Merge pull request google#38 from mabrains/modifying_models_regression

Modifying models regression

models/ngspice/testing/regression/bjt_beta/model_reg.py

@@ -0,0 +1,339 @@
+# Copyright 2022 GlobalFoundries PDK Authors
+#
+# 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 unittest.mock import DEFAULT
+from docopt import docopt
+import pandas as pd
+import numpy as np
+import os
+from jinja2 import Template
+import concurrent.futures
+import shutil
+import multiprocessing as mp
+
+import subprocess
+import glob
+
+import warnings
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
+def find_bjt_beta(filename):
+ """
+ Find res in log
+ """
+ cmd = 'grep "bjt_bat = " {} | head -n 1'.format(filename)
+ process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
+ return float(process.communicate()[0][:-1].decode("utf-8").split(" ")[2])
+
+def call_simulator(file_name):
+ """Call simulation commands to perform simulation.
+ Args:
+ file_name (str): Netlist file name.
+ """
+ return os.system(f"ngspice -b -a {file_name} -o {file_name}.log > {file_name}.log")
+
+def ext_beta_corners(dev_data_path, device,vb , vc ,Id_sim, step,list_devices):
+ # Read Data
+ df = pd.read_excel(dev_data_path)
+ corners = df["corners"].count()
+
+ all_dfs = []
+ for i in range(corners):
+
+ 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
+ idf = df[[f"{vb}", f"{vc}{step[0]}", f"{vc}{step[1]}", f"{vc}{step[2]}"]].copy()
+ idf.rename(
+ columns={
+ f"{vb}": "base_volt",
+ f"{vc}{step[0]}": "coll_volt_s1",
+ f"{vc}{step[1]}": "coll_volt_s2",
+ f"{vc}{step[2]}": "coll_volt_s3"
+ },
+ inplace=True,
+ )
+ idf.to_csv(
+ f"{device}/measured_{Id_sim[0]}/{i}_measured_{list_devices[k]}.csv",
+ index=False,
+ )
+
+ idf["corner"] = corners
+ all_dfs.append(idf)
+
+ print(all_dfs)
+
+ if device == "pnp":
+ vb = temp_vb
+
+ # measured Id_sim 1
+ idf = df[[f"{vb}", f"{vc}{step[0]}", f"{vc}{step[1]}", f"{vc}{step[2]}"]].copy()
+ idf.rename(
+ columns={
+ f"{vb}": "base_volt",
+ f"{vc}{step[0]}.{2*i+1}": "coll_volt_s1",
+ f"{vc}{step[1]}.{2*i+1}": "coll_volt_s2",
+ f"{vc}{step[2]}.{2*i+1}": "coll_volt_s3"
+ },
+ inplace=True,
+ )
+ else:
+ # measured Id_sim 0
+ idf = df[[f"{vb}", f"{vc}{step[0]}", f"{vc}{step[1]}", f"{vc}{step[2]}"]].copy()
+ idf.rename(
+ columns={
+ f"{vb}": "base_volt",
+ f"{vc}{step[0]}.{2*i}": "coll_volt_s1",
+ f"{vc}{step[1]}.{2*i}": "coll_volt_s2",
+ f"{vc}{step[2]}.{2*i}": "coll_volt_s3"
+ },
+ inplace=True,
+ )
+
+ idf["corner"] = corners
+ all_dfs.append(idf)
+
+ # measured Id_sim 1
+ idf = df[[f"{vb}", f"{vc}{step[0]}", f"{vc}{step[1]}", f"{vc}{step[2]}"]].copy()
+ idf.rename(
+ columns={
+ f"{vb}": "base_volt",
+ f"{vc}{step[0]}.{2*i+1}": "coll_volt_s1",
+ f"{vc}{step[1]}.{2*i+1}": "coll_volt_s2",
+ f"{vc}{step[2]}.{2*i+1}": "coll_volt_s3"
+ },
+ inplace=True,
+ )
+
+
+
+ df = pd.concat(all_dfs)
+ df["device"] = device
+ df.dropna(axis=0, inplace=True)
+ df = df[["device", "corner", "base_volt","coll_volt_s1","coll_volt_s2","coll_volt_s3"]]
+ return df
+
+
+def run_sim(dirpath, device, vc, ib, temp=25):
+ """ Run simulation at specific information and corner """
+ netlist_tmp = "./device_netlists/res_op_analysis.spice"
+
+
+ info = {}
+ info["device"] = device
+ info["collector_voltage"] = vc
+ info["base_current"] = ib
+ info["temp"] = temp
+
+
+ collector_volt_str = "{:.3f}".format(vc)
+ base_curr_str = "{:.3f}".format(ib)
+ temp_str = "{:.1f}".format(temp)
+
+ netlist_path = f"{dirpath}/{device}_netlists/netlist_w{collector_volt_str}_l{base_curr_str}_t{temp_str}.spice"
+
+ with open(netlist_tmp) as f:
+ tmpl = Template(f.read())
+ os.makedirs(f"{dirpath}/{device}_netlists", exist_ok=True)
+
