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) :
        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 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 plot_homogeneous_spikes(ax):
    homspikes = hompoisson(rate, trials, duration)
    ax.set_title('stationary')
    ax.set_xlim(0.0, duration)
    ax.set_ylim(-0.5, trials-0.5)
    ax.set_xticks([0, 1, 2])
    ax.set_yticks(np.arange(0, trials+1, 5))
    ax.set_xlabel('Time', 's')
    ax.set_ylabel('Trial')
    ax.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)
    ax.set_title('non-stationary')
    ax.set_xlim(0.0, duration)
    ax.set_ylim(-0.5, trials-0.5)
    ax.set_xticks([0, 1, 2])
    ax.set_yticks(np.arange(0, trials+1, 5))
    ax.set_xlabel('Time', 's')
    #ax.set_ylabel('Trial')
    ax.eventplot(inhspikes, colors=[lsA['color']], linelength=0.8, lw=1)


if __name__ == "__main__":
    fig, (ax1, ax2) = plt.subplots(1, 2)
    fig.subplots_adjust(**adjust_fs(fig, left=5.0, right=1.0, top=1.2))
    plot_homogeneous_spikes(ax1)
    plot_inhomogeneous_spikes(ax2)
    plt.savefig('rasterexamples.pdf')
    plt.close()