"""Visualization functions."""
# Authors: Mainak Jas <mjas@mgh.harvard.edu>
# Sam Neymotin <samnemo@gmail.com>
# Christopher Bailey <cjb@cfin.au.dk>
import numpy as np
from itertools import cycle
from .externals.mne import _validate_type
def _get_plot_data_trange(times, data, tmin, tmax):
"""Get slices of times and data based on tmin and tmax"""
if isinstance(times, list):
times = np.array(times)
if isinstance(data, list):
data = np.array(data)
plot_tmin = times[0]
if tmin is not None:
plot_tmin = max(tmin, plot_tmin)
plot_tmax = times[-1]
if tmax is not None:
plot_tmax = min(tmax, plot_tmax)
mask = np.logical_and(times >= plot_tmin, times < plot_tmax)
return data[mask], times[mask]
def _decimate_plot_data(decim, data, times, sfreq=None):
from scipy.signal import decimate
if not isinstance(decim, list):
decim = [decim]
for dec in decim:
if not isinstance(dec, int) or dec < 1:
raise ValueError('each decimation factor must be a positive int, '
f'but {dec} is a {type(dec)}')
data = decimate(data, dec)
times = times[::dec]
if sfreq is None:
return data, times
else:
sfreq /= np.prod(decim)
return data, times, sfreq
def plt_show(show=True, fig=None, **kwargs):
"""Show a figure while suppressing warnings.
NB copied from :func:`mne.viz.utils.plt_show`.
Parameters
----------
show : bool
Show the figure.
fig : instance of Figure | None
If non-None, use fig.show().
**kwargs : dict
Extra arguments for :func:`matplotlib.pyplot.show`.
"""
from matplotlib import get_backend
import matplotlib.pyplot as plt
if show and get_backend() != 'agg':
(fig or plt).show(**kwargs)
def plot_extracellular(times, data, tmin=None, tmax=None, ax=None,
decim=None, color=None,
voltage_offset=None, voltage_scalebar=None,
contact_labels=None, show=True):
"""Plot extracellular electrode array voltage time series.
Parameters
----------
times : list | Numpy array
Sampling times (in ms).
data : Two-dimensional Numpy array
The extracellular voltages as an (n_contacts, n_times) array.
tmin : float | None
Start time of plot in milliseconds. If None, plot entire simulation.
tmax : float | None
End time of plot in milliseconds. If None, plot entire simulation.
ax : instance of matplotlib figure | None
The matplotlib axis
decim : int | list of int | None (default)
Optional (integer) factor by which to decimate the raw dipole traces.
The SciPy function :func:`~scipy.signal.decimate` is used, which
recommends values <13. To achieve higher decimation factors, a list of
ints can be provided. These are applied successively.
color : string | array of floats | ``matplotlib.colors.ListedColormap``
The color to use for plotting (optional). The usual Matplotlib standard
color strings may be used (e.g., 'b' for blue). A color can also be
defined as an RGBA-quadruplet, or an array of RGBA-values (one for each
electrode contact trace to plot). An instance of
:class:`~matplotlib.colors.ListedColormap` may also be provided.
voltage_offset : float | None (optional)
Amount to offset traces by on the voltage-axis. Useful for plotting
laminar arrays.
voltage_scalebar : float | None (optional)
Height, in units of uV, of a scale bar to plot in the top-left corner
of the plot.
contact_labels : list (optional)
Labels associated with the contacts to plot. Passed as-is to
:func:`~matplotlib.axes.Axes.set_yticklabels`.
show : bool
If True, show the figure
Returns
-------
fig : instance of plt.fig
The matplotlib figure handle into which time series were plotted.
