[pointprocesses] adapted plot scripts
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14
plotstyle.py
14
plotstyle.py
@ -265,7 +265,7 @@ def __axes__init__(ax, *args, **kwargs):
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ax.show_spines('lb')
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def __axis_label(label, unit=None):
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def axis_label(label, unit=None):
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""" Format an axis label from a label and a unit
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Parameters
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@ -291,7 +291,7 @@ def __axis_label(label, unit=None):
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def set_xlabel(ax, label, unit=None, **kwargs):
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""" Format the xlabel from a label and an unit.
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Uses the __axis_label() function to format the axis label.
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Uses the axis_label() function to format the axis label.
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Parameters
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----------
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@ -302,13 +302,13 @@ def set_xlabel(ax, label, unit=None, **kwargs):
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kwargs: key-word arguments
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Further arguments passed on to the set_xlabel() function.
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"""
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ax.set_xlabel_orig(__axis_label(label, unit), **kwargs)
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ax.set_xlabel_orig(axis_label(label, unit), **kwargs)
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def set_ylabel(ax, label, unit=None, **kwargs):
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""" Format the ylabel from a label and an unit.
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Uses the __axis_label() function to format the axis label.
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Uses the axis_label() function to format the axis label.
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Parameters
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----------
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@ -319,13 +319,13 @@ def set_ylabel(ax, label, unit=None, **kwargs):
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kwargs: key-word arguments
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Further arguments passed on to the set_ylabel() function.
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"""
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ax.set_ylabel_orig(__axis_label(label, unit), **kwargs)
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ax.set_ylabel_orig(axis_label(label, unit), **kwargs)
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def set_zlabel(ax, label, unit=None, **kwargs):
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""" Format the zlabel from a label and an unit.
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Uses the __axis_label() function to format the axis label.
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Uses the axis_label() function to format the axis label.
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Parameters
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----------
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@ -336,7 +336,7 @@ def set_zlabel(ax, label, unit=None, **kwargs):
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kwargs: key-word arguments
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Further arguments passed on to the set_zlabel() function.
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"""
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ax.set_zlabel_orig(__axis_label(label, unit), **kwargs)
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ax.set_zlabel_orig(axis_label(label, unit), **kwargs)
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def common_format():
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@ -1,34 +1,9 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import scipy.io as spio
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import scipy.stats as spst
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import scipy as sp
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from IPython import embed
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def set_axis_fontsize(axis, label_size, tick_label_size=None, legend_size=None):
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"""
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Sets axis, tick label and legend font sizes to the desired size.
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:param axis: the axes object
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:param label_size: the size of the axis label
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:param tick_label_size: the size of the tick labels. If None, lable_size is used
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:param legend_size: the size of the font used in the legend.If None, the label_size is used.
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"""
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if not tick_label_size:
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tick_label_size = label_size
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if not legend_size:
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legend_size = label_size
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axis.xaxis.get_label().set_fontsize(label_size)
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axis.yaxis.get_label().set_fontsize(label_size)
