[pointprocesses] python version of sketch

plotstyle.py

@@ -37,6 +37,7 @@ edgewidth = 0.0 if xkcd_style else 1.0
 mainline = {'linestyle': '-', 'linewidth': lwthick}
 minorline = {'linestyle': '-', 'linewidth': lwthin}
 largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'], 'markeredgewidth': edgewidth}
+largeopenmarker = {'marker': 'o', 'markersize': 7, 'markerfacecolor': colors['white'], 'markeredgewidth': 2}
 smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': edgewidth}
 largelinepoints = {'linestyle': '-', 'linewidth': lwthick, 'marker': 'o', 'markersize': 10, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
 smalllinepoints = {'linestyle': '-', 'linewidth': 1.4, 'marker': 'o', 'markersize': 7, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
@@ -88,6 +89,7 @@ fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
 lsB = dict({'color': colors['red']}, **mainline)
 lsBm = dict({'color': colors['red']}, **minorline)
 psB = dict({'color': colors['red'], 'linestyle': 'none'}, **largemarker)
+psBo = dict({'markeredgecolor': colors['red'], 'linestyle': 'none'}, **largeopenmarker)
 psBm = dict({'color': colors['red'], 'linestyle': 'none'}, **smallmarker)
 lpsB = dict({'color': colors['red']}, **largelinepoints)
 lpsBm = dict({'color': colors['red']}, **smalllinepoints)

pointprocesses/lecture/firingrates.py

@@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
 from plotstyle import *
 
 
-def get_instantaneous_rate(times, max_t=30., dt=1e-4):
+def instantaneous_rate(times, max_t=30., dt=1e-4):
     time = np.arange(0., max_t, dt)
     indices = np.asarray(times / dt, dtype=int)
     intervals = np.diff(np.hstack(([0], times)))
@@ -19,11 +19,11 @@ def get_instantaneous_rate(times, max_t=30., dt=1e-4):
 
 def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])[:50000]
-    time, rate = get_instantaneous_rate(times, max_t=50000*dt)
+    time, rate = instantaneous_rate(times, max_t=50000*dt)
 
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_instantaneous_rate(np.squeeze(spike_times[i][0])[:50000],
+        _, rates[:, i] = instantaneous_rate(np.squeeze(spike_times[i][0])[:50000],
                                             max_t=50000*dt)
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
@@ -48,7 +48,7 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
     plt.close()
 
 
-def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
+def binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
     time = np.arange(0., max_t, dt)
     bins = np.arange(0., max_t, bin_width)
     bin_indices = np.asarray(bins / dt, np.int)
@@ -62,10 +62,10 @@ def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
 
 def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, rate = get_binned_rate(times)
+    time, rate = binned_rate(times)
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_binned_rate(np.squeeze(spike_times[i][0]))
+        _, rates[:, i] = binned_rate(np.squeeze(spike_times[i][0]))
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
 
@@ -93,7 +93,7 @@ def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
     plt.close()
   
 
-def get_convolved_rate(times, sigma, max_t=30., dt=1.e-4):
+def convolved_rate(times, sigma, max_t=30., dt=1.e-4):
     time = np.arange(0., max_t, dt)
     kernel = spst.norm.pdf(np.arange(-8*sigma, 8*sigma, dt),loc=0,scale=sigma)
     indices = np.asarray(times/dt, dtype=int)
@@ -105,11 +105,11 @@ def get_convolved_rate(times, sigma, max_t=30., dt=1.e-4):
 
 def plot_conv_rate(spike_times, sigma=0.05, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, rate = get_convolved_rate(times, sigma)
+    time, rate = convolved_rate(times, sigma)
    
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_convolved_rate(np.squeeze(spike_times[i][0]), sigma)
+        _, rates[:, i] = convolved_rate(np.squeeze(spike_times[i][0]), sigma)
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
     
