examples/msre/model_frq_response.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from parameters_U233 import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from jitcdde import t\n",
    "from msrDynamics.objects import Node, System\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = 8\n",
    "\n",
    "# unpack ORNL data\n",
    "df_ORNL_mag = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude.csv\",names=['f','mag'])\n",
    "df_ORNL_mag = df_ORNL_mag.sort_values(df_ORNL_mag.columns[0])\n",
    "df_ORNL_phase = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase.csv\",names=['f','phase'])\n",
    "df_ORNL_phase = df_ORNL_phase.sort_values(df_ORNL_phase.columns[0])\n",
    "df_ORNL_mag_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude_theoretical.csv\",names=['f','mag'])\n",
    "df_ORNL_mag_t = df_ORNL_mag_t.sort_values(df_ORNL_mag_t.columns[0])\n",
    "df_ORNL_phase_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase_theoretical.csv\",names=['f','phase'])\n",
    "df_ORNL_phase_t = df_ORNL_phase_t.sort_values(df_ORNL_phase_t.columns[0])\n",
    "df_jit = pd.read_csv(f\"frequency_response_results_{P}_MW.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,1,figsize=(6,12))\n",
    "\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
    "\n",
    "# function to update the style of each axis\n",
    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
    "    ax.set_title(title,fontsize=20)\n",
    "    ax.set_xlabel(x_label,fontsize=14)\n",
    "    ax.set_ylabel(y_label,fontsize=14)\n",
    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
    "\n",
    "update_axis_style(axs[0],\"Magnitude\")\n",
    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=\"msrDynamics\")\n",
    "axs[0].plot(df_ORNL_mag_t['f'],df_ORNL_mag_t['mag'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n",
    "axs[0].scatter(df_ORNL_mag['f'],df_ORNL_mag['mag'],marker=\"2\",color=colors[2],label=\"ORNL: measured\")\n",
    "axs[0].set_yscale(\"log\")\n",
    "axs[0].set_xscale(\"log\")\n",
    "axs[0].set_xlim([9e-3,1e0])\n",
    "axs[0].set_ylim([1e2,3.5e3])\n",
    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
    "axs[0].legend()\n",
    "\n",
    "update_axis_style(axs[1],\"Phase\",x_label='Frequency (rad/s)',y_label=\"Phase(deg)\")\n",
    "axs[1].plot(df_jit['Frequency'],df_jit['Phase Shift'],label=\"msrDynamics\")\n",
    "axs[1].plot(df_ORNL_phase_t['f'],df_ORNL_phase_t['phase'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n",
    "axs[1].scatter(df_ORNL_phase['f'],df_ORNL_phase['phase'],marker=\"2\",color=colors[2],label=\"ORNL-TM-2997\")\n",
    "axs[1].set_xscale(\"log\")\n",
    "axs[1].set_xlim([9e-3,1e0])\n",
    "axs[1].legend()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'frequency_response_results_8_MW_double_tau.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_adj \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfrequency_response_results_8_MW_double_tau.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m fig,axs \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m1\u001b[39m,figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m      6\u001b[0m colors \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:blue\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:orange\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:green\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:red\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:purple\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:brown\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m~/git/envs/thesis_env/lib/python3.11/site-packages/pandas/io/parsers/readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    944\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m    945\u001b[0m )\n\u001b[1;32m    946\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 948\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/git/envs/thesis_env/lib/python3.11/site-packages/pandas/io/parsers/readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    608\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    610\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 611\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    613\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    614\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/git/envs/thesis_env/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/git/envs/thesis_env/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1703\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1704\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1706\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1711\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1712\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1713\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1714\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1715\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1716\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/git/envs/thesis_env/lib/python3.11/site-packages/pandas/io/common.py:863\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    859\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    860\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    862\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 863\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    864\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    865\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    866\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    867\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    868\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    869\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    871\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    872\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'frequency_response_results_8_MW_double_tau.csv'"
     ]
    }
   ],
   "source": [
    "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n",
    "\n",
    "\n",
    "fig,axs = plt.subplots(2,1,figsize=(10,10))\n",
    "\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
    "\n",
    "# function to update the style of each axis\n",
    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
    "\n",
    "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n",
    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n",
    "axs[0].set_yscale(\"log\")\n",
    "axs[0].set_xscale(\"log\")\n",
    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