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for tick in axis.xaxis.get_major_ticks() + axis.yaxis.get_major_ticks():
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tick.label.set_fontsize(tick_label_size)
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l = axis.get_legend()
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if l:
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for t in l.get_texts():
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t.set_fontsize(legend_size)
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import matplotlib.pyplot as plt
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from plotstyle import *
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def get_instantaneous_rate(times, max_t=30., dt=1e-4):
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@ -53,33 +28,24 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
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avg_rate = np.mean(rates, axis=1)
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rate_std = np.std(rates, axis=1)
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fig = plt.figure()
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ax1 = fig.add_subplot(311)
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ax2 = fig.add_subplot(312)
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ax3 = fig.add_subplot(313)
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height))
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ax1.vlines(times[times < (50000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
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ax1.set_ylabel("skpikes", fontsize=12)
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set_axis_fontsize(ax1, 12)
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ax1.set_xlim([0, 5])
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ax1.set_ylabel('Spikes')
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ax1.set_xlim(0, 5)
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ax2.plot(time, rate, label="instantaneous rate, trial 1")
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ax2.set_ylabel("firing rate [Hz]", fontsize=12)
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ax2.legend(fontsize=12)
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set_axis_fontsize(ax2, 12)
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ax2.set_ylabel('Firing rate', 'Hz')
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ax2.legend()
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ax3.fill_between(time, avg_rate+rate_std, avg_rate-rate_std, color="dodgerblue",
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alpha=0.5, label="standard deviation")
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ax3.plot(time, avg_rate, label="average rate")
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ax3.set_xlabel("times [s]", fontsize=12)
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ax3.set_ylabel("firing rate [Hz]", fontsize=12)
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ax3.legend(fontsize=12)
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ax3.set_ylim([0, 450])
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set_axis_fontsize(ax3, 12)
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ax3.set_xlabel('Time', 's')
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ax3.set_ylabel('Firing rate', 'Hz')
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ax3.legend()
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ax3.set_ylim(0, 450)
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fig.set_size_inches(15, 10)
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fig.subplots_adjust(left=0.1, bottom=0.125, top=0.95, right=0.95)
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fig.set_facecolor("white")
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fig.savefig("figures/instantaneous_rate.png")
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fig.savefig("isimethod.pdf")
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plt.close()
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@ -104,36 +70,27 @@ def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
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avg_rate = np.mean(rates, axis=1)
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rate_std = np.std(rates, axis=1)
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fig = plt.figure()
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ax1 = fig.add_subplot(311)
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ax2 = fig.add_subplot(312)
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ax3 = fig.add_subplot(313)
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height))
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ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
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ax1.set_ylabel("skpikes", fontsize=12)
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ax1.set_xlim([0, 5])
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set_axis_fontsize(ax1, 12)
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ax1.set_ylabel('Skpikes')
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ax1.set_xlim(0, 5)
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ax2.plot(time, rate, label="binned rate, trial 1")
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ax2.set_ylabel("firing rate [Hz]", fontsize=12)
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ax2.legend(fontsize=12)
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ax2.set_xlim([0, 5])
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set_axis_fontsize(ax2, 12)
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ax2.set_ylabel('Firing rate', 'Hz')
