import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from scipy.stats import linregress
from numba import jit
from thunderlab.tabledata import TableData
from plotstyle import plot_style, lighter, darker


data_path = Path('data')
sims_path = data_path / 'simulations'


def load_models(file):
    """ Load model parameter from csv file.

    Parameters
    ----------
    file: string
        Name of file with model parameters.

    Returns
    -------
    parameters: list of dict
        For each cell a dictionary with model parameters.
    """
    parameters = []
    with file.open('r') as file:
        header_line = file.readline()
        header_parts = header_line.strip().split(",")
        keys = header_parts
        for line in file:
            line_parts = line.strip().split(",")
            parameter = {}
            for i in range(len(keys)):
                parameter[keys[i]] = float(line_parts[i]) if i > 0 else line_parts[i]
            parameters.append(parameter)
    return parameters


def cell_parameters(parameters, cell_name):
    for params in parameters:
        if params['cell'] == cell_name:
            return params
    print('cell', cell_name, 'not found!')
    exit()
    return None


@jit(nopython=True)
def simulate(stimulus, deltat=0.00005, v_zero=0.0, a_zero=2.0,
             threshold=1.0, v_base=0.0, delta_a=0.08, tau_a=0.1,
             v_offset=-10.0, mem_tau=0.015, noise_strength=0.05,
             input_scaling=60.0, dend_tau=0.001, ref_period=0.001):
    """ Simulate a P-unit.

    Returns
    -------
    spike_times: 1-D array
        Simulated spike times in seconds.
    """    
    # initial conditions:
    v_dend = stimulus[0]
    v_mem = v_zero
    adapt = a_zero

    # prepare noise:    
    noise = np.random.randn(len(stimulus))
    noise *= noise_strength / np.sqrt(deltat)

    # rectify stimulus array:
    stimulus = stimulus.copy()
    stimulus[stimulus < 0.0] = 0.0

    # integrate:
    spike_times = []
    for i in range(len(stimulus)):
        v_dend += (-v_dend + stimulus[i]) / dend_tau * deltat
        v_mem += (v_base - v_mem + v_offset + (
                    v_dend * input_scaling) - adapt + noise[i]) / mem_tau * deltat
        adapt += -adapt / tau_a * deltat

        # refractory period:
        if len(spike_times) > 0 and (deltat * i) - spike_times[-1] < ref_period + deltat/2:
            v_mem = v_base

        # threshold crossing:
        if v_mem > threshold:
            v_mem = v_base
            spike_times.append(i * deltat)
            adapt += delta_a / tau_a

    return np.array(spike_times)


def punit_spikes(parameter, alpha, beatf1, beatf2, tmax, trials):
    tini = 0.2
    model_params = dict(parameter)
    cell = model_params.pop('cell')
    eodf0 = model_params.pop('EODf')
    time = np.arange(-tini, tmax, model_params['deltat'])
    stimulus = np.sin(2*np.pi*eodf0*time)
    stimulus += alpha*np.sin(2*np.pi*(eodf0 + beatf1)*time)
    stimulus += alpha*np.sin(2*np.pi*(eodf0 + beatf2)*time)
    spikes = []
    for i in range(trials):
        model_params['v_zero'] = np.random.rand()
        model_params['a_zero'] += 0.02*parameter['a_zero']*np.random.randn()
        spiket = simulate(stimulus, **model_params)
        spikes.append(spiket[spiket > tini] - tini)
    return spikes

    
def plot_am(ax, s, alpha, beatf1, beatf2, tmax):
    time = np.arange(0, tmax, 0.0001)
    am = alpha*np.sin(2*np.pi*beatf1*time)
    am += alpha*np.sin(2*np.pi*beatf2*time)
    ax.show_spines('l')
    ax.plot(1000*time, -100*am, **s.lsAM)
    ax.set_xlim(0, 1000*tmax)
    ax.set_ylim(-50, 50)
    #ax.set_xlabel('Time', 'ms')
    ax.set_ylabel('AM', r'\%')
    ax.text(1, 1.2, f'Contrast = {100*alpha:g}\\,\\%',
            transform=ax.transAxes, ha='right')

    
def plot_raster(ax, s, spikes, tmax):
    spikes_ms = [1000*s[s<tmax] for s in spikes[:16]]
    ax.show_spines('')
    ax.eventplot(spikes_ms, linelengths=0.9, **s.lsRaster)
    ax.set_xlim(0, 1000*tmax)
    #ax.set_xlabel('Time', 'ms')
    #ax.set_ylabel('Trials')