@@ -139,9 +139,9 @@ def plot_conv_rate(spike_times, sigma=0.05, max_t=30, dt=1e-4):
 
 def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, conv_rate = get_convolved_rate(times, bin_width/np.sqrt(12.))
+    time, conv_rate = convolved_rate(times, bin_width/np.sqrt(12.))
-    time, inst_rate = get_instantaneous_rate(times)
+    time, inst_rate = instantaneous_rate(times)
-    time, binn_rate = get_binned_rate(times, bin_width)
+    time, binn_rate = binned_rate(times, bin_width)
    
     fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=cm_size(figure_width, 1.8*figure_height))
     fig.subplots_adjust(**adjust_fs(fig, left=6.0, right=1.5, bottom=3.0, top=1.0))

pointprocesses/lecture/isihexamples.py

@@ -2,6 +2,15 @@ import numpy as np
 import matplotlib.pyplot as plt
 from plotstyle import *
 
+
+rate = 20.0
+trials = 10
+duration = 100.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+
 def hompoisson(rate, trials, duration) :
     spikes = []
     for k in range(trials) :
@@ -13,6 +22,7 @@ def hompoisson(rate, trials, duration) :
         spikes.append( times )
     return spikes
 
+
 def inhompoisson(rate, trials, dt) :
     spikes = []
     p = rate*dt
@@ -40,12 +50,27 @@ def pifspikes(input, trials, dt, D=0.1) :
         spikes.append( times )
     return spikes
 
+
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
+
+
 def isis( spikes ) :
     isi = []
     for k in range(len(spikes)) :
         isi.extend(np.diff(spikes[k]))
     return isi
 
+
 def plotisih( ax, isis, binwidth=None ) :
     if binwidth == None :
         nperbin = 200.0    # average number of isis per bin
@@ -61,42 +86,29 @@ def plotisih( ax, isis, binwidth=None ) :
     ax.set_ylabel('p(ISI)', '1/s')
     ax.bar( 1000.0*b[:-1], h, bar_fac*1000.0*np.diff(b), **fsA)
 
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 100.0
-dt = 0.001
-tau = 0.1;
     
-# homogeneous spike trains:
+def plot_hom_isih(ax):
-homspikes = hompoisson(rate, trials, duration)
+    homspikes = hompoisson(rate, trials, duration)
+    ax.set_xlim(0.0, 150.0)
+    ax.set_ylim(0.0, 31.0)
+    ax.set_xticks(np.arange(0.0, 151.0, 50.0))
+    ax.set_yticks(np.arange(0.0, 31.0, 10.0))
+    plotisih(ax, isis(homspikes), 0.005)
 
-# OU noise:
+    
-rng = np.random.RandomState(54637281)
+def plot_inhom_isih(ax):
-time = np.arange(0.0, duration, dt)
+    inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
-x = np.zeros(time.shape)+rate
+    ax.set_xlim(0.0, 150.0)
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+    ax.set_ylim(0.0, 31.0)
-for k in range(1,len(x)) :
+    ax.set_xticks(np.arange(0.0, 151.0, 50.0))
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    ax.set_yticks(np.arange(0.0, 31.0, 10.0))
-x[x<0.0] = 0.0
+    plotisih(ax, isis(inhspikes), 0.005)
-
+
-# pif spike trains:
+    
-inhspikes = pifspikes(x, trials, dt, D=0.3)
+if __name__ == "__main__":
-
+    fig, (ax1, ax2) = plt.subplots(1, 2)
-fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
-fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
+    plot_hom_isih(ax1)
-ax1.set_xlim(0.0, 150.0)
+    plot_inhom_isih(ax2)
-ax1.set_ylim(0.0, 31.0)
+    plt.savefig('isihexamples.pdf')
-ax1.set_xticks(np.arange(0.0, 151.0, 50.0))
+    plt.close()
-ax1.set_yticks(np.arange(0.0, 31.0, 10.0))
-plotisih(ax1, isis(homspikes), 0.005)
-
-ax2.set_xlim(0.0, 150.0)
-ax2.set_ylim(0.0, 31.0)
-ax2.set_xticks(np.arange(0.0, 151.0, 50.0))
-ax2.set_yticks(np.arange(0.0, 31.0, 10.0))
-plotisih(ax2, isis(inhspikes), 0.005)
-
-plt.savefig('isihexamples.pdf')
-plt.close()

pointprocesses/lecture/pointprocesses.tex

@@ -60,8 +60,8 @@ process]{Punktprozess}{point processes}.
   \texpicture{pointprocessscetch}
   \titlecaption{\label{pointprocessscetchfig} Statistics of point
     processes.}{A point process is a sequence of instances in time
-    $t_i$ that can be characterized through the inter-event-intervals
+    $t_i$ that can be also characterized by inter-event intervals
-    $T_i=t_{i+1}-t_i$ and the number of events $n_i$. }
+    $T_i=t_{i+1}-t_i$ and event counts $n_i$.}
 \end{figure}
 