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ax2.legend()
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ax2.set_xlim(0, 5)
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ax3.fill_between(time, avg_rate+rate_std, avg_rate-rate_std, color="dodgerblue",
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alpha=0.5, label="standard deviation")
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ax3.plot(time, avg_rate, label="average rate")
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ax3.set_xlabel("times [s]", fontsize=12)
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ax3.set_ylabel("firing rate [Hz]", fontsize=12)
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ax3.legend(fontsize=12)
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ax3.set_xlim([0, 5])
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ax3.set_ylim([0, 450])
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set_axis_fontsize(ax3, 12)
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ax3.set_xlabel('Times', 's')
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ax3.set_ylabel('Firing rate', 'Hz')
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ax3.legend()
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ax3.set_xlim(0, 5)
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ax3.set_ylim(0, 450)
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fig.set_size_inches(15, 10)
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fig.subplots_adjust(left=0.1, bottom=0.125, top=0.95, right=0.95)
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fig.set_facecolor("white")
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fig.savefig("figures/binned_rate.png")
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fig.savefig("binmethod.pdf")
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plt.close()
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@ -157,36 +114,27 @@ def plot_conv_rate(spike_times, sigma=0.05, max_t=30, dt=1e-4):
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avg_rate = np.mean(rates, axis=1)
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rate_std = np.std(rates, axis=1)
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fig = plt.figure()
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ax1 = fig.add_subplot(311)
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ax2 = fig.add_subplot(312)
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ax3 = fig.add_subplot(313)
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height))
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ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
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ax1.set_ylabel("skpikes", fontsize=12)
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ax1.set_xlim([0, 5])
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set_axis_fontsize(ax1, 12)
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ax1.set_ylabel('Skpikes')
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ax1.set_xlim(0, 5)
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ax2.plot(time, rate, label="convolved rate, trial 1")
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ax2.set_ylabel("firing rate [Hz]", fontsize=12)
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ax2.legend(fontsize=12)
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ax2.set_xlim([0, 5])
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set_axis_fontsize(ax2, 12)
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ax2.set_ylabel('Firing rate', 'Hz')
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ax2.legend()
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ax2.set_xlim(0, 5)
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ax3.fill_between(time, avg_rate+rate_std, avg_rate-rate_std, color="dodgerblue",
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alpha=0.5, label="standard deviation")
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ax3.plot(time, avg_rate, label="average rate")
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ax3.set_xlabel("times [s]", fontsize=12)
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ax3.set_ylabel("firing rate [Hz]", fontsize=12)
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ax3.legend(fontsize=12)
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ax3.set_xlim([0, 5])
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ax3.set_ylim([0, 450])
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set_axis_fontsize(ax3, 12)
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ax3.set_xlabel('Times', 's')
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ax3.set_ylabel('Firing rate', 'Hz')
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ax3.legend()
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ax3.set_xlim(0, 5)
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ax3.set_ylim(0, 450)
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fig.set_size_inches(15, 10)
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fig.subplots_adjust(left=0.1, bottom=0.125, top=0.95, right=0.95)
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fig.set_facecolor("white")
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fig.savefig("figures/convolved_rate.png")
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fig.savefig("convmethod.pdf")
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plt.close()
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@ -196,78 +144,52 @@ def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
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time, inst_rate = get_instantaneous_rate(times)
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time, binn_rate = get_binned_rate(times, bin_width)
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fig = plt.figure()
