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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.

Connections Module

bmtool.bmplot.connections.is_notebook()

Detect if code is running in a Jupyter notebook environment.

Returns:

Type Description
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.

Examples:

>>> if is_notebook():
...     plt.show()
Source code in bmtool/bmplot/connections.py 
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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.

    Examples
    --------
    >>> if is_notebook():
    ...     plt.show()
    """
    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.connections.total_connection_matrix(config, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, synaptic_info='0', include_gap=True)

Generate a plot displaying total connections or other synaptic statistics.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network names to use as sources.

 None
targets str

Comma-separated string of network names to use as targets.

 None
sids str

Comma-separated string of source node identifiers to filter.

 None
tids str

Comma-separated string of target node identifiers to filter.

 None
no_prepend_pop bool

If True, don't display population name before sid or tid in the plot. Default is False.

 False
synaptic_info str

Type of information to display. Options: - '0': Total connections (default) - '1': Mean and standard deviation of connections - '2': All synapse .mod files used - '3': All synapse .json files used

 '0'
include_gap bool

If True, include gap junctions and chemical synapses in the analysis. If False, only include chemical synapses. Default is True.

 True

Returns:

Type Description
tuple of (Figure, Axes)

The matplotlib Figure and Axes objects for further customization or saving.

Raises:

Type Description
Exception

If config is not defined or sources/targets are not defined.

Examples:

>>> total_connection_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     title='PN to LN Connections'
... )
Source code in bmtool/bmplot/connections.py 
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def total_connection_matrix(
    config: str,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    synaptic_info: str = "0",
    include_gap: bool = True,
) -> Tuple[Any, Any]:
    """
    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, optional
        Comma-separated string of network names to use as sources.
    targets : str, optional
        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. Default is False.
    synaptic_info : str, optional
        Type of information to display. Options:
        - '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. Default is True.

    Returns
    -------
    tuple of (Figure, Axes)
        The matplotlib Figure and Axes objects for further customization or saving.

    Raises
    ------
    Exception
        If config is not defined or sources/targets are not defined.

    Examples
    --------
    >>> total_connection_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     title='PN to LN Connections'
    ... )
    """
    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 is 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"

    return plot_connection_info(
        text, num, source_labels, target_labels, title, syn_info=synaptic_info
    )

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

Generates a plot showing the percent connectivity of a network.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
nodes DataFrame

Pre-loaded node data. If None, will be loaded from config.

 None
edges DataFrame

Pre-loaded edge data. If None, will be loaded from config.

 None
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network name(s) to plot.

 None
targets str

Comma-separated string of network name(s) to plot.

 None
sids str

Comma-separated string of source node identifier(s) to filter.

 None
tids str

Comma-separated string of target node identifier(s) to filter.

 None
no_prepend_pop bool

If True, population name is not displayed before sid or tid in the plot. Default is False.

 False
method str

Method for calculating percent connectivity. Options: 'total', 'uni', 'bi'. Default is 'total'.

 'total'
include_gap bool

If True, include gap junctions in analysis. If False, only include chemical synapses. Default is True.

 True
return_dict bool

If True, return connection information as a dictionary. Default is False.

 False

Returns:

Type Description
Union[Tuple[Figure, Axes], Dict]

If return_dict=True, returns a dictionary of connection information. Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

Raises:

Type Description
Exception

If config is not defined or sources/targets are not defined.

Examples:

>>> result = percent_connection_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     return_dict=True
... )
Source code in bmtool/bmplot/connections.py 
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def percent_connection_matrix(
    config: str,
    nodes: Optional[pd.DataFrame] = None,
    edges: Optional[pd.DataFrame] = None,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    method: str = "total",
    include_gap: bool = True,
    return_dict: bool = False,
) -> Union[Tuple[Any, Any], Dict]:
    """
    Generates a plot showing the percent connectivity of a network.

    Parameters
    ----------
    config : str
        Path to a BMTK simulation config file.
    nodes : pd.DataFrame, optional
        Pre-loaded node data. If None, will be loaded from config.
    edges : pd.DataFrame, optional
        Pre-loaded edge data. If None, will be loaded from config.
    title : str, optional
        Title for the plot. If None, a default title will be used.
    sources : str, optional
        Comma-separated string of network name(s) to plot.
    targets : str, optional
        Comma-separated string of network name(s) to plot.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter.
    no_prepend_pop : bool, optional
        If True, population name is not displayed before sid or tid in the plot. Default is False.
    method : str, optional
        Method for calculating percent connectivity. Options: 'total', 'uni', 'bi'.
        Default is 'total'.
    include_gap : bool, optional
        If True, include gap junctions in analysis. If False, only include chemical synapses.
        Default is True.
    return_dict : bool, optional
        If True, return connection information as a dictionary. Default is False.

    Returns
    -------
    Union[Tuple[Figure, Axes], Dict]
        If return_dict=True, returns a dictionary of connection information.
        Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

    Raises
    ------
    Exception
        If config is not defined or sources/targets are not defined.

    Examples
    --------
    >>> result = percent_connection_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     return_dict=True
    ... )
    """
    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 is None or title == "":
        title = "Percent Connectivity"

    if return_dict:
        result_dict = plot_connection_info(
            text, num, source_labels, target_labels, title, return_dict=return_dict
        )
        return result_dict
    else:
        return plot_connection_info(text, num, source_labels, target_labels, title)

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

Generates probability graphs showing connectivity as a function of distance.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
nodes DataFrame

Pre-loaded node data. If None, will be loaded from config.

 None
edges DataFrame

Pre-loaded edge data. If None, will be loaded from config.

 None
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network name(s) to plot.

 None
targets str

Comma-separated string of network name(s) to plot.

 None
sids str

Comma-separated string of source node identifier(s) to filter.

 None
tids str

Comma-separated string of target node identifier(s) to filter.

 None
no_prepend_pop bool

If True, population name is not displayed before sid or tid. Default is False.

 False
save_file str

Path to save the plot. If None, plot is not saved.

 required
dist_X bool

If True, include X distance in calculations. Default is True.

 True
dist_Y bool

If True, include Y distance in calculations. Default is True.

 True
dist_Z bool

If True, include Z distance in calculations. Default is True.

 True
bins int

Number of distance bins for the probability calculation. Default is 8.

 8
line_plot bool

If True, plot lines instead of bars. Default is False.

 False
verbose bool

If True, print debugging information. Default is False.

 False
include_gap bool

If True, include gap junctions in analysis. Default is True.

 True

Returns:

Type Description
None

Raises:

Type Description
Exception

If config is not defined or sources/targets are not defined.
Notes

This function needs model_template to be defined to work properly.

