models/ngspice/testing/regression/mimcap_c/models_regression.py

# Copyright 2022 Efabless Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
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 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"ngspice -b -a {file_name} -o {file_name}.log > {file_name}.log")

def ext_measured(device,vn,d_in, cv_sim, corner,start):
    
    # Get dimensions used for each device 
    dirpath = f"{device}_{cv_sim}_{corner}"
    
    # Extracting measured values for each W & L 
    for i in range (start,4+start):
        if i == 0+start: width  = 100 ; length = 100
        if i == 1+start: width  = 5   ; length = 5
        if i == 2+start: width  = 100 ; length = 5
        if i == 3+start: width  = 5   ; length = 100
        
        # measured cv
        col_list = [f"{vn}",f"{d_in}"]
        df_measured = pd.read_csv(f"{dirpath}/{device}.csv",usecols=col_list)
        df_measured.loc[i:i].to_csv(f"{dirpath}/measured_{cv_sim}/{i-start}_measured_w{width}_l{length}.csv", index=False)

def ext_simulated(device,vn,d_in,cv_sim, corner,start):
    
    # Get dimensions used for each device 
    dirpath = f"{device}_{cv_sim}_{corner}"
    netlist_tmp = f"./device_netlists/mimcap.spice"
    
    # Extracting measured values for each W & L 
    for i in range (start,4+start):
        if i == 0+start: width  = 100 ; length = 100
        if i == 1+start: width  = 5   ; length = 5
        if i == 2+start: width  = 100 ; length = 5
        if i == 3+start: width  = 5   ; length = 100
    
        with open(netlist_tmp) as f:
            tmpl = Template(f.read())
            os.makedirs(f"{dirpath}/{device}_netlists_{cv_sim}",exist_ok=True)
            with open(f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{device}_netlist_w{width}_l{length}.spice", "w") as netlist:
                netlist.write(tmpl.render(device = device, width = width, length = length , corner = corner ))
            netlist_path  = f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{device}_netlist_w{width}_l{length}.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 
            df_simulated = pd.read_csv(f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{device}_netlist_w{width}_l{length}.spice.log")
            clean_data = str(df_simulated.loc[4]).replace("Compatibility modes selected: hs","").replace("\nName: 4, dtype: object","").split("=")
            df_clean_ = {vn: [f"moscap_{corner}"],d_in: [clean_data[1]]}
            df_clean = pd.DataFrame(df_clean_)            
            df_clean.to_csv(f"{dirpath}/simulated_{cv_sim}/{i-start}_simulated_w{width}_l{length}.csv",index= False)    
            
def error_cal(device,vn,d_in,Id_sim, corner,start):
    
    # Get dimensions used for each device 
    dirpath = f"{device}_{Id_sim}_{corner}"
    df_final = pd.DataFrame()
    for i in range (start,4+start):
        if i == 0+start: width  = 100 ; length = 100
        if i == 1+start: width  = 5   ; length = 5
        if i == 2+start: width  = 100 ; length = 5
        if i == 3+start: width  = 5   ; length = 100
             
        measured  = pd.read_csv(f"{dirpath}/measured_{Id_sim}/{i-start}_measured_w{width}_l{length}.csv")
        simulated = pd.read_csv(f"{dirpath}/simulated_{Id_sim}/{i-start}_simulated_w{width}_l{length}.csv")

        error_1 = round (100 * abs((abs(measured.iloc[:, 1]) - abs(simulated.iloc[:, 1]))/abs(measured.iloc[:, 1])),8)  

        df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1]).transpose()        
        df_error.to_csv(f"{dirpath}/error_{Id_sim}/{i-start}_{device}_error_w{width}_l{length}.csv",index= False)
        
        # Mean error 
        mean_error = (df_error[f"{d_in}"].mean())
        # Max error 
        max_error = df_error[f"{d_in}"].max()

        df_final_ = {'Run no.': f'{i-start}', 'Device name': f'{dirpath}', 'Width': f'{width}', 'Length': f'{length}', 'Simulated_Val':f'{Id_sim}','Mean error%':f'{"{:.2f}".format(mean_error)}', 'Max error%':f'{"{:.2f}".format(max_error)} '}
        df_final = df_final.append(df_final_, ignore_index = True)
    # Max mean error 
    print (df_final)   
    df_final.to_csv (f"{dirpath}/Final_report_{Id_sim}.csv", index = False)
    out_report = pd.read_csv (f"{dirpath}/Final_report_{Id_sim}.csv")
    print ("\n",f"Max. mean error = {out_report['Mean error%'].max()}%")    
    print ("=====================================================================================================================================================")


def main():

    # mim
    corners = ["ss" , "typical","ff"]        
    devices = ["mim_1p5fF" , "mim_1p0fF" , "mim_2p0fF"]
    measure = ["cv","corners", "CV (fF)"]
    start = 0
    for corner in corners: 
        for device in devices: 
            # Folder structure of measured values
            cv_sim, cap_vn, cap_in = measure[0], measure[1], measure[2]
            dirpath = f"{device}_{cv_sim}_{corner}"
            if os.path.exists(dirpath) and os.path.isdir(dirpath):
                shutil.rmtree(dirpath)
            os.makedirs(f"{dirpath}/measured_{cv_sim}",exist_ok=False)

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

            # Folder structure of simulated values 
            os.makedirs(f"{dirpath}/simulated_{cv_sim}",exist_ok=False)
            os.makedirs(f"{dirpath}/error_{cv_sim}",exist_ok=False)
    
            ext_measured (device,cap_vn,cap_in, cv_sim, corner,start)
            ext_simulated(device,cap_vn,cap_in,cv_sim, corner,start)
            error_cal    (device,cap_vn,cap_in,cv_sim, corner,start)            
            start = start + 4            
        start = start + 24

# # ================================================================
# -------------------------- 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()