import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from spectral import diag_projection, peak_size
from plotstyle import plot_style
from plotstyle import plot_chi2


example_cell = [['2020-10-27-ag-invivo-1', 0],
                ['2020-10-27-ag-invivo-1', 1]]

example_cells = [
                 #['2021-06-18-ae-invivo-1', 3],    #  98Hz, 1%, ok
                 ['2021-06-18-ae-invivo-1', 6],    #  98Hz, 2: 10%, ok OR 6: 5%
                 #['2012-03-30-ah', 5],             # 177Hz, 5%, 2.0, nice
                 ##['2012-07-03-ak', 0],             # 120Hz, 2.5%, 1.8, broader, the one model cell, nice triangle up to 1%!
                 ##['2012-12-20-ac', 0],             # 213Hz, 2.5%, 2.1, ok, model cell, weak triangle up to 1%!
                 ['2017-07-18-ai-invivo-1', 2],    #  78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%!
                 ##['2019-06-28-ae', 0],             # 477Hz, 10%, 2.6, weak
                 ##['2020-10-27-aa-invivo-1', 4],    # 259Hz, 0.5%, 2.0, ok
                 ##['2020-10-27-ae-invivo-1', 4],    # 375Hz, 0.5%, 4.3, nice, additional low freq line
                 ###['2020-10-27-ag-invivo-1', 2],    # 405Hz, 5%, 3.9, strong, is already the example
                 ##['2021-08-03-ab-invivo-1', 1],    # 140Hz, 0.5%, ok
                 #['2020-10-29-ag-invivo-1', 2],    #  164Hz, 5%, 1.6, no diagonal
                 ['2018-08-24-ak', 1],             #  145Hz, 5%, no diagonal
                 ['2018-08-14-ac', 1],             # 239Hz, 0: 10%, no diagonal OR 1: 5%
                 ##['2018-08-29-af', 1],             #  383Hz, 5%, no diagonal

                 ]

data_path = Path('data') / 'cells'


def load_baseline(path, cell_name):
    d = path / f'{cell_name}-baseline.npz'
    data = np.load(d)
    eodf = float(data['eodf'])
    rate = float(data['ratebase/Hz'])
    cv = float(data['cvbase'])
    isis = data['isis']
    pdf = data['isih']
    freqs = data['freqs']
    prr = data['prr']
    return eodf, rate, cv, isis, pdf, freqs, prr
    

def load_noise(path, cell_name, run):
    data = np.load(path / f'{cell_name}-spectral-data-s{run:02d}.npz')
    contrast = data['contrast']
    time = data['time']
    stimulus = data['stimulus']
    spikes = []
    for k in range(1000):
        key = f'spikes_{k:03d}'
        if not key in data.keys():
            break
        spikes.append(data[key])
    return contrast, time, stimulus, spikes


def load_spectra(path, mode, cell_name, run=None):
    data = np.load(path / f'{cell_name}-spectral-{mode}-s{run:02d}.npz')
    contrast = float(data['contrast'])
    fcutoff = float(data['fcutoff'])
    freqs = data['freqs']
    pss = data['pss']
    prs = data['prs']
    prss = data['prss']
    nsegs = int(data['nsegs'])
    gain = np.abs(prs)/pss
    chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
    return fcutoff, contrast, freqs, gain, chi2


def plot_isih(ax, s, rate, cv, isis, pdf):
    ax.show_spines('b')
    ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
    ax.set_xlim(0, 8)
    ax.set_xticks_delta(2)
    ax.set_xlabel('ISI', 'ms')
    ax.text(0.6, 1.08, f'CV$_{{\\rm base}}$={cv:.2f}, $r={rate:.0f}$Hz',
            transform=ax.transAxes)


def plot_isih_small(ax, s, contrast, rate, cv, isis, pdf):
    ax.show_spines('b')
    ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
    ax.set_xlim(0, 20)
    ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '5', '10', '15', '20\\,ms'])
    xt = 1 if rate > 80 else 1.3
    ax.text(xt, 1.05, f'CV$_{{\\rm base}}$={cv:.2f}', ha='right', 
            transform=ax.transAxes)
    ax.text(xt, 0.6, f'$r={rate:.0f}$Hz', ha='right',
            transform=ax.transAxes)
    ax.text(xt, 0.15, f'$c={100*contrast:.0f}$\\%', ha='right',
            transform=ax.transAxes)