Source code in bmtool/bmplot/connections.py 
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def probability_connection_matrix(
    config: str,
    nodes: Optional[pd.DataFrame] = None,
    edges: Optional[pd.DataFrame] = None,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    dist_X: bool = True,
    dist_Y: bool = True,
    dist_Z: bool = True,
    bins: int = 8,
    line_plot: bool = False,
    verbose: bool = False,
    include_gap: bool = True,
) -> Tuple[Any, Any]:
    """
    Generates probability graphs showing connectivity as a function of distance.

    Parameters
    ----------
    config : str
        Path to a BMTK simulation config file.
    nodes : pd.DataFrame, optional
        Pre-loaded node data. If None, will be loaded from config.
    edges : pd.DataFrame, optional
        Pre-loaded edge data. If None, will be loaded from config.
    title : str, optional
        Title for the plot. If None, a default title will be used.
    sources : str, optional
        Comma-separated string of network name(s) to plot.
    targets : str, optional
        Comma-separated string of network name(s) to plot.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter.
    no_prepend_pop : bool, optional
        If True, population name is not displayed before sid or tid. Default is False.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    dist_X : bool, optional
        If True, include X distance in calculations. Default is True.
    dist_Y : bool, optional
        If True, include Y distance in calculations. Default is True.
    dist_Z : bool, optional
        If True, include Z distance in calculations. Default is True.
    bins : int, optional
        Number of distance bins for the probability calculation. Default is 8.
    line_plot : bool, optional
        If True, plot lines instead of bars. Default is False.
    verbose : bool, optional
        If True, print debugging information. Default is False.
    include_gap : bool, optional
        If True, include gap junctions in analysis. Default is True.

    Returns
    -------
    None

    Raises
    ------
    Exception
        If config is not defined or sources/targets are not defined.

    Notes
    -----
    This function needs model_template to be defined to work properly.
    """
    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 = data[x][y]["bins"]

            XX = bins_data[:-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")

    return fig, axes

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

Generates connection plot displaying synaptic convergence data.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network name(s) to plot.

 None
targets str

Comma-separated string of network name(s) to plot.

 None
sids str

Comma-separated string of source node identifier(s) to filter.

 None
tids str

Comma-separated string of target node identifier(s) to filter.

 None
no_prepend_pop bool

If True, population name is not displayed before sid or tid. Default is False.

 False
save_file str

Path to save the plot. If None, plot is not saved.

 required
convergence bool

If True, compute convergence; if False, compute divergence. Default is True.

 True
method str

Statistical method for display. Options: 'mean', 'min', 'max', 'stdev', 'mean+std'. Default is 'mean+std'.

 'mean+std'
include_gap bool

If True, include gap junctions in analysis. Default is True.

 True
return_dict bool

If True, return connection information as a dictionary. Default is None.

 None

Returns:

Type Description
Union[Tuple[Figure, Axes], Dict, None]

If return_dict=True, returns a dictionary of connection information. Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

Raises:

Type Description
Exception

If config is not defined or sources/targets are not defined.

Examples:

>>> result = convergence_connection_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     method='mean+std'
... )
Source code in bmtool/bmplot/connections.py 
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def convergence_connection_matrix(
    config: str,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    convergence: bool = True,
    method: str = "mean+std",
    include_gap: bool = True,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[Any, Any], Dict, None]:
    """
    Generates connection plot displaying synaptic convergence data.

    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, optional
        Comma-separated string of network name(s) to plot.
    targets : str, optional
        Comma-separated string of network name(s) to plot.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter.
    no_prepend_pop : bool, optional
        If True, population name is not displayed before sid or tid. Default is False.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    convergence : bool, optional
        If True, compute convergence; if False, compute divergence. Default is True.
    method : str, optional
        Statistical method for display. Options: 'mean', 'min', 'max', 'stdev', 'mean+std'.
        Default is 'mean+std'.
    include_gap : bool, optional
        If True, include gap junctions in analysis. Default is True.
    return_dict : bool, optional
        If True, return connection information as a dictionary. Default is None.

    Returns
    -------
    Union[Tuple[Figure, Axes], Dict, None]
        If return_dict=True, returns a dictionary of connection information.
        Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

    Raises
    ------
    Exception
        If config is not defined or sources/targets are not defined.

    Examples
    --------
    >>> result = convergence_connection_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     method='mean+std'
    ... )
    """
    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,
        convergence,
        method,
        include_gap=include_gap,
        return_dict=return_dict,
    )

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

Generates connection plot displaying synaptic divergence data.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network name(s) to plot.

 None
targets str

Comma-separated string of network name(s) to plot.

 None
sids str

Comma-separated string of source node identifier(s) to filter.

 None
tids str

Comma-separated string of target node identifier(s) to filter.

 None
no_prepend_pop bool

If True, population name is not displayed before sid or tid. Default is False.

 False
save_file str

Path to save the plot. If None, plot is not saved.

 required
convergence bool

If True, compute convergence; if False, compute divergence. Default is False.

 False
method str

Statistical method for display. Options: 'mean', 'min', 'max', 'stdev', 'mean+std'. Default is 'mean+std'.

 'mean+std'
include_gap bool

If True, include gap junctions in analysis. Default is True.

 True
return_dict bool

If True, return connection information as a dictionary. Default is None.

 None

Returns:

Type Description
Union[Tuple[Figure, Axes], Dict, None]

If return_dict=True, returns a dictionary of connection information. Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

Raises:

Type Description
Exception

If config is not defined or sources/targets are not defined.

Examples:

>>> result = divergence_connection_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     method='mean+std'
... )
Source code in bmtool/bmplot/connections.py 
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def divergence_connection_matrix(
    config: str,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    convergence: bool = False,
    method: str = "mean+std",
    include_gap: bool = True,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[Any, Any], Dict, None]:
    """
    Generates connection plot displaying synaptic divergence data.

    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, optional
        Comma-separated string of network name(s) to plot.
    targets : str, optional
        Comma-separated string of network name(s) to plot.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter.
    no_prepend_pop : bool, optional
        If True, population name is not displayed before sid or tid. Default is False.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    convergence : bool, optional
        If True, compute convergence; if False, compute divergence. Default is False.
    method : str, optional
        Statistical method for display. Options: 'mean', 'min', 'max', 'stdev', 'mean+std'.
        Default is 'mean+std'.
    include_gap : bool, optional
        If True, include gap junctions in analysis. Default is True.
    return_dict : bool, optional
        If True, return connection information as a dictionary. Default is None.

    Returns
    -------
    Union[Tuple[Figure, Axes], Dict, None]
        If return_dict=True, returns a dictionary of connection information.
        Otherwise, returns a tuple of (Figure, Axes) for further customization or saving.

    Raises
    ------
    Exception
        If config is not defined or sources/targets are not defined.

    Examples
    --------
    >>> result = divergence_connection_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     method='mean+std'
    ... )
    """
    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 is 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:
        result_dict = plot_connection_info(
            syn_info,
            data,
            source_labels,
            target_labels,
            title,
            return_dict=return_dict,
        )
        return result_dict
    else:
        return plot_connection_info(
            syn_info, data, source_labels, target_labels, title
        )

bmtool.bmplot.connections.gap_junction_matrix(config, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, method='convergence')

Generates connection plot displaying gap junction data.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
title str

Title for the plot. If None, a default title will be used.

