def plot_state(times, values, time_unit=ms, var_unit=None, var_name=None, axes=None, **kwds): ''' Parameters ---------- times : `~brian2.units.fundamentalunits.Quantity` The array of times for the data points given in ``values``. values : `~brian2.units.fundamentalunits.Quantity`, `~numpy.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 : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. var_unit : `~brian2.units.fundamentalunits.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 : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) if var_unit is None: if isinstance(values, Quantity): var_unit = _get_best_unit(values) if var_unit is not None: values /= var_unit axes.plot(times / time_unit, values, **kwds) axes.set_xlabel('time (%s)' % time_unit) if var_unit is not None: axes.set_ylabel('%s (%s)' % (var_name, var_unit)) else: axes.set_ylabel('%s' % var_name) return axes
def plot_state(times, values, time_unit=ms, var_unit=None, var_name=None, axes=None, **kwds): ''' Parameters ---------- times : `~brian2.units.fundamentalunits.Quantity` The array of times for the data points given in ``values``. values : `~brian2.units.fundamentalunits.Quantity`, `~numpy.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 : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. var_unit : `~brian2.units.fundamentalunits.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 : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) if var_unit is None: if isinstance(values, Quantity): var_unit = values._get_best_unit() if var_unit is not None: values /= var_unit axes.plot(times / time_unit, values, **kwds) axes.set_xlabel('time (%s)' % time_unit) if var_unit is not None: axes.set_ylabel('%s (%s)' % (var_name, var_unit)) else: axes.set_ylabel('%s' % var_name) return axes
def plot_raster(spike_indices, spike_times, time_unit=ms, axes=None, **kwds): ''' 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 : `~numpy.ndarray` The indices of spiking neurons, corresponding to the times given in ``spike_times``. spike_times : `~brian2.units.fundamentalunits.Quantity` A sequence of spike times. time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. axes : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) axes.plot(spike_times/time_unit, spike_indices, '.', **kwds) axes.set_xlabel('time (%s)' % time_unit) axes.set_ylabel('neuron index') return axes
def plot_raster(spike_indices, spike_times, time_unit=ms, axes=None, **kwds): ''' 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 : `~numpy.ndarray` The indices of spiking neurons, corresponding to the times given in ``spike_times``. spike_times : `~brian2.units.fundamentalunits.Quantity` A sequence of spike times. time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. axes : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) axes.plot(spike_times / time_unit, spike_indices, '.', **kwds) axes.set_xlabel('time (%s)' % time_unit) axes.set_ylabel('neuron index') return axes
def plot_rate(times, rate, time_unit=ms, rate_unit=Hz, axes=None, **kwds): ''' Parameters ---------- times : `~brian2.units.fundamentalunits.Quantity` The time points at which the ``rate`` is measured. rate : `~brian2.units.fundamentalunits.Quantity` The population rate for each time point in ``times`` time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the rate axis. Defaults to ``Hz``. axes : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) axes.plot(times / time_unit, rate / rate_unit, **kwds) axes.set_xlabel('time (%s)' % time_unit) axes.set_ylabel('population rate (%s)' % rate_unit) return axes
def plot_rate(times, rate, time_unit=ms, rate_unit=Hz, axes=None, **kwds): ''' Parameters ---------- times : `~brian2.units.fundamentalunits.Quantity` The time points at which the ``rate`` is measured. rate : `~brian2.units.fundamentalunits.Quantity` The population rate for each time point in ``times`` time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the time axis. Defaults to ``ms``, but longer simulations could use ``second``, for example. time_unit : `~brian2.units.fundamentalunits.Unit`, optional The unit to use for the rate axis. Defaults to ``Hz``. axes : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to matplotlib's `~matplotlib.axes.Axes.plot` command. This can be used to set plot properties such as the ``color``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) axes.plot(times/time_unit, rate/rate_unit, **kwds) axes.set_xlabel('time (%s)' % time_unit) axes.set_ylabel('population rate (%s)' % rate_unit) return axes