"""
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
_validate_type(times, (list, np.ndarray), 'times')
_validate_type(data, (list, np.ndarray), 'data')
if isinstance(times, list):
times = np.array(times)
if isinstance(data, list):
data = np.array(data)
if data.ndim != 2:
raise ValueError(f'data must be 2D, got shape {data.shape}')
if len(times) != data.shape[1]:
raise ValueError(f'length of times ({len(times)}) and data '
f'({len(data)}) do not match')
n_contacts = data.shape[0]
if color is not None:
_validate_type(color,
(str, tuple, list, np.ndarray, ListedColormap),
'color')
if isinstance(color, (tuple, list)):
if (not np.all([isinstance(c, float) for c in color]) or
len(color) < 3 or len(color) > 4):
raise ValueError(
f'color must be length 3 or 4, got {color}')
elif isinstance(color, np.ndarray):
if (color.shape[0] != n_contacts or
(color.shape[1] < 3 or color.shape[1] > 4)):
raise ValueError(
f'color must be n_contacts x (3 or 4), got {color}')
elif isinstance(color, ListedColormap):
if color.N != n_contacts:
raise ValueError(f'ListedColormap has N={color.N}, but '
f'there are {n_contacts} contacts')
if ax is None:
_, ax = plt.subplots(1, 1)
n_offsets = data.shape[0]
trace_offsets = np.zeros((n_offsets, 1))
if voltage_offset is not None:
trace_offsets = np.arange(n_offsets)[:, np.newaxis] * voltage_offset
for contact_no, trace in enumerate(np.atleast_2d(data)):
plot_data, plot_times = _get_plot_data_trange(times, trace, tmin, tmax)
if decim is not None:
plot_data, plot_times = _decimate_plot_data(decim, plot_data,
plot_times)
if isinstance(color, np.ndarray):
col = color[contact_no]
elif isinstance(color, ListedColormap):
col = color(contact_no)
else:
col = color
ax.plot(plot_times, plot_data + trace_offsets[contact_no],
label=f'C{contact_no}', color=col)
if voltage_offset is not None:
ax.set_ylim(-voltage_offset, n_offsets * voltage_offset)
ylabel = 'Individual contact traces'
if contact_labels is None:
ax.set_yticks([])
elif len(contact_labels) != n_offsets:
raise ValueError(f'contact_labels is length {len(contact_labels)},'
f' but {n_offsets} contacts to be plotted')
else:
trace_ticks = np.arange(0, len(contact_labels) * voltage_offset,
voltage_offset)
ax.set_yticks(trace_ticks)
ax.set_yticklabels(contact_labels)
if voltage_scalebar is None:
voltage_scalebar = voltage_offset
if voltage_scalebar is not None:
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
scalebar = AnchoredSizeBar(ax.transData, 1,
f'{voltage_scalebar:.0f} ' + r'$\mu V$',
'upper left',
size_vertical=voltage_scalebar,
pad=0.1,
color='black',
label_top=False,
frameon=False)
ax.add_artist(scalebar)
else:
ylabel = r'Electric potential ($\mu V$)'
ax.ticklabel_format(axis='both', scilimits=(-2, 3))
ax.set_ylabel(ylabel, multialignment='center')
ax.set_xlabel('Time (ms)')
plt_show(show)
return ax.get_figure()
[docs]def plot_dipole(dpl, tmin=None, tmax=None, ax=None, layer='agg', decim=None,
color=None, show=True):
"""Simple layer-specific plot function.
Parameters
----------
dpl : instance of Dipole | list of Dipole instances
The Dipole object.
tmin : float or None
Start time of plot in milliseconds. If None, plot entire simulation.
tmax : float or None
End time of plot in milliseconds. If None, plot entire simulation.
ax : instance of matplotlib figure | None
The matplotlib axis
layer : str
The layer to plot. Can be one of
'agg', 'L2', and 'L5'
decim : int or list of int or None (default)
Optional (integer) factor by which to decimate the raw dipole traces.
The SciPy function :func:`~scipy.signal.decimate` is used, which
recommends values <13. To achieve higher decimation factors, a list of
ints can be provided. These are applied successively.
color : tuple of float
RGBA value to use for plotting (optional)
show : bool
If True, show the figure
Returns
-------
fig : instance of plt.fig
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
from .dipole import Dipole
if ax is None:
_, ax = plt.subplots(1, 1, constrained_layout=True)
if isinstance(dpl, Dipole):
dpl = [dpl]
scale_applied = dpl[0].scale_applied
for dpl_trial in dpl:
if dpl_trial.scale_applied != scale_applied:
raise RuntimeError('All dipoles must be scaled equally!')