def compute_power(spikes, nfft, dt):
    psds = []
    time = np.arange(nfft + 1)*dt
    tmax = nfft*dt
    rates = []
    cvs = []
    for s in spikes:
        rates.append(len(s)/tmax)
        isis = np.diff(s)
        cvs.append(np.std(isis)/np.mean(isis))
        b, _ = np.histogram(s, time)
        fourier = np.fft.rfft(b - np.mean(b))
        psds.append(np.abs(fourier)**2)
        #psds.append(fourier)
    freqs = np.fft.rfftfreq(nfft, dt)
    #print('mean rate', np.mean(rates))
    #print('CV', np.mean(cvs))
    return freqs, np.mean(psds, 0)
    #return freqs, np.abs(np.mean(psds, 0))**2/dt


def decibel(x):
    return 10*np.log10(x/1e8)

def plot_psd(ax, s, spikes, nfft, dt, beatf1, beatf2):
    offs = 3
    freqs, psd = compute_power(spikes, nfft, dt)
    psd /= freqs[1]
    ax.plot(freqs, decibel(psd), **s.lsPower)
    ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + offs,
            label=r'$r$', clip_on=False, **s.psF0)
    ax.plot(beatf1, decibel(peak_ampl(freqs, psd, beatf1)) + offs,
            label=r'$\Delta f_1$', clip_on=False, **s.psF01)
    ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + offs + 4.5,
            label=r'$\Delta f_2$', clip_on=False, **s.psF02)
    ax.plot(beatf2 - beatf1, decibel(peak_ampl(freqs, psd, beatf2 - beatf1)) + offs,
            label=r'$\Delta f_2 - \Delta f_1$', clip_on=False, **s.psF01_2)
    ax.plot(beatf1 + beatf2, decibel(peak_ampl(freqs, psd, beatf1 + beatf2)) + offs,
            label=r'$\Delta f_1 + \Delta f_2$', clip_on=False, **s.psF012)
    ax.set_xlim(0, 300)
    ax.set_ylim(-40, 0)
    ax.set_xlabel('Frequency', 'Hz')
    ax.set_ylabel('Power [dB]')


def plot_example(axs, axr, axp, s, cell, alpha, beatf1, beatf2, nfft, trials):
    dt = 0.0001
    tmax = nfft*dt
    t1 = 0.1
    spikes = punit_spikes(cell, alpha, beatf1, beatf2, tmax, trials)
    plot_am(axs, s, alpha, beatf1, beatf2, t1)
    plot_raster(axr, s, spikes, t1)
    plot_psd(axp, s, spikes, nfft, dt, beatf1, beatf2)


def peak_ampl(freqs, psd, f):
    df = 2
    psd_snippet = psd[(freqs > f - df) & (freqs < f + df)]
    return np.max(psd_snippet)


def compute_peaks(name, cell, alpha_max, beatf1, beatf2, nfft, trials):
    data_file = sims_path / f'{name}-contrastpeaks.csv'
    data = TableData(data_file)
    return data
    """
    if data_file.exists():
        data = TableData(data_file)
        return data
    dt = 0.0001
    tmax = nfft*dt
    alphas = np.linspace(0, alpha_max, 200)
    ampl_f1 = np.zeros(len(alphas))
    ampl_f2 = np.zeros(len(alphas))
    ampl_sum = np.zeros(len(alphas))
    ampl_diff = np.zeros(len(alphas))
    for k, alpha in enumerate(alphas):
        print(alpha)
        spikes =  punit_spikes(cell, alpha, beatf1, beatf2, tmax, trials)
        freqs, psd = compute_power(spikes, nfft, dt)
        ampl_f1[k] = peak_ampl(freqs, psd, beatf1)
        ampl_f2[k] = peak_ampl(freqs, psd, beatf2)
        ampl_sum[k] = peak_ampl(freqs, psd, beatf1 + beatf2)
        ampl_diff[k] = peak_ampl(freqs, psd, beatf2 - beatf1)
    data = TableData()
    data.append('contrast', '%', '%.1f', 100*alphas)
    data.append('f1', 'Hz', '%g', ampl_f1)
    data.append('f2', 'Hz', '%g', ampl_f2)
    data.append('f1+f2', 'Hz', '%g', ampl_sum)
    data.append('f2-f1', 'Hz', '%g', ampl_diff)
    data.write(data_file)
    return data
    """

def amplitude(power):
    power -= power[0]
    power[power<0] = 0
    return np.sqrt(power)


def amplitude_linearfit(contrast, power, max_contrast):
    power -= power[0]
    power[power<0] = 0
    ampl = np.sqrt(power)
    a = ampl[contrast <= max_contrast]
    c = contrast[contrast <= max_contrast]
    r = linregress(c, a)
    return r.intercept + r.slope*contrast


