"""
Usage:
  models_regression.py [--num_cores=<num>]

  -h, --help             Show help text.
  -v, --version          Show version.
  --num_cores=<num>      Number of cores to be used by simulator
"""

from cmath import inf
from re import T
from docopt import docopt
import pandas as pd 
import numpy as np
import os 
from jinja2 import Template
import concurrent.futures
import shutil
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

def call_simulator(file_name):
    """Call simulation commands to perform simulation.
    Args:
        file_name (str): Netlist file name.
    """
    os.system(f"Xyce -hspice-ext all {file_name} -l {file_name}.log")

def ext_measured(device,vb,step,Id_sim,list_devices,vc):

    # Get dimensions used for each device 
    dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["corners"])
    loops = dimensions["corners"].count()

    # Extracting measured values for each Device 
    for i in range (loops):
        k = i
        if i >= len(list_devices):
            while k >= len(list_devices):
                k = k - len(list_devices)

        # Special case for 1st measured values 
        if i == 0 :
            if device == "pnp":
                temp_vb = vb
                vb = "-vb "
            # measured Id_sim 0
            col_list = [f"{vb}",f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]
            df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list)
            df_measured.columns = [f"{vb}",f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]
            df_measured.to_csv(f"{device}/measured_{Id_sim[0]}/{i}_measured_{list_devices[k]}.csv", index = False)

            if device == "pnp":
                vb = temp_vb

            # measured Id_sim 1
            col_list = [f"{vb}",f"{vc}{step[0]}.{2*i+1}",f"{vc}{step[1]}.{2*i+1}",f"{vc}{step[2]}.{2*i+1}"]
            df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list)
            df_measured.columns = [f"{vb}",f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]        
            df_measured.to_csv(f"{device}/measured_{Id_sim[1]}/{i}_measured_{list_devices[k]}.csv", index = False)
        else:
            # measured Id_sim 0
            col_list = [f"{vb}",f"{vc}{step[0]}.{2*i}",f"{vc}{step[1]}.{2*i}",f"{vc}{step[2]}.{2*i}"]
            df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list)
            df_measured.columns = [f"{vb}",f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]        
            df_measured.to_csv(f"{device}/measured_{Id_sim[0]}/{i}_measured_{list_devices[k]}.csv", index = False)

            # measured Id_sim 1
            col_list = [f"{vb}",f"{vc}{step[0]}.{2*i+1}",f"{vc}{step[1]}.{2*i+1}",f"{vc}{step[2]}.{2*i+1}"]
            df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list)
            df_measured.columns = [f"{vb}",f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]        
            df_measured.to_csv(f"{device}/measured_{Id_sim[1]}/{i}_measured_{list_devices[k]}.csv", index = False)

def ext_simulated(device,vc,step,sweep,Id_sim,list_devices,ib):

    # Get dimensions used for each device 
    dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["corners"])
    loops = dimensions["corners"].count()
    temp_range = int(loops/4) 
    netlist_tmp = f"./device_netlists/{device}.spice"
    for i in range (loops):
        if i in range (0,temp_range):               temp = 25
        elif i in range (temp_range,2*temp_range):  temp = -40
        elif i in range (2*temp_range,3*temp_range):temp = 125
        else:                                       temp = 175 

        k = i
        if i >= len(list_devices):
            while k >= len(list_devices):
                k = k - len(list_devices)

        with open(netlist_tmp) as f:
            tmpl = Template(f.read())
            os.makedirs(f"{device}/{device}_netlists_sim",exist_ok=True)
            with open(f"{device}/{device}_netlists_sim/{i}_{device}_netlist_{list_devices[k]}.spice", "w") as netlist:
                netlist.write(tmpl.render(device = list_devices[k], i = i , temp = temp ))
            netlist_path  = f"{device}/{device}_netlists_sim/{i}_{device}_netlist_{list_devices[k]}.spice" 

            # Running ngspice for each netlist 
            with concurrent.futures.ProcessPoolExecutor(max_workers=workers_count) as executor:
                executor.submit(call_simulator, netlist_path)

