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 plotserialcorr(ax, isis, maxlag=10) :
    lags = np.arange(maxlag+1)
    corr = [1.0]
    for lag in lags[1:] :
        corr.append(np.corrcoef(isis[:-lag], isis[lag:])[0,1])
    ax.set_xlabel(r'lag $k$')
    ax.set_ylabel(r'ISI correlation $\rho_k$')
    ax.set_xlim(0.0, maxlag)
    ax.set_ylim(-1.0, 1.0)
    ax.plot([0, 10], [0.0, 0.0], **lsGrid)
    ax.plot(lags, corr, clip_on=False, zorder=100, **lpsAm)

    
# parameter:
rate = 20.0
drate = 50.0
trials = 10
duration = 500.0
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=7.0, right=1.0))

plotserialcorr(ax1, isis(homspikes))
ax1.set_ylim(-0.2, 1.0)

plotserialcorr(ax2, isis(inhspikes))
ax2.set_ylabel('')
ax2.set_ylim(-0.2, 1.0)

plt.savefig('serialcorrexamples.pdf')
plt.close()