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ax1 = fig.add_subplot(411)
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ax2 = fig.add_subplot(412)
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ax3 = fig.add_subplot(413)
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ax4 = fig.add_subplot(414)
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ax1.spines["right"].set_visible(False)
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ax1.spines["top"].set_visible(False)
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ax1.yaxis.set_ticks_position('left')
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ax1.xaxis.set_ticks_position('bottom')
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ax2.spines["right"].set_visible(False)
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ax2.spines["top"].set_visible(False)
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ax2.yaxis.set_ticks_position('left')
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ax2.xaxis.set_ticks_position('bottom')
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ax3.spines["right"].set_visible(False)
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ax3.spines["top"].set_visible(False)
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ax3.yaxis.set_ticks_position('left')
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ax3.xaxis.set_ticks_position('bottom')
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ax4.spines["right"].set_visible(False)
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ax4.spines["top"].set_visible(False)
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ax4.yaxis.set_ticks_position('left')
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ax4.xaxis.set_ticks_position('bottom')
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fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=cm_size(figure_width, 2.0*figure_height))
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fig.subplots_adjust(**adjust_fs(fig, left=6.0, right=1.5, bottom=3.0, top=1.0))
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ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
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ax1.set_ylabel("spikes", fontsize=10)
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ax1.set_xlim([2.5, 3.5])
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ax1.set_ylim([0, 1])
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ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color=colors['blue'], lw=1.5)
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ax1.set_ylabel('Spikes')
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ax1.set_xlim(2.5, 3.5)
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ax1.set_ylim(0, 1)
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ax1.set_yticks([0, 1])
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set_axis_fontsize(ax1, 10)
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ax1.set_xticks([2.5, 2.75, 3.0, 3.25, 3.5])
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ax1.set_xticklabels([])
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ax2.plot(time, inst_rate, lw=1.5, label="instantaneous rate")
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ax2.set_ylabel("firing rate [Hz]", fontsize=10)
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ax2.legend(fontsize=10)
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ax2.set_xlim([2.5, 3.5])
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ax2.set_ylim([0, 300])
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ax2.plot(time, inst_rate, color=colors['orange'], lw=1.5, label="instantaneous rate")
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ax2.legend()
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ax2.set_ylabel('Rate', 'Hz')
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ax2.set_xlim(2.5, 3.5)
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ax2.set_ylim(0, 300)
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ax2.set_yticks(np.arange(0, 400, 100))
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set_axis_fontsize(ax2, 10)
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ax2.set_xticks([2.5, 2.75, 3.0, 3.25, 3.5])
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ax2.set_xticklabels([])
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ax3.plot(time, binn_rate, lw=1.5, label="binned rate")
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ax3.set_ylabel("firing rate [Hz]", fontsize=10)
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ax3.legend(fontsize=10)
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ax3.set_xlim([2.5, 3.5])
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ax3.set_ylim([0, 300])
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ax3.plot(time, binn_rate, color=colors['orange'], lw=1.5, label="binned rate")
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ax3.set_ylabel('Rate', 'Hz')
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ax3.legend()
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ax3.set_xlim(2.5, 3.5)
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ax3.set_ylim(0, 300)
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ax3.set_yticks(np.arange(0, 400, 100))
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set_axis_fontsize(ax3, 10)
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ax3.set_xticks([2.5, 2.75, 3.0, 3.25, 3.5])
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ax3.set_xticklabels([])
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ax4.plot(time, conv_rate, lw=1.5, label="convolved rate")
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ax4.set_xlabel("time [s]", fontsize=10)
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ax4.set_ylabel("firing rate [Hz]", fontsize=10)
