2025-01-07 Linear optics for IP drifting beam - new quads.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4911baaf-e51c-4074-9dc3-3d5279dd38f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from UTILITY_quickstart import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9429d65-37a2-47a1-878c-5fecac910f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Base Tao lattice\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    }
   ],
   "source": [
    "#Start from golden lattice\n",
    "tao = initializeTao(\n",
    "    loadDefaultLatticeTF = False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c8794a-0ec6-49b1-a481-2b80e39faf91",
   "metadata": {},
   "source": [
    "## Golden lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37c36fa4-2d10-458d-acee-0eb70d296202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 0.500113514905197,\n",
       " 'alpha_a': -8.61413026751329e-05,\n",
       " 'gamma_a': 1.99954605827816,\n",
       " 'phi_a': 73.1003856252223,\n",
       " 'eta_a': -4.29100544671707e-07,\n",
       " 'etap_a': -7.60864677687215e-06,\n",
       " 'beta_b': 0.499955681369933,\n",
       " 'alpha_b': 0.000116201966385052,\n",
       " 'gamma_b': 2.00017731724298,\n",
       " 'phi_b': 60.4082301501082,\n",
       " 'eta_b': -6.77061438266207e-18,\n",
       " 'etap_b': -1.31862568529426e-17,\n",
       " 'eta_x': -4.29100544671707e-07,\n",
       " 'etap_x': -7.60864677687215e-06,\n",
       " 'eta_y': -6.77059836943936e-18,\n",
       " 'etap_y': -1.31862402801866e-17}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The golden lattice linear optics gives 50 x 50 cm betas (zero alphas) at PENT\n",
    "tao.ele_twiss(\"PENT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c2e3ec0-0f18-41f5-b360-fa0a56acb17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 11.5533562066008,\n",
       " 'alpha_a': -0.64078864083071,\n",
       " 'gamma_a': 0.122095264526835,\n",
       " 'phi_a': 71.31011993097,\n",
       " 'eta_a': 1.82993853116333e-05,\n",
       " 'etap_a': 1.18535480249718e-06,\n",
       " 'beta_b': 25.2039557159784,\n",
       " 'alpha_b': -1.56858472563668,\n",
       " 'gamma_b': 0.137298211459199,\n",
       " 'phi_b': 56.2536406733655,\n",
       " 'eta_b': -2.27197431822334e-17,\n",
       " 'etap_b': -4.93465667198356e-20,\n",
       " 'eta_x': 1.82993853116333e-05,\n",
       " 'etap_x': 1.18535480249718e-06,\n",
       " 'eta_y': -2.27197534407484e-17,\n",
       " 'etap_y': -4.93522682312884e-20}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#At MFFF, have\n",
    "#     Beta    Alpha\n",
    "# x   11.55   -0.64\n",
    "# y   25.2    -1.56\n",
    "tao.ele_twiss(\"MFFF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0120cf-0a17-4c04-a46b-87f513d3a5d8",
   "metadata": {},
   "source": [
    "## Disable FF quads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45c270e6-5f01-40f4-8044-ab82f04991a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "setQuadkG(tao, \"Q5FF\", 0)\n",
    "setQuadkG(tao, \"Q4FF\", 0)\n",
    "setQuadkG(tao, \"Q3FF\", 0)\n",
    "setQuadkG(tao, \"Q2FF\", 0)\n",
    "setQuadkG(tao, \"Q1FF\", 0)\n",
    "setQuadkG(tao, \"Q0FF\", 0)\n",
    "setQuadkG(tao, \"Q0D\", 0)\n",
    "setQuadkG(tao, \"Q1D\", 0)\n",
    "setQuadkG(tao, \"Q2D\", 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2b7742b-8087-48de-b990-c5fba99e72b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 62.1183955561056,\n",
       " 'alpha_a': -2.56600115693764,\n",
       " 'gamma_a': 0.122095264526835,\n",
       " 'phi_a': 71.9394382055096,\n",
       " 'eta_a': 3.69901994937505e-05,\n",
       " 'etap_a': 1.18535480249718e-06,\n",
       " 'beta_b': 108.80815352558,\n",
       " 'alpha_b': -3.7335191001574,\n",
       " 'gamma_b': 0.137298211459199,\n",
       " 'phi_b': 56.5594895777048,\n",
       " 'eta_b': -3.062786696503e-17,\n",
       " 'etap_b': -4.93465667198356e-20,\n",
       " 'eta_x': 3.69901994937505e-05,\n",
