.append(h)

    if not handles:
        return

    plt.title(title)
    plt.legend(handles=handles)
    plt.draw()

    if save_file:
        plt.savefig(save_file)
    notebook = is_notebook
    if notebook == False:
        plt.show()

bmtool.bmplot.plot_network_graph(config=None, nodes=None, edges=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, edge_property='model_template')

Creates a directed graph visualization of the network connectivity using NetworkX.

This function generates a network diagram showing the connections between different cell populations, with edge labels indicating the connection types based on the specified edge property.

Parameters:

config : str Path to a BMTK simulation configuration file. nodes : dict, optional Dictionary of node information (if already loaded). edges : dict, optional Dictionary of edge information (if already loaded). title : str, optional Custom title for the plot. If None, defaults to "Network Graph". sources : str Comma-separated list of source network names. targets : str Comma-separated list of target network names. sids : str, optional Comma-separated list of source node identifiers to filter by. tids : str, optional Comma-separated list of target node identifiers to filter by. no_prepend_pop : bool, default=False If True, population names are not prepended to node identifiers in the display. save_file : str, optional Path to save the generated plot. edge_property : str, default='model_template' The edge property to use for labeling connections in the graph.

Returns:

None Displays a network graph visualization.

Source code in bmtool/bmplot.py

def plot_network_graph(config=None,nodes=None,edges=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,edge_property='model_template'):
    """
    Creates a directed graph visualization of the network connectivity using NetworkX.

    This function generates a network diagram showing the connections between different 
    cell populations, with edge labels indicating the connection types based on the specified
    edge property.

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation configuration file.
    nodes : dict, optional
        Dictionary of node information (if already loaded).
    edges : dict, optional
        Dictionary of edge information (if already loaded).
    title : str, optional
        Custom title for the plot. If None, defaults to "Network Graph".
    sources : str
        Comma-separated list of source network names.
    targets : str
        Comma-separated list of target network names.
    sids : str, optional
        Comma-separated list of source node identifiers to filter by.
    tids : str, optional
        Comma-separated list of target node identifiers to filter by.
    no_prepend_pop : bool, default=False
        If True, population names are not prepended to node identifiers in the display.
    save_file : str, optional
        Path to save the generated plot.
    edge_property : str, default='model_template'
        The edge property to use for labeling connections in the graph.

    Returns:
    --------
    None
        Displays a network graph visualization.
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    throw_away, data, source_labels, target_labels = util.connection_graph_edge_types(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,edge_property=edge_property)

    if title == None or title=="":
        title = "Network Graph"

    import networkx as nx

    net_graph = nx.MultiDiGraph() #or G = nx.MultiDiGraph()

    edges = []
    edge_labels = {}
    for node in list(set(source_labels+target_labels)):
        net_graph.add_node(node)

    for s, source in enumerate(source_labels):
        for t, target in enumerate(target_labels):
            relationship = data[s][t]
            for i, relation in enumerate(relationship):
                edge_labels[(source,target)]=relation
                edges.append([source,target])

    net_graph.add_edges_from(edges)
    #pos = nx.spring_layout(net_graph,k=0.50,iterations=20)
    pos = nx.shell_layout(net_graph)
    plt.figure()
    nx.draw(net_graph,pos,edge_color='black', width=1,linewidths=1,\
        node_size=500,node_color='white',arrowstyle='->',alpha=0.9,\
        labels={node:node for node in net_graph.nodes()})

    nx.draw_networkx_edge_labels(net_graph,pos,edge_labels=edge_labels,font_color='red')
    plt.show()

    return

Report Visualization

bmtool.bmplot.plot_report(config_file=None, report_file=None, report_name=None, variables=None, gids=None)

Source code in bmtool/bmplot.py

def plot_report(config_file=None, report_file=None, report_name=None, variables=None, gids=None):
    if report_file is None:
        report_name, report_file = _get_cell_report(config_file, report_name)

    var_report = CellVarsFile(report_file)
    variables = listify(variables) if variables is not None else var_report.variables
    gids = listify(gids) if gids is not None else var_report.gids
    time_steps = var_report.time_trace

    def __units_str(var):
        units = var_report.units(var)
        if units == CellVarsFile.UNITS_UNKNOWN:
            units = missing_units.get(var, '')
        return '({})'.format(units) if units else ''

    n_plots = len(variables)
    if n_plots > 1:
        # If more than one variale to plot do so in different subplots
        f, axarr = plt.subplots(n_plots, 1)
        for i, var in enumerate(variables):
            for gid in gids:
                axarr[i].plot(time_steps, var_report.data(gid=gid, var_name=var), label='gid {}'.format(gid))

            axarr[i].legend()
            axarr[i].set_ylabel('{} {}'.format(var, __units_str(var)))
            if i < n_plots - 1:
                axarr[i].set_xticklabels([])

        axarr[i].set_xlabel('time (ms)')

    elif n_plots == 1:
        # For plotting a single variable
        plt.figure()
        for gid in gids:
            plt.plot(time_steps, var_report.data(gid=gid, var_name=variables[0]), label='gid {}'.format(gid))
        plt.ylabel('{} {}'.format(variables[0], __units_str(variables[0])))
        plt.xlabel('time (ms)')
        plt.legend()
    else:
        return

    plt.show()

bmtool.bmplot.plot_report_default(config, report_name, variables, gids)

A simplified interface for plotting cell report variables from BMTK simulations.

This function handles the common case of plotting specific variables for specific cells from a BMTK report file, with minimal parameter requirements.

Parameters:

config : str Path to a BMTK simulation configuration file. report_name : str Name of the report to plot (without file extension). variables : str Comma-separated list of variable names to plot (e.g., 'v,i_na,i_k'). gids : str Comma-separated list of cell IDs (gids) to plot data for.

Returns:

None Displays plots of the specified variables for the specified cells.

Source code in bmtool/bmplot.py

def plot_report_default(config, report_name, variables, gids):
    """
    A simplified interface for plotting cell report variables from BMTK simulations.

    This function handles the common case of plotting specific variables for specific cells
    from a BMTK report file, with minimal parameter requirements.

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation configuration file.
    report_name : str
        Name of the report to plot (without file extension).
    variables : str
        Comma-separated list of variable names to plot (e.g., 'v,i_na,i_k').
    gids : str
        Comma-separated list of cell IDs (gids) to plot data for.

    Returns:
    --------
    None
        Displays plots of the specified variables for the specified cells.
    """

    if variables:
        variables = variables.split(',')
    if gids:
        gids = [int(i) for i in gids.split(',')]    

    if report_name:
         cfg = util.load_config(config)
         report_file = os.path.join(cfg['output']['output_dir'],report_name+'.h5')
    plot_report(config_file=config, report_file=report_file, report_name=report_name, variables=variables, gids=gids);

    return