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ax4.legend(fontsize=10)
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ax4.set_xlim([2.5, 3.5])
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ax4.plot(time, conv_rate, color=colors['orange'], lw=1.5, label="convolved rate")
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ax4.set_xlabel('Time', 's')
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ax4.set_ylabel('Rate', 'Hz')
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ax4.legend()
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ax4.set_xlim(2.5, 3.5)
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ax4.set_xticks([2.5, 2.75, 3.0, 3.25, 3.5])
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ax4.set_ylim([0, 300])
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ax4.set_ylim(0, 300)
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ax4.set_yticks(np.arange(0, 400, 100))
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set_axis_fontsize(ax4, 10)
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fig.set_size_inches(7.5, 5)
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fig.subplots_adjust(left=0.1, bottom=0.125, top=0.95, right=0.95, )
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fig.set_facecolor("white")
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fig.savefig("firingrates.pdf")
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plt.close()
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if __name__ == "__main__":
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spike_times = spio.loadmat('lifoustim.mat')["spikes"]
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# plot_isi_rate(spike_times)
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# plot_bin_rate(spike_times, 0.05)
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# plot_conv_rate(spike_times, 0.025)
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#plot_isi_rate(spike_times)
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#plot_bin_rate(spike_times, 0.05)
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#plot_conv_rate(spike_times, 0.025)
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plot_comparison(spike_times, 0.05, 0.025)
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@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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def hompoisson(rate, trials, duration) :
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spikes = []
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@ -56,9 +57,9 @@ def plotisih( ax, isis, binwidth=None ) :
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ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
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ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
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ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
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ax.set_xlabel('ISI [ms]')
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ax.set_ylabel('p(ISI) [1/s]')
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ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
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ax.set_xlabel('ISI', 'ms')
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ax.set_ylabel('p(ISI)', '1/s')
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ax.bar( 1000.0*b[:-1], h, bar_fac*1000.0*np.diff(b), facecolor=colors['blue'])
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# parameter:
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rate = 20.0
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@ -83,19 +84,17 @@ x[x<0.0] = 0.0
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# pif spike trains:
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inhspikes = pifspikes(x, trials, dt, D=0.3)
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fig = plt.figure( figsize=(9,4) )
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ax = fig.add_subplot(1, 2, 1)
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ax.set_title('stationary')
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ax.set_xlim(0.0, 200.0)
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ax.set_ylim(0.0, 40.0)
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plotisih(ax, isis(homspikes))
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fig, (ax1, ax2) = plt.subplots(1, 2)
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fig.subplots_adjust(**adjust_fs(fig, top=1.5))
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ax1.set_title('stationary')
|
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ax1.set_xlim(0.0, 200.0)
|
||||
ax1.set_ylim(0.0, 40.0)
|
||||
plotisih(ax1, isis(homspikes))
|
||||
|
||||
ax = fig.add_subplot(1, 2, 2)
|
||||
ax.set_title('non-stationary')
|
||||
ax.set_xlim(0.0, 200.0)
|
||||
ax.set_ylim(0.0, 40.0)
|
||||
plotisih(ax, isis(inhspikes))
|
||||
ax2.set_title('non-stationary')
|
||||
ax2.set_xlim(0.0, 200.0)
|
||||
ax2.set_ylim(0.0, 40.0)
|
||||
plotisih(ax2, isis(inhspikes))
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig('isihexamples.pdf')
|
||||
plt.close()
|
||||
|
||||
@ -1,20 +1,9 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
figsize=(6,3)
|
||||
|
||||
def set_rc():
|
||||
plt.rcParams['xtick.major.size'] = 5
|
||||
plt.rcParams['xtick.minor.size'] = 5
|
||||
plt.rcParams['xtick.major.width'] = 2
|
||||
plt.rcParams['xtick.minor.width'] = 2
|
||||
plt.rcParams['ytick.major.size'] = 5
|
||||
plt.rcParams['ytick.minor.size'] = 5
|
||||
plt.rcParams['ytick.major.width'] = 2
|
||||
plt.rcParams['ytick.minor.width'] = 2
|
||||
plt.rcParams['xtick.direction'] = "out"
|
||||
plt.rcParams['ytick.direction'] = "out"
|
||||
fig_size = cm_size(figure_width, 1.2*figure_height)
|
||||
|
||||
|
||||