def 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): ''' Plot a given `~brian2.spatialneuron.morphology.Morphology` in 2D or 3D. Parameters ---------- morphology : `~brian2.spatialneuron.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 : ~brian2.units.fundamentalunits.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 `~.matplotlib.figure.Figure.colorbar` method (2D plot), or for Mayavi's `~.mayavi.mlab.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 : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene`, optional A matplotlib `~matplotlib.axes.Axes` (for 2D plots) or mayavi `~mayavi.core.api.Scene` ( for 3D plots) instance, where the plot will be added. Returns ------- axes : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene` The `~matplotlib.axes.Axes` or `~mayavi.core.api.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. ''' # Avoid circular import issues from brian2tools.plotting.base import (_setup_axes_matplotlib, _setup_axes_mayavi) if plot_3d is None: # Decide whether to use 2d or 3d plotting based on the coordinates flat_morphology = FlatMorphology(morphology) plot_3d = any(np.abs(flat_morphology.z) > 1e-12) if values is not None: if hasattr(values, 'name'): value_varname = values.name else: value_varname = 'values' if value_unit is not None: if not isinstance(value_unit, Unit): raise TypeError(f'\'value_unit\' has to be a unit but is' f'\'{type(value_unit)}\'.') fail_for_dimension_mismatch(value_unit, values, 'The \'value_unit\' arguments needs ' 'to have the same dimensions as ' 'the \'values\'.') else: if have_same_dimensions(values, DIMENSIONLESS): value_unit = 1. else: value_unit = values[:].get_best_unit() orig_values = values values = values/value_unit if isinstance(value_norm, tuple): if not len(value_norm) == 2: raise TypeError('Need a (vmin, vmax) tuple for the value ' 'normalization, but got a tuple of length ' f'{len(value_norm)}.') vmin, vmax = value_norm if vmin is not None: err_msg = ('The minimum value in \'value_norm\' needs to ' 'have the same units as \'values\'.') fail_for_dimension_mismatch(vmin, orig_values, error_message=err_msg) vmin /= value_unit if vmax is not None: err_msg = ('The maximum value in \'value_norm\' needs to ' 'have the same units as \'values\'.') fail_for_dimension_mismatch(vmax, orig_values, error_message=err_msg) vmax /= value_unit if plot_3d: value_norm = (vmin, vmax) else: value_norm = Normalize(vmin=vmin, vmax=vmax, clip=True) value_norm.autoscale_None(values) elif plot_3d: raise TypeError('3d plots only support normalizations given by ' 'a (min, max) tuple.') value_colormap = plt.get_cmap(value_colormap) if plot_3d: try: import mayavi.mlab as mayavi except ImportError: raise ImportError('3D plotting needs the mayavi library') axes = _setup_axes_mayavi(axes) axes.scene.disable_render = True surf = _plot_morphology3D(morphology, axes, colors=colors, values=values, value_norm=value_norm, value_colormap=value_colormap, show_diameters=show_diameter, show_compartments=show_compartments) if values is not None and value_colorbar: if not isinstance(value_colorbar, Mapping): value_colorbar = {} if not have_same_dimensions(value_unit, DIMENSIONLESS): unit_str = f' ({value_unit!s})' else: unit_str = '' if value_varname: value_colorbar['title'] = f'{value_varname}{unit_str}' cb = mayavi.scalarbar(surf, **value_colorbar) # Make text dark gray cb.title_text_property.color = (0.1, 0.1, 0.1) cb.label_text_property.color = (0.1, 0.1, 0.1) axes.scene.disable_render = False else: axes = _setup_axes_matplotlib(axes) _plot_morphology2D(morphology, axes, colors, values, value_norm, value_colormap, show_compartments=show_compartments, show_diameter=show_diameter) axes.set_xlabel('x (um)') axes.set_ylabel('y (um)') axes.set_aspect('equal') if values is not None and value_colorbar: divider = make_axes_locatable(axes) cax = divider.append_axes("right", size="5%", pad=0.1) mappable = ScalarMappable(norm=value_norm, cmap=value_colormap) mappable.set_array([]) fig = axes.get_figure() if not isinstance(value_colorbar, Mapping): value_colorbar = {} if not have_same_dimensions(value_unit, DIMENSIONLESS): unit_str = f' ({value_unit!s})' else: unit_str = '' if value_varname: value_colorbar['label'] = f'{value_varname}{unit_str}' fig.colorbar(mappable, cax=cax, **value_colorbar) return axes
def plot_synapses(sources, targets, values=None, var_unit=None, var_name=None, plot_type='scatter', axes=None, **kwds): ''' Parameters ---------- sources : `~numpy.ndarray` of int The source indices of the connections (as returned by ``Synapses.i``). targets : `~numpy.ndarray` of int The target indices of the connections (as returned by ``Synapses.j``). values : `~brian2.units.fundamentalunits.Quantity`, `~numpy.ndarray` The values to plot, a 1D array of the same size as ``sources`` and ``targets``. var_unit : `~brian2.units.fundamentalunits.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 `~matplotlib.axes.Axes.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 : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. kwds : dict, optional Any additional keywords command will be handed over to the respective matplotlib command (`~matplotlib.axes.Axes.scatter` if the ``plot_type`` is ``'scatter'``, `~matplotlib.axes.Axes.imshow` for ``'image'``, and `~matplotlib.axes.Axes.hexbin` for ``'hexbin'``). This can be used to set plot properties such as the ``marker``. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. ''' # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) sources = np.asarray(sources) targets = np.asarray(targets) if not len(sources) == len(targets): raise TypeError('Length of sources and targets does not match.') if plot_type not in ['scatter', 'image', 'hexbin']: raise ValueError("plot_type has to be either 'scatter', 'image', or " "'hexbin' (was: %r)" % plot_type) # Get some information out of the values if provided if values is not None: if len(values) != len(sources): raise TypeError('Length of values and sources/targets does not ' 'match.') if var_name is None: var_name = getattr(values, 'name', None) # works for a VariableView if var_unit is None: try: var_unit = values[:]._get_best_unit() except AttributeError: pass if var_unit is not None: values = values / var_unit if plot_type != 'hexbin': # For "hexbin", we are binning multiple synapses anyway, so we don't # have to make a difference for multiple synapses connection_count = Counter(zip(sources, targets)) multiple_synapses = np.any(np.array(list(connection_count.values())) > 1) edgecolor = kwds.pop('edgecolor', 'none') if plot_type != 'hexbin' and multiple_synapses: if values is not None: raise NotImplementedError("Plotting variables with multiple " "synapses per source-target pair is only " "implemented for 'hexbin' plots.") unique_sources, unique_targets = zip(*connection_count.keys()) n_synapses = list(connection_count.values()) bounds, cmap, norm = _discrete_color_mapping(kwds.pop('cmap', None), n_synapses) # Make the plot if plot_type == 'scatter': marker = kwds.pop('marker', ',') axes.scatter(unique_sources, unique_targets, marker=marker, c=n_synapses, edgecolor=edgecolor, cmap=cmap, norm=norm, **kwds) else: assert np.max(n_synapses) < 256 matrix = _int_connection_matrix(unique_sources, unique_targets, n_synapses) origin = kwds.pop('origin', 'lower') interpolation = kwds.pop('interpolation', 'nearest') axes.imshow(matrix, origin=origin, interpolation=interpolation, cmap=cmap, norm=norm, extent=(min(unique_sources) - 0.5, max(unique_sources) + 0.5, min(unique_targets) - 0.5, max(unique_targets) + 0.5), **kwds) # Add the colorbar locatable_axes = make_axes_locatable(axes) cax = locatable_axes.append_axes('right', size='5%', pad=0.05) mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm, ticks=bounds-0.5) cax.set_ylabel('number of