+ with open(netlist_path, "w") as netlist:
+ netlist.write(
+ tmpl.render(
+ device=device,
+ i = ib,
+ temp=temp_str,
+
+ )
+ )
+
+ # Running ngspice for each netlist
+ try:
+ call_simulator(netlist_path)
+ # Find res in log
+ try:
+ bjt_beta = find_bjt_beta(f"{netlist_path}.log")
+ except Exception as e:
+ bjt_beta = 0.0
+ except Exception as e:
+ bjt_beta = 0.0
+
+ info["bjt_beta_sim_unscaled"] = bjt_beta
+
+ return info
+
+def run_sims(df, dirpath, num_workers=mp.cpu_count()):
+
+ results = []
+ with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
+ futures_list = []
+ for j, row in df.iterrows():
+ futures_list.append(
+ executor.submit(
+ run_sim,
+ dirpath,
+ row["device"],
+ row["voltage"],
+ row["collector_current"],
+ row["base_voltage"]
+
+ )
+ )
+
+ for future in concurrent.futures.as_completed(futures_list):
+ try:
+ data = future.result()
+ results.append(data)
+ except Exception as exc:
+ print("Test case generated an exception: %s" % (exc))
+
+ df = pd.DataFrame(results)
+ print(df.columns)
+ df = df[
+ ["device","base_voltage" ,"collector_current", "temp"]
+ ]
+
+ return df
+
+
+def main():
+
+ # pandas setup
+ pd.set_option("display.max_columns", None)
+ pd.set_option("display.max_rows", None)
+ pd.set_option("max_colwidth", None)
+ pd.set_option("display.width", 1000)
+
+ main_regr_dir = "bjt_beta_regr"
+
+ # bjt_beta var 
+ vb = ["vbp (V)", "-vb "]
+ vc = ["vcp =", "vc =-"]
+ Id_sim = ["|Ic (A)|", "|Ib (A)|"]
+ sweep = 101
+ step = [1, 2, 3]
+
+
+ devices = ["npn", "pnp"]
+ list_devices = [
+ [
+ "npn_10p00x10p00",
+ "npn_05p00x05p00",
+ "npn_00p54x16p00",
+ "npn_00p54x08p00",
+ "npn_00p54x04p00",
+ "npn_00p54x02p00",
+ ],
+ ["pnp_10p00x00p42", "pnp_05p00x00p42", "pnp_10p00x10p00", "pnp_05p00x05p00"],
+ ]
+
+ for i, dev in enumerate(devices):
+ dev_path = f"{main_regr_dir}/{dev}"
+
+ if os.path.exists(dev_path) and os.path.isdir(dev_path):
+ shutil.rmtree(dev_path)
+
+ os.makedirs(f"{dev_path}", exist_ok=False)
+
+ print("######" * 10)
+ print(f"# Checking Device {dev}")
+
+ beta_data_files = glob.glob(
+ f"../../180MCU_SPICE_DATA/BJT/bjt_{dev}_beta_f.nl_out.xlsx"
+ )
+ if len(beta_data_files) < 1:
+ print("# Can't find beta file for device: {}".format(dev))
+ beta_file = ""
+ else:
+ beta_file = beta_data_files[0]
+ print("# beta data points file : ", beta_file)
+
+
+ if beta_file == "" :
+ print(f"# No datapoints available for validation for device {dev}")
+ continue
+
+ if beta_file != "":
+ meas_df = ext_beta_corners(beta_file, dev ,vb[i] , vc[i] ,Id_sim, step,list_devices)
+ else:
+ meas_df = []
+
+
+
+
+ print("# Device {} number of measured_datapoints : ".format(dev), len(meas_df))
+
+ sim_df = run_sims(meas_df, dev_path, 3)
+ #print("# Device {} number of simulated datapoints : ".format(dev), len(sim_df))
+
+ #merged_df = meas_df.merge(
+ # sim_df, on=["device", "corner", "length", "width", "temp"], how="left"
+ #)
+ #merged_df["error"] = (
+ # np.abs(merged_df["res_sim"] - merged_df["res_measured"])
+ # * 100.0
+ # / merged_df["res_measured"]
+ #)
+
+ #merged_df.to_csv(f"{dev_path}/error_analysis.csv", index=False)
+
+ #print(
+ # "# Device {} min error: {:.2f} , max error: {:.2f}, mean error {:.2f}".format(
+ # dev,
+ # merged_df["error"].min(),
+ # merged_df["error"].max(),
+ # merged_df["error"].mean(),
+ # )
+ #)
+
+ #if merged_df["error"].max() < PASS_THRESH:
+ # print("# Device {} has passed regression.".format(dev))
+ #else:
+ # print("# Device {} has failed regression. Needs more analysis.".format(dev))
+
+ #print("\n\n")
+
+
+# # ================================================================
+# -------------------------- 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()