 None
sources str

Comma-separated string of network name(s) to plot.

 None
targets str

Comma-separated string of network name(s) to plot.

 None
sids str

Comma-separated string of source node identifier(s) to filter.

 None
tids str

Comma-separated string of target node identifier(s) to filter.

 None
no_prepend_pop bool

If True, population name is not displayed before sid or tid. Default is False.

 False
save_file str

Path to save the plot. If None, plot is not saved.

 required
method str

Method for computing gap junction statistics. Options: 'convergence', 'percent'. Default is 'convergence'.

 'convergence'

Returns:

Type Description
None

Raises:

Type Description
Exception

If config is not defined, sources/targets are not defined, or method is invalid.

Examples:

>>> gap_junction_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     method='convergence'
... )
Source code in bmtool/bmplot/connections.py 
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def gap_junction_matrix(
    config: str,
    title: Optional[str] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = False,
    method: str = "convergence",
) -> Tuple[Any, Any]:
    """
    Generates connection plot displaying gap junction data.

    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, optional
        Comma-separated string of network name(s) to plot.
    targets : str, optional
        Comma-separated string of network name(s) to plot.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter.
    no_prepend_pop : bool, optional
        If True, population name is not displayed before sid or tid. Default is False.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    method : str, optional
        Method for computing gap junction statistics. Options: 'convergence', 'percent'.
        Default is 'convergence'.

    Returns
    -------
    None

    Raises
    ------
    Exception
        If config is not defined, sources/targets are not defined, or method is invalid.

    Examples
    --------
    >>> gap_junction_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     method='convergence'
    ... )
    """
    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: np.ndarray,
        data: np.ndarray,
        source_labels: List,
        target_labels: List,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, List]:
        """
        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 : np.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : np.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 (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: np.ndarray,
        data: np.ndarray,
        source_labels: List,
        target_labels: List,
    ) -> Tuple[np.ndarray, np.ndarray, List, List]:
        """
        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 : np.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : np.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 (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 is None or title == "":
        title = "Gap Junction"
        if method == "convergence":
            title += " Syn Convergence"
        elif method == "percent":
            title += " Percent Connectivity"
    return plot_connection_info(syn_info, data, source_labels, target_labels, title)

bmtool.bmplot.connections.connection_histogram(config, nodes=None, edges=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=True, synaptic_info='0', source_cell=None, target_cell=None, include_gap=True)

Generates histogram of the number of connections individual cells receive from another population.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
nodes DataFrame

Pre-loaded node data. If None, will be loaded from config.

 None
edges DataFrame

Pre-loaded edge data. If None, will be loaded from config.

 None
sources str

Comma-separated string of network name(s) to plot as sources.

 None
targets str

Comma-separated string of network name(s) to plot as targets.

 None
sids str

Comma-separated string of source node identifier(s) to filter by.

 None
tids str

Comma-separated string of target node identifier(s) to filter by.

 None
no_prepend_pop bool

If True, population name is not prepended to sid or tid. Default is True.

 True
synaptic_info str

Type of synaptic information to display. Default is '0'.

 '0'
source_cell str

Specific source cell type to plot connections from.

 None
target_cell str

Specific target cell type to plot connections onto.

 None
include_gap bool

If True, include gap junctions in analysis. Default is True.

 True

Returns:

Type Description
tuple

(matplotlib.figure.Figure, matplotlib.axes.Axes) containing the histogram.
Source code in bmtool/bmplot/connections.py 
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def connection_histogram(
    config: str,
    nodes: Optional[pd.DataFrame] = None,
    edges: Optional[pd.DataFrame] = None,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: bool = True,
    synaptic_info: str = "0",
    source_cell: Optional[str] = None,
    target_cell: Optional[str] = None,
    include_gap: bool = True,
) -> Tuple[Any, Any]:
    """
    Generates histogram of the number of connections individual cells receive from another population.

    Parameters
    ----------
    config : str
        Path to a BMTK simulation config file.
    nodes : pd.DataFrame, optional
        Pre-loaded node data. If None, will be loaded from config.
    edges : pd.DataFrame, optional
        Pre-loaded edge data. If None, will be loaded from config.
    sources : str, optional
        Comma-separated string of network name(s) to plot as sources.
    targets : str, optional
        Comma-separated string of network name(s) to plot as targets.
    sids : str, optional
        Comma-separated string of source node identifier(s) to filter by.
    tids : str, optional
        Comma-separated string of target node identifier(s) to filter by.
    no_prepend_pop : bool, optional
        If True, population name is not prepended to sid or tid. Default is True.
    synaptic_info : str, optional
        Type of synaptic information to display. Default is '0'.
    source_cell : str, optional
        Specific source cell type to plot connections from.
    target_cell : str, optional
        Specific target cell type to plot connections onto.
    include_gap : bool, optional
        If True, include gap junctions in analysis. Default is True.

    Returns
    -------
    tuple
        (matplotlib.figure.Figure, matplotlib.axes.Axes) containing the histogram.
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")

    sources_list = sources.split(",") if sources else []
    targets_list = targets.split(",") if targets else []
    if sids:
        sids_list = sids.split(",")
    else:
        sids_list = []
    if tids:
        tids_list = tids.split(",")
    else:
        tids_list = []

    def connection_pair_histogram(ax=None, **kwargs: Dict) -> None:
        """
        Creates a histogram showing the distribution of connection counts between specific cell types.

        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.

        Parameters
        ----------
        ax : matplotlib.axes.Axes, optional
            The axes object on which to create the histogram. If None, uses current axes.
        kwargs : dict
            Dictionary containing edge data and filtering information.
            - 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

        Returns
        -------
        None
        """
        if ax is None:
            ax = plt.gca()
        edges_data = 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_data[
                (edges_data[source_id_type] == source_id) & (edges_data[target_id_type] == target_id)
            ]
            if not include_gap:
                gap_col = temp["is_gap_junction"].fillna(False).astype(bool)
                temp = temp[~gap_col]
            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 (statistics.StatisticsError, ValueError):  # 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)
            ax.hist(node_pairs.values, density=False, bins="auto", stacked=True, label=label)
            ax.legend()
            ax.set_xlabel("# of conns from {} to {}".format(source_cell, target_cell))
            ax.set_ylabel("# of cells")
        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")

    # Create figure for the histogram
    fig, ax = plt.subplots()

    # Wrapper to pass ax to the connection_pair_histogram function
    def relation_func_wrapper(**kwargs):
        return connection_pair_histogram(ax=ax, **kwargs)

    util.relation_matrix(
        config,
        nodes,
        edges,
        sources_list,
        targets_list,
        sids_list,
        tids_list,
        not no_prepend_pop,
        relation_func=relation_func_wrapper,
        synaptic_info=synaptic_info,
    )

    return fig, ax

bmtool.bmplot.connections.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:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
sources str

Network name(s) to plot as sources.

 required
targets str

Network name(s) to plot as targets.

 required
source_cell_id int

ID of the source cell for calculating distances to target nodes.

 required
target_id_type str

String to filter target nodes based off the target_query.

 required
ignore_z bool

If True, ignore Z axis when calculating distance. Default is False.

