import glob
import json
import os

import re
from bmtk.simulator.bionet.pyfunction_cache import add_synapse_model
from neuron import h
import random
import numpy as np

scale = 10
#max_exc = 0

weight_means = {"exc": {}, "inh": {}}

def lognormal(m, s):
    mean = np.log(m) - 0.5 * np.log((s/m)**2+1)
    std = np.sqrt(np.log((s/m)**2 + 1))
    return max(np.random.lognormal(mean, std, 1), 0.0000000001)
    
def Bg2Pyr(syn_params, sec_x, sec_id):
    """Create a bg2pyr synapse
    :param syn_params: parameters of a synapse
    :param sec_x: normalized distance along the section
    :param sec_id: target section
    :return: NEURON synapse object
    """

    lsyn = h.bg2pyr(sec_x, sec=sec_id)

    if syn_params.get('initW'):
        lsyn.initW = float(syn_params['initW'])
    if syn_params.get('taun1'):
        lsyn.taun1 = float(syn_params['taun1'])
    if syn_params.get('taun2'):
        lsyn.taun2 = float(syn_params['taun2'])
    if syn_params.get('gNMDAmax'):
        lsyn.gNMDAmax = float(syn_params['gNMDAmax'])
    if syn_params.get('enmda'):
        lsyn.enmda = float(syn_params['enmda'])
    if syn_params.get('taua1'):
        lsyn.taua1 = float(syn_params['taua1'])
    if syn_params.get('taua2'):
        lsyn.taua2 = float(syn_params['taua2'])
    if syn_params.get('gAMPAmax'):
        lsyn.gAMPAmax = float(syn_params['gAMPAmax'])
    if syn_params.get('eampa'):
        lsyn.eampa = float(syn_params['eampa'])
    return lsyn


def bg2pyr(syn_params, xs, secs):
    """Create a list of bg2pyr synapses
    :param syn_params: parameters of a synapse
    :param xs: list of normalized distances along the section
    :param secs: target sections
    :return: list of NEURON synpase objects
    """
    syns = []
    for x, sec in zip(xs, secs):
        syn = Pyr2Pyr(syn_params, x, sec)
        syns.append(syn)
    return syns

def Pyr2Int(syn_params, sec_x, sec_id):
    """Create a pyr2int synapse
    :param syn_params: parameters of a synapse
    :param sec_x: normalized distance along the section
    :param sec_id: target section
    :return: NEURON synapse object
    """

    lsyn = h.pyr2int(sec_x, sec=sec_id)

    if syn_params.get('AlphaTmax_ampa'):
        lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
    if syn_params.get('Beta_ampa'):
        lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
    if syn_params.get('Cdur_ampa'):
        lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
    if syn_params.get('gbar_ampa'):
        lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
    if syn_params.get('Erev_ampa'):
        lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)

    if syn_params.get('AlphaTmax_nmda'):
        lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
    if syn_params.get('Beta_nmda'):
        lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
    if syn_params.get('Cdur_nmda'):
        lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
    if syn_params.get('gbar_nmda'):
        lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
    if syn_params.get('Erev_nmda'):
        lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
    
    if syn_params.get('initW'):
        lsyn.initW = float(syn_params['initW']) * random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick() 

    if syn_params.get('Wmax'):
        lsyn.Wmax = float(syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
    if syn_params.get('Wmin'):
        lsyn.Wmin = float(syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
    #delay = float(syn_params['initW']) # par.x(3) + delayDistance
    #lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)

    if syn_params.get('lambda1'):
        lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
    if syn_params.get('lambda2'):
        lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
    if syn_params.get('threshold1'):
        lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
    if syn_params.get('threshold2'):
        lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
    if syn_params.get('tauD1'):
        lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
    if syn_params.get('d1'):
        lsyn.d1 = float(syn_params['d1']) # par.x(11)
    if syn_params.get('tauD2'):
        lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
    if syn_params.get('d2'):
        lsyn.d2 = float(syn_params['d2']) # par.x(13)
    if syn_params.get('tauF'):
        lsyn.tauF = float(syn_params['tauF']) # par.x(14)
    if syn_params.get('f'):
        lsyn.f = float(syn_params['f']) # par.x(15)

    if syn_params.get('bACH'):
        lsyn.bACH = float(syn_params['bACH']) # par.x(17)
    if syn_params.get('aDA'):
        lsyn.aDA = float(syn_params['aDA']) # par.x(18)
    if syn_params.get('bDA'):
        lsyn.bDA = float(syn_params['bDA']) # par.x(19)
    if syn_params.get('wACH'):
        lsyn.wACH = float(syn_params['wACH']) # par.x(20)
    