def create_spikes(nspikes=11, duration=0.5, seed=1000):
|
||||
@ -38,27 +27,18 @@ def gaussian(sigma, dt):
|
||||
|
||||
|
||||
def setup_axis(spikes_ax, rate_ax):
|
||||
spikes_ax.spines["left"].set_visible(False)
|
||||
spikes_ax.spines["right"].set_visible(False)
|
||||
spikes_ax.spines["top"].set_visible(False)
|
||||
spikes_ax.yaxis.set_ticks_position('left')
|
||||
spikes_ax.xaxis.set_ticks_position('bottom')
|
||||
spikes_ax.show_spines('b')
|
||||
spikes_ax.set_yticks([])
|
||||
spikes_ax.set_ylim(-0.2, 1.0)
|
||||
#spikes_ax.set_ylabel("Spikes")
|
||||
spikes_ax.text(-0.1, 0.5, "Spikes", transform=spikes_ax.transAxes, rotation='vertical', va='center')
|
||||
#spikes_ax.text(-0.125, 1.2, "A", transform=spikes_ax.transAxes)
|
||||
spikes_ax.text(-0.1, 0.5, 'Spikes', transform=spikes_ax.transAxes, rotation='vertical', va='center')
|
||||
spikes_ax.set_xlim(-1, 500)
|
||||
#spikes_ax.set_xticklabels(np.arange(0., 600, 100))
|
||||
|
||||
rate_ax.spines["right"].set_visible(False)
|
||||
rate_ax.spines["top"].set_visible(False)
|
||||
rate_ax.yaxis.set_ticks_position('left')
|
||||
rate_ax.xaxis.set_ticks_position('bottom')
|
||||
rate_ax.set_xlabel('Time [ms]')
|
||||
#rate_ax.set_ylabel('Firing rate [Hz]')
|
||||
rate_ax.text(-0.1, 0.5, "Rate [Hz]", transform=rate_ax.transAxes, rotation='vertical', va='center')
|
||||
#rate_ax.text(-0.125, 1.15, "B", transform=rate_ax.transAxes)
|
||||
spikes_ax.show_spines('lb')
|
||||
rate_ax.set_xlabel('Time', 'ms')
|
||||
#rate_ax.set_ylabel('Firing rate', 'Hz')
|
||||
rate_ax.text(-0.1, 0.5, axis_label('Rate', 'Hz'), transform=rate_ax.transAxes,
|
||||
rotation='vertical', va='center')
|
||||
rate_ax.set_xlim(0, 500)
|
||||
#rate_ax.set_xticklabels(np.arange(0., 600, 100))
|
||||
rate_ax.set_ylim(0, 60)
|
||||
@ -75,29 +55,23 @@ def plot_bin_method():
|
||||
bins = np.arange(0, 0.55, 0.05)
|
||||
count, _ = np.histogram(spike_times, bins)
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(*figsize)
|
||||
fig.set_facecolor('white')
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
fig = plt.figure(figsize=fig_size)
|
||||
spikes_ax = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate_ax)
|
||||
setup_axis(spikes_ax, rate_ax)
|
||||
|
||||
for ti in spike_times:
|
||||
ti *= 1000.0
|
||||
spikes.plot([ti, ti], [0., 1.], '-b', lw=2)
|
||||
spikes_ax.plot([ti, ti], [0., 1.], color=colors['blue'], lw=2)
|
||||
|
||||
#spikes.vlines(1000.0*spike_times, 0., 1., color="darkblue", lw=1.25)
|
||||
for tb in 1000.0*bins :
|
||||
spikes.plot([tb, tb], [-2.0, 0.75], '-', color="#777777", lw=1, clip_on=False)
|
||||
#spikes.vlines(1000.0*np.hstack((0,bins)), -2.0, 1.25, color="#777777", lw=1, linestyles='-', clip_on=False)
|
||||
spikes_ax.plot([tb, tb], [-2.0, 0.75], '-', color="#777777", lw=1, clip_on=False)
|
||||
for i,c in enumerate(count):
|
||||
spikes.text(1000.0*(bins[i]+0.5*bins[1]), 1.1, str(c), color='#CC0000', ha='center')
|
||||
spikes_ax.text(1000.0*(bins[i]+0.5*bins[1]), 1.1, str(c), color=colors['red'],
|
||||
ha='center')
|
||||
|
||||
rate = count / 0.05
|
||||
rate_ax.step(1000.0*bins, np.hstack((rate, rate[-1])), color='#FF9900', where='post')
|
||||
fig.tight_layout()
|
||||
rate_ax.step(1000.0*bins, np.hstack((rate, rate[-1])), color=colors['orange'], where='post')
|
||||
fig.savefig("binmethod.pdf")
|
||||
plt.close()
|
||||
|
||||
@ -114,59 +88,45 @@ def plot_conv_method():
|
||||
rate = np.convolve(rate, kernel, mode='same')
|
||||
rate = np.roll(rate, -1)
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(*figsize)
|
||||
fig.set_facecolor('white')
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
fig = plt.figure(figsize=fig_size)
|
||||
spikes_ax = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate_ax)
|
||||
setup_axis(spikes_ax, rate_ax)
|
||||
|
||||
#spikes.vlines(1000.0*spike_times, 0., 1., color="darkblue", lw=1.5, zorder=2)
|
||||
for ti in spike_times:
|
||||
ti *= 1000.0
|
||||
spikes.plot([ti, ti], [0., 1.], '-b', lw=2)
|
||||
spikes.plot(1000*kernel_time + ti, kernel/np.max(kernel), color='#cc0000', lw=1, zorder=1)
|
||||
|
||||
rate_ax.plot(1000.0*t, rate, color='#FF9900', lw=2, zorder=2)
|
||||
rate_ax.fill_between(1000.0*t, rate, np.zeros(len(rate)), color='#FFFF66')
|
||||
#rate_ax.fill_between(t, rate, np.zeros(len(rate)), color="red", alpha=0.5)
|
||||
#rate_ax.set_ylim([0, 50])
|
||||
#rate_ax.set_yticks(np.arange(0,75,25))
|
||||
fig.tight_layout()
|
||||
spikes_ax.plot([ti, ti], [0., 1.], color=colors['blue'], lw=2)
|
||||
spikes_ax.plot(1000*kernel_time + ti, kernel/np.max(kernel), color=colors['red'],
|
||||
lw=1, zorder=1)
|
||||
|
||||
rate_ax.plot(1000.0*t, rate, color=colors['orange'], lw=2, zorder=2)
|
||||
rate_ax.fill_between(1000.0*t, rate, np.zeros(len(rate)), color=colors['yellow'])
|
||||
|
||||
fig.savefig("convmethod.pdf")
|
||||
|
||||
|
||||
def plot_isi_method():
|
||||
spike_times = create_spikes()
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(*figsize)
|
||||
fig.set_facecolor('white')
|
||||
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
fig = plt.figure(figsize=fig_size)
|
||||
spikes_ax = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate)
|
||||
setup_axis(spikes_ax, rate)
|
||||
|
||||
spike_times = np.hstack((0.005, spike_times))
|
||||
#spikes.vlines(1000*spike_times, 0., 1., color="blue", lw=2)
|
||||
for i in range(1, len(spike_times)):
|
||||
t_start = 1000*spike_times[i-1]
|
||||
t = 1000*spike_times[i]
|
||||
spikes.plot([t_start, t_start], [0., 1.], '-b', lw=2)
|
||||
spikes.annotate(s='', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'), color='red')
|
||||
spikes.text(0.5*(t_start+t), 0.75,
|
||||
spikes_ax.plot([t_start, t_start], [0., 1.], color=colors['blue'], lw=2)
|
||||
spikes_ax.annotate(s='', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'), color=colors['red'])
|
||||