    "axs[0].set_xlim([1e-2,2e0])\n",
    "p = tau_c+tau_l\n",
    "f_dip = (2*math.pi)/(p)\n",
    "f_dip2 = (2*math.pi)/((p)/2)\n",
    "f_peak = (2*math.pi)/(2*(p))\n",
    "f_peak2 = (2*math.pi)/((2/3)*(p))\n",
    "axs[0].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n",
    "axs[0].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n",
    "axs[0].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n",
    "axs[0].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n",
    "axs[0].legend()\n",
    "\n",
    "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n",
    "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n",
    "axs[1].set_yscale(\"log\")\n",
    "axs[1].set_xscale(\"log\")\n",
    "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
    "axs[1].set_xlim([1e-2,2e0])\n",
    "p2 = 2*(tau_c+tau_l)\n",
    "f_dip = (2*math.pi)/(p2)\n",
    "f_dip2 = (2*math.pi)/((p2)/2)\n",
    "f_peak = (2*math.pi)/(2*(p2))\n",
    "f_peak2 = (2*math.pi)/((2/3)*(p2))\n",
    "axs[1].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n",
    "axs[1].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n",
    "axs[1].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n",
    "axs[1].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n",
    "axs[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n",
    "\n",
    "\n",
    "fig,axs = plt.subplots(2,1,figsize=(10,10))\n",
    "\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n",
    "\n",
    "# function to update the style of each axis\n",
    "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n",
    "    ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n",
    "\n",
    "# Filling between ranges where inc_dec is True\n",
    "inc_dec = np.ones(len(df_jit))\n",
    "for f in enumerate(df_jit['Frequency']):\n",
    "    delay_input = np.cos(f[1]*((-p))) # t = 2pi-tau\n",
    "    if (delay_input > 0):\n",
    "        inc_dec[f[0]] = 0\n",
    "\n",
    "# Find start and end points of True and False segments in inc_dec and fill between them\n",
    "start_true = None\n",
    "start_false = None\n",
    "for i in range(len(df_jit['Frequency'])):\n",
    "    if inc_dec[i]:\n",
    "        if start_false is not None:\n",
    "            # Fill the False segment before starting the True segment\n",
    "            axs[0].fill_between(df_jit['Frequency'][start_false:i], 0, df_jit['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n",
    "            start_false = None\n",
    "        if start_true is None:\n",
    "            start_true = i  # Mark the start of a True segment\n",
    "    else:\n",
    "        if start_true is not None:\n",
    "            # Fill the True segment before starting the False segment\n",
    "            axs[0].fill_between(df_jit['Frequency'][start_true:i], 0, df_jit['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n",
    "            start_true = None\n",
    "        if start_false is None:\n",
    "            start_false = i  # Mark the start of a False segment\n",
    "\n",
    "# Check if there is a segment that goes until the end\n",
    "if start_true is not None:\n",
    "    axs[0].fill_between(df_jit['Frequency'][start_true:], 0, df_jit['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n",
    "if start_false is not None:\n",
    "    axs[0].fill_between(df_jit['Frequency'][start_false:], 0, df_jit['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n",
    "\n",
    "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n",
    "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n",
    "axs[0].set_yscale(\"log\")\n",
    "axs[0].set_xscale(\"log\")\n",
    "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
    "axs[0].set_xlim([1e-2,2e0])\n",
    "\n",
    "red_patch = mpatches.Patch(color='red', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) > 0$\")\n",
    "green_patch = mpatches.Patch(color='green', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) < 0$\")\n",
    "\n",
    "axs[0].legend(handles=[axs[0].get_lines()[0], red_patch, green_patch])\n",
    "\n",
    "######################################################################################\n",
    "# Double tau\n",
    "######################################################################################\n",
    "\n",
    "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n",
    "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n",
    "axs[1].set_yscale(\"log\")\n",
    "axs[1].set_xscale(\"log\")\n",
    "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n",
    "axs[1].set_xlim([1e-2,2e0])\n",
    "axs[1].legend(handles=[axs[1].get_lines()[0], red_patch, green_patch])\n",
    "\n",
    "# Filling between ranges where inc_dec is True\n",
    "inc_dec = np.ones(len(df_adj))\n",
    "for f in enumerate(df_adj['Frequency']):\n",
    "    delay_input = np.cos(f[1]*(-p2)) # t = 2pi-tau\n",
    "    if (delay_input > 0):\n",
    "        inc_dec[f[0]] = 0\n",
    "\n",
    "# Find start and end points of True and False segments in inc_dec and fill between them\n",
    "start_true = None\n",
    "start_false = None\n",
    "for i in range(len(df_adj['Frequency'])):\n",
    "    if inc_dec[i]:\n",
    "        if start_false is not None:\n",
    "            # Fill the False segment before starting the True segment\n",
    "            axs[1].fill_between(df_adj['Frequency'][start_false:i], 0, df_adj['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n",
    "            start_false = None\n",
    "        if start_true is None:\n",
    "            start_true = i  # Mark the start of a True segment\n",
    "    else:\n",
    "        if start_true is not None:\n",
    "            # Fill the True segment before starting the False segment\n",
    "            axs[1].fill_between(df_adj['Frequency'][start_true:i], 0, df_adj['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n",
    "            start_true = None\n",
    "        if start_false is None:\n",
    "            start_false = i  # Mark the start of a False segment\n",
    "\n",
    "# Check if there is a segment that goes until the end\n",
    "if start_true is not None:\n",
    "    axs[1].fill_between(df_adj['Frequency'][start_true:], 0, df_adj['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n",
    "if start_false is not None:\n",
    "    axs[1].fill_between(df_adj['Frequency'][start_false:], 0, df_adj['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  }
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