 False

Returns:

Type Description
tuple

Two tuples, each containing (matplotlib.figure.Figure, matplotlib.axes.Axes): - First tuple: 3D/2D scatter plot of node positions - Second tuple: Histogram of distances

Examples:

>>> (fig1, ax1), (fig2, ax2) = connection_distance(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     source_cell_id=0,
...     target_id_type='LN',
...     ignore_z=False
... )
Source code in bmtool/bmplot/connections.py 
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def connection_distance(
    config: str,
    sources: str,
    targets: str,
    source_cell_id: int,
    target_id_type: str,
    ignore_z: bool = False,
) -> Tuple[Tuple[Any, Any], Tuple[Any, Any]]:
    """
    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
        Path to a BMTK simulation config file.
    sources : str
        Network name(s) to plot as sources.
    targets : str
        Network name(s) to plot as targets.
    source_cell_id : int
        ID of the source cell for calculating distances to target nodes.
    target_id_type : str
        String to filter target nodes based off the target_query.
    ignore_z : bool, optional
        If True, ignore Z axis when calculating distance. Default is False.

    Returns
    -------
    tuple
        Two tuples, each containing (matplotlib.figure.Figure, matplotlib.axes.Axes):
        - First tuple: 3D/2D scatter plot of node positions
        - Second tuple: Histogram of distances

    Examples
    --------
    >>> (fig1, ax1), (fig2, ax2) = connection_distance(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     source_cell_id=0,
    ...     target_id_type='LN',
    ...     ignore_z=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()

    # Plot distances in a separate 2D plot
    fig2, ax2 = plt.subplots(figsize=(8, 6))
    ax2.hist(distances, bins=20, color="blue", edgecolor="black")
    ax2.set_xlabel("Distance")
    ax2.set_ylabel("Count")
    ax2.set_title("Distance from Source Node to Each Target Node")
    ax2.grid(True)

    return (fig, ax), (fig2, ax2)

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

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

This function creates a grid of histograms where each cell represents the distribution of a specific edge property between source and target populations.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 required
sources str

Comma-separated list of source network names.

 None
targets str

Comma-separated list of target network names.

 None
sids str

Comma-separated list of source node identifiers to filter by.

 None
tids str

Comma-separated list of target node identifiers to filter by.

 None
no_prepend_pop bool

If True, population names are not prepended to node identifiers.

 None
edge_property str

The edge property to analyze (e.g., 'syn_weight', 'delay').

 None
time int

Time point to analyze from a time series report.

 None
time_compare int

Second time point for comparison with time.

 None
report str

Name of the report to analyze.

 None
title str

Custom title for the plot.

 None
save_file str

Path to save the generated plot.

 required

Returns:

Type Description
tuple

(matplotlib.figure.Figure, matplotlib.axes.Axes) containing the histogram matrix.

Examples:

>>> fig, axes = edge_histogram_matrix(
...     config='config.json',
...     sources='PN',
...     targets='LN',
...     edge_property='syn_weight'
... )
Source code in bmtool/bmplot/connections.py 
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def edge_histogram_matrix(
    config: str,
    sources: Optional[str] = None,
    targets: Optional[str] = None,
    sids: Optional[str] = None,
    tids: Optional[str] = None,
    no_prepend_pop: Optional[bool] = None,
    edge_property: Optional[str] = None,
    time: Optional[int] = None,
    time_compare: Optional[int] = None,
    report: Optional[str] = None,
    title: Optional[str] = None,

) -> Tuple[Any, Any]:
    """
    Generates a matrix of histograms showing the distribution of edge properties between populations.

    This function creates a grid of histograms where each cell represents the distribution
    of a specific edge property between source and target populations.

    Parameters
    ----------
    config : str
        Path to a BMTK simulation config file.
    sources : str, optional
        Comma-separated list of source network names.
    targets : str, optional
        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.
    edge_property : str, optional
        The edge property to analyze (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
    -------
    tuple
        (matplotlib.figure.Figure, matplotlib.axes.Axes) containing the histogram matrix.

    Examples
    --------
    >>> fig, axes = edge_histogram_matrix(
    ...     config='config.json',
    ...     sources='PN',
    ...     targets='LN',
    ...     edge_property='syn_weight'
    ... )
    """

    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()

    return fig, axes

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

Plot connection information as a heatmap with text annotations.

Parameters:

Name Type Description Default
text ndarray

2D array of text annotations for each cell.

 required
num ndarray

2D array of numerical values for the heatmap colors.

 required
source_labels list of str

Labels for source populations (rows).

 required
target_labels list of str

Labels for target populations (columns).

 required
title str

Title for the plot.

 required
syn_info str

Type of synaptic information being displayed. Options: '0', '1', '2', '3'. Default is '0'.

 '0'
save_file str

Path to save the plot. If None, plot is not saved.

 required
return_dict bool

If True, return connection information as a dictionary. Default is None.

 None

Returns:

Type Description
Union[Tuple, Dict, None]

If return_dict=True, returns a dictionary of connection information. Otherwise, returns a tuple of (Figure, Axes), or None if just displaying.
Notes

Handles missing source and target values by setting them to 0.

Source code in bmtool/bmplot/connections.py 
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def plot_connection_info(
    text: np.ndarray,
    num: np.ndarray,
    source_labels: List[str],
    target_labels: List[str],
    title: str,
    syn_info: str = "0",

    return_dict: Optional[bool] = None,
) -> Union[Tuple, Dict, None]:
    """
    Plot connection information as a heatmap with text annotations.

    Parameters
    ----------
    text : np.ndarray
        2D array of text annotations for each cell.
    num : np.ndarray
        2D array of numerical values for the heatmap colors.
    source_labels : list of str
        Labels for source populations (rows).
    target_labels : list of str
        Labels for target populations (columns).
    title : str
        Title for the plot.
    syn_info : str, optional
        Type of synaptic information being displayed. Options: '0', '1', '2', '3'.
        Default is '0'.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    return_dict : bool, optional
        If True, return connection information as a dictionary. Default is None.

    Returns
    -------
    Union[Tuple, Dict, None]
        If return_dict=True, returns a dictionary of connection information.
        Otherwise, returns a tuple of (Figure, Axes), or None if just displaying.