 \noindent
@@ -150,9 +150,8 @@ the interval $T_i$. The parameter $k$ is called the \enterm{lag}
 maps are distinctly different \figref{returnmapfig}.
 
 \begin{figure}[tp]
-  \includegraphics[width=1\textwidth]{returnmapexamples}
   \includegraphics[width=1\textwidth]{serialcorrexamples}
-  \titlecaption{\label{returnmapfig}Interspike interval
+  \titlecaption{\label{returnmapfig}Interspike-interval
     correlations}{of the spike trains shown in
     \figref{rasterexamplesfig}. Upper panels show the return maps and
     lower panels the serial correlations of successive intervals
@@ -191,7 +190,7 @@ with itself and is always 1.
 % \begin{figure}[t]
 %   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz10ms}\hfill
 %   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz100ms}
-%   \titlecaption{\label{countstatsfig}Count Statistik.}{}
+%   \titlecaption{\label{countstatsfig}Count statistic.}{}
 % \end{figure}
 Counting the number of events $n_i$ (counts) in time windows $i$ of duration $W$
 yields positive integer random numbers that are commonly quantified

pointprocesses/lecture/rasterexamples.py

@@ -2,6 +2,15 @@ import numpy as np
 import matplotlib.pyplot as plt
 from plotstyle import *
 
+
+rate = 20.0
+trials = 10
+duration = 2.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+
 def hompoisson(rate, trials, duration) :
     spikes = []
     for k in range(trials) :
@@ -13,6 +22,7 @@ def hompoisson(rate, trials, duration) :
         spikes.append(times)
     return spikes
 
+
 def inhompoisson(rate, trials, dt) :
     spikes = []
     p = rate*dt
@@ -40,47 +50,44 @@ def pifspikes(input, trials, dt, D=0.1) :
         spikes.append(times)
     return spikes
 
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 2.0
-dt = 0.001
-tau = 0.1;
-
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
 
-# OU noise:
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
-rng = np.random.RandomState(54637281)
+    # OU noise:
-time = np.arange(0.0, duration, dt)
+    rng = np.random.RandomState(54637281)
-x = np.zeros(time.shape)+rate
+    time = np.arange(0.0, duration, dt)
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+    x = np.zeros(time.shape)+rate
-for k in range(1,len(x)) :
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
         x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
+    x[x<0.0] = 0.0
-
+    spikes = pifspikes(x, trials, dt, D)
-# inhomogeneous spike trains:
+    return spikes
-#inhspikes = inhompoisson(x, trials, dt)
+
-# pif spike trains:
+    
-inhspikes = pifspikes(x, trials, dt, D=0.3)
+def plot_homogeneous_spikes(ax):
-
+    homspikes = hompoisson(rate, trials, duration)
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 0.5*figure_width))
+    ax.set_title('stationary')
-fig.subplots_adjust(**adjust_fs(fig, left=4.0, right=1.0, top=1.2))
+    ax.set_xlim(0.0, duration)
-
+    ax.set_ylim(-0.5, trials-0.5)
-ax1.set_title('stationary')
+    ax.set_xlabel('Time [s]')
-ax1.set_xlim(0.0, duration)
+    ax.set_ylabel('Trial')
-ax1.set_ylim(-0.5, trials-0.5)
+    ax.eventplot(homspikes, colors=[lsA['color']], linelength=0.8, lw=1)
-ax1.set_xlabel('Time [s]')
+
-ax1.set_ylabel('Trial')
+    
-ax1.eventplot(homspikes, colors=[lsA['color']], linelength=0.8, lw=1)
+def plot_inhomogeneous_spikes(ax):
-
+    inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
-ax2.set_title('non-stationary')
+    ax.set_title('non-stationary')
-ax2.set_xlim(0.0, duration)
+    ax.set_xlim(0.0, duration)
-ax2.set_ylim(-0.5, trials-0.5)
+    ax.set_ylim(-0.5, trials-0.5)
-ax2.set_xlabel('Time [s]')
+    ax.set_xlabel('Time [s]')
-ax2.set_ylabel('Trial')
+    ax.set_ylabel('Trial')
-ax2.eventplot(inhspikes, colors=[lsA['color']], linelength=0.8, lw=1)
+    ax.eventplot(inhspikes, colors=[lsA['color']], linelength=0.8, lw=1)
-
+
-plt.savefig('rasterexamples.pdf')
+
-plt.close()
+if __name__ == "__main__":
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 0.5*figure_width))
+    fig.subplots_adjust(**adjust_fs(fig, left=4.0, right=1.0, top=1.2))
+    plot_homogeneous_spikes(ax1)
+    plot_inhomogeneous_spikes(ax2)
+    plt.savefig('rasterexamples.pdf')
+    plt.close()

