brian2tools.plotting package

Package containing plotting modules.

brian2tools.plotting.add_background_pattern(axes, hatch='xxx', fill=True, fc=(0.9, 0.9, 0.9), ec=(0.8, 0.8, 0.8), zorder=-10, **kwds)[source]

Add a “hatching” pattern to the background of the axes (can be useful to make a difference between “no value” and a value mapped to a color value that is identical to the background color). By default, it uses a cross hatching pattern in gray which can be changed by providing the respective arguments. All additional keyword arguments are passed on to the Rectangle initializer.

Parameters:
brian2tools.plotting.brian_plot(brian_obj, axes=None, **kwds)[source]

Plot the data of the given object (e.g. a monitor). This function will call an adequate plotting function for the object, e.g. plot_raster for a SpikeMonitor. The plotting may apply heuristics to get a generally useful plot (e.g. for a PopulationRateMonitor, it will plot the rates smoothed with a Gaussian window of 1 ms), the exact details are subject to change. This function is therefore mostly meant as a quick and easy way to plot an object, for full control use one of the specific plotting functions.

When a StateMonitor that records several variables is given, a column of subplots sharing the time axis is created automatically.

Parameters:

  • brian_obj (object) – The Brian object to plot.

  • axes (Axes or array-like of Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot. For a multi-variable StateMonitor, pass an array-like of Axes with one entry per recorded variable.

  • kwds (dict, optional) –

    Any additional keywords command will be handed over to matplotlib’s plot command. This can be used to set plot properties such as the color.

    For multi-variable StateMonitor objects, var_name and var_unit may be dictionaries keyed by variable name, e.g. var_name={'v': 'membrane potential', 'I': 'input current'}.

Returns:

axes – The Axes instance(s) used for plotting. A list is returned when multiple state variables are plotted.

Return type:

Axes or list of Axes

brian2tools.plotting.plot_dendrogram(morphology, axes=None, **kwds)[source]

Plot a “dendrogram” of a morphology, i.e. an abstract representation which visualizes the branching structure and the length of each section.

Parameters:

  • morphology (Morphology) – The morphology to visualize.

  • axes (Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot.

  • **kwds – Any additional keyword arguments are passed to matplotlib’s plot, vlines, and hlines calls. Only arguments accepted by all three functions should be used (e.g. color, alpha, linewidth).

Returns:

axes – The Axes instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes

brian2tools.plotting.plot_morphology(morphology, plot_3d=None, show_compartments=False, show_diameter=False, colors=('darkblue', 'darkred'), values=None, value_norm=(None, None), value_colormap='hot', value_colorbar=True, value_unit=None, axes=None)[source]

Plot a given Morphology in 2D or 3D.

Parameters:

  • morphology (Morphology) – The morphology to plot

  • plot_3d (bool, optional) – Whether to plot the morphology in 3D or in 2D. If not set (the default) a morphology where all z values are 0 is plotted in 2D, otherwise it is plot in 3D.

  • show_compartments (bool, optional) – Whether to plot a dot at the center of each compartment. Defaults to False.

  • show_diameter (bool, optional) – Whether to plot the compartments with the diameter given in the morphology. Defaults to False.

  • colors (sequence of color specifications) – A list of colors that is cycled through for each new section. Can be any color specification that matplotlib understands (e.g. a string such as 'darkblue' or a tuple such as (0, 0.7, 0).

  • values (Quantity, optional) – Values to fill compartment patches with a color that corresponds to their given value.

  • value_norm (tuple or callable, optional) – Normalization function to scale the displayed values. Can be a tuple of a minimum and a maximum value (where either of them can be None to denote taking the minimum/maximum from the data) or a function that takes a value and returns the scaled value (e.g. as returned by matplotlib.colors.PowerNorm). For a tuple of values, will use matplotlib.colors.Normalize```(vmin, vmax, clip=True)`` with the given (vmin, vmax) values.

  • value_colormap (str or matplotlib.colors.Colormap, optional) – Desired colormap for plots. Either the name of a standard colormap or a matplotlib.colors.Colormap instance. Defaults to 'hot'. Note that this uses matplotlib color maps even for 3D plots with Mayavi.

  • value_colorbar (bool or dict, optional) – Whether to add a colorbar for the values. Defaults to True, but will be ignored if no values are provided. Can also be a dictionary with the keyword arguments for matplotlib’s colorbar method (2D plot), or for Mayavi’s scalarbar method (3D plot).

  • value_unit (Unit, optional) – A Unit to rescale the values for display in the colorbar. Does not have any visible effect if no colorbar is used. If not specified, will try to determine the “best unit” to itself.