synapses') else: if plot_type == 'scatter': marker = kwds.pop('marker', ',') color = kwds.pop('color', values if values is not None else None) plotted = axes.scatter(sources, targets, marker=marker, c=color, edgecolor=edgecolor, **kwds) elif plot_type == 'image': if values is not None: matrix = _float_connection_matrix(sources, targets, values) else: matrix = _int_connection_matrix(sources, targets, 1) origin = kwds.pop('origin', 'lower') interpolation = kwds.pop('interpolation', 'nearest') vmin = kwds.pop('vmin', 1 if values is None else None) plotted = axes.imshow(matrix, origin=origin, interpolation=interpolation, vmin=vmin, extent=(min(sources) - 0.5, max(sources) + 0.5, min(targets) - 0.5, max(targets) + 0.5), **kwds) elif plot_type == 'hexbin': if values is None: # Counting synapses mincnt = kwds.pop('mincnt', 1) else: mincnt = kwds.pop('mincnt', None) plotted = axes.hexbin(sources, targets, C=values, mincnt=mincnt, **kwds) if values is not None or plot_type == 'hexbin': # Add a colorbar locatable_axes = make_axes_locatable(axes) cax = locatable_axes.append_axes('right', size='7.5%', pad=0.05) plt.colorbar(plotted, cax=cax) if var_name is None: if var_unit is not None: cax.set_ylabel('in units of %s' % str(var_unit)) else: label = var_name if var_unit is not None: label += ' (%s)' % str(var_unit) cax.set_ylabel(label) axes.set_xlim(-0.5, max(sources) + 0.5) axes.set_ylim(-0.5, max(targets) + 0.5) axes.set_xlabel('source neuron index') axes.set_ylabel('target neuron index') # Prevent floating point values on the axes (e.g. when zooming in) axes.xaxis.set_major_locator(MaxNLocator(integer=True)) axes.yaxis.set_major_locator(MaxNLocator(integer=True)) return axes
def plot_dendrogram(morphology, axes=None): """ Plot a "dendrogram" of a morphology, i.e. an abstract representation which visualizes the branching structure and the length of each section. Parameters ---------- morphology : `~brian2.spatialneuron.morphology.Morphology` The morphology to visualize. axes : `~matplotlib.axes.Axes`, optional The `~matplotlib.axes.Axes` instance used for plotting. Defaults to ``None`` which means that a new `~matplotlib.axes.Axes` will be created for the plot. Returns ------- axes : `~matplotlib.axes.Axes` The `~matplotlib.axes.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. """ # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib axes = _setup_axes_matplotlib(axes) # Get some information from the flattened morphology flat_morpho = FlatMorphology(morphology) section_depth = flat_morpho.depth[flat_morpho.starts] section_distance = flat_morpho.end_distance / float(um) n_sections = flat_morpho.sections max_depth = max(flat_morpho.depth) max_children = max(flat_morpho.morph_children_num) children = flat_morpho.morph_children length_metric = section_distance # Each point should be in the middle of its two outermost terminal points # We go backwards through the tree, noting for each point all terminal # indices in its subtree terminals = [set() for _ in range(n_sections)] terminal_counter = 0 for d in range(max_depth, -1, -1): for idx in np.nonzero(section_depth == d)[0]: child_start_idx = (idx + 1) * max_children num_children = flat_morpho.morph_children_num[idx + 1] if num_children == 0: terminals[idx] = {terminal_counter} terminal_counter += 1 else: child_indices = children[child_start_idx : child_start_idx + num_children] terminals[idx].update(*[terminals[c - 1] for c in child_indices]) # Now we make sure that subtrees starting at a lower x value will be left # of other subtrees # This is probably not the most efficient algorithm, but it seems to work order_strings = [[] for _ in range(terminal_counter)] for idx in np.argsort(length_metric): child_terminals = terminals[idx] for t, order_string in enumerate(order_strings): if t in child_terminals: order_string.extend("A") else: order_string.extend("B") order_strings = ["".join(s) for s in order_strings] terminal_x_values = np.argsort(np.argsort(order_strings)) # Use the re-arranged values to calculate the actual x value for the tree min_index = [min(terminal_x_values[np.array(list(ts), dtype=int)]) for