spikes_ax.text(0.5*(t_start+t), 0.75,
|
||||
"{0:.0f}".format((t - t_start)),
|
||||
color='#CC0000', ha='center')
|
||||
color=colors['red'], ha='center')
|
||||
|
||||
#spike_times = np.hstack((0, spike_times))
|
||||
i_rate = 1./np.diff(spike_times)
|
||||
rate.step(1000*spike_times, np.hstack((i_rate, i_rate[-1])),color='#FF9900', lw=2, where="post")
|
||||
rate.step(1000*spike_times, np.hstack((i_rate, i_rate[-1])),color=colors['orange'], lw=2, where="post")
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig("isimethod.pdf")
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
def hompoisson(rate, trials, duration) :
|
||||
spikes = []
|
||||
@ -45,28 +46,13 @@ def isis( spikes ) :
|
||||
isi.extend(np.diff(spikes[k]))
|
||||
return np.array( isi )
|
||||
|
||||
def plotisih( ax, isis, binwidth=None ) :
|
||||
if binwidth == None :
|
||||
nperbin = 200.0 # average number of isis per bin
|
||||
bins = len(isis)/nperbin # number of bins
|
||||
binwidth = np.max(isis)/bins
|
||||
if binwidth < 5e-4 : # half a millisecond
|
||||
binwidth = 5e-4
|
||||
h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
|
||||
ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.set_xlabel('ISI [ms]')
|
||||
ax.set_ylabel('p(ISI) [1/s]')
|
||||
ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
|
||||
|
||||
def plotreturnmap(ax, isis, lag=1, max=None) :
|
||||
ax.set_xlabel(r'ISI$_i$ [ms]')
|
||||
ax.set_ylabel(r'ISI$_{i+1}$ [ms]')
|
||||
ax.set_xlabel(r'ISI$_i$', 'ms')
|
||||
ax.set_ylabel(r'ISI$_{i+1}$', 'ms')
|
||||
if max != None :
|
||||
ax.set_xlim(0.0, 1000.0*max)
|
||||
ax.set_ylim(0.0, 1000.0*max)
|
||||
ax.scatter( 1000.0*isis[:-lag], 1000.0*isis[lag:] )
|
||||
ax.scatter(1000.0*isis[:-lag], 1000.0*isis[lag:], c=colors['blue'])
|
||||
|
||||
# parameter:
|
||||
rate = 20.0
|
||||
@ -91,15 +77,13 @@ x[x<0.0] = 0.0
|
||||
# pif spike trains:
|
||||
inhspikes = pifspikes(x, trials, dt, D=0.3)
|
||||
|
||||
fig = plt.figure( figsize=(9,4) )
|
||||
ax = fig.add_subplot(1, 2, 1)
|
||||
ax.set_title('stationary')
|
||||
plotreturnmap(ax, isis(homspikes), 1, 0.3)
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
fig.subplots_adjust(**adjust_fs(fig, left=6.5, top=1.5))
|
||||
ax1.set_title('stationary')
|
||||
plotreturnmap(ax1, isis(homspikes), 1, 0.3)
|
||||
|
||||
ax = fig.add_subplot(1, 2, 2)
|
||||
ax.set_title('non-stationary')
|
||||
plotreturnmap(ax, isis(inhspikes), 1, 0.3)
|
||||
ax2.set_title('non-stationary')
|
||||
plotreturnmap(ax2, isis(inhspikes), 1, 0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig('returnmapexamples.pdf')
|
||||
#plt.show()
|
||||
plt.close()
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
def hompoisson(rate, trials, duration) :
|
||||
spikes = []
|
||||
@ -45,29 +46,6 @@ def isis( spikes ) :
|
||||
isi.extend(np.diff(spikes[k]))
|
||||
return np.array( isi )
|
||||
|
||||
def plotisih( ax, isis, binwidth=None ) :
|
||||
if binwidth == None :
|
||||
nperbin = 200.0 # average number of isis per bin
|
||||
bins = len(isis)/nperbin # number of bins
|
||||
binwidth = np.max(isis)/bins
|
||||
if binwidth < 5e-4 : # half a millisecond
|
||||
binwidth = 5e-4
|
||||
h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
|
||||
ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
|
||||
ax.set_xlabel('ISI [ms]')
|
||||
ax.set_ylabel('p(ISI) [1/s]')
|
||||
ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
|
||||
|
||||
def plotreturnmap(ax, isis, lag=1, max=None) :
|
||||
ax.set_xlabel(r'ISI$_i$ [ms]')
|
||||
ax.set_ylabel(r'ISI$_{i+1}$ [ms]')
|
||||
if max != None :
|
||||
ax.set_xlim(0.0, 1000.0*max)
|
||||
ax.set_ylim(0.0, 1000.0*max)
|
||||
ax.scatter( 1000.0*isis[:-lag], 1000.0*isis[lag:] )
|
||||
|
||||
def plotserialcorr(ax, isis, maxlag=10) :
|
||||
lags = np.arange(maxlag+1)
|
||||
corr = [1.0]
|
||||
@ -77,7 +55,7 @@ def plotserialcorr(ax, isis, maxlag=10) :
|
||||
ax.set_ylabel(r'ISI correlation $\rho_k$')
|
||||
ax.set_xlim(0.0, maxlag)
|
||||
ax.set_ylim(-1.0, 1.0)
|
||||
ax.plot(lags, corr, '.-', markersize=20)
|
||||
ax.plot(lags, corr, '.-', markersize=15, c=colors['blue'])
|
||||
|
||||
# parameter:
|
||||
rate = 20.0
|
||||
@ -102,16 +80,14 @@ x[x<0.0] = 0.0
|
||||
# pif spike trains:
|
||||
inhspikes = pifspikes(x, trials, dt, D=0.3)
|
||||
|
||||
fig = plt.figure( figsize=(9,3) )
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
fig.subplots_adjust(**adjust_fs(fig, left=7.0, right=1.0))
|
||||
|
||||
ax = fig.add_subplot(1, 2, 1)
|
||||
plotserialcorr(ax, isis(homspikes))
|
||||
ax.set_ylim(-0.2, 1.0)
|
||||
plotserialcorr(ax1, isis(homspikes))
|
||||
ax1.set_ylim(-0.2, 1.0)
|
||||
|
||||
ax = fig.add_subplot(1, 2, 2)
|
||||
plotserialcorr(ax, isis(inhspikes))
|
||||
ax.set_ylim(-0.2, 1.0)
|
||||
plotserialcorr(ax2, isis(inhspikes))
|
||||
ax2.set_ylim(-0.2, 1.0)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig('serialcorrexamples.pdf')
|
||||
#plt.show()
|
||||
plt.close()
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy.io as scio
|
||||
from IPython import embed
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
|
||||
def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
|
||||
@ -38,30 +38,19 @@ def reconstruct_stimulus(spike_times, sta, stimulus, t_max=30., dt=1e-4):
|
||||
|
||||
|
||||
def plot_results(sta_time, st_average, stim_time, s_est, stimulus, duration, dt):
|
||||
plt.xkcd()
|
||||
sta_ax = plt.subplot2grid((1, 3), (0, 0), rowspan=1, colspan=1)
|
||||
stim_ax = plt.subplot2grid((1, 3), (0, 1), rowspan=1, colspan=2)
|
||||
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(8, 3)
|
||||
fig.subplots_adjust(left=0.08, bottom=0.15, top=0.9, right=0.975)
|
||||
fig.set_facecolor("white")
|
||||
fig.subplots_adjust(**adjust_fs(fig, left=6.5, right=2.0, bottom=3.0, top=2.0))
|
||||
|
||||
sta_ax.plot(sta_time * 1000, st_average, color="#FF9900", lw=2.)