    Notes
    -----
    Handles missing source and target values by setting them 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")

    # Calculate square cell size to ensure proper aspect ratio
    base_cell_size = 0.6  # Base size per cell

    # Calculate figure dimensions with proper aspect ratio
    # Make sure width and height are proportional to the matrix dimensions
    fig_width = max(8, num_target * base_cell_size + 4)  # Width based on columns
    fig_height = max(6, num_source * base_cell_size + 3)  # Height based on rows

    # Ensure minimum readable size
    min_fig_size = 8
    if fig_width < min_fig_size or fig_height < min_fig_size:
        scale_factor = min_fig_size / min(fig_width, fig_height)
        fig_width *= scale_factor
        fig_height *= scale_factor

    # Create figure and axis
    fig1, ax1 = plt.subplots(figsize=(fig_width, fig_height))

    # Replace NaN with 0 and create heatmap
    num_clean = np.nan_to_num(num, nan=0)
    # if string is nan\nnan make it 0

    # Use 'auto' aspect ratio to let matplotlib handle it properly
    # This prevents the stretching issue
    im1 = ax1.imshow(num_clean, aspect="auto", interpolation="nearest")

    # Set ticks and labels
    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)

    # Improved font sizing based on matrix size
    label_font_size = max(8, min(14, 120 / max(num_source, num_target)))

    # Style the tick labels
    ax1.tick_params(axis="y", labelsize=label_font_size, pad=5)
    plt.setp(
        ax1.get_xticklabels(),
        rotation=45,
        ha="right",
        rotation_mode="anchor",
        fontsize=label_font_size,
    )

    # Dictionary to store connection information
    graph_dict = {}

    # Improved text size calculation - more readable for larger matrices
    text_size = max(6, min(12, 80 / max(num_source, num_target)))

    # Loop over data dimensions and create text annotations
    for i in range(num_source):
        for j in range(num_target):
            edge_info = text[i, j] if text[i, j] is not None else "0\n0"

            if source_labels[i] not in graph_dict:
                graph_dict[source_labels[i]] = {}
            graph_dict[source_labels[i]][target_labels[j]] = edge_info

            # Skip displaying text for NaN values to reduce clutter
            if edge_info == "nan\nnan":
                edge_info = "0\n±0"

            # Format the text display
            if isinstance(edge_info, str) and "\n" in edge_info:
                # For mean/std format (e.g. "15.5\n4.0")
                parts = edge_info.split("\n")
                if len(parts) == 2:
                    try:
                        mean_val = float(parts[0])
                        std_val = float(parts[1])
                        display_text = f"{mean_val:.1f}\n±{std_val:.1f}"
                    except ValueError:
                        display_text = edge_info
                else:
                    display_text = edge_info
            else:
                display_text = str(edge_info)

            # Add text to plot with better contrast
            text_color = "white" if num_clean[i, j] < (np.nanmax(num_clean) * 0.9) else "black"

            if syn_info == "2" or syn_info == "3":
                ax1.text(
                    j,
                    i,
                    display_text,
                    ha="center",
                    va="center",
                    color=text_color,
                    rotation=37.5,
                    fontsize=text_size,
                    weight="bold",
                )
            else:
                ax1.text(
                    j,
                    i,
                    display_text,
                    ha="center",
                    va="center",
                    color=text_color,
                    fontsize=text_size,
                    weight="bold",
                )

    # Set labels and title
    title_font_size = max(12, min(18, label_font_size + 4))
    ax1.set_ylabel("Source", fontsize=title_font_size, weight="bold", labelpad=10)
    ax1.set_xlabel("Target", fontsize=title_font_size, weight="bold", labelpad=10)
    ax1.set_title(title, fontsize=title_font_size + 2, weight="bold", pad=20)

    # Add colorbar
    cbar = plt.colorbar(im1, shrink=0.8)
    cbar.ax.tick_params(labelsize=label_font_size)

    # Adjust layout to minimize whitespace and prevent stretching
    plt.tight_layout(pad=1.5)

    # Force square cells by setting equal axis limits if needed
    ax1.set_xlim(-0.5, num_target - 0.5)
    ax1.set_ylim(num_source - 0.5, -0.5)  # Inverted for proper matrix orientation

    # Display or save the plot
    try:
        # Check if running in notebook
        from IPython import get_ipython

        notebook = get_ipython() is not None
    except ImportError:
        notebook = False

    if not notebook:
        plt.show()

    if return_dict:
        return graph_dict
    else:
        return fig1, ax1

bmtool.bmplot.connections.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.

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

Parameters:

Name Type Description Default
csv_path str

Path to the CSV file containing connection data. The CSV should be an output from the bmtool.connector classes, specifically generated by the save_connection_report() function.

 None
exclude_strings list of str

List of strings to exclude rows where 'Source' or 'Target' contain these strings.

 None
assemb_key str

Key to identify and process assembly connections.

 None
title str

Title for the generated plot. Default is 'Percent connection matrix'.

 'Percent connection matrix'
pop_order list of str

List of population labels to specify the order for x- and y-ticks in the plot.

 None

Returns:

Type Description
tuple

(matplotlib.figure.Figure, matplotlib.axes.Axes) containing the heatmap.

Examples:

>>> fig, ax = connector_percent_matrix(
...     csv_path='connections.csv',
...     exclude_strings=['Gap'],
...     title='Network Connectivity'
... )
Source code in bmtool/bmplot/connections.py 
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def connector_percent_matrix(
    csv_path: Optional[str] = None,
    exclude_strings: Optional[List[str]] = None,
    assemb_key: Optional[str] = None,
    title: str = "Percent connection matrix",
    pop_order: Optional[List[str]] = None,
) -> Tuple[Any, Any]:
    """
    Generates and plots a connection matrix based on connection probabilities from a CSV file.

    This function visualizes percent connectivity while factoring in population distance and other parameters.
    It processes connection data by filtering '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, optional
        Path to the CSV file containing 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.
    assemb_key : str, optional
        Key to identify and process assembly connections.
    title : str, optional
        Title for the generated plot. Default is 'Percent connection matrix'.
    pop_order : list of str, optional
        List of population labels to specify the order for x- and y-ticks in the plot.

    Returns
    -------
    tuple
        (matplotlib.figure.Figure, matplotlib.axes.Axes) containing the heatmap.

    Examples
    --------
    >>> fig, ax = connector_percent_matrix(
    ...     csv_path='connections.csv',
    ...     exclude_strings=['Gap'],
    ...     title='Network Connectivity'
    ... )
    """
    # 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 isinstance(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()

    return fig, ax

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

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

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 None
sources str

Which network(s) to plot. If None or 'all', plots all networks.

 None
sid str

Column name to group cell types (node grouping criteria).

 None
title str

Plot title. Default is '3D positions'.

 None
subset int

Take every Nth row. This makes plotting large networks easier to visualize.

 None

Returns:

Type Description
tuple

(matplotlib.figure.Figure, matplotlib.axes.Axes) containing the 3D plot.