def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
    rate_i = np.argmax(prr[freqs < 0.7*eodf])
    eod_i = np.argmax(prr[freqs > 500]) + np.argmax(freqs > 500)
    power_db = 10*np.log10(prr/np.max(prr))
    ax.show_spines('b')
    mask = (freqs > 30) & (freqs < 890)
    ax.plot(freqs[mask], power_db[mask], **s.lsC1)
    ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
    ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
    ax.set_ylim(-25, 5)
    #ax.plot(freqs[mask], 1e-3*prr[mask], **s.lsC1)
    #ax.plot(freqs[eod_i], 1e-3*prr[eod_i] + 2, **s.psFEOD)
    #ax.plot(freqs[rate_i], 1e-3*prr[rate_i] + 2, **s.psF0)
    #ax.set_ylim(0, 30)
    ax.set_xlim(0, 900)
    ax.set_xticks_delta(300)
    ax.set_xlabel('$f$', 'Hz')
    ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
            ha='center')
    ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
            ha='center')
    ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
    #ax.text(freqs[eod_i], 1e-3*prr[eod_i] + 4, '$f_{\\rm EOD}$',
    #        ha='center')
    #ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 4, '$r$',
    #        ha='center')
    #ax.yscalebar(1.05, 0, 5, 'kHz', ha='right')


def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1,
                  contrast2, spikes2, am=True):
    t0 = 0.3
    t1 = 0.4
    maxtrials = 8
    trials = np.arange(maxtrials)
    ax.show_spines('')
    ax.eventplot(spikes1[2:2+maxtrials], lineoffsets=trials - maxtrials + 1,
                 linelength=0.8, linewidths=1, color=s.cell_color1)
    ax.eventplot(spikes2[2:2+maxtrials], lineoffsets=trials - 2*maxtrials,
                 linelength=0.8, linewidths=1, color=s.cell_color2)
    stim = contrast1*stimulus1
    if am:
        eod = np.sin(2*np.pi*eodf*time1) * (1 + stim)
    else:
        eod = np.sin(2*np.pi*eodf*time1) + stim
    ax.plot(time1, 4*eod + 7, **s.lsEOD)
    ax.plot(time1, 4*(1 + stim) + 7, **s.lsAM)
    ax.set_xlim(t0, t1)
    ax.set_ylim(-2*maxtrials - 0.5, 14)
    ax.xscalebar(1, -0.05, 0.01, None, '10\\,ms', ha='right')
    ax.text(t1 + 0.003, -0.5*maxtrials, f'${100*contrast1:.0f}$\\,\\%',
            va='center', color=s.cell_color1)
    ax.text(t1 + 0.003, -1.55*maxtrials, f'${100*contrast2:.0f}$\\,\\%',
            va='center', color=s.cell_color2)
    

def plot_gain(ax, s, contrast1, freqs1, gain1, contrast2,
              freqs2, gain2, fcutoff, ymax, dy):
    ax.plot(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
    ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
    ax.set_xlim(0, fcutoff)
    ax.set_ylim(0, ymax)
    ax.set_xticks_delta(fcutoff//3)
    ax.set_yticks_delta(dy)
    ax.set_xlabel('$f$', 'Hz')
    ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
    

def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21,
                   contrast2, freqs2, chi22, fcutoff, ymax, toffs):
    diags = []
    sis = []
    sips = []
    sifs = []
    for contrast, freqs, chi2 in [[contrast1, freqs1, chi21],
                                  [contrast2, freqs2, chi22]]:
        dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
        diags.append([dfreqs, diag])
        sinorm, sirel, sif = peak_size(dfreqs, diag, fbase, median=False)
        sip = diag[np.argmin(np.abs(dfreqs - sif))]
        sis.append(sinorm)
        sips.append(sip)
        sifs.append(sif)
        print(f'    SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
    ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
    ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
    offs = 0.05*ymax
    ax.plot(sifs[1], 1e-4*sips[1] + offs, clip_on=False, **s.psC2)
    ax.plot(sifs[0], 1e-4*sips[0] + offs, clip_on=False, **s.psC1)
    ax.set_xlim(0, 2*fcutoff)
    ax.set_ylim(0, ymax)
    ax.set_xticks_delta(fcutoff)
    ax.set_yticks_delta(ymax//3)
    ax.set_xlabel('$f_1 + f_2$', 'Hz')
    ax.text(sifs[1] - 0.15*fcutoff, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
            ha='right', color=s.cell_color2)
    ax.text(sifs[1] + 0.25*fcutoff, 1e-4*sips[1], f'SI={sis[1]:.1f}')
    ax.text(sifs[0] - 0.15*fcutoff, 1e-4*sips[0] + toffs, f'{100*contrast1:.0f}\\%',
            ha='right', color=s.cell_color1)
    ax.text(sifs[0] + 0.25*fcutoff, 1e-4*sips[0] + toffs, f'SI={sis[0]:.1f}')