        # Writing simulated data 0
        df_simulated = pd.read_csv(f"{device}/simulated_{Id_sim[0]}/{i}_simulated_{list_devices[k]}.csv",header=0)

        # empty array to append in it shaped (sweep, number of trials + 1)
        new_array = np.empty((sweep, 1+int(df_simulated.shape[0]/sweep)))  
        new_array[:, 0] = df_simulated.iloc[:sweep, 0]
        times = int(df_simulated.shape[0]/sweep)

        for j in range(times):
            new_array[:, (j+1)] = df_simulated.iloc[j*sweep:(j+1)*sweep, 1]

        # Writing final simulated data 0
        df_simulated = pd.DataFrame(new_array)
        df_simulated.to_csv(f"{device}/simulated_{Id_sim[0]}/{i}_simulated_{list_devices[k]}.csv",index= False)    
        df_simulated.columns = [f"{vc}",f"{ib}{step[0]}",f"{ib}{step[1]}",f"{ib}{step[2]}"]
        df_simulated.to_csv(f"{device}/simulated_{Id_sim[0]}/{i}_simulated_{list_devices[k]}.csv",index= False)


        # Writing simulated data 1
        df_simulated = pd.read_csv(f"{device}/simulated_{Id_sim[1]}/{i}_simulated_{list_devices[k]}.csv",header=0)

        # empty array to append in it shaped (sweep, number of trials + 1)
        new_array = np.empty((sweep, 1+int(df_simulated.shape[0]/sweep)))  
        new_array[:, 0] = df_simulated.iloc[:sweep, 0]
        times = int(df_simulated.shape[0]/sweep)

        for j in range(times):
            new_array[:, (j+1)] = df_simulated.iloc[j*sweep:(j+1)*sweep, 1]

        # Writing final simulated data 1
        df_simulated = pd.DataFrame(new_array)
        df_simulated.to_csv(f"{device}/simulated_{Id_sim[1]}/{i}_simulated_{list_devices[k]}.csv",index= False)    
        df_simulated.columns = [f"{vc}",f"{ib}{step[0]}",f"{ib}{step[1]}",f"{ib}{step[2]}"]
        df_simulated.to_csv(f"{device}/simulated_{Id_sim[1]}/{i}_simulated_{list_devices[k]}.csv",index= False)

def error_cal(device,vb,step,Id_sim,list_devices,vc):

    df_final = pd.DataFrame()
    # Get dimensions used for each device 
    dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["corners"])
    loops = dimensions["corners"].count()
    temp_range = int(loops/4) 
    for i in range (loops):
        if i in range (0,temp_range):               temp = 25
        elif i in range (temp_range,2*temp_range):  temp = -40
        elif i in range (2*temp_range,3*temp_range):temp = 125
        else:                                       temp = 175 

        k = i
        if i >= len(list_devices):
            while k >= len(list_devices):
                k = k - len(list_devices)

        measured  = pd.read_csv(f"{device}/measured_{Id_sim}/{i}_measured_{list_devices[k]}.csv")
        simulated = pd.read_csv(f"{device}/simulated_{Id_sim}/{i}_simulated_{list_devices[k]}.csv")

        error_1 = round (100 * abs((abs(measured.iloc[0:, 1]) - abs(simulated.iloc[0:, 1]))/abs(measured.iloc[:, 1])),6)
        error_2 = round (100 * abs((abs(measured.iloc[0:, 2]) - abs(simulated.iloc[0:, 2]))/abs(measured.iloc[:, 2])),6)
        error_3 = round (100 * abs((abs(measured.iloc[0:, 3]) - abs(simulated.iloc[0:, 3]))/abs(measured.iloc[:, 3])),6)

        df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1,error_2,error_3]).transpose()
        df_error.replace([np.inf, -np.inf], df_error.max().nlargest(2).iloc[1], inplace=True)
        df_error.to_csv(f"{device}/error_{Id_sim}/{i}_{device}_error_{list_devices[k]}.csv",index= False)

