{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np, pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "if not os.path.isdir(\"./lut_plot\"):\n",
    "    os.mkdir(\"./lut_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_meas(path):\n",
    "    measurements = pickle.load(open(path, \"rb\"))\n",
    "    df = pd.DataFrame(measurements, columns=[\"ampl\",\"mag_gen_sq\",\"mag_feedback_sq\",\"phase_diff\"])\n",
    "    df[\"mag_gen\"] = np.sqrt(df[\"mag_gen_sq\"])\n",
    "    df[\"mag_feedback\"] = np.sqrt(df[\"mag_feedback_sq\"])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_meas(\"./measurements.pkl\")\n",
    "df_lut = get_meas(\"./measurements_lut.pkl\")\n",
    "df_lut_sq = get_meas(\"./measurements_lut_sq.pkl\")\n",
    "df_tab = get_meas(\"./measurements_tab.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_slope(ampl, mag_feedback):\n",
    "    slope, intersect = np.polyfit(x = ampl[0:20], y = mag_feedback[0:20], deg = 1)\n",
    "    return slope, intersect\n",
    "\n",
    "#get_slope(df[\"ampl\"], df[\"mag_feedback\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fac(ampl, mag_feedback):\n",
    "    slope, intersect = get_slope(ampl, mag_feedback)\n",
    "    return[(x*slope + intersect) / y for (x,y) in zip(ampl, mag_feedback)]\n",
    "def interp(x): return np.interp(x, df[\"ampl\"], fac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0.1, 0.5, num = 50)\n",
    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
    "\n",
    "plt.plot(df[\"ampl\"], df[\"mag_feedback\"], label=\"measurement\")\n",
    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"Original Measurement\")\n",
    "\n",
    "plt.savefig(\"./lut_plot/original_measurement.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
    "\n",
    "plt.plot(df_lut[\"ampl\"], df_lut[\"mag_feedback\"], label=\"measurement\")\n",
    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"Lut Measurement\")\n",
    "\n",
    "plt.savefig(\"./lut_plot/lut_measurement.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
    "\n",
    "plt.plot(df_lut_sq[\"ampl\"], df_lut_sq[\"mag_feedback\"], label=\"measurement\")\n",
    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"Lut Squared Measurement\")\n",
    "\n",
    "plt.savefig(\"./lut_plot/lut_sq_measurement.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intersect = get_slope(df_tab[\"ampl\"], df_tab[\"mag_feedback\"])\n",
    "\n",
    "plt.plot(df_tab[\"ampl\"], df_tab[\"mag_feedback\"], label=\"measurement\")\n",
    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"Lut Tab Measurement\")\n",
    "\n",
    "plt.savefig(\"./lut_plot/lut_tab_measurement.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
    "\n",
    "plt.plot(df[\"ampl\"], df[\"mag_feedback\"], label=\"measurement\")\n",
    "plt.plot(df_lut[\"ampl\"], df_lut[\"mag_feedback\"], label=\"measurement lut\")\n",
    "plt.plot(df_lut_sq[\"ampl\"], df_lut_sq[\"mag_feedback\"], label=\"measurement lut sq\")\n",
    "plt.plot(df_tab[\"ampl\"], df_tab[\"mag_feedback\"], label=\"measurement tab sq\")\n",
    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"All Measurements\")\n",
    "\n",
    "plt.savefig(\"./lut_plot/all_measurement.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac = get_fac(df[\"ampl\"], df[\"mag_feedback\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f for f in fac]}, open(\"lut.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f**2 for f in fac]}, open(\"lut_sq.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
    "ampl_corr = []\n",
    "for ampl in df[\"ampl\"]:\n",
    "    y = ampl * slope + intersect\n",
    "    mag_feedback_idx = np.argmin(np.abs(df[\"mag_feedback\"] - y))\n",
    "    ampl_corr.append(df[\"ampl\"][mag_feedback_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ampl_corr = np.array(ampl_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_start = np.argmax(ampl_corr != ampl_corr.min()) - 1\n",
    "idx_end  = np.argmin(ampl_corr != ampl_corr.max()) \n",
    "start = 1.0 * idx_start / len(ampl_corr)\n",
    "end   = 1.0 * idx_end / len(ampl_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_range = np.linspace(start,end,num=len(ampl_corr))\n",
    "new_range  = np.linspace(0,1,num=len(ampl_corr))\n",
    "\n",
    "ampl_corr_new = []\n",
    "for f in new_range:\n",
    "    ratio = np.interp(f, new_range, orig_range)\n",
    "    new_ampl = np.interp(ratio, new_range, ampl_corr)\n",
    "    ampl_corr_new.append(new_ampl)\n",
    "ampl_corr_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac = ampl_corr_new / df[\"ampl\"]\n",
    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f for f in list(fac)]}, open(\"lut_tab.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.load(open(\"./lut_tab.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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