ts in terminals] max_index = [max(terminal_x_values[np.array(list(ts), dtype=int)]) for ts in terminals] x_values = (np.array(min_index) + np.array(max_index)) / 2.0 # Plot the dendogram with lengths of the vertical lines representing the # total distance to the root plt.plot(x_values[0], length_metric[0], "ko", clip_on=False) for sec, (x, depth) in enumerate(zip(x_values, length_metric)): child_start_idx = (sec + 1) * max_children num_children = flat_morpho.morph_children_num[sec + 1] if num_children > 0: child_indices = children[child_start_idx : child_start_idx + num_children] child_depth = length_metric[child_indices - 1] child_x = x_values[child_indices - 1] axes.vlines(child_x, depth, child_depth, clip_on=False, lw=2) axes.hlines(depth, min(child_x), max(child_x), lw=2) axes.set_xticks([]) axes.set_ylabel("distance from root (um)") axes.set_xlim(-1, terminal_counter) return axes
def plot_morphology( morphology, plot_3d=None, show_compartments=False, show_diameter=False, colors=("darkblue", "darkred"), axes=None ): """ Plot a given `~brian2.spatialneuron.morphology.Morphology` in 2D or 3D. Parameters ---------- morphology : `~brian2.spatialneuron.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)`. axes : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene`, optional A matplotlib `~matplotlib.axes.Axes` (for 2D plots) or mayavi `~mayavi.core.api.Scene` ( for 3D plots) instance, where the plot will be added. Returns ------- axes : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene` The `~matplotlib.axes.Axes` or `~mayavi.core.api.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. """ # Avoid circular import issues from brian2tools.plotting.base import _setup_axes_matplotlib, _setup_axes_mayavi if plot_3d is None: # Decide whether to use 2d or 3d plotting based on the coordinates flat_morphology = FlatMorphology(morphology) plot_3d = any(np.abs(flat_morphology.z) > 1e-12) if plot_3d: try: import mayavi.mlab as mayavi except ImportError: raise ImportError("3D plotting needs the mayavi library") axes = _setup_axes_mayavi(axes) axes.scene.disable_render = True _plot_morphology3D( morphology, axes, colors=colors, show_diameters=show_diameter, show_compartments=show_compartments ) axes.scene.disable_render = False else: axes = _setup_axes_matplotlib(axes) _plot_morphology2D(morphology, axes, colors, show_compartments=show_compartments, show_diameter=show_diameter) axes.set_xlabel("x (um)") axes.set_ylabel("y (um)") axes.set_aspect("equal") return axes
def plot_morphology(morphology, plot_3d=None, show_compartments=False, show_diameter=False, colors=('darkblue', 'darkred'), axes=None): ''' Plot a given `~brian2.spatialneuron.morphology.Morphology` in 2D or 3D. Parameters ---------- morphology : `~brian2.spatialneuron.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)`. axes : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene`, optional A matplotlib `~matplotlib.axes.Axes` (for 2D plots) or mayavi `~mayavi.core.api.Scene` ( for 3D plots) instance, where the plot will be added. Returns ------- axes : `~matplotlib.axes.Axes` or `~mayavi.core.api.Scene` The `~matplotlib.axes.Axes` or `~mayavi.core.api.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. ''' # Avoid circular import issues from brian2tools.plotting.base import (_setup_axes_matplotlib, _setup_axes_mayavi) if plot_3d is None: # Decide whether to use 2d or 3d plotting based on the coordinates flat_morphology = FlatMorphology(morphology) plot_3d = any(np.abs(flat_morphology.z) > 1e-12) if plot_3d: try: import mayavi.mlab as mayavi except ImportError: raise ImportError('3D plotting needs the mayavi library') axes = _setup_axes_mayavi(axes) axes.scene.disable_render = True _plot_morphology3D(morphology, axes, colors=colors, show_diameters=show_diameter, show_compartments=show_compartments) axes.scene.disable_render = False else: axes = _setup_axes_matplotlib(axes) _plot_morphology2D(morphology, axes, colors, show_compartments=show_compartments, show_diameter=show_diameter) axes.set_xlabel('x (um)') axes.set_ylabel('y (um)') axes.set_aspect('equal') return axes