notebooks/models_transf_balun_srf38.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import em\n",
    "from models import RBFInterpolatorModel, load_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer 0.65um \n",
    "\n",
    "\n",
    "Octagonal transformer in TSMC 65 um. 1T + overlaped (balun)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "#turn and freq independent @ 28GHz SRF > 38GHz\n",
    "sub_spaces = [(1,1, \"_balun\"), (1,1, \"\"), (2,2, \"_balun\")]\n",
    "\n",
    "\n",
    "def ranges(df_train, df_test):\n",
    "    df_test = df_test[df_test.Dinp > 30]\n",
    "    df_test = df_test[df_test.Wp > 5]\n",
    "    df_test = df_test[df_test.Dinp < 170 ]\n",
    "\n",
    "    return  df_train, df_test\n",
    "\n",
    "\n",
    "model_table = {}\n",
    "for subsp in sub_spaces:\n",
    "    print(f\"Subspace: {subsp}\")\n",
    "    for f in range(201):\n",
    "\n",
    "        freq, x_train, y_train, x_test, y_test, srf_data = load_data(\"../data/transf_65nm/\", f, srf=3, nt=subsp, n_samples=1000, filter=ranges)\n",
    "        \n",
    "        if 'balun' in subsp[2]:\n",
    "            m = RBFInterpolatorModel(degree=4)\n",
    "            m.fit(x_train[:,[0,1]], y_train)\n",
    "            pred = m.predict(x_test[:,[0,1]])\n",
    "        else:\n",
    "            m = RBFInterpolatorModel(degree=5)\n",
    "            m.fit(x_train, y_train)\n",
    "            pred = m.predict(x_test)\n",
    "\n",
    "        if (max_mean_error := max(em.mape_lq_diff(freq, pred, y_test)[0])) > 10 :\n",
    "            print(f\"{f} : {max_mean_error}\")\n",
    "\n",
    "        mdl = {\n",
    "                'freq_index': f,\n",
    "                'freq': freq[0],\n",
    "                'nt': subsp,\n",
    "                'RBFmodel': m\n",
    "        }\n",
    "        model_table[(subsp, f)] = mdl\n",
    "\n",
    "    srf_m = RBFInterpolatorModel(degree=3)\n",
    "    if 'balun' in subsp[2]:\n",
    "        srf_m.fit(srf_data[0][:, [0,1]], srf_data[1][:, [0]])\n",
    "    else:\n",
    "        srf_m.fit(srf_data[0], srf_data[1])\n",
    "        \n",
    "    srf_mdl = {\n",
    "            'srf_limit': 3,\n",
    "            'nt': subsp,\n",
    "            'RBFmodel': srf_m\n",
    "    }\n",
    "    model_table[(subsp, 'srf')] = srf_mdl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_table['inputs'] = ['Dinp', 'Wp', 'Dins', 'Ws']\n",
    "model_table['ouputs'] = em.COLUMNS_6P[1:]\n",
    "model_table['device'] = 'transf'\n",
    "model_table['model'] = 'transf'\n",
    "model_table['ranges'] = {'dinp': (25, 240), 'wp': (4, 14), 'np': (1,2), 'dins': (25, 240), 'ws': (4, 14), 'ns': (1,2), 'freq': (0, 200)}\n",
    "model_table['key'] = {'nt':sub_spaces, 'f':(0, 201)}\n",
    "\n",
    "with open(\"../PASSIVES_RBF_TRANSF_BALUN_28G_SRF38_v2.model\",'wb') as outfile:\n",
    "    pickle.dump(model_table, outfile)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open(\"../../em-model/PASSIVES_RBF_TRANSF_BALUN_28G_SRF38_v1.model\",'rb') as infile:\n",
    "#    model = pickle.load(infile)\n",
    "\n",
    " #   model['ranges'] = {'dinp': (25, 240), 'wp': (4, 14), 'np': (1,2), 'dins': (25, 240), 'ws': (4, 14), 'ns': (1,2), 'freq': (0, 200)}\n",
    "\n",
    "#with open(\"../../em-model/PASSIVES_RBF_TRANSF_BALUN_28G_SRF38_full_range.model\",'wb') as outfile:\n",
    " #   pickle.dump(model, outfile) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from models import PassivesModel\n",
    "\n",
    "tranf_model = PassivesModel(\"../../em-model/PASSIVES_RBF_TRANSF_BALUN_28G_SRF38_v1.model\")\n",
    "resp, resp_arg = tranf_model.simulate(np=2, ns=2, dinp=45, wp=5, dins=45, ws=5) \n",
    "tranf_model.save(sri_fname=\"../test_out_in.sri\", lq_fname= \"../test_out_in.out\")\n",
    "tranf_model.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Copyright (C) 2022 Instituto de Telecomunicações & IMSE CSIC"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('py38env')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "9748f21e6de7edd35ba16c4ad604c5ab63165e362197e45a9f4680b926973bdc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}