    return lsyn


def pyr2int(syn_params, xs, secs):
    """Create a list of pyr2int synapses
    :param syn_params: parameters of a synapse
    :param xs: list of normalized distances along the section
    :param secs: target sections
    :return: list of NEURON synpase objects
    """
    syns = []
    for x, sec in zip(xs, secs):
        syn = Pyr2Int(syn_params, x, sec)
        syns.append(syn)
    return syns

def Int2Pyr(syn_params, sec_x, sec_id):
    """Create a int2pyr synapse
    :param syn_params: parameters of a synapse
    :param sec_x: normalized distance along the section
    :param sec_id: target section
    :return: NEURON synapse object
    """

    trg_cell_nid = int(str(sec_id).split("[")[1].split("]")[0])
    # if trg_cell_nid > 0:
    #     import pdb; pdb.set_trace()
    if trg_cell_nid in weight_means["inh"].keys():
        mean_weight = weight_means["inh"][trg_cell_nid]
    else:
        weight_means["inh"][trg_cell_nid] = mean_weight = np.random.uniform(0.2, 0.35)

    lsyn = h.int2pyr(sec_x, sec=sec_id)

    if syn_params.get('AlphaTmax_ampa'):
        lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
    if syn_params.get('Beta_ampa'):
        lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
    if syn_params.get('Cdur_ampa'):
        lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
    if syn_params.get('gbar_ampa'):
        lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
    if syn_params.get('Erev_ampa'):
        lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)

    if syn_params.get('AlphaTmax_nmda'):
        lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
    if syn_params.get('Beta_nmda'):
        lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
    if syn_params.get('Cdur_nmda'):
        lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
    if syn_params.get('gbar_nmda'):
        lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
    if syn_params.get('Erev_nmda'):
        lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
    
    if syn_params.get('initW'):
        #lsyn.initW = float(syn_params['initW']) * random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick() 
        lsyn.initW = float(min(lognormal(mean_weight, 0.1), 5) * scale)
        #float(lognormal(3.171729, 0.5173616067) * scale * 20)
        #lsyn.initW = float(lognormal(mean_weight, 0.5173616067) * scale)
        #lsyn.initW = min(float(lognormal(mean_weight, 1)), 11) * scale
        #lsyn.initW = 3.171729*10
    if syn_params.get('Wmax'):
        lsyn.Wmax = float(syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
    if syn_params.get('Wmin'):
        lsyn.Wmin = float(syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
    #delay = float(syn_params['initW']) # par.x(3) + delayDistance
    #lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)

    if syn_params.get('lambda1'):
        lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
    if syn_params.get('lambda2'):
        lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
    if syn_params.get('threshold1'):
        lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
    if syn_params.get('threshold2'):
        lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
    if syn_params.get('tauD1'):
        lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
    if syn_params.get('d1'):
        lsyn.d1 = float(syn_params['d1']) # par.x(11)
    if syn_params.get('tauD2'):
        lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
    if syn_params.get('d2'):
        lsyn.d2 = float(syn_params['d2']) # par.x(13)
    if syn_params.get('tauF'):
        lsyn.tauF = float(syn_params['tauF']) # par.x(14)
    if syn_params.get('f'):
        lsyn.f = float(syn_params['f']) # par.x(15)

    
    return lsyn


def int2pyr(syn_params, xs, secs):
    """Create a list of int2pyr synapses
    :param syn_params: parameters of a synapse
    :param xs: list of normalized distances along the section
    :param secs: target sections
    :return: list of NEURON synpase objects
    """
    syns = []
    for x, sec in zip(xs, secs):
        syn = Int2Pyr(syn_params, x, sec)
        syns.append(syn)
    return syns


def Pyr2Pyr(syn_params, sec_x, sec_id):
    """Create a pyr2pyr synapse
    :param syn_params: parameters of a synapse
    :param sec_x: normalized distance along the section
    :param sec_id: target section
    :return: NEURON synapse object
    """
    #import pdb; pdb.set_trace()
    trg_cell_nid = int(str(sec_id).split("[")[1].split("]")[0])
    # if trg_cell_nid > 0:
    #     import pdb; pdb.set_trace()
    if trg_cell_nid in weight_means["exc"].keys():
        mean_weight = weight_means["exc"][trg_cell_nid]
    else:
        weight_means["exc"][trg_cell_nid] = mean_weight = np.random.uniform(0.435, 0.5)
        #weight_means["exc"][trg_cell_nid] = mean_weight = np.random.uniform(0.18181829517744805 + 0.32, 0.18181829517744805 + 0.35)

