"""
Simple tools for plotting Neo-format data.
These tools are intended for quickly producing basic plots with simple
formatting. If you need to produce more complex and/or publication-quality
figures, it will probably be easier to use matplotlib or another plotting
package directly rather than trying to extend this module.
:copyright: Copyright 2006-2016 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""
from collections import defaultdict
from numbers import Number
from itertools import repeat
from os import path, makedirs
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
from quantities import ms
from neo import AnalogSignalArray, AnalogSignal, SpikeTrain
try:
from sys import maxint
except ImportError: # Py3
from sys import maxsize as maxint
DEFAULT_FIG_SETTINGS = {
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8,
'savefig.dpi': 150,
}
def handle_options(ax, options):
if "xticks" not in options or options.pop("xticks") is False:
plt.setp(ax.get_xticklabels(), visible=False)
if "xlabel" in options:
ax.set_xlabel(options.pop("xlabel"))
if "yticks" not in options or options.pop("yticks") is False:
plt.setp(ax.get_yticklabels(), visible=False)
if "ylabel" in options:
ax.set_ylabel(options.pop("ylabel"))
if "ylim" in options:
ax.set_ylim(options.pop("ylim"))
if "xlim" in options:
ax.set_xlim(options.pop("xlim"))
def plot_signal(ax, signal, index=None, label='', **options):
"""
Plot an AnalogSignal or one signal from an AnalogSignalArray.
"""
if "ylabel" in options:
if options["ylabel"] == "auto":
options["ylabel"] = "%s (%s)" % (signal.name,
signal.units._dimensionality.string)
handle_options(ax, options)
if index is None:
label = "%s (Neuron %d)" % (label, signal.channel_index)
else:
label = "%s (Neuron %d)" % (label, signal.channel_index[index])
signal = signal[:, index]
ax.plot(signal.times.rescale(ms), signal, label=label, **options)
ax.legend()
def plot_signals(ax, signal_array, label_prefix='', **options):
"""
Plot all signals in an AnalogSignalArray in a single panel.
"""
if "ylabel" in options:
if options["ylabel"] == "auto":
options["ylabel"] = "%s (%s)" % (signal_array.name,
signal_array.units._dimensionality.string)
handle_options(ax, options)
offset = options.pop("y_offset", None)
show_legend = options.pop("legend", True)
for i in signal_array.channel_index.argsort():
channel = signal_array.channel_index[i]
signal = signal_array[:, i]
if label_prefix:
label = "%s (Neuron %d)" % (label_prefix, channel)
else:
label = "Neuron %d" % channel
if offset:
signal += i * offset
ax.plot(signal.times.rescale(ms), signal, label=label, **options)
if show_legend:
ax.legend()
def plot_spiketrains(ax, spiketrains, label='', **options):
"""
Plot all spike trains in a Segment in a raster plot.
"""
ax.set_xlim(0, spiketrains[0].t_stop / ms)
handle_options(ax, options)
max_index = 0
min_index = maxint
for spiketrain in spiketrains:
ax.plot(spiketrain,
np.ones_like(spiketrain) * spiketrain.annotations['source_index'],
'k.', **options)
max_index = max(max_index, spiketrain.annotations['source_index'])
min_index = min(min_index, spiketrain.annotations['source_index'])
ax.set_ylabel("Neuron index")
ax.set_ylim(-0.5 + min_index, max_index + 0.5)
if label:
plt.text(0.95, 0.95, label,
transform=ax.transAxes, ha='right', va='top',
bbox=dict(facecolor='white', alpha=1.0))
def plot_array_as_image(ax, arr, label='', **options):
"""
Plots a numpy array as an image.
"""
handle_options(ax, options)
show_legend = options.pop("legend", True)
plt.pcolormesh(arr, **options)
ax.set_aspect('equal')
if label:
plt.text(0.95, 0.95, label,
transform=ax.transAxes, ha='right', va='top',
bbox=dict(facecolor='white', alpha=1.0))
if show_legend:
plt.colorbar()
def scatterplot(ax, data_table, label='', **options):
handle_options(ax, options)
if options.pop("show_fit", False):
plt.plot(data_table.x, data_table.y_fit, 'k-')
plt.scatter(data_table.x, data_table.y, **options)
if label:
plt.text(0.95, 0.95, label,
transform=ax.transAxes, ha='right', va='top',
bbox=dict(facecolor='white', alpha=1.0))
def plot_hist(ax, histogram, label='', **options):
handle_options(ax, options)
for t, n in histogram:
ax.bar(t, n, width=histogram.bin_width, color=None)
if label:
plt.text(0.95, 0.95, label,
transform=ax.transAxes, ha='right', va='top',
bbox=dict(facecolor='white', alpha=1.0))
def variable_names(segment):
"""
List the names of all the AnalogSignalArrays (used for the variable name by
PyNN) in the given segment.