Examples:

>>> fig, ax = plot_3d_positions(
...     config='config.json',
...     sources='cortex',
...     sid='node_type_id',
...     title='3D Neuron Positions'
... )
Source code in bmtool/bmplot/connections.py 
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def plot_3d_positions(
    config: Optional[str] = None,
    sources: Optional[str] = None,
    sid: Optional[str] = None,
    title: Optional[str] = None,

    subset: Optional[int] = None,
) -> Tuple[Any, Any]:
    """
    Plots a 3D graph of all cells with x, y, z location.

    Parameters
    ----------
    config : str, optional
        Path to a BMTK simulation config file.
    sources : str, optional
        Which network(s) to plot. If None or 'all', plots all networks.
    sid : str, optional
        Column name to group cell types (node grouping criteria).
    title : str, optional
        Plot title. Default is '3D positions'.
    subset : int, optional
        Take every Nth row. This makes plotting large networks easier to visualize.

    Returns
    -------
    tuple
        (matplotlib.figure.Figure, matplotlib.axes.Axes) containing the 3D plot.

    Examples
    --------
    >>> fig, ax = plot_3d_positions(
    ...     config='config.json',
    ...     sources='cortex',
    ...     sid='node_type_id',
    ...     title='3D Neuron Positions'
    ... )
    """

    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 fig, ax

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

    # Add axis labels
    ax.set_xlabel("X Position (μm)")
    ax.set_ylabel("Y Position (μm)")
    ax.set_zlabel("Z Position (μm)")

    # Draw the plot
    plt.draw()
    plt.tight_layout()

    return fig, ax

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

Plot 3D visualization of cell rotations with quiver arrows showing rotation orientations.

Parameters:

Name Type Description Default
config str

Path to a BMTK simulation config file.

 None
sources list of str

Network names to plot. If None or contains 'all', plots all networks.

 None
sids str

Comma-separated column names to group cell types.

 None
title str

Plot title. Default is 'Cell rotations'.

 None
quiver_length float

Length of the quiver arrows. If None, use matplotlib default.

 None
arrow_length_ratio float

Ratio of arrow head size to quiver length.

 None
group str

Comma-separated group names to include. If None, include all groups.

 None
subset int

Take every Nth row. Useful for visualizing large networks.

 None

Returns:

Type Description
tuple

(matplotlib.figure.Figure, matplotlib.axes.Axes) containing the 3D plot.

Examples:

>>> fig, ax = plot_3d_cell_rotation(
...     config='config.json',
...     sources=['cortex'],
...     sids='node_type_id',
...     title='Cell Rotation Vectors'
... )
Source code in bmtool/bmplot/connections.py 
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def plot_3d_cell_rotation(
    config: Optional[str] = None,
    sources: Optional[List[str]] = None,
    sids: Optional[str] = None,
    title: Optional[str] = None,

    quiver_length: Optional[float] = None,
    arrow_length_ratio: Optional[float] = None,
    group: Optional[str] = None,
    subset: Optional[int] = None,
) -> Tuple[Any, Any]:
    """
    Plot 3D visualization of cell rotations with quiver arrows showing rotation orientations.

    Parameters
    ----------
    config : str, optional
        Path to a BMTK simulation config file.
    sources : list of str, optional
        Network names to plot. If None or contains 'all', plots all networks.
    sids : str, optional
        Comma-separated column names to group cell types.
    title : str, optional
        Plot title. Default is 'Cell rotations'.
    quiver_length : float, optional
        Length of the quiver arrows. If None, use matplotlib default.
    arrow_length_ratio : float, optional
        Ratio of arrow head size to quiver length.
    group : str, optional
        Comma-separated group names to include. If None, include all groups.
    subset : int, optional
        Take every Nth row. Useful for visualizing large networks.

    Returns
    -------
    tuple
        (matplotlib.figure.Figure, matplotlib.axes.Axes) containing the 3D plot.

    Examples
    --------
    >>> fig, ax = plot_3d_cell_rotation(
    ...     config='config.json',
    ...     sources=['cortex'],
    ...     sids='node_type_id',
    ...     title='Cell Rotation Vectors'
    ... )
    """
    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 fig, ax

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

    return fig, ax

Spikes Module

bmtool.bmplot.spikes.raster(spikes_df=None, config=None, network_name=None, groupby='pop_name', sortby=None, ax=None, tstart=None, tstop=None, color_map=None, dot_size=0.3)

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. groupby : str, optional Column name to group spikes by for coloring. Default is 'pop_name'. sortby : str, optional Column name to sort node_ids within each group. If provided, nodes within each population will be sorted by this column. 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. dot_size: float, optional Size of the dot to display on the scatterplot

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/spikes.py 
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def raster(
    spikes_df: Optional[pd.DataFrame] = None,
    config: Optional[str] = None,
    network_name: Optional[str] = None,
    groupby: str = "pop_name",
    sortby: Optional[str] = None,
    ax: Optional[Axes] = None,
    tstart: Optional[float] = None,
    tstop: Optional[float] = None,
    color_map: Optional[Dict[str, str]] = None,
    dot_size: float = 0.3,
) -> 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.
    groupby : str, optional
        Column name to group spikes by for coloring. Default is 'pop_name'.
    sortby : str, optional
        Column name to sort node_ids within each group. If provided, nodes within each population will be sorted by this column.
    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.
    dot_size: float, optional
        Size of the dot to display on the scatterplot

    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
    sns.set_style("whitegrid")
    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
    legend_handles = []
    y_offset = 0  # Track y-position offset for stacking populations

    for pop_name, group in spikes_df.groupby(groupby):
        if sortby:
            # Sort by the specified column, putting NaN values at the end
            group_sorted = group.sort_values(by=sortby, na_position='last')
            # Create a mapping from node_ids to consecutive y-positions based on sorted order
            # Use the sorted order to maintain the same sequence for all spikes from same node
            unique_nodes_sorted = group_sorted['node_ids'].drop_duplicates()
            node_to_y = {node_id: y_offset + i for i, node_id in enumerate(unique_nodes_sorted)}
            # Map node_ids to new y-positions for ALL spikes (not just the sorted group)
            y_positions = group['node_ids'].map(node_to_y)
            # Verify no data was lost
            assert len(y_positions) == len(group), f"Data loss detected in population {pop_name}"
            assert y_positions.isna().sum() == 0, f"Unmapped node_ids found in population {pop_name}"
        else:
            y_positions = group['node_ids']

        ax.scatter(group["timestamps"], y_positions, color=color_map[pop_name], s=dot_size)
        # Dummy scatter for consistent legend appearance
        handle = ax.scatter([], [], color=color_map[pop_name], label=pop_name, s=20)
        legend_handles.append(handle)

        # Update y_offset for next population if sortby is used
        if sortby:
            y_offset += len(unique_nodes_sorted)

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

    return ax

bmtool.bmplot.spikes.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/spikes.py 
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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
    sns.set_style("whitegrid")
    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.spikes.plot_firing_rate_distribution(individual_stats, groupby, ax=None, color_map=None, plot_type='box', swarm_alpha=0.6, logscale=False)

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. logscale : bool, optional If True, use logarithmic scale for the y-axis (default is False).