if layer in dpl_trial.data.keys():
# extract scaled data and times
data, times = _get_plot_data_trange(dpl_trial.times,
dpl_trial.data[layer],
tmin, tmax)
if decim is not None:
data, times = _decimate_plot_data(decim, data, times)
ax.plot(times, data, color=color)
ax.ticklabel_format(axis='both', scilimits=(-2, 3))
ax.set_xlabel('Time (ms)')
if scale_applied == 1:
ylabel = 'Dipole moment (nAm)'
else:
ylabel = 'Dipole moment\n(nAm ' +\
r'$\times$ {:.0f})'.format(scale_applied)
ax.set_ylabel(ylabel, multialignment='center')
if layer == 'agg':
title_str = 'Aggregate (L2 + L5)'
else:
title_str = layer
ax.set_title(title_str)
plt_show(show)
return ax.get_figure()
[docs]def plot_spikes_hist(cell_response, ax=None, spike_types=None, show=True):
"""Plot the histogram of spiking activity across trials.
Parameters
----------
cell_response : instance of CellResponse
The CellResponse object from net.cell_response
ax : instance of matplotlib axis | None
An axis object from matplotlib. If None,
a new figure is created.
spike_types: string | list | dictionary | None
String input of a valid spike type is plotted individually.
Ex: 'poisson', 'evdist', 'evprox', ...
List of valid string inputs will plot each spike type individually.
Ex: ['poisson', 'evdist']
Dictionary of valid lists will plot list elements as a group.
Ex: {'Evoked': ['evdist', 'evprox'], 'Tonic': ['poisson']}
If None, all input spike types are plotted individually if any
are present. Otherwise spikes from all cells are plotted.
Valid strings also include leading characters of spike types
Example: 'ev' is equivalent to ['evdist', 'evprox']
show : bool
If True, show the figure.
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
spike_times = np.array(sum(cell_response._spike_times, []))
spike_types_data = np.array(sum(cell_response._spike_types, []))
unique_types = np.unique(spike_types_data)
spike_types_mask = {s_type: np.in1d(spike_types_data, s_type)
for s_type in unique_types}
cell_types = ['L5_pyramidal', 'L5_basket', 'L2_pyramidal', 'L2_basket']
input_types = np.setdiff1d(unique_types, cell_types)
if isinstance(spike_types, str):
spike_types = {spike_types: [spike_types]}
if spike_types is None:
if any(input_types):
spike_types = input_types.tolist()
else:
spike_types = unique_types.tolist()
if isinstance(spike_types, list):
spike_types = {s_type: [s_type] for s_type in spike_types}
if isinstance(spike_types, dict):
for spike_label in spike_types:
if not isinstance(spike_types[spike_label], list):
raise TypeError(f'spike_types[{spike_label}] must be a list. '
f'Got '
f'{type(spike_types[spike_label]).__name__}.')
if not isinstance(spike_types, dict):
raise TypeError('spike_types should be str, list, dict, or None')
spike_labels = dict()
for spike_label, spike_type_list in spike_types.items():
for spike_type in spike_type_list:
n_found = 0
for unique_type in unique_types:
if unique_type.startswith(spike_type):
if unique_type in spike_labels:
raise ValueError(f'Elements of spike_types must map to'
f' mutually exclusive input types.'
f' {unique_type} is found more than'
f' once.')
spike_labels[unique_type] = spike_label
n_found += 1
if n_found == 0:
raise ValueError(f'No input types found for {spike_type}')
if ax is None:
_, ax = plt.subplots(1, 1, constrained_layout=True)
color_cycle = cycle(['r', 'g', 'b', 'y', 'm', 'c'])
bins = np.linspace(0, spike_times[-1], 50)
spike_color = dict()
for spike_type, spike_label in spike_labels.items():
label = "_nolegend_"
if spike_label not in spike_color:
spike_color[spike_label] = next(color_cycle)
label = spike_label
color = spike_color[spike_label]
ax.hist(spike_times[spike_types_mask[spike_type]], bins,
label=label, color=color)
ax.set_ylabel("Counts")
ax.legend()
plt_show(show)
return ax.get_figure()
[docs]def plot_spikes_raster(cell_response, ax=None, show=True):
"""Plot the aggregate spiking activity according to cell type.