"""
return set(signal.name for signal in segment.analogsignalarrays)
[docs]class Panel(object):
"""
Represents a single panel in a multi-panel figure.
A panel is a Matplotlib Axes or Subplot instance. A data item may be an
AnalogSignal, AnalogSignalArray, or a list of SpikeTrains. The Panel will
automatically choose an appropriate representation. Multiple data items may
be plotted in the same panel.
Valid options are any valid Matplotlib formatting options that should be
applied to the Axes/Subplot, plus in addition:
`data_labels`:
a list of strings of the same length as the number of data items.
`line_properties`:
a list of dicts containing Matplotlib formatting options, of the
same length as the number of data items.
"""
def __init__(self, *data, **options):
self.data = list(data)
self.options = options
self.data_labels = options.pop("data_labels", repeat(None))
self.line_properties = options.pop("line_properties", repeat({}))
[docs] def plot(self, axes):
"""
Plot the Panel's data in the provided Axes/Subplot instance.
"""
for datum, label, properties in zip(self.data, self.data_labels, self.line_properties):
properties.update(self.options)
if isinstance(datum, DataTable):
scatterplot(axes, datum, label=label, **properties)
elif isinstance(datum, Histogram):
plot_hist(axes, datum, label=label, **properties)
elif isinstance(datum, AnalogSignal):
plot_signal(axes, datum, label=label, **properties)
elif isinstance(datum, AnalogSignalArray):
plot_signals(axes, datum, label_prefix=label, **properties)
elif isinstance(datum, list) and len(datum) > 0 and isinstance(datum[0], SpikeTrain):
plot_spiketrains(axes, datum, label=label, **properties)
elif isinstance(datum, np.ndarray):
if datum.ndim == 2:
plot_array_as_image(axes, datum, label=label, **properties)
else:
raise Exception("Can't handle arrays with %s dimensions" % datum.ndim)
else:
raise Exception("Can't handle type %s" % type(datum))
[docs]def comparison_plot(segments, labels, title='', annotations=None,
fig_settings=None, with_spikes=True):
"""
Given a list of segments, plot all the data they contain so as to be able
to compare them.
Return a Figure instance.
"""
variables_to_plot = set.union(*(variable_names(s) for s in segments))
print("Plotting the following variables: %s" % ", ".join(variables_to_plot))
# group signal arrays by name
n_seg = len(segments)
by_var_and_channel = defaultdict(lambda: defaultdict(list))
line_properties = []
for k, (segment, label) in enumerate(zip(segments, labels)):
lw = 2 * (n_seg - k) - 1
col = 'rbgmck'[k % 6]
line_properties.append({"linewidth": lw, "color": col})
for array in segment.analogsignalarrays:
for i in array.channel_index.argsort():
channel = array.channel_index[i]
signal = array[:, i]
signal.channel_index = channel # Neo should do this in the previous line
by_var_and_channel[array.name][channel].append(signal)
# each panel plots the signals for a given variable.
panels = []
for by_channel in by_var_and_channel.values():
panels += [Panel(*array_list,
line_properties=line_properties,
data_labels=labels) for array_list in by_channel.values()]
if with_spikes and len(segments[0].spiketrains) > 0:
panels += [Panel(segment.spiketrains, data_labels=[label])
for segment, label in zip(segments, labels)]
panels[-1].options["xticks"] = True
panels[-1].options["xlabel"] = "Time (ms)"
fig = Figure(*panels,
title=title,
settings=fig_settings,
annotations=annotations)
return fig
class DataTable(object):
"""A lightweight encapsulation of x, y data for scatterplots."""
def __init__(self, x, y):
self.x = x
self.y = y
def fit_curve(self, f, p0, **fitting_parameters):
from scipy.optimize import curve_fit
self._f = f
self._p0 = p0
self._popt, self._pcov = curve_fit(f, self.x, self.y, p0, **fitting_parameters)
return self._popt, self._pcov
@property
def y_fit(self):
return self._f(self.x, *self._popt)
class Histogram(object):
"""A lightweight encapsulation of histogram data."""
def __init__(self, data):
self.data = data
self.evaluated = False
def evaluate(self):
if not self.evaluated:
n_bins = int(np.sqrt(len(self.data)))
self.values, self.bins = np.histogram(self.data, bins=n_bins)
self.bin_width = self.bins[1] - self.bins[0]
self.evaluated = True
def __iter__(self):
"""Iterate over the bars of the histogram"""
self.evaluate()
for x, y in zip(self.bins[:-1], self.values):
yield (x, y)
def isi_histogram(segment):
all_isis = np.concatenate([np.diff(np.array(st)) for st in segment.spiketrains])
return Histogram(all_isis)