Returns:

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

Source code in bmtool/bmplot/spikes.py 
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def plot_firing_rate_distribution(
    individual_stats: pd.DataFrame,
    groupby: Union[str, List[str]],
    ax: Optional[Axes] = None,
    color_map: Optional[Dict[str, str]] = None,
    plot_type: Union[str, List[str]] = "box",
    swarm_alpha: float = 0.6,
    logscale: bool = False,
) -> 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.
    logscale : bool, optional
        If True, use logarithmic scale for the y-axis (default is False).

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the selected plot type(s) overlayed.
    """
    sns.set_style("whitegrid")
    # 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="box",
                alpha=0.4,
                cut=0,  # This prevents the KDE from extending beyond the data range
            )
        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)

    if logscale:
        ax.set_yscale('log')

    return ax

Entrainment Module

bmtool.bmplot.entrainment.plot_spike_power_correlation(spike_df, lfp_data, fs, pop_names, filter_method='wavelet', bandwidth=2.0, lowcut=None, highcut=None, freq_range=(10, 100), freq_step=5, type_name='raw', figsize=(12, 8))

Calculate and plot spike rate-LFP power correlation across frequencies for full signal.

Analyzes the relationship between population spike rates and LFP power across a range of frequencies, using Spearman correlation for the entire signal duration.

Parameters:

Name Type Description Default
spike_df DataFrame

DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.

 required
lfp_data DataArray

LFP data with time dimension.

 required
fs float

Sampling frequency in Hz.

 required
pop_names List[str]

List of population names to analyze.

 required
filter_method str

Filtering method: 'wavelet' or 'butter' (default: 'wavelet').

 'wavelet'
bandwidth float

Bandwidth parameter for wavelet filter (default: 2.0).

 2.0
lowcut float

Lower frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.

 None
highcut float

Upper frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.

 None
freq_range Tuple[float, float]

Min and max frequency to analyze in Hz (default: (10, 100)).

 (10, 100)
freq_step float

Step size for frequency analysis in Hz (default: 5).

 5
type_name str

Which type of spike rate to use (default: 'raw').

 'raw'
figsize Tuple[float, float]

Figure size (width, height) in inches (default: (12, 8)).

 (12, 8)

Returns:

Type Description
Figure

Figure containing the correlation plot.
Notes
  • Uses Spearman correlation (rank-based, robust to outliers).
  • Pre-computes LFP power at all frequencies for efficiency.

Examples:

>>> fig = plot_spike_power_correlation(
...     spike_df=spike_df,
...     lfp_data=lfp,
...     fs=400,
...     pop_names=['PV', 'SST'],
...     freq_range=(10, 100),
...     freq_step=5
... )
Source code in bmtool/bmplot/entrainment.py 
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def plot_spike_power_correlation(
    spike_df: pd.DataFrame,
    lfp_data: xr.DataArray,
    fs: float,
    pop_names: List[str],
    filter_method: str = "wavelet",
    bandwidth: float = 2.0,
    lowcut: Optional[float] = None,
    highcut: Optional[float] = None,
    freq_range: Tuple[float, float] = (10, 100),
    freq_step: float = 5,
    type_name: str = "raw",
    figsize: Tuple[float, float] = (12, 8),
) -> Figure:
    """
    Calculate and plot spike rate-LFP power correlation across frequencies for full signal.

    Analyzes the relationship between population spike rates and LFP power across a range
    of frequencies, using Spearman correlation for the entire signal duration.

    Parameters
    ----------
    spike_df : pd.DataFrame
        DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.
    lfp_data : xr.DataArray
        LFP data with time dimension.
    fs : float
        Sampling frequency in Hz.
    pop_names : List[str]
        List of population names to analyze.
    filter_method : str, optional
        Filtering method: 'wavelet' or 'butter' (default: 'wavelet').
    bandwidth : float, optional
        Bandwidth parameter for wavelet filter (default: 2.0).
    lowcut : float, optional
        Lower frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.
    highcut : float, optional
        Upper frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.
    freq_range : Tuple[float, float], optional
        Min and max frequency to analyze in Hz (default: (10, 100)).
    freq_step : float, optional
        Step size for frequency analysis in Hz (default: 5).
    type_name : str, optional
        Which type of spike rate to use (default: 'raw').
    figsize : Tuple[float, float], optional
        Figure size (width, height) in inches (default: (12, 8)).

    Returns
    -------
    matplotlib.figure.Figure
        Figure containing the correlation plot.

    Notes
    -----
    - Uses Spearman correlation (rank-based, robust to outliers).
    - Pre-computes LFP power at all frequencies for efficiency.

    Examples
    --------
    >>> fig = plot_spike_power_correlation(
    ...     spike_df=spike_df,
    ...     lfp_data=lfp,
    ...     fs=400,
    ...     pop_names=['PV', 'SST'],
    ...     freq_range=(10, 100),
    ...     freq_step=5
    ... )
    """
    # Compute spike rate for all spikes
    spike_rate = bmspikes.get_population_spike_rate(spike_df, fs=fs)

    # Setup frequencies for analysis
    frequencies = np.arange(freq_range[0], freq_range[1] + 1, freq_step)

    # Pre-calculate LFP power for all frequencies
    power_by_freq = {}
    for freq in frequencies:
        power_by_freq[freq] = get_lfp_power(
            lfp_data, freq, fs, filter_method, lowcut=lowcut, highcut=highcut, bandwidth=bandwidth
        )

    # Calculate correlations for each population and frequency
    results = {}
    for pop in pop_names:
        results[pop] = {}
        pop_spike_rate = spike_rate.sel(population=pop, type=type_name)

        for freq in frequencies:
            lfp_power = power_by_freq[freq]

            if len(pop_spike_rate) != len(lfp_power):
                print(f"Warning: Length mismatch for {pop} at {freq} Hz")
                print(f"{len(pop_spike_rate)} {len(lfp_power)}")
                continue

            corr, p_val = stats.spearmanr(pop_spike_rate.values, lfp_power.values)
            results[pop][freq] = {"correlation": corr, "p_value": p_val}

    # Create plot
    sns.set_style("whitegrid")
    fig = plt.figure(figsize=figsize)

    colors = plt.get_cmap("tab10")
    for i, pop in enumerate(pop_names):
        plot_freqs = []
        plot_corrs = []

        for freq in frequencies:
            if freq in results[pop] and not np.isnan(results[pop][freq]["correlation"]):
                plot_freqs.append(freq)
                plot_corrs.append(results[pop][freq]["correlation"])

        if len(plot_freqs) == 0:
            continue

        plot_freqs = np.array(plot_freqs)
        plot_corrs = np.array(plot_corrs)
        color = colors(i)

        plt.plot(
            plot_freqs, plot_corrs, marker="o", label=pop, linewidth=2, markersize=6, color=color
        )