Parameters
----------
cell_response : instance of CellResponse
The CellResponse object from net.cell_response
ax : instance of matplotlib axis | None
An axis object from matplotlib. If None,
a new figure is created.
show : bool
If True, show the figure.
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure object.
"""
import matplotlib.pyplot as plt
spike_times = np.array(sum(cell_response._spike_times, []))
spike_types = np.array(sum(cell_response._spike_types, []))
spike_gids = np.array(sum(cell_response._spike_gids, []))
cell_types = ['L2_basket', 'L2_pyramidal', 'L5_basket', 'L5_pyramidal']
cell_type_colors = {'L5_pyramidal': 'r', 'L5_basket': 'b',
'L2_pyramidal': 'g', 'L2_basket': 'w'}
if ax is None:
_, ax = plt.subplots(1, 1, constrained_layout=True)
ypos = 0
for cell_type in cell_types:
cell_type_gids = np.unique(spike_gids[spike_types == cell_type])
cell_type_times, cell_type_ypos = [], []
for gid in cell_type_gids:
gid_time = spike_times[spike_gids == gid]
cell_type_times.append(gid_time)
cell_type_ypos.append(np.repeat(ypos, len(gid_time)))
ypos = ypos - 1
if cell_type_times:
cell_type_times = np.concatenate(cell_type_times)
cell_type_ypos = np.concatenate(cell_type_ypos)
else:
cell_type_times = []
cell_type_ypos = []
ax.scatter(cell_type_times, cell_type_ypos, label=cell_type,
color=cell_type_colors[cell_type])
ax.legend(loc=1)
ax.set_facecolor('k')
ax.set_xlabel('Time (ms)')
ax.get_yaxis().set_visible(False)
ax.set_xlim(left=0)
plt_show(show)
return ax.get_figure()
[docs]def plot_cells(net, ax=None, show=True):
"""Plot the cells using Network.pos_dict.
Parameters
----------
net : instance of Network
The Network object.
ax : instance of matplotlib Axes3D | None
An axis object from matplotlib. If None,
a new figure is created.
show : bool
If True, show the figure.
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = {'L5_pyramidal': 'b', 'L2_pyramidal': 'c',
'L5_basket': 'r', 'L2_basket': 'm'}
markers = {'L5_pyramidal': '^', 'L2_pyramidal': '^',
'L5_basket': 'x', 'L2_basket': 'x'}
for cell_type in net.cell_types:
x = [pos[0] for pos in net.pos_dict[cell_type]]
y = [pos[1] for pos in net.pos_dict[cell_type]]
z = [pos[2] for pos in net.pos_dict[cell_type]]
if cell_type in colors:
color = colors[cell_type]
marker = markers[cell_type]
ax.scatter(x, y, z, c=color, s=50, marker=marker, label=cell_type)
if net.rec_arrays:
cols = plt.get_cmap('inferno', len(net.rec_arrays) + 2)
for ii, (arr_name, arr) in enumerate(net.rec_arrays.items()):
x = [p[0] for p in arr.positions]
y = [p[1] for p in arr.positions]
z = [p[2] for p in arr.positions]
ax.scatter(x, y, z, color=cols(ii + 1), s=25, marker='o',
label=arr_name)
plt.legend(bbox_to_anchor=(-0.15, 1.025), loc="upper left")
plt_show(show)
return ax.get_figure()
[docs]def plot_tfr_morlet(dpl, freqs, *, n_cycles=7., tmin=None, tmax=None,
layer='agg', decim=None, padding='zeros', ax=None,
colormap='inferno', colorbar=True, show=True):
"""Plot Morlet time-frequency representation of dipole time course
Parameters
----------
dpl : instance of Dipole | list of Dipole instances
The Dipole object. If a list of dipoles is given, the power is
calculated separately for each trial, then averaged.
freqs : array
Frequency range of interest.
n_cycles : float or array of float, default 7.0
Number of cycles. Fixed number or one per frequency.
tmin : float or None
Start time of plot in milliseconds. If None, plot entire simulation.
tmax : float or None
End time of plot in milliseconds. If None, plot entire simulation.
layer : str, default 'agg'
The layer to plot. Can be one of 'agg', 'L2', and 'L5'
decim : int or list of int or None (default)
Optional (integer) factor by which to decimate the raw dipole traces.