    lsyn = h.pyr2pyr(sec_x, sec=sec_id)

    if syn_params.get('AlphaTmax_ampa'):
        lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
    if syn_params.get('Beta_ampa'):
        lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
    if syn_params.get('Cdur_ampa'):
        lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
    if syn_params.get('gbar_ampa'):
        lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
    if syn_params.get('Erev_ampa'):
        lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)

    if syn_params.get('AlphaTmax_nmda'):
        lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
    if syn_params.get('Beta_nmda'):
        lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
    if syn_params.get('Cdur_nmda'):
        lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
    if syn_params.get('gbar_nmda'):
        lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
    if syn_params.get('Erev_nmda'):
        lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
    global max_exc
    if syn_params.get('initW'):
        #lsyn.initW = float(syn_params['initW']) * random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick() 
        lsyn.initW = float(lognormal(mean_weight, 0.14) * scale)
        #lsyn.initW = 5
        # if (lsyn.initW > max_exc):
        #     max_exc = lsyn.initW
        #lsyn.initW = float(lognormal(mean_weight, 0.13993260156705545) * scale)
        #lsyn.initW = float(min(lognormal(mean_weight, 0.22), 1.20) * scale)
        #lsyn.initW = 0.18181829517744805 * 10
        #print(lsyn.initW)
        
    if syn_params.get('Wmax'):
        lsyn.Wmax = 8#float(syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
    if syn_params.get('Wmin'):
        lsyn.Wmin = float(syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
    #delay = float(syn_params['initW']) # par.x(3) + delayDistance
    #lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)

    if syn_params.get('lambda1'):
        lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
    if syn_params.get('lambda2'):
        lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
    if syn_params.get('threshold1'):
        lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
    if syn_params.get('threshold2'):
        lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
    if syn_params.get('tauD1'):
        lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
    if syn_params.get('d1'):
        lsyn.d1 = float(syn_params['d1']) # par.x(11)
    if syn_params.get('tauD2'):
        lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
    if syn_params.get('d2'):
        lsyn.d2 = float(syn_params['d2']) # par.x(13)
    if syn_params.get('tauF'):
        lsyn.tauF = float(syn_params['tauF']) # par.x(14)
    if syn_params.get('f'):
        lsyn.f = float(syn_params['f']) # par.x(15)

    if syn_params.get('bACH'):
        lsyn.bACH = float(syn_params['bACH']) # par.x(17)
    if syn_params.get('aDA'):
        lsyn.aDA = float(syn_params['aDA']) # par.x(18)
    if syn_params.get('bDA'):
        lsyn.bDA = float(syn_params['bDA']) # par.x(19)
    if syn_params.get('wACH'):
        lsyn.wACH = float(syn_params['wACH']) # par.x(20)
    
    return lsyn


def pyr2pyr(syn_params, xs, secs):
    """Create a list of pyr2pyr synapses
    :param syn_params: parameters of a synapse
    :param xs: list of normalized distances along the section
    :param secs: target sections
    :return: list of NEURON synpase objects
    """
    syns = []
    for x, sec in zip(xs, secs):
        syn = Pyr2Pyr(syn_params, x, sec)
        syns.append(syn)
    return syns


def load():
    #import pdb; pdb.set_trace()
    add_synapse_model(Bg2Pyr, 'bg2pyr', overwrite=False)
    add_synapse_model(Bg2Pyr, overwrite=False)
    add_synapse_model(Pyr2Pyr, 'pyr2pyr', overwrite=False)
    add_synapse_model(Pyr2Pyr, overwrite=False)
    add_synapse_model(Pyr2Int, 'pyr2int', overwrite=False)
    add_synapse_model(Pyr2Int, overwrite=False)
    add_synapse_model(Int2Pyr, 'int2pyr', overwrite=False)
    #import pdb; pdb.set_trace()
    add_synapse_model(Int2Pyr, overwrite=False)
    #import pdb; pdb.set_trace()
    return

def syn_params_dicts(syn_dir='../biophys_components/synaptic_models'):
    """
    returns: A dictionary of dictionaries containing all
    properties in the synapse json files
    """
    files = glob.glob(os.path.join(syn_dir,'*.json'))
    data = {}
    for fh in files:
        with open(fh) as f:
            data[os.path.basename(fh)] = json.load(f) #data["filename.json"] = {"prop1":"val1",...}
    return data