|
||||
sta_ax.set_xlabel("Time (ms)")
|
||||
sta_ax.set_ylabel("Stimulus")
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sta_ax.plot(sta_time * 1000, st_average, color=colors['orange'], lw=2.)
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||||
sta_ax.set_xlabel('Time', 'ms')
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||||
sta_ax.set_ylabel('Stimulus')
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||||
sta_ax.set_xlim(-40, 20)
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||||
sta_ax.set_xticks(np.arange(-40, 21, 20))
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||||
sta_ax.set_ylim(-0.1, 0.2)
|
||||
sta_ax.set_yticks(np.arange(-0.1, 0.21, 0.1))
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||||
# sta_ax.xaxis.grid('off')
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||||
sta_ax.spines["right"].set_visible(False)
|
||||
sta_ax.spines["top"].set_visible(False)
|
||||
sta_ax.yaxis.set_ticks_position('left')
|
||||
sta_ax.xaxis.set_ticks_position('bottom')
|
||||
sta_ax.spines["bottom"].set_linewidth(2.0)
|
||||
sta_ax.spines["left"].set_linewidth(2.0)
|
||||
sta_ax.tick_params(direction="out", width=2.0)
|
||||
ylim = sta_ax.get_ylim()
|
||||
xlim = sta_ax.get_xlim()
|
||||
sta_ax.plot(list(xlim), [0., 0.], zorder=1, color='darkgray', ls='--', lw=1)
|
||||
@ -80,23 +69,15 @@ def plot_results(sta_time, st_average, stim_time, s_est, stimulus, duration, dt)
|
||||
connectionstyle="angle3,angleA=60,angleB=-40") )
|
||||
#sta_ax.text(-0.25, 1.04, "A", transform=sta_ax.transAxes, size=24)
|
||||
|
||||
stim_ax.plot(stim_time * 1000, stimulus[:,1], label='stimulus', color='#0000FF', lw=2.)
|
||||
stim_ax.plot(stim_time * 1000, s_est, label='reconstruction', color='#FF9900', lw=2)
|
||||
stim_ax.set_xlabel('Time (ms)')
|
||||
stim_ax.plot(stim_time * 1000, stimulus[:,1], label='stimulus', color=colors['blue'], lw=2.)
|
||||
stim_ax.plot(stim_time * 1000, s_est, label='reconstruction', color=colors['orange'], lw=2)
|
||||
stim_ax.set_xlabel('Time', 'ms')
|
||||
stim_ax.set_xlim(0.0, 200)
|
||||
stim_ax.set_ylim([-1., 1.])
|
||||
stim_ax.legend(loc=(0.3, 0.85), frameon=False, fontsize=12)
|
||||
stim_ax.legend(loc=(0.3, 0.85), frameon=False)
|
||||
stim_ax.plot([0.0, 250], [0., 0.], color="darkgray", lw=1, ls='--', zorder=1)
|
||||
stim_ax.spines["right"].set_visible(False)
|
||||
stim_ax.spines["top"].set_visible(False)
|
||||
stim_ax.yaxis.set_ticks_position('left')
|
||||
stim_ax.xaxis.set_ticks_position('bottom')
|
||||
stim_ax.spines["bottom"].set_linewidth(2.0)
|
||||
stim_ax.spines["left"].set_linewidth(2.0)
|
||||
stim_ax.tick_params(direction="out", width=2.0)
|
||||
#stim_ax.text(-0.1, 1.04, "B", transform=stim_ax.transAxes, size=24)
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig("sta.pdf")
|
||||
plt.close()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user