# -*- coding: utf-8 -*-
"""
Created on Sat Jun  5 18:14:31 2021

@author: nils
"""


import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil


# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(56.9, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 502
V_init = -70
V_T = -57.9

# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))

# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
    ind_dict[var] = i
    i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
    ind_dict[var] = i
    i += 1

# gating parameters
b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'][:] = np.array([-57.37, 20.98, 1.])
b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])

# mut_act_Vhalf_wt = -30.01851851851851
# mut_act_k_wt = -7.7333333333333325
# s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
# s_diff_k = mut_act_k_wt - b_param['s'][1]
# b_param['s'][1] = b_param['s'][1] + s_diff_k
# b_param['u'][1] = b_param['u'][1] + s_diff_k
# b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
# b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
# b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
# b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
# b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
# b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf

# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.4

# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True

# model conductances
Kv_ratio = 0.10
g["Na"] = 58. * surf_area
g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
g["Kv"] = 3.9 * Kv_ratio * surf_area
g["Kv_mut"] = 0.
g["L"] = 0. * surf_area
g["Leak"] = 0.038 * surf_area

# save folder
folder = '../Sensitivity_Analysis/FS_Kv'
if not os.path.isdir(folder):
    os.makedirs(folder)

#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n',  's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
                       np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,20, base=2)
log_array = np.append(log_array,1)

# %% multiprocessing
prominence = 50
desired_AUC_width = high/5

Parallel(n_jobs=8, verbose=9)(
    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
                           scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
                           low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
        for alt_ind in range(alt_types.shape[0]) for alt in range(21))


# #%% Get pd Dataframes for certain variables
# import pandas as pd
# AUC = pd.DataFrame(columns=mutations.keys())
# AUC_rel = pd.DataFrame(columns=mutations.keys())
# rheobase = pd.DataFrame(columns=mutations.keys())
# rheobase_fit = pd.DataFrame(columns=mutations.keys())
# rheobase_null_fit = pd.DataFrame(columns=mutations.keys())

# for mut in list(mutations.keys()):
#     fname = os.path.join(folder, "{}.hdf5".format(mut))
#     f = h5py.File(fname, "r")
#     AUC['{}'.format(mut)] = f['analysis']['AUC']
#     AUC_rel['{}'.format(mut)] = f['analysis']['AUC_rel']
#     rheobase['{}'.format(mut)] = f['analysis']['rheobase']
#     rheobase_fit['{}'.format(mut)] = f['analysis']['rheobase_fit']
#     rheobase_null_fit['{}'.format(mut)] = f['analysis']['rheobase_null_fit']

# AUC.to_json(os.path.join(folder, 'AUC.json'))
# AUC_rel.to_json(os.path.join(folder, 'AUC_rel.json'))
# rheobase.to_json(os.path.join(folder, 'rheobase.json'))
# rheobase_fit.to_json(os.path.join(folder, 'rheobase_fit.json'))
# rheobase_null_fit.to_json(os.path.join(folder, 'rheobase_null_fit.json'))