import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
from read_chirp_data import *
from utility import *
from IPython import embed

# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-09-ad-invivo-1"
# parameters for binning, smoothing and plotting
cut_window = 60
chirp_size = 14 #ms
neuronal_delay = 5 #ms
chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
chirp_end = int((chirp_size/2+neuronal_delay+cut_window+1)*sampling_rate)
num_bin = 12
window = 1 #ms
time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
spike_bins = np.arange(-cut_window, cut_window+1) #ms

# read data from files
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
eod = read_chirp_eod(os.path.join(data_dir, dataset))
chirp_times = read_chirp_times(os.path.join(data_dir, dataset))

# make a delta f map for the quite more complicated keys
df_map = map_keys(spikes)

# differentiate between phases
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1)

# make dictionaries for spiketimes
df_phase_time = {}
df_phase_binary = {}

# iterate over delta f, repetition, phases and a single chirp
for deltaf in df_map.keys():
    df_phase_time[deltaf] = {}
    df_phase_binary[deltaf] = {}
    for rep in df_map[deltaf]:
        for phase in spikes[rep]:
            for idx in np.arange(num_bin):
                # check the phase
                if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:

                    # get spikes between 50 ms befor and after the chirp
                    spikes_to_cut = np.asarray(spikes[rep][phase])
                    spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
                    spikes_idx = np.round(spikes_cut*sampling_rate)
                    # also save as binary, 0 no spike, 1 spike
                    binary_spikes = np.isin(cut_range, spikes_idx)*1

                    # add the spikes to the dictionaries with the correct df and phase
                    if idx in df_phase_time[deltaf].keys():
                        df_phase_time[deltaf][idx].append(spikes_cut)
                        df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
                    else:
                        df_phase_time[deltaf][idx] = [spikes_cut]
                        df_phase_binary[deltaf][idx] = binary_spikes


# for plotting and calculating iterate over delta f and phases
for df in df_phase_time.keys():
    beat_duration = int(abs(1/df*1000)) #ms
    beat_window = 0
    while beat_window+beat_duration <= cut_window:
        beat_window = beat_window+beat_duration
    for phase in df_phase_time[df].keys():

        trials_binary = df_phase_binary[df][phase]

        train_chirp = []
        train_beat = []
        spikerate_chirp = np.zeros(len(trials_binary))
        spikerate_beat = np.zeros(len(trials_binary))
        for i, trial in enumerate(trials_binary):
            smoothed_trial = smooth(trial, window, 1/sampling_rate)
            train_chirp.append(smoothed_trial[chirp_start:chirp_end])
            train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
            spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
            spikerate_beat[i] = np.mean(smoothed_trial[chirp_start-beat_window:chirp_start])

        rcs = []
        rbs = []
        for i, train in enumerate(train_chirp):
            for j, train2 in enumerate(train_chirp):
                if np.array_equal(train, train2):
                    continue
                else:
                    rc, _ = ss.pearsonr(train, train2)
                    rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
                    rcs.append(rc)
                    rbs.append(rb)

        r_train_chirp = np.mean(rcs)
        r_train_beat = np.mean(rbs)

        csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
        print(csi_train)
        csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
        print(csi_rate)

        # plot
        #plot_trials = df_phase_time[df][phase]
        #plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)

        # calculation
        #overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000

        '''
        smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)

        fig, ax = plt.subplots(2, 1, sharex=True)
        for i, trial in enumerate(plot_trials):
            ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
        ax[1].plot(time_axis, smoothed_spikes*1000)

        ax[0].set_title(df)
        ax[0].set_ylabel('repetition', fontsize=12)

        ax[1].set_xlabel('time [ms]', fontsize=12)
        ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
        plt.show()
        '''


'''
for trial in range(len(trials_binary)):
spike_rate = np.zeros(len(spike_bins)-1)
for idx in range(len(spike_bins)-1):
bin_start = spike_bins[idx]*sampling_rate
bin_end = spike_bins[idx+1]*sampling_rate
spike_rate[idx] = np.sum(trials_binary[trial][bin_start:bin_end])
embed()
exit()
'''