#### exec(open('temp.py').read())

# import sys

# sys.path.insert(1, "../helperScripts")
# sys.path.insert(1, "../Kinetics")

# import matplotlib.pyplot as plt
# import numpy as np
# import features as fts
# import expcells
# from tqdm import tqdm
# import pandas as pd
# import seaborn as sns
# from matplotlib.gridspec import GridSpec
# from scipy.signal import butter, filtfilt
# from pprint import pprint
# import os
# import pickle
# import json
# from goMultiprocessing import Multiprocessthis_appendsave
# from copy import deepcopy
# from pprint import pprint
# import MOOSEModel as mm
# import os
# import subprocess
# import scipy.stats as scs
# from mpl_toolkits.axes_grid1.inset_locator import inset_axes


# # Load models from the JSON file
# basemodels_list = []
# file_path = "activemodels.json"
# with open(file_path, "r") as file:
#     for line in file:
#         basemodel = json.loads(line)
#         basemodels_list.append(basemodel)

# DBLO_list = [a["Features"]["DBLO_1.5e-10"] for a in basemodels_list]
# model = basemodels_list[np.argsort(DBLO_list)[-1]]
model["Parameters"]["Channels"] = {
    "Na_T_Chan": {
        "Gbar": 1.3166608675954075e-06*1.2,
        "Erev": 0.06,
        "Kinetics": "../Kinetics/Na_T_Chan_Royeck_wslow",
    },
    "K_DR_Chan": {
        "Gbar": 1.0331133821518452e-05*1.2,
        "Erev": -0.1,
        "Kinetics": "../Kinetics/K_DR_Chan_Custom3",
        "KineticVars": {
            "n_vhalf_inf": 0.013+0.003,
            "n_slope_inf": 0.0087666,
            "n_A": 0.011334428127858195,
            "n_B": 0.046758040004099964,
            "n_C": 0.002291150799108055,
            "n_D": 0.003842409793118826,
            "n_E": 0.009657679734147879/2,
            "n_F": 0.4179831998177472,
        },
    },
    "h_Chan": {
        "Gbar": 4.130480081616187e-09,
        "Erev": -0.04,
        "Kinetics": "../Kinetics/h_Chan_Hay2011_exact",
    },
}


mm.plotModel(model, 150e-12)