def amplitude_squarefit(contrast, power, max_contrast):
    power -= power[0]
    power[power<0] = 0
    ampl = np.sqrt(power)
    a = np.sqrt(ampl[contrast <= max_contrast])
    c = contrast[contrast <= max_contrast]
    r = linregress(c, a)
    return (r.intercept + r.slope*contrast)**2


def plot_peaks(ax, s, data, alphas):
    contrast = data[:, 'contrast']
    ax.plot(contrast, amplitude_linearfit(contrast, data[:, 'f1'], 4), **s.lsF01m)
    ax.plot(contrast, amplitude_linearfit(contrast, data[:, 'f2'], 2), **s.lsF02m)
    ax.plot(contrast, amplitude_squarefit(contrast, data[:, 'f1+f2'], 4), **s.lsF012m)
    ax.plot(contrast, amplitude_squarefit(contrast, data[:, 'f2-f1'], 4), **s.lsF01_2m)
    ax.plot(contrast, amplitude(data[:, 'f1']), **s.lsF01)
    ax.plot(contrast, amplitude(data[:, 'f2']), **s.lsF02)
    ax.plot(contrast, amplitude(data[:, 'f1+f2']), **s.lsF012)
    ax.plot(contrast, amplitude(data[:, 'f2-f1']), **s.lsF01_2)
    for alpha, tag in zip(alphas, ['A', 'B', 'C', 'D']):
        contrast = 100*alpha
        ax.plot(contrast, 630, 'vk', ms=4, clip_on=False)
        ax.text(contrast, 660, tag, ha='center')
        #ax.axvline(contrast, **s.lsGrid)
        #ax.text(contrast, 630, tag, ha='center')
    ax.axvline(1.5, **s.lsLine)
    ax.axvline(4, **s.lsLine)
    yoffs = 340
    ax.text(1.5/2, yoffs, 'linear\nregime',
            ha='center', va='center')
    ax.text((1.5 + 4)/2, yoffs, 'weakly\nnonlinear\nregime',
            ha='center', va='center')
    ax.text(10, yoffs, 'strongly\nnonlinear\nregime',
            ha='center', va='center')
    ax.set_xlim(0, 16.5)
    ax.set_ylim(0, 600)
    ax.set_xticks_delta(5)
    ax.set_yticks_delta(300)
    ax.set_xlabel('Contrast', r'\%')
    ax.set_ylabel('Amplitude', 'Hz')

    
if __name__ == '__main__':
    parameters = load_models(data_path / 'punitmodels.csv')
    cell_name = '2013-01-08-aa-invivo-1'     # 132Hz, CV=0.16: perfect!
    beatf1 = 40
    beatf2 = 132
    # cell_name = '2012-07-03-ak-invivo-1'    # 128Hz, CV=0.24
    # cell_name = '2018-05-08-ae-invivo-1'    # 142Hz, CV=0.48
    
    cell = cell_parameters(parameters, cell_name)

    s = plot_style()
    
    nfft = 2**18
    fig, axs = plt.subplots(5, 4, cmsize=(s.plot_width, 0.8*s.plot_width),
                            height_ratios=[1, 1.5, 2, 1.5, 4])
    fig.subplots_adjust(leftm=8, rightm=2, topm=2, bottomm=3.5,
                        wspace=0.3, hspace=0.3)
    ax0 = fig.merge(axs[3, :])
    ax0.set_visible(False)
    axa = fig.merge(axs[4, :])
    fig.show_spines('lb')
    alphas = [0.01, 0.03, 0.05, 0.16]
    #alphas = [0.002, 0.01, 0.05, 0.1]
    for c, alpha in enumerate(alphas):
        plot_example(axs[0, c], axs[1, c], axs[2, c], s, cell,
                     alpha, beatf1, beatf2, nfft, 100)
    axs[1, 0].xscalebar(1, -0.1, 30, 'ms', ha='right')
    axs[2, 0].legend(loc='center left', bbox_to_anchor=(0, -0.8),
                     ncol=5, columnspacing=2)
    data = compute_peaks(cell_name, cell, 0.2, beatf1, beatf2, nfft, 1000)
    plot_peaks(axa, s, data, alphas)
    fig.common_yspines(axs[0, :])
    fig.common_yticks(axs[2, :])
    #fig.common_xlabels(axs[2, :])
    fig.tag(axs[0, :], xoffs=-2, yoffs=1.6)
    fig.tag(axa)
    fig.savefig()