The SciPy function :func:`~scipy.signal.decimate` is used, which
recommends values <13. To achieve higher decimation factors, a list of
ints can be provided. These are applied successively.
padding : str or None
Optional padding of the dipole time course beyond the plotting limits.
Possible values are: 'zeros' for padding with 0's (default), 'mirror'
for mirror-image padding.
ax : instance of matplotlib figure | None
The matplotlib axis
colormap : str
The name of a matplotlib colormap, e.g., 'viridis'. Default: 'inferno'
colorbar : bool
If True (default), adjust figure to include colorbar.
show : bool
If True, show the figure
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from .externals.mne import tfr_array_morlet
from .dipole import Dipole
if isinstance(dpl, Dipole):
dpl = [dpl]
if ax is None:
fig, ax = plt.subplots(1, 1, constrained_layout=True)
scale_applied = dpl[0].scale_applied
sfreq = dpl[0].sfreq
trial_power = []
for dpl_trial in dpl:
if dpl_trial.scale_applied != scale_applied:
raise RuntimeError('All dipoles must be scaled equally!')
if dpl_trial.sfreq != sfreq:
raise RuntimeError('All dipoles must be sampled equally!')
data, times = _get_plot_data_trange(dpl_trial.times,
dpl_trial.data[layer],
tmin, tmax)
sfreq = dpl_trial.sfreq
if decim is not None:
data, times, sfreq = _decimate_plot_data(decim, data, times,
sfreq=sfreq)
if padding is not None:
if not isinstance(padding, str):
raise ValueError('padding must be a string (or None)')
if padding == 'zeros':
data = np.r_[np.zeros((len(data) - 1,)), data.ravel(),
np.zeros((len(data) - 1,))]
elif padding == 'mirror':
data = np.r_[data[-1:0:-1], data, data[-2::-1]]
# MNE expects an array of shape (n_trials, n_channels, n_times)
data = data[None, None, :]
power = tfr_array_morlet(data, sfreq=sfreq, freqs=freqs,
n_cycles=n_cycles, output='power')
if padding is not None:
# get the middle portion after padding
power = power[:, :, :, times.shape[0] - 1:2 * times.shape[0] - 1]
trial_power.append(power)
power = np.mean(trial_power, axis=0)
im = ax.pcolormesh(times, freqs, power[0, 0, ...], cmap=colormap,
shading='auto')
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Frequency (Hz)')
if colorbar:
fig = ax.get_figure()
xfmt = ScalarFormatter()
xfmt.set_powerlimits((-2, 2))
cbar = fig.colorbar(im, ax=ax, format=xfmt, shrink=0.8, pad=0)
cbar.ax.yaxis.set_ticks_position('left')
cbar.ax.set_ylabel(r'Power ([nAm $\times$ {:.0f}]$^2$)'.format(
scale_applied), rotation=-90, va="bottom")
plt_show(show)
return ax.get_figure()
[docs]def plot_psd(dpl, *, fmin=0, fmax=None, tmin=None, tmax=None, layer='agg',
ax=None, show=True):
"""Plot power spectral density (PSD) of dipole time course
Applies `~scipy.signal.periodogram` from SciPy with ``window='hamming'``.
Note that no spectral averaging is applied across time, as most
``hnn_core`` simulations are short-duration. However, passing a list of
`Dipole` instances will plot their average (Hamming-windowed) power, which
resembles the `Welch`-method applied over time.