    
if __name__ == '__main__':
    """
    # find a nice example cell:
    from thunderlab.tabledata import TableData
    data = TableData('data/Apteronotus_leptorhynchus-Punit-data.csv')
    data = data[(data['sinorm_nmax'] > 0) & (data['sinorm_nmax'] < 1.5), :]
    data = data[(data['contrast'] > 0.04) & (data['contrast'] < 0.06), :]
    #data = data[(data['respmod2'] > 150) & (data['respmod2'] < 200), :]
    data = data[(data['cvbase'] > 0.4) & (data['cvbase'] < 0.8), :]
    data = data[(data['ratebase'] > 220) & (data['ratebase'] < 300), :]
    for k in range(data.rows()):
        print(f'{data[k, "cell"]:<22s} s{data[k, "stimindex"]:02.0f}: '
              f'{100*data[k, "contrast"]:3g}%, r={data[k, "ratebase"]:3.0f}Hz, '
              f'CV={data[k, "cvbase"]:4.2f}, '
              f'rmod={data[k, "respmod2"]:3.0f}Hz, '
              f'SI={data[k, "sinorm_nmax"]:5.2f}')
    print()
    #exit()
    """
    
    #mode = 'all'
    mode = '100'

    cell_name = example_cell[0][0]
    print('Example P-unit:')
    eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
    print(f'    {cell_name:<22s}:         fbase={rate:3.0f}Hz, CV={cv:.2f}')
    contrast1, time1, stimulus1, spikes1 = load_noise(data_path,
                                                      *example_cell[0])
    contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
                                                      *example_cell[1])
    fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
                                                             *example_cell[0])
    fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, mode,
                                                             *example_cell[1])
    print(f'    contrast1: {100*contrast1:4.1f}%   contrast2: {100*contrast2:4.1f}%')
    print(f'    fcutoff1 :  {fcutoff1:3.0f}Hz  fcutoff2 :  {fcutoff2:3.0f}Hz')
    print(f'    duration1: {time1[-1]:4.1f}s   duration2: {time2[-1]:4.1f}s')
    
    s = plot_style()
    s.cell_color1 = s.punit_color1
    s.cell_color2 = s.punit_color2
    s.lsC1 = s.lsP1
    s.lsC2 = s.lsP2
    s.psC1 = s.psP1
    s.psC2 = s.psP2
    fig, (ax1, ax2, ax3) = \
        plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.3, 4.7],
                     cmsize=(s.plot_width, 0.85*s.plot_width))
    fig.subplots_adjust(leftm=8, rightm=2, topm=2, bottomm=3.5,
                        wspace=0.4, hspace=0.42)
    axi, axp, axr = ax1.subplots(1, 3, width_ratios=[2, 3, 0, 10, 0.2])
    axg, axc1, axc2, axd = ax2.subplots(1, 4, wspace=0.2,
                                        width_ratios=[3.5, 0.5, 4, 4, 0.8, 3.5])
    axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35,
                       width_ratios=[1, 1, 0.1, 1, 1, 0.1],
                       height_ratios=[1, 4])
    
    axi.text(0, 1.08, 'P-unit:', transform=axi.transAxes,
             color=s.cell_color1, fontsize='large')
    plot_isih(axi, s, rate, cv, isis, pdf)
    plot_response_spectrum(axp, s, eodf, rate, freqs, prr)
    plot_response(axr, s, eodf, time1, stimulus1, contrast1, spikes1,
                  contrast2, spikes2, am=True)
    
    plot_gain(axg, s, contrast1, freqs1, gain1,
              contrast2, freqs2, gain2, fcutoff1, ymax=10, dy=5)
    axc = plot_chi2(axc1, s, freqs2, chi22, fcutoff2, None, 6)
    axc.remove()
    axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
    axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
                   fontsize='medium', color=s.cell_color2)
    plot_chi2(axc2, s, freqs1, chi21, fcutoff1, None, 6)
    axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
                   fontsize='medium', color=s.cell_color1)
    axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
    plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
                   contrast2, freqs2, chi22, fcutoff1, ymax=14, toffs=2.5)
    
    fig.common_yticks(axc1, axc2)
    fig.tag([axi, axp, axr], xoffs=-3, yoffs=0)
    fig.tag([axg, axc1, axc2, axd], xoffs=-3, yoffs=2)
    print()

    print('Additional example cells:')
    axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes,
                   color=s.cell_color1, fontsize='large')
    for k, (cell, run) in enumerate(example_cells):
        eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell)
        fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, mode,
                                                            cell, run)
        print(f'    {cell:<22s}: run={run:2d}, contrast={100*contrast:3.2g}%, '
              f'fbase={rate:3.0f}Hz, CV={cv:.2f}')
        plot_isih_small(axs[0, k], s, contrast, rate, cv, isis, pdf)
        vmax = 20 if k < 2 else 30
        axc = plot_chi2(axs[1, k], s, freqs, chi2, fcutoff, rate, vmax)
        if k % 2 == 0:
            axc.remove()
        if k == 1:
            axc.set_ylabel('')
    fig.common_yticks(axs[1, :])
    fig.tag([axs[0, :2], axs[0, 2:]], xoffs=-3, yoffs=1)
    
    fig.savefig()
    print()