       " 'etap_x': 1.18535480249718e-06,\n",
       " 'eta_y': -3.06279671256493e-17,\n",
       " 'etap_y': -4.93522682312884e-20}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tao.ele_twiss(\"PENT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8fa493-6ecf-4f23-9403-6aa8ab598fe0",
   "metadata": {},
   "source": [
    "## Changing beam into BC20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1d8941e-5870-46db-a75e-64c0b7bea931",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Following code adapted from \"2024-08-09 Second IP linear optics.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a5a4a41-221d-4973-a1b2-bab32860ca51",
   "metadata": {},
   "outputs": [],
   "source": [
    "pbounds = {\n",
    "    #Made up limits\n",
    "    # 'Q19701':  [-35, -15],   #BCON = -26.7\n",
    "    # 'Q19801':  [25, 45],    #BCON = 35.7\n",
    "    # 'Q18801':  [0, 100],   #BCON = -26.7\n",
    "    # 'Q18901':  [-100, 0],    #BCON = 35.7\n",
    "\n",
    "    #\"Q6E\": [-301, 0],\n",
    "    #\"Q5ER\": [-33, -13],\n",
    "    # \"Q4ER_1\": [120, 140], #Code below will keep all _1, _2, _3 in sync\n",
    "    # \"Q3ER_1\": [100, 120], #Code below will keep all _1, _2 in sync\n",
    "    # \"Q2ER\": [-165, -145],\n",
    "    # \"Q1ER\": [150, 170],\n",
    "\n",
    "    # Limits and values as of 2025-01-07 \n",
    "    # SCP settings\n",
    "    # \"Q18601\": [0, 106.5],       #BCON = 24.7\n",
    "    # \"Q18701\": [-106.5, 0],      #BCON = -26.1\n",
    "    # \"Q18801\": [0, 106.5],       #BCON = 27.9\n",
    "    # \"Q18901\": [-106.5, 0],      #BCON = -26.85\n",
    "    # 'Q19701':  [-107.5, 0],   #BCON = -26.7\n",
    "    # 'Q19801':  [0, 107.5],    #BCON = 35.7\n",
    "\n",
    "\n",
    "    #BC20 quads - bipolar boost range    \n",
    "    # \"Q4ER_1\": [118, 146], # 132 +- 27/2\n",
    "    #     #Code below will keep all _1, _2, _3 in sync\n",
    "    # \"Q3ER_1\": [94, 126], # 110 +- 32/2\n",
    "    #     #Code below will keep all _1, _2 in sync\n",
    "    # \"Q2ER\": [-172, -136], # -154 +- -36/2\n",
    "    # \"Q1ER\": [133, 189], #161 +- 56/2\n",
    "\n",
    "    #BC20 quads - full boost range    \n",
    "    \"Q4ER_1\": [105, 169], # 132 +- 27\n",
    "        #Code below will keep all _1, _2, _3 in sync\n",
    "    \"Q3ER_1\": [78, 142], # 110 +- 32\n",
    "        #Code below will keep all _1, _2 in sync\n",
    "    \"Q2ER\": [-190, -118], # -154 +- -36\n",
    "    \"Q1ER\": [105, 217], #161 +- 56\n",
    "    \n",
    "    # EPICS for 851 and 871\n",
    "    # 'Q19851':  [-101.9, 0],   #BCON = -64.1\n",
    "    # 'Q19871':  [0, 101.9],    #BCON = 44.9\n",
    "\n",
    "\n",
    "    #################################################################\n",
    "    ### If using bounds above, use setQuadkG() in specificOptimizer()\n",
    "    #################################################################\n",
    "    ### If using bounds below, use setLattice() !!!\n",
    "    #################################################################\n",
    "\n",
    "    \n",
    "    # \"Q1EkG\" :  (0, 388) , #BCON = 133\n",
    "    # \"Q2EkG\" :  (-364, 0), #BCON = -136\n",
    "    # \"Q3EkG\" :  (0, 299) , #BCON = 94\n",
    "    # \"Q4EkG\" :  (0, 304) , #BCON = 118\n",
    "    # \"Q5EkG\" :  (-117, 0), #BCON = -17\n",
    "    # \"Q6EkG\" :  (-301, 0), #BCON = -142\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c2207ac-777b-4317-a64a-2ce4f2fb0460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[132.28941580900812, 110.23470630887479, -154.252921494901, 161.3297517863203]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quadNames = pbounds.keys()\n",
    "[getQuadkG(tao, name) for name in quadNames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cfb15a9-c6a7-4d88-99b8-52c3b66b42b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rampToZero(val, thresh, scale = 1):\n",
    "    return (max(val, thresh) - thresh) / scale\n",
    "\n",
    "def specificOptimizer(\n",
