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[figures] make py3 compatible

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This commit is contained in:
Jan Grewe 2018-11-02 11:01:31 +01:00
parent 61bdeb160a
commit 780fba24c4
10 changed files with 19 additions and 19 deletions
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4
bootstrap/lecture/bootstrapsem.py
View File

@ -15,13 +15,13 @@ x = rng.randn(nsamples)
# bootstrap the mean:
mus = []
for i in xrange(nresamples) :
for i in range(nresamples) :
mus.append(np.mean(x[rng.randint(0, nsamples, nsamples)]))
hmus, _ = np.histogram(mus, bins, density=True)
# many SRS:
musrs = []
for i in xrange(nresamples) :
for i in range(nresamples) :
musrs.append(np.mean(rng.randn(nsamples)))
hmusrs, _ = np.histogram(musrs, bins, density=True)

2
bootstrap/lecture/permutecorrelation.py
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@ -19,7 +19,7 @@ rd = np.corrcoef(x, y)[0, 1]
# permutation:
nperm = 1000
rs = []
for i in xrange(nperm) :
for i in np.arange(nperm) :
xr=rng.permutation(x)
yr=rng.permutation(y)
rs.append( np.corrcoef(xr, yr)[0, 1] )

2
likelihood/lecture/mlemean.py
View File

@ -41,7 +41,7 @@ for mu in mus :
ax.text(mu-0.1, 0.04, '?', zorder=1, ha='right')
else :
ax.text(mu+0.1, 0.04, '?', zorder=1)
for k in xrange(len(mus)) :
for k in np.arange(len(mus)) :
ax.plot(x, g[:,k], zorder=5)
ax.scatter(xd, 0.05*rng.rand(len(xd))+0.2, s=30, zorder=10)

2
pointprocesses/lecture/firingrates.py
View File

@ -86,7 +86,7 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
def get_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 = bins / dt
bin_indices = np.asarray(bins / dt, np.int)
hist, _ = sp.histogram(times, bins)
rate = np.zeros(time.shape)

6
pointprocesses/lecture/isihexamples.py
View File

@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
times = []
v = vreset
noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
for k in xrange(len(noise)) :
for k in np.arange(len(noise)) :
v += (input[k]+noise[k])*dt/tau
if v >= vthresh :
v = vreset
@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
def isis( spikes ) :
isi = []
for k in xrange(len(spikes)) :
for k in np.arange(len(spikes)) :
isi.extend(np.diff(spikes[k]))
return isi
@ -76,7 +76,7 @@ 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 xrange(1,len(x)) :
for k in np.arange(1,len(x)) :
x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
x[x<0.0] = 0.0

4
pointprocesses/lecture/rasterexamples.py
View File

@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
times = []
v = vreset
noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
for k in xrange(len(noise)) :
for k in range(len(noise)) :
v += (input[k]+noise[k])*dt/tau
if v >= vthresh :
v = vreset
@ -55,7 +55,7 @@ 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 xrange(1,len(x)) :
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

6
pointprocesses/lecture/returnmapexamples.py
View File

@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
times = []
v = vreset
noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
for k in xrange(len(noise)) :
for k in range(len(noise)) :
v += (input[k]+noise[k])*dt/tau
if v >= vthresh :
v = vreset
@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
def isis( spikes ) :
isi = []
for k in xrange(len(spikes)) :
for k in range(len(spikes)) :
isi.extend(np.diff(spikes[k]))
return np.array( isi )
@ -84,7 +84,7 @@ 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 xrange(1,len(x)) :
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

6
pointprocesses/lecture/serialcorrexamples.py
View File

@ -31,7 +31,7 @@ def pifspikes(input, trials, dt, D=0.1) :
times = []
v = vreset
noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
for k in xrange(len(noise)) :
for k in range(len(noise)) :
v += (input[k]+noise[k])*dt/tau
if v >= vthresh :
v = vreset
@ -41,7 +41,7 @@ def pifspikes(input, trials, dt, D=0.1) :
def isis( spikes ) :
isi = []
for k in xrange(len(spikes)) :
for k in range(len(spikes)) :
isi.extend(np.diff(spikes[k]))
return np.array( isi )
@ -95,7 +95,7 @@ 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 xrange(1,len(x)) :
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

2
pointprocesses/lecture/sta.py
View File

@ -6,7 +6,7 @@ from IPython import embed
def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
count = 0
sta = np.zeros((abs(t_min) + abs(t_max))/dt)
sta = np.zeros(int((abs(t_min) + abs(t_max))/dt))
time = np.arange(t_min, t_max, dt)
if len(stim.shape) > 1 and stim.shape[1] > 1:
stim = stim[:,1]

4
statistics/lecture/displayunivariatedata.py
View File

@ -53,7 +53,7 @@ ax.annotate('mean plus\nstd. dev.',
arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
connectionstyle="angle3,angleA=-60,angleB=80") )
ax = fig.add_axes([xpos, ypos, width, height], axis_bgcolor='none')
ax = fig.add_axes([xpos, ypos, width, height])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
@ -92,7 +92,7 @@ ax.annotate('median',
arrowprops=dict(arrowstyle="->", relpos=(0.8,0.0),
connectionstyle="angle3,angleA=-60,angleB=20") )
ax = fig.add_axes([xpos+width+0.03, ypos, 0.98-(xpos+width+0.03), height], axis_bgcolor='none')
ax = fig.add_axes([xpos+width+0.03, ypos, 0.98-(xpos+width+0.03), height])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')