    # Formatting
    plt.xlabel("Frequency (Hz)", fontsize=12)
    plt.ylabel("Spike Rate-Power Correlation", fontsize=12)

    plt.title(
        "Spike Rate-LFP Power Correlation",
        fontsize=14,
    )
    plt.grid(True, alpha=0.3)
    plt.axhline(y=0, color="gray", linestyle="-", alpha=0.5)

    # Setup legend
    from matplotlib.lines import Line2D

    legend_elements = [
        Line2D([0], [0], color=colors(i), marker="o", linestyle="-", label=pop)
        for i, pop in enumerate(pop_names)
    ]
    plt.legend(handles=legend_elements, fontsize=10, loc="best")

    # Axis formatting
    if len(frequencies) > 10:
        plt.xticks(frequencies[::2])
    else:
        plt.xticks(frequencies)
    plt.xlim(frequencies[0], frequencies[-1])

    y_min, y_max = plt.ylim()
    plt.ylim(min(y_min, -0.1), max(y_max, 0.1))

    plt.tight_layout()
    return fig

LFP Module

bmtool.bmplot.lfp.plot_spectrogram(sxx_xarray, remove_aperiodic=None, log_power=False, plt_range=None, clr_freq_range=None, pad=0.03, ax=None, vmin=None, vmax=None)

Plot a power spectrogram with optional aperiodic removal and frequency-based coloring.

Parameters:

Name Type Description Default
sxx_xarray array - like

Spectrogram data as an xarray DataArray with PSD values.

 required
remove_aperiodic optional

FOOOF model object for aperiodic subtraction. If None, raw spectrum is displayed.

 None
log_power bool or str

If True or 'dB', convert power to log scale. Default is False.

 False
plt_range tuple of float

Frequency range to display as (f_min, f_max). If None, displays full range.

 None
clr_freq_range tuple of float

Frequency range to use for determining color limits. If None, uses full range.

 None
pad float

Padding for colorbar. Default is 0.03.

 0.03
ax Axes

Axes to plot on. If None, creates a new figure and axes.

 None
vmin float

Minimum value for colorbar scaling. If None, computed from data.

 None
vmax float

Maximum value for colorbar scaling. If None, computed from data.

 None

Returns:

Type Description
Figure

The figure object containing the spectrogram.

Examples:

>>> fig = plot_spectrogram(
...     sxx_xarray, log_power='dB',
...     plt_range=(10, 100), clr_freq_range=(20, 50)
... )
Source code in bmtool/bmplot/lfp.py 
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def plot_spectrogram(
    sxx_xarray: Any,
    remove_aperiodic: Optional[Any] = None,
    log_power: bool = False,
    plt_range: Optional[Tuple[float, float]] = None,
    clr_freq_range: Optional[Tuple[float, float]] = None,
    pad: float = 0.03,
    ax: Optional[plt.Axes] = None,
    vmin: Optional[float] = None,
    vmax: Optional[float] = None,
) -> Figure:
    """
    Plot a power spectrogram with optional aperiodic removal and frequency-based coloring.

    Parameters
    ----------
    sxx_xarray : array-like
        Spectrogram data as an xarray DataArray with PSD values.
    remove_aperiodic : optional
        FOOOF model object for aperiodic subtraction. If None, raw spectrum is displayed.
    log_power : bool or str, optional
        If True or 'dB', convert power to log scale. Default is False.
    plt_range : tuple of float, optional
        Frequency range to display as (f_min, f_max). If None, displays full range.
    clr_freq_range : tuple of float, optional
        Frequency range to use for determining color limits. If None, uses full range.
    pad : float, optional
        Padding for colorbar. Default is 0.03.
    ax : matplotlib.axes.Axes, optional
        Axes to plot on. If None, creates a new figure and axes.
    vmin : float, optional
        Minimum value for colorbar scaling. If None, computed from data.
    vmax : float, optional
        Maximum value for colorbar scaling. If None, computed from data.

    Returns
    -------
    matplotlib.figure.Figure
        The figure object containing the spectrogram.

    Examples
    --------
    >>> fig = plot_spectrogram(
    ...     sxx_xarray, log_power='dB',
    ...     plt_range=(10, 100), clr_freq_range=(20, 50)
    ... )
    """
    sxx = sxx_xarray.PSD.values.copy()
    t = sxx_xarray.time.values.copy()
    f = sxx_xarray.frequency.values.copy()

    cbar_label = "PSD" if remove_aperiodic is None else "PSD Residual"
    if log_power:
        with np.errstate(divide="ignore"):
            sxx = np.log10(sxx)
        cbar_label += " dB" if log_power == "dB" else " log(power)"

    if remove_aperiodic is not None:
        f1_idx = 0 if f[0] else 1
        ap_fit = gen_aperiodic(f[f1_idx:], remove_aperiodic.aperiodic_params)
        sxx[f1_idx:, :] -= (ap_fit if log_power else 10**ap_fit)[:, None]
        sxx[:f1_idx, :] = 0.0

    if log_power == "dB":
        sxx *= 10

    if ax is None:
        _, ax = plt.subplots(1, 1)
    plt_range = np.array(f[-1]) if plt_range is None else np.array(plt_range)
    if plt_range.size == 1:
        plt_range = [f[0 if f[0] else 1] if log_power else 0.0, plt_range.item()]
    f_idx = (f >= plt_range[0]) & (f <= plt_range[1])

    # Determine vmin and vmax: explicit parameters take precedence, then clr_freq_range, then None
    if vmin is None:
        if clr_freq_range is not None:
            c_idx = (f >= clr_freq_range[0]) & (f <= clr_freq_range[1])
            vmin = sxx[c_idx, :].min()

    if vmax is None:
        if clr_freq_range is not None:
            c_idx = (f >= clr_freq_range[0]) & (f <= clr_freq_range[1])
            vmax = sxx[c_idx, :].max()

    f = f[f_idx]
    pcm = ax.pcolormesh(t, f, sxx[f_idx, :], shading="gouraud", vmin=vmin, vmax=vmax, rasterized=True)
    if "cone_of_influence_frequency" in sxx_xarray:
        coif = sxx_xarray.cone_of_influence_frequency
        ax.plot(t, coif)
        ax.fill_between(t, coif, step="mid", alpha=0.2)
    ax.set_xlim(t[0], t[-1])
    # ax.set_xlim(t[0],0.2)
    ax.set_ylim(f[0], f[-1])
    plt.colorbar(mappable=pcm, ax=ax, label=cbar_label, pad=pad)
    ax.set_xlabel("Time (sec)")
    ax.set_ylabel("Frequency (Hz)")
    return ax.figure