pointprocesses/lecture/returnmapexamples.py

@@ -1,94 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from plotstyle import *
-
-def hompoisson(rate, trials, duration) :
-    spikes = []
-    for k in range(trials) :
-        times = []
-        t = 0.0
-        while t < duration :
-            t += np.random.exponential(1/rate)
-            times.append( t )
-        spikes.append( times )
-    return spikes
-
-def inhompoisson(rate, trials, dt) :
-    spikes = []
-    p = rate*dt
-    for k in range(trials) :
-        x = np.random.rand(len(rate))
-        times = dt*np.nonzero(x<p)[0]
-        spikes.append( times )
-    return spikes
-
-
-def pifspikes(input, trials, dt, D=0.1) :
-    vreset = 0.0
-    vthresh = 1.0
-    tau = 1.0
-    spikes = []
-    for k in range(trials) :
-        times = []
-        v = vreset
-        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
-        for k in range(len(noise)) :
-            v += (input[k]+noise[k])*dt/tau
-            if v >= vthresh :
-                v = vreset
-                times.append(k*dt)
-        spikes.append( times )
-    return spikes
-
-
-def isis( spikes ) :
-    isi = []
-    for k in range(len(spikes)) :
-        isi.extend(np.diff(spikes[k]))
-    return np.array( isi )
-
-
-def plotreturnmap(ax, isis, lag=1, max=1.0) :
-    ax.set_xlabel(r'ISI$_i$', 'ms')
-    ax.set_ylabel(r'ISI$_{i+1}$', 'ms')
-    ax.set_xlim(0.0, 1000.0*max)
-    ax.set_ylim(0.0, 1000.0*max)
-    isiss = isis[isis<max]
-    ax.plot(1000.0*isiss[:-lag], 1000.0*isiss[lag:], clip_on=False, **psAm)
-
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 10.0
-dt = 0.001
-tau = 0.1;
-
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
-
-# OU noise:
-rng = np.random.RandomState(54637281)
-time = np.arange(0.0, duration, dt)
-x = np.zeros(time.shape)+rate
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
-for k in range(1,len(x)) :
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
-
-# pif spike trains:
-inhspikes = pifspikes(x, trials, dt, D=0.3)
-
-fig, (ax1, ax2) = plt.subplots(1, 2)
-fig.subplots_adjust(**adjust_fs(fig, left=6.5, top=1.5))
-plotreturnmap(ax1, isis(homspikes), 1, 0.3)
-ax1.set_xticks(np.arange(0.0, 301.0, 100.0))
-ax1.set_yticks(np.arange(0.0, 301.0, 100.0))
-
-plotreturnmap(ax2, isis(inhspikes), 1, 0.3)
-ax2.set_ylabel('')
-ax2.set_xticks(np.arange(0.0, 301.0, 100.0))
-ax2.set_yticks(np.arange(0.0, 301.0, 100.0))
-
-plt.savefig('returnmapexamples.pdf')
-plt.close()

pointprocesses/lecture/serialcorrexamples.py

@@ -2,6 +2,16 @@ import numpy as np
 import matplotlib.pyplot as plt
 from plotstyle import *
 