    "    self,\n",
    "    **kwargs\n",
    "):\n",
    "\n",
    "    self.totalNumEvals += 1\n",
    "    self.displayEvals()\n",
    "\n",
    "    savedData = kwargs\n",
    "    \n",
    "    badValue = -1e300  #The value returned for illegal config. Should be colossal. Double limit ~= 1e308\n",
    "    bigCost  = 1e50   #Should be large enough to dominate any \"normal\" return value but be dominated by badValue\n",
    "    \n",
    "    try: #This try block deals with bad configurations. Instead of causing the optimizer to halt we now 'except' a low value\n",
    "        #setLattice(tao, **kwargs)\n",
    "\n",
    "        for name in quadNames:\n",
    "            setQuadkG(tao, name, kwargs[name])\n",
    "\n",
    "        if \"Q3ER_1\" in quadNames:\n",
    "            setQuadkG(tao, \"Q3ER_2\", kwargs[\"Q3ER_1\"])\n",
    "\n",
    "        if \"Q4ER_1\" in quadNames:\n",
    "            setQuadkG(tao, \"Q4ER_2\", kwargs[\"Q4ER_1\"])\n",
    "            setQuadkG(tao, \"Q4ER_3\", kwargs[\"Q4ER_1\"])\n",
    "\n",
    "    except:\n",
    "        return badValue * 5\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    try: \n",
    "\n",
    "\n",
    "        # savedData[\"betaX\"]  = tao.ele_twiss(\"MFFF\")[\"beta_a\"]\n",
    "        # savedData[\"alphaX\"] = tao.ele_twiss(\"MFFF\")[\"alpha_a\"]\n",
    "        # savedData[\"betaY\"]  = tao.ele_twiss(\"MFFF\")[\"beta_b\"]\n",
    "        # savedData[\"alphaY\"] = tao.ele_twiss(\"MFFF\")[\"alpha_b\"]\n",
    "\n",
    "        # savedData[\"betaX\"]  = tao.ele_twiss(\"BEGBC20\")[\"beta_a\"]\n",
    "        # savedData[\"alphaX\"] = tao.ele_twiss(\"BEGBC20\")[\"alpha_a\"]\n",
    "        # savedData[\"betaY\"]  = tao.ele_twiss(\"BEGBC20\")[\"beta_b\"]\n",
    "        # savedData[\"alphaY\"] = tao.ele_twiss(\"BEGBC20\")[\"alpha_b\"]\n",
    "\n",
    "        twissDict = tao.twiss_at_s(ele = \"PENT\", s_offset = 0.0)\n",
    "        savedData[\"betaX\"]  = twissDict[\"beta_a\"]\n",
    "        savedData[\"alphaX\"] = twissDict[\"alpha_a\"]\n",
    "        savedData[\"betaY\"]  = twissDict[\"beta_b\"]\n",
    "        savedData[\"alphaY\"] = twissDict[\"alpha_b\"]\n",
    "        \n",
    "        \n",
    "            \n",
    "    except:\n",
    "        return badValue\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    savedData[\"alphaPenalty\"] = 1e3 * ( rampToZero(abs(savedData[\"alphaX\"]), 0.01 ) ** 2 + rampToZero(abs(savedData[\"alphaY\"]), 0.01 ) ** 2 )\n",
    "    #savedData[\"magnetError\"] = 0 * ( abs( kwargs[\"Q18801\"] - 27.9 ) + abs( kwargs[\"Q18901\"] - -26.85 ) ) \n",
    "\n",
    "    #Target specific betas\n",
    "    #savedData[\"betaError\"] = abs(savedData[\"betaX\"] - 20) ** 2 + abs(savedData[\"betaY\"] - 20) ** 2\n",
    "\n",
    "    #Target symmetric (but arbitrary) beta\n",
    "    savedData[\"betaError\"] = 1e2 * abs(savedData[\"betaX\"] - savedData[\"betaY\"]) / (savedData[\"betaX\"] + savedData[\"betaY\"])\n",
    "\n",
    "    #Using product instead of sum causes it to prefer more symmetric solutions\n",
    "    #savedData[\"maximizeMe\"] = savedData[\"betaX\"] + savedData[\"betaY\"] - savedData[\"alphaPenalty\"]\n",
    "    #savedData[\"maximizeMe\"] = savedData[\"betaX\"] * savedData[\"betaY\"] - (savedData[\"alphaPenalty\"] ** 2)\n",
    "    #savedData[\"maximizeMe\"] = np.min([savedData[\"betaX\"], savedData[\"betaY\"]]) - (savedData[\"alphaPenalty\"] ** 2)\n",
    "    #savedData[\"maximizeMe\"] = 100 * np.min([savedData[\"betaX\"], savedData[\"betaY\"]]) + savedData[\"betaX\"] + savedData[\"betaY\"] - (savedData[\"alphaPenalty\"] ** 2)\n",
    "    savedData[\"maximizeMe\"] =  np.min([savedData[\"betaX\"], savedData[\"betaY\"]]) - savedData[\"betaError\"] - savedData[\"alphaPenalty\"]# - savedData[\"magnetError\"]\n",
    "    \n",
    "\n",
    "    \n",
    "    #Collect desired data as a pandas Series\n",
    "    tmpData = pd.Series( savedData ) \n",
