import os

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from plotstyle import plot_style
from threefish.core import find_folder_name
from threefish.defaults import default_figsize
from threefish.load import save_visualization
from threefish.RAM.calc_model import trial_nrs_ram_model
from threefish.RAM.plot_labels import title_find_cell_add
from threefish.RAM.reformat_matrix import get_stack, load_model_susept


##from update_project import *


def table_printen(table):
    print(table.keys())
    for l in range(len(table)):
        list_here = np.array(table.iloc[l])
        l1 = "& ".join(list_here)
        print(l1)


def trialnr():

    plot_style()
    default_figsize(column=2, length=3.1)  #.254.75 0.75


    ##################################
    # model part

    cells = ['2013-01-08-aa-invivo-1']
    trial_nrs_here = trial_nrs_ram_model()



    fig, ax = plt.subplots(1, 1)
    ax = [ax]

    perc05, perc90, perc95 = get_lines_trialnr(cells, trial_nrs_here)

    #######################
    # plot 99the percentile
    ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile')
    ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False)
    ax[0].set_xscale('log')#colors[s]
    ax[0].set_yscale('log')
    ax[0].set_xlabel('Trials [$N$]')
    ax[0].set_ylabel(r'$|\chi_{2}|$\,[Hz]')
    ax[0].set_xlim(8, 10000000)

    ############################################
    # plot percentiles
    ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile')
    ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile')
    ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False)
    ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False)
    ax[0].legend()


    plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95)

    save_visualization(pdf=True)


def get_lines_trialnr(cells_given, trial_nrs_here):
    stacks = []
    perc95 = []
    perc90 = []
    vars = []
    cv_stims = []
    fr_stims = []
    perc05 = []
    median = []
    stacks_wo_norm = []
    perc95_wo_norm = []
    perc05_wo_norm = []
    median_wo_norm = []
    counter = []
    for tr in trial_nrs_here:
        save_names = [
            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
                tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV']
        save_name = find_folder_name('calc_model') + '/' + save_names[0]
        cell_add, cells_save = title_find_cell_add(cells_given)
        path = save_name + '.pkl'  # '../'+

        suscept_matrix = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)

        if len(suscept_matrix) > 0:
            model_show, stack_plot, stack_plot_wo_norm = get_stack(cells_given[0], suscept_matrix)

            stacks.append(stack_plot)
            if 'counter_validate' in suscept_matrix.keys():
                counter.append(suscept_matrix['counter_validate'].iloc[0])
            else:
                counter.append(float('nan'))
            cv_stims.append(suscept_matrix['cv_stim_mean'].iloc[0])
            fr_stims.append(suscept_matrix['fr_stim_mean'].iloc[0])
            vars.append(suscept_matrix['var_RAM'].iloc[0])
            perc95.append(np.percentile(stack_plot, 99.9))  # 99.99
            perc90.append(np.percentile(stack_plot, 90))
            perc05.append(np.percentile(stack_plot, 10))
            median.append(np.percentile(stack_plot, 50))
            stacks_wo_norm.append(stack_plot_wo_norm)
            perc95_wo_norm.append(np.percentile(stack_plot_wo_norm, 99.99))
            perc05_wo_norm.append(np.percentile(stack_plot_wo_norm, 10))
            median_wo_norm.append(np.percentile(stack_plot_wo_norm, 50))

        else:
            vars.append(float('nan'))
            cv_stims.append(float('nan'))
            fr_stims.append(float('nan'))
            stacks.append([])
            perc95.append(float('nan'))
            perc90.append(float('nan'))
            perc05.append(float('nan'))
            median.append(float('nan'))
            stacks_wo_norm.append([])
            perc95_wo_norm.append(float('nan'))
            perc05_wo_norm.append(float('nan'))
            median_wo_norm.append(float('nan'))
            counter.append(float('nan'))
    return perc05, perc90, perc95


def start_pos_modeldata():
    return 1.03


def signal_component_name():
    return r'$\xi_{signal}$'#signal noise'


def noise_component_name():#$\xi_{noise}$noise_name =
    return r'$\xi_{noise}$'#'Noise component'#'intrinsic noise'


def ypos_x_modelanddata():
    return -0.45


def print_table_for_methods():
    global path
    path = 'print_table_suscept-model_params_suscept_table.csv'
    if os.path.exists(path):
        table = pd.read_csv(path)
        table_printen(table)
    path = 'print_table_all-model_params_suscept_table.csv'
    if os.path.exists(path):
        table = pd.read_csv()
        table_printen(table)


if __name__ == '__main__':


    ##########################
    # hier kann man die tabellen Werte zum kopieren in den Text printen lassen
    print_table_for_methods()

    trialnr()