+    
+# parameter:
+rate = 20.0
+trials = 10
+duration = 500.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+
 def hompoisson(rate, trials, duration) :
     spikes = []
     for k in range(trials) :
@@ -41,11 +51,35 @@ def pifspikes(input, trials, dt, D=0.1) :
         spikes.append( times )
     return spikes
 
+
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
+
+
 def isis( spikes ) :
     isi = []
     for k in range(len(spikes)) :
         isi.extend(np.diff(spikes[k]))
-    return np.array( isi )
+    return np.array(isi)
+
+
+def plotreturnmap(ax, isis, lag=1, max=1.0) :
+    ax.set_xlabel(r'ISI$_i$', 'ms')
+    ax.set_ylabel(r'ISI$_{i+1}$', 'ms')
+    ax.set_xlim(0.0, 1000.0*max)
+    ax.set_ylim(0.0, 1000.0*max)
+    isiss = isis[isis<max]
+    ax.plot(1000.0*isiss[:-lag], 1000.0*isiss[lag:], clip_on=False, **psAm)
+
 
 def plotserialcorr(ax, isis, maxlag=10) :
     lags = np.arange(maxlag+1)
@@ -60,38 +94,38 @@ def plotserialcorr(ax, isis, maxlag=10) :
     ax.plot(lags, corr, clip_on=False, zorder=100, **lpsAm)
 
 
-# parameter:
+def plot_hom_returnmap(ax, spikes):
-rate = 20.0
+    plotreturnmap(ax, isis(spikes)[:200], 1, 0.3)
-drate = 50.0
+    ax.set_xticks(np.arange(0.0, 301.0, 100.0))
-trials = 10
+    ax.set_yticks(np.arange(0.0, 301.0, 100.0))
-duration = 500.0
-dt = 0.001
-tau = 0.1;
 
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
 
-# OU noise:
+def plot_inhom_returnmap(ax, spikes):
-rng = np.random.RandomState(54637281)
+    plotreturnmap(ax, isis(spikes)[:200], 1, 0.3)
-time = np.arange(0.0, duration, dt)
+    ax.set_ylabel('')
-x = np.zeros(time.shape)+rate
+    ax.set_xticks(np.arange(0.0, 301.0, 100.0))
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+    ax.set_yticks(np.arange(0.0, 301.0, 100.0))
-for k in range(1,len(x)) :
+
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
 
-# pif spike trains:
+def plot_hom_serialcorr(ax, spikes):
-inhspikes = pifspikes(x, trials, dt, D=0.3)
+    plotserialcorr(ax, isis(spikes))
+    ax.set_ylim(-0.2, 1.0)
 
-fig, (ax1, ax2) = plt.subplots(1, 2)
-fig.subplots_adjust(**adjust_fs(fig, left=7.0, right=1.0))
 
-plotserialcorr(ax1, isis(homspikes))
+def plot_inhom_serialcorr(ax, spikes):
-ax1.set_ylim(-0.2, 1.0)
+    plotserialcorr(ax, isis(spikes))
+    ax.set_ylabel('')
+    ax.set_ylim(-0.2, 1.0)
 
-plotserialcorr(ax2, isis(inhspikes))
-ax2.set_ylabel('')
-ax2.set_ylim(-0.2, 1.0)
 
-plt.savefig('serialcorrexamples.pdf')
+if __name__ == "__main__":
-plt.close()
+    homspikes = hompoisson(rate, trials, duration)
+    inhomspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
+    fig, axs = plt.subplots(2, 2, figsize=cm_size(figure_width, 1.8*figure_height))
+    fig.subplots_adjust(**adjust_fs(fig, left=6.5, right=1.5))
+    plot_hom_returnmap(axs[0,0], homspikes)
+    plot_inhom_returnmap(axs[0,1], inhomspikes)
+    plot_hom_serialcorr(axs[1,0], homspikes)
+    plot_inhom_serialcorr(axs[1,1], inhomspikes)
+    plt.savefig('serialcorrexamples.pdf')
+    plt.close()