        # Mean error 
        mean_error = (df_error[f"{vc}{step[0]}"].mean() + df_error[f"{vc}{step[1]}"].mean() + df_error[f"{vc}{step[2]}"].mean())/6
        # Max error 
        max_error = df_error[[f"{vc}{step[0]}",f"{vc}{step[1]}",f"{vc}{step[2]}"]].max().max()
        # Max error location 
        max_index = max((df_error == max_error).idxmax())
        max_location_vc = (df_error == max_error).idxmax(axis=1)[max_index]
        if Id_sim == "Ic":
            if i == 0 :
                if device == "pnp":
                    temp_vb = vb
                    vb = "-vb "
            else:
                if device == "pnp":
                    vb = temp_vb
        max_location_vb = df_error[f"{vb}"][max_index]

        df_final_ = {'Run no.': f'{i}', 'Temp': f'{temp}', 'Device name': f'{device}', 'device': f'{list_devices[k]}','Simulated_Val':f'{Id_sim}','Mean error%':f'{"{:.2f}".format(mean_error)}', 'Max error%':f'{"{:.2f}".format(max_error)} @ {max_location_vc} & Vc (V) = {max_location_vb}'}
        df_final = df_final.append(df_final_, ignore_index = True)

    # Max mean error 
    print (df_final)   
    df_final.to_csv (f"{device}/Final_report_{Id_sim}.csv", index = False)
    out_report = pd.read_csv (f"{device}/Final_report_{Id_sim}.csv")
    print ("\n",f"Max. mean error = {out_report['Mean error%'].max()}%")    
    print ("=====================================================================================================================================================")

def main():

    devices = ["npn","pnp"]
    list_devices = [["vnpn_10x10" , "vnpn_5x5" , "vnpn_0p54x16" , "vnpn_0p54x8" , "vnpn_0p54x4", "vnpn_0p54x2"],
                    ["vpnp_0p42x10" , "vpnp_0p42x5", "vpnp_10x10" , "vpnp_5x5"]]
    vb = ["vbp ","-vb (V)"]
    vc = ["vcp =","vc =-"]
    Id_sim  = ["Ic","Ib"]
    sweep = 101
    step = [1, 2, 3]

    for i, device in enumerate(devices): 
        # Folder structure of measured values 
        dirpath = f"{device}"
        if os.path.exists(dirpath) and os.path.isdir(dirpath):
            shutil.rmtree(dirpath)
        os.makedirs(f"{device}/measured_{Id_sim[0]}",exist_ok=False)
        os.makedirs(f"{device}/measured_{Id_sim[1]}",exist_ok=False)

        # From xlsx to csv 
        read_file = pd.read_excel (f"../../180MCU_SPICE_DATA/BJT/bjt_{device}_beta_f.nl_out.xlsx")
        read_file.to_csv (f"{device}/{device}.csv", index = False, header=True)

        # Folder structure of simulated values
        os.makedirs(f"{device}/simulated_{Id_sim[0]}",exist_ok=False)
        os.makedirs(f"{device}/error_{Id_sim[0]}",exist_ok=False)
        os.makedirs(f"{device}/simulated_{Id_sim[1]}",exist_ok=False)
        os.makedirs(f"{device}/error_{Id_sim[1]}",exist_ok=False)

        # =========== Simulate ==============      
        ext_measured (device,vb[i],step,Id_sim,list_devices[i],vc[i])

        ext_simulated(device,vb[i],step,sweep,Id_sim,list_devices[i],vc[i])

        # ============ Results ============= 
        error_cal    (device,vb[i],step,Id_sim[0],list_devices[i],vc[i])
        error_cal    (device,vb[i],step,Id_sim[1],list_devices[i],vc[i])

# ================================================================
# -------------------------- MAIN --------------------------------
# ================================================================

if __name__ == "__main__":

    # Args
    arguments     = docopt(__doc__, version='comparator: 0.1')
    workers_count = os.cpu_count()*2 if arguments["--num_cores"] == None else int(arguments["--num_cores"])

    # Calling main function 
    main()