Parameters
----------
dpl : instance of Dipole | list of Dipole instances
The Dipole object.
fmin : float
Minimum frequency to plot (in Hz). Default: 0 Hz
fmax : float
Maximum frequency to plot (in Hz). Default: None (plot up to Nyquist)
tmin : float or None
Start time of data to include (in ms). If None, use entire simulation.
tmax : float or None
End time of data to include (in ms). If None, use entire simulation.
layer : str, default 'agg'
The layer to plot. Can be one of 'agg', 'L2', and 'L5'
ax : instance of matplotlib figure | None
The matplotlib axis.
show : bool
If True, show the figure
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
from scipy.signal import periodogram
from .dipole import Dipole
if ax is None:
_, ax = plt.subplots(1, 1, constrained_layout=True)
if isinstance(dpl, Dipole):
dpl = [dpl]
scale_applied = dpl[0].scale_applied
sfreq = dpl[0].sfreq
trial_power = []
for dpl_trial in dpl:
if dpl_trial.scale_applied != scale_applied:
raise RuntimeError('All dipoles must be scaled equally!')
if dpl_trial.sfreq != sfreq:
raise RuntimeError('All dipoles must be sampled equally!')
data, _ = _get_plot_data_trange(dpl_trial.times,
dpl_trial.data[layer],
tmin, tmax)
freqs, Pxx = periodogram(data, sfreq, window='hamming', nfft=len(data))
trial_power.append(Pxx)
ax.plot(freqs, np.mean(np.array(Pxx, ndmin=2), axis=0))
if fmax is not None:
ax.set_xlim((fmin, fmax))
ax.ticklabel_format(axis='both', scilimits=(-2, 3))
ax.set_xlabel('Frequency (Hz)')
if scale_applied == 1:
ylabel = 'Power spectral density\n(nAm' + r'$^2 \ Hz^{-1}$)'
else:
ylabel = 'Power spectral density\n' +\
r'([nAm$\times$ {:.0f}]'.format(scale_applied) +\
r'$^2 \ Hz^{-1}$)'
ax.set_ylabel(ylabel, multialignment='center')
plt_show(show)
return ax.get_figure()
def _linewidth_from_data_units(ax, linewidth):
# see: https://stackoverflow.com/a/35501485
fig = ax.get_figure()
length = fig.bbox_inches.width * ax.get_position().width
value_range = np.diff(ax.get_xlim())[0]
length *= 72 # Convert length to points
# Scale linewidth to value range
return linewidth * (length / value_range)
[docs]def plot_cell_morphology(cell, ax, show=True):
"""Plot the cell morphology.
Parameters
----------
cell : instance of Cell
The cell object
ax : instance of Axes3D
Matplotlib 3D axis
show : bool
If True, show the plot
Returns
-------
axes : list of instance of Axes3D
The matplotlib 3D axis handle.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
if ax is None:
plt.figure()
ax = plt.axes(projection='3d')
# Cell is in XZ plane
ax.set_xlim((cell.pos[1] - 250, cell.pos[1] + 150))
ax.set_zlim((cell.pos[2] - 100, cell.pos[2] + 1200))
for sec_name, section in cell.sections.items():
linewidth = _linewidth_from_data_units(ax, section.diam)
end_pts = section.end_pts
xs, ys, zs = list(), list(), list()
for pt in end_pts:
dx = cell.pos[0] - cell.sections['soma'].end_pts[0][0]
dy = cell.pos[1] - cell.sections['soma'].end_pts[0][1]
dz = cell.pos[2] - cell.sections['soma'].end_pts[0][2]
xs.append(pt[0] + dx)
ys.append(pt[1] + dz)
zs.append(pt[2] + dy)
ax.plot(xs, ys, zs, 'b-', linewidth=linewidth)
ax.view_init(0, -90)
ax.axis('off')
plt.tight_layout()
plt_show(show)
return ax
[docs]def plot_connectivity_matrix(net, conn_idx, ax=None, show_weight=True,
colorbar=True, colormap='Greys',
show=True):
"""Plot connectivity matrix with color bar for synaptic weights
Parameters
----------
net : Instance of Network object
The Network object
conn_idx : int
Index of connection to be visualized
from `net.connectivity`
ax : instance of Axes3D
Matplotlib 3D axis
show_weight : bool
If True, visualize connectivity weights as gradient.
