[figures] make py3 compatible
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@@ -86,7 +86,7 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
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def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
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time = np.arange(0., max_t, dt)
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bins = np.arange(0., max_t, bin_width)
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bin_indices = bins / dt
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bin_indices = np.asarray(bins / dt, np.int)
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hist, _ = sp.histogram(times, bins)
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rate = np.zeros(time.shape)
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@@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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times = []
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v = vreset
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noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
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for k in xrange(len(noise)) :
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for k in np.arange(len(noise)) :
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v += (input[k]+noise[k])*dt/tau
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if v >= vthresh :
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v = vreset
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@@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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def isis( spikes ) :
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isi = []
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for k in xrange(len(spikes)) :
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for k in np.arange(len(spikes)) :
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isi.extend(np.diff(spikes[k]))
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return isi
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@@ -76,7 +76,7 @@ rng = np.random.RandomState(54637281)
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time = np.arange(0.0, duration, dt)
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x = np.zeros(time.shape)+rate
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n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
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for k in xrange(1,len(x)) :
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for k in np.arange(1,len(x)) :
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x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
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x[x<0.0] = 0.0
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@@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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times = []
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v = vreset
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noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
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for k in xrange(len(noise)) :
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for k in range(len(noise)) :
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v += (input[k]+noise[k])*dt/tau
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if v >= vthresh :
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v = vreset
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@@ -55,7 +55,7 @@ rng = np.random.RandomState(54637281)
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time = np.arange(0.0, duration, dt)
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x = np.zeros(time.shape)+rate
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n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
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for k in xrange(1,len(x)) :
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for k in range(1,len(x)) :
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x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
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x[x<0.0] = 0.0
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@@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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times = []
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v = vreset
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noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
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for k in xrange(len(noise)) :
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for k in range(len(noise)) :
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v += (input[k]+noise[k])*dt/tau
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if v >= vthresh :
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v = vreset
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@@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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def isis( spikes ) :
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isi = []
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for k in xrange(len(spikes)) :
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for k in range(len(spikes)) :
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isi.extend(np.diff(spikes[k]))
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return np.array( isi )
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@@ -84,7 +84,7 @@ rng = np.random.RandomState(54637281)
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time = np.arange(0.0, duration, dt)
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x = np.zeros(time.shape)+rate
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n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
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for k in xrange(1,len(x)) :
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for k in range(1,len(x)) :
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x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
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x[x<0.0] = 0.0
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@@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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times = []
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v = vreset
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noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
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for k in xrange(len(noise)) :
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for k in range(len(noise)) :
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v += (input[k]+noise[k])*dt/tau
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if v >= vthresh :
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v = vreset
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@@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
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def isis( spikes ) :
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isi = []
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for k in xrange(len(spikes)) :
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for k in range(len(spikes)) :
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isi.extend(np.diff(spikes[k]))
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return np.array( isi )
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@@ -95,7 +95,7 @@ rng = np.random.RandomState(54637281)
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time = np.arange(0.0, duration, dt)
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x = np.zeros(time.shape)+rate
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n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
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for k in xrange(1,len(x)) :
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for k in range(1,len(x)) :
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x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
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x[x<0.0] = 0.0
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@@ -6,7 +6,7 @@ from IPython import embed
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def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
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count = 0
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sta = np.zeros((abs(t_min) + abs(t_max))/dt)
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sta = np.zeros(int((abs(t_min) + abs(t_max))/dt))
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time = np.arange(t_min, t_max, dt)
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if len(stim.shape) > 1 and stim.shape[1] > 1:
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stim = stim[:,1]
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