  • axes (Axes or Scene, optional) – A matplotlib Axes (for 2D plots) or mayavi Scene ( for 3D plots) instance, where the plot will be added.

Returns:

axes – The Axes or Scene instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes or Scene

brian2tools.plotting.plot_raster(spike_indices, spike_times, time_unit=<Mock name='mock.ms' id='130266780174144'>, axes=None, **kwds)[source]

Plot a “raster plot”, a plot of neuron indices over spike times. The default marker used for plotting is '.', it can be overriden with the marker keyword argument.

Parameters:

  • spike_indices (ndarray) – The indices of spiking neurons, corresponding to the times given in spike_times.

  • spike_times (Quantity) – A sequence of spike times.

  • time_unit (Unit, optional) – The unit to use for the time axis. Defaults to ms, but longer simulations could use second, for example.

  • axes (Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot.

  • kwds (dict, optional) – Any additional keywords command will be handed over to matplotlib’s plot command. This can be used to set plot properties such as the color.

Returns:

axes – The Axes instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes

brian2tools.plotting.plot_rate(times, rate, time_unit=<Mock name='mock.ms' id='130266780174144'>, rate_unit=<Mock name='mock.Hz' id='130266780174336'>, axes=None, **kwds)[source]
Parameters:

  • times (Quantity) – The time points at which the rate is measured.

  • rate (Quantity) – The population rate for each time point in times

  • time_unit (Unit, optional) – The unit to use for the time axis. Defaults to ms, but longer simulations could use second, for example.

  • time_unit – The unit to use for the rate axis. Defaults to Hz.

  • axes (Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot.

  • kwds (dict, optional) – Any additional keywords command will be handed over to matplotlib’s plot command. This can be used to set plot properties such as the color.

Returns:

axes – The Axes instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes

brian2tools.plotting.plot_state(times, values, time_unit=<Mock name='mock.ms' id='130266780174144'>, var_unit=None, var_name=None, axes=None, **kwds)[source]
Parameters:

  • times (Quantity) – The array of times for the data points given in values.

  • values (Quantity, ndarray) – The values to plot, either a 1D array with the same length as times, or a 2D array with len(times) rows.

  • time_unit (Unit, optional) – The unit to use for the time axis. Defaults to ms, but longer simulations could use second, for example.

  • var_unit (Unit, optional) – The unit to use to plot the values (e.g. mV for a membrane potential). If none is given (the default), an attempt is made to find a good scale automatically based on the values.

  • var_name (str, optional) – The name of the variable that is plotted. Used for the axis label.

  • axes (Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot.

  • kwds (dict, optional) – Any additional keywords command will be handed over to matplotlib’s plot command. This can be used to set plot properties such as the color.

Returns:

axes – The Axes instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes

brian2tools.plotting.plot_synapses(sources, targets, values=None, var_unit=None, var_name=None, plot_type='scatter', axes=None, **kwds)[source]
Parameters:

  • sources (ndarray of int) – The source indices of the connections (as returned by Synapses.i).

  • targets (ndarray of int) – The target indices of the connections (as returned by Synapses.j).

  • values (Quantity, ndarray) – The values to plot, a 1D array of the same size as sources and targets.

  • var_unit (Unit, optional) – The unit to use to plot the values (e.g. mV for a membrane potential). If none is given (the default), an attempt is made to find a good scale automatically based on the values.

  • var_name (str, optional) – The name of the variable that is plotted. Used for the axis label.

  • plot_type ({'scatter', 'image', 'hexbin'}, optional) – What type of plot to use. Can be 'scatter' (the default) to draw a scatter plot, 'image' to display the connections as a matrix or 'hexbin' to display a 2D histogram using matplotlib’s hexbin function. For a large number of synapses, 'scatter' will be very slow. Similarly, an 'image' plot will use a lot of memory for connections between two large groups. For a small number of neurons and synapses, 'hexbin' will be hard to interpret.

  • axes (Axes, optional) – The Axes instance used for plotting. Defaults to None which means that a new Axes will be created for the plot.

  • kwds (dict, optional) – Any additional keywords command will be handed over to the respective matplotlib command (scatter if the plot_type is 'scatter', imshow for 'image', and hexbin for 'hexbin'). This can be used to set plot properties such as the marker.

Returns:

axes – The Axes instance that was used for plotting. This object allows to modify the plot further, e.g. by setting the plotted range, the axis labels, the plot title, etc.

Return type:

Axes

Submodules