If False, all weights set to constant value.
colormap : str
The name of a matplotlib colormap. Default: 'Greys'
colorbar : bool
If True (default), adjust figure to include colorbar.
show : bool
If True, show the plot
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
"""
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from .network import Network
from .cell import _get_gaussian_connection
_validate_type(net, Network, 'net', 'Network')
_validate_type(conn_idx, int, 'conn_idx', 'int')
_validate_type(show_weight, bool, 'show_weight', 'bool')
if ax is None:
_, ax = plt.subplots(1, 1)
# Load objects for distance calculation
conn = net.connectivity[conn_idx]
nc_dict = conn['nc_dict']
src_type = conn['src_type']
target_type = conn['target_type']
src_type_pos = net.pos_dict[src_type]
target_type_pos = net.pos_dict[target_type]
src_range = np.array(net.gid_ranges[conn['src_type']])
target_range = np.array(net.gid_ranges[conn['target_type']])
connectivity_matrix = np.zeros((len(src_range), len(target_range)))
for src_gid, target_src_pair in conn['gid_pairs'].items():
src_idx = np.where(src_range == src_gid)[0][0]
target_indeces = np.where(np.in1d(target_range, target_src_pair))[0]
for target_idx in target_indeces:
src_pos = src_type_pos[src_idx]
target_pos = target_type_pos[target_idx]
# Identical calculation used in Cell.par_connect_from_src()
if show_weight:
weight, _ = _get_gaussian_connection(
src_pos, target_pos, nc_dict,
inplane_distance=net._inplane_distance)
else:
weight = 1.0
connectivity_matrix[src_idx, target_idx] = weight
im = ax.imshow(connectivity_matrix, cmap=colormap, interpolation='none')
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Frequency (Hz)')
if colorbar:
fig = ax.get_figure()
xfmt = ScalarFormatter()
xfmt.set_powerlimits((-2, 2))
cbar = fig.colorbar(im, ax=ax, format=xfmt)
cbar.ax.yaxis.set_ticks_position('right')
cbar.ax.set_ylabel('Weight', rotation=-90, va="bottom")
ax.set_xlabel(f"{conn['target_type']} target gids "
f"({target_range[0]}-{target_range[-1]})")
ax.set_xticklabels(list())
ax.set_ylabel(f"{conn['src_type']} source gids "
f"({src_range[0]}-{src_range[-1]})")
ax.set_yticklabels(list())
ax.set_title(f"{conn['src_type']} -> {conn['target_type']} "
f"({conn['loc']}, {conn['receptor']})")
plt.tight_layout()
plt_show(show)
return ax.get_figure()
def _update_target_plot(ax, conn, src_gid, src_type_pos, target_type_pos,
src_range, target_range, nc_dict, colormap,
inplane_distance):
from .cell import _get_gaussian_connection
# Extract indeces to get position in network
# Index in gid range aligns with net.pos_dict
target_src_pair = conn['gid_pairs'][src_gid]
target_indeces = np.where(np.in1d(target_range, target_src_pair))[0]
src_idx = np.where(src_range == src_gid)[0][0]
src_pos = src_type_pos[src_idx]
# Aggregate positions and weight of each connected target
weights, target_x_pos, target_y_pos = list(), list(), list()
for target_idx in target_indeces:
target_pos = target_type_pos[target_idx]
target_x_pos.append(target_pos[0])
target_y_pos.append(target_pos[1])
weight, _ = _get_gaussian_connection(src_pos, target_pos, nc_dict,
inplane_distance)
weights.append(weight)
ax.clear()
im = ax.scatter(target_x_pos, target_y_pos, c=weights, s=50,
cmap=colormap)
x_pos = target_type_pos[:, 0]
y_pos = target_type_pos[:, 1]
ax.scatter(x_pos, y_pos, color='k', marker='x', zorder=-1, s=20)
ax.scatter(src_pos[0], src_pos[1], marker='s', color='red', s=150)
ax.set_ylabel('Y Position')
ax.set_xlabel('X Position')
return im
[docs]def plot_cell_connectivity(net, conn_idx, src_gid=None, axes=None,
colorbar=True, colormap='viridis', show=True):
"""Plot synaptic weight of connections.
This is an interactive plot with source cells shown in the left
subplot and connectivity from a source cell to all the target cells
in the right subplot. Click on the cells in the left subplot to
explore how the connectivity pattern changes for different source cells.
