added opulation vector project

projects/project_populationvector/code/generatedata.py

@@ -0,0 +1,105 @@
+import numpy as np
+from scipy.io import savemat
+import matplotlib.pyplot as plt
+
+def lifadaptspikes(stimulus, gain=10.0, trials=50, duration=200.0, before=200.0, after=400.0):
+    dt = 0.1
+    s0 = 9.5
+    tau = 10.0
+    D = 1.0
+    taua = 60.0
+    da = 100.0
+    Vth = 10.0
+    n = int((duration+before+after)/dt)
+    delay = 10.0
+    sig = np.zeros(n) + s0
+    n1 = int((before+delay)/dt)
+    n2 = int((before+duration+delay)/dt)
+    sig[n1:n2] += (gain * stimulus - 1.0) * np.exp(-np.arange(n2-n1)*dt/0.4/duration)
+    spikes = []
+    for j in range(trials):
+        noise = np.sqrt(2.0*D/dt)*np.random.randn(n)
+        V = np.random.rand()*Vth
+        A = 0.0
+        s = s0
+        As = np.zeros(n)
+        times = []
+        for k in range(n):
+            As[k] = A
+            V += (-V-A+sig[k]+noise[k])*dt/tau
+            A += (-A)*dt/taua
+            if V >= Vth:
+                V = 0.0
+                A += da/taua
+                times.append(k*dt-before)
+        spikes.append(np.array(times))
+        #plt.plot(np.arange(n)*dt-before, As)
+        #plt.plot(np.arange(n)*dt-before, sig)
+        #return spikes
+    return spikes
+
+def firingrate(spikes, tmin, tmax):
+    rates = []
+    for st in spikes:
+        times = st[(st>=tmin)&(st<=tmax)]
+        r = len(times)/(tmax-tmin)
+        rates.append(1000.0*r)
+    return np.mean(rates), np.std(rates)
+
+"""
+nangles = 12
+angles = 180.0*np.arange(nangles)/nangles
+rates = np.zeros(nangles)
+ratessd = np.zeros(nangles)
+allspikes = []
+for k, angle in enumerate(angles):
+    spikes = lifadaptspikes(0.5*(1.0-np.cos(2.0*np.pi*angle/180.0)))
+    rm, rsd = firingrate(spikes, 0.0, 200.0)
+    rates[k] = rm
+    ratessd[k] = rsd
+    allspikes.append(spikes)
+    #plt.subplot(2, 5, k+1)
+    #plt.title('%g' % (angle/2.0/np.pi))
+    #plt.eventplot(spikes, colors=['r'])
+plt.plot(angles, rates, 'r', lw=2)
+plt.plot(angles, rates+ratessd, 'b')
+plt.plot(angles, rates-ratessd, 'b')
+plt.show()
+"""
+
+# tuning curves:
+nunits = 6
+unitphases = np.linspace(0.0, 1.0, nunits) + 0.05*np.random.randn(nunits)/float(nunits)
+unitgains = 15.0 + 5.0*(2.0*np.random.rand(nunits)-1.0)
+nangles = 12
+angles = 180.0*np.arange(nangles)/nangles
+for unit, (phase, gain) in enumerate(zip(unitphases, unitgains)):
+    print '%.1f %.0f' % (gain, phase*180.0)
+    allspikes = []
+    for k, angle in enumerate(angles):
+        spikes = lifadaptspikes(0.5*(1.0-np.cos(2.0*np.pi*(angle/180.0-phase))), gain)
+        allspikes.append(spikes)
+    spikesobj = np.zeros((len(allspikes), len(allspikes[0])), dtype=np.object)
+    for k in range(len(allspikes)):
+        for j in range(len(allspikes[k])):
+            spikesobj[k, j] = 0.001*allspikes[k][j]
+    savemat('unit%d.mat'%(unit+1), mdict={'angles': angles, 'spikes': spikesobj})
+
+# population activity:
+nangles = 50
+angles = 180.0*np.random.rand(nangles)
+for k, angle in enumerate(angles):
+    print '%.0f' % angle
+    allspikes = []
+    for unit, (phase, gain) in enumerate(zip(unitphases, unitgains)):
+        spikes = lifadaptspikes(0.5*(1.0-np.cos(2.0*np.pi*(angle/180.0-phase))), gain)
+        allspikes.append(spikes)
+    spikesobj = np.zeros((len(allspikes), len(allspikes[0])), dtype=np.object)
+    for i in range(len(allspikes)):
+        for j in range(len(allspikes[i])):
+            spikesobj[i, j] = 0.001*allspikes[i][j]
+    savemat('population%02d.mat'%(k+1), mdict={'spikes': spikesobj,
+                                               'angle': angle,
+                                               'phases': unitphases,
+                                               'gains': unitgains})
+