Parameters
----------
net : Instance of Network object
The Network object
conn_idx : int
Index of connection to be visualized from net.connectivity
src_gid : int | None
The cell ID of the source cell. It must be an element of
net.connectivity[conn_idx]['gid_pairs'].keys()
If None, the first cell from the list of valid src_gids is selected.
axes : instance of Axes3D
Matplotlib 3D axis
colormap : str
The name of a matplotlib colormap. Default: 'viridis'
colorbar : bool
If True (default), adjust figure to include colorbar.
show : bool
If True, show the plot
Returns
-------
fig : instance of matplotlib Figure
The matplotlib figure handle.
Notes
-----
Target cells will be determined by the connections in
net.connectivity[conn_idx].
If the target cell is not connected to the source cell,
it will appear as a smaller black cross.
Source cell is plotted as a red square. Source cell will not be plotted if
the connection corresponds to a drive, ex: poisson, bursty, etc.
"""
import matplotlib.pyplot as plt
from .network import Network
from matplotlib.ticker import ScalarFormatter
_validate_type(net, Network, 'net', 'Network')
_validate_type(conn_idx, int, 'conn_idx', 'int')
# Load objects for distance calculation
conn = net.connectivity[conn_idx]
nc_dict = conn['nc_dict']
src_type = conn['src_type']
target_type = conn['target_type']
src_type_pos = np.array(net.pos_dict[src_type])
target_type_pos = np.array(net.pos_dict[target_type])
src_range = np.array(net.gid_ranges[conn['src_type']])
valid_src_gids = list(net.connectivity[conn_idx]['gid_pairs'].keys())
src_pos_valid = src_type_pos[np.in1d(src_range, valid_src_gids)]
if src_gid is None:
src_gid = valid_src_gids[0]
_validate_type(src_gid, int, 'src_gid', 'int')
if src_gid not in valid_src_gids:
raise ValueError(f'src_gid {src_gid} not a valid cell ID for this '
f'connection. Please select one of {valid_src_gids}')
target_range = np.array(net.gid_ranges[conn['target_type']])
if axes is None:
if src_type in net.cell_types:
fig, axes = plt.subplots(1, 2, sharex=True, sharey=True)
else:
fig, axes = plt.subplots(1, 1, sharex=True, sharey=True)
axes = [axes]
if len(axes) == 2:
ax_src, ax = axes
else:
ax = axes[0]
im = _update_target_plot(ax, conn, src_gid, src_type_pos,
target_type_pos, src_range,
target_range, nc_dict, colormap,
net._inplane_distance)
x_src = src_type_pos[:, 0]
y_src = src_type_pos[:, 1]
x_src_valid = src_pos_valid[:, 0]
y_src_valid = src_pos_valid[:, 1]
if src_type in net.cell_types:
ax_src.scatter(x_src, y_src, marker='s', color='red', s=50,
alpha=0.2)
ax_src.scatter(x_src_valid, y_src_valid, marker='s', color='red',
s=50)
plt.suptitle(f"{conn['src_type']}-> {conn['target_type']}"
f" ({conn['loc']}, {conn['receptor']})")
def _onclick(event):
if event.inaxes in [ax] or event.inaxes is None:
return
dist = np.linalg.norm(src_type_pos[:, :2] -
np.array([event.xdata, event.ydata]),
axis=1)
src_idx = np.argmin(dist)
src_gid = src_range[src_idx]
if src_gid not in valid_src_gids:
return
_update_target_plot(ax, conn, src_gid, src_type_pos,
target_type_pos, src_range, target_range,
nc_dict, colormap, net._inplane_distance)
fig.canvas.draw()
if colorbar:
fig = ax.get_figure()
xfmt = ScalarFormatter()
xfmt.set_powerlimits((-2, 2))
cbar = fig.colorbar(im, ax=ax, format=xfmt)
cbar.ax.yaxis.set_ticks_position('right')
cbar.ax.set_ylabel('Weight', rotation=-90, va="bottom")
plt.tight_layout()
fig.canvas.mpl_connect('button_press_event', _onclick)
plt_show(show)
return ax.get_figure()