    "    self.history = pd.concat([self.history, tmpData.to_frame().T])\n",
    "    \n",
    "    #Optional: Write to file\n",
    "    #self.history.to_json('optimizerHistory.json', orient='records')\n",
    "    n = self.totalNumEvals\n",
    "    if (n < 100) or (n < 1000 and  n % 10 == 0) or (n % 100 == 0): #This gets expensive to write when n >> 10k\n",
    "        self.history.to_json('optimizerHistory.json', orient='records')\n",
    "    \n",
    "    self.updatePlot()\n",
    "\n",
    "\n",
    "    return savedData[\"maximizeMe\"]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Function to create optimizerWrapper based on pbounds\n",
    "def create_optimizer_wrapper(pbounds):\n",
    "    param_names = list(pbounds.keys())\n",
    "    \n",
    "    def optimizerWrapper(self, **kwargs):\n",
    "        params = {name: kwargs.get(name, None) for name in param_names}\n",
    "        if None in params.values():\n",
    "            raise ValueError(\"All parameters must be provided\")\n",
    "        return specificOptimizer(self, **params)\n",
    "    \n",
    "    return optimizerWrapper\n",
    "\n",
    "# Create the optimizerWrapper function\n",
    "optimizerWrapper = create_optimizer_wrapper(pbounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6645923-1c80-487e-a6aa-cfd078c2306f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "class OptimizationProblem:\n",
    "    def __init__(self):\n",
    "        self.history = pd.DataFrame()\n",
    "        self.totalNumEvals = 0\n",
    "        self.plot_display_handle = None\n",
    "        self.evals_display_handle = None\n",
    "\n",
    "\n",
    "    def updatePlot(self):\n",
    "        #This function seems to cause a substantial memory leak.\n",
    "        #This hack below reduces the plotting frequency which makes it tolerable\n",
    "\n",
    "        n = self.totalNumEvals\n",
    "        \n",
    "        if (n < 100) or (n < 1000 and  n % 10 == 0) or (n % 100 == 0): \n",
    "            plt.figure()\n",
    "            plotKey = \"maximizeMe\"\n",
    "            plt.plot(np.arange(len(self.history[plotKey])), self.history[plotKey], '-')\n",
    "            \n",
    "            plt.title('Optimization History')\n",
    "            plt.xlabel('Evaluation #')\n",
    "            plt.ylabel(plotKey)\n",
    "            plt.yscale('log')\n",
    "            plt.grid(True)\n",
    "            \n",
    "            \n",
    "            if self.plot_display_handle is None:\n",
    "                self.plot_display_handle = display(plt.gcf(), display_id=True)\n",
    "            else:\n",
    "                update_display(plt.gcf(), display_id=self.plot_display_handle.display_id)\n",
    "            plt.close()\n",
    "\n",
    "        return\n",
    "\n",
    "    def displayEvals(self):\n",
    "        if self.evals_display_handle is None:\n",
    "            self.evals_display_handle = display(f\"Total Num Evals: {self.totalNumEvals}\", display_id=True)\n",
    "        else:\n",
    "            update_display(f\"Total Num Evals: {self.totalNumEvals}\", display_id=self.evals_display_handle.display_id)\n",
    "\n",
    "# Attach the function to the class as a method\n",
    "OptimizationProblem.optimizerWrapper = optimizerWrapper\n",
    "\n",
    "# Instantiate the optimization problem\n",
    "problem = OptimizationProblem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67eb92c7-862a-42af-8bf0-e1ec027ddec8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import differential_evolution\n",
    "\n",
    "# Convert pbounds to the format required by differential_evolution\n",
    "bounds = [(low, high) for (low, high) in pbounds.values()]\n",
    "param_names = list(pbounds.keys())\n",
    "\n",
    "#scipy.optimize wants to optimize a function which is passed a vector of all the parameters\n",
    "#This programmatically wraps the existing wrapper (ugh...) to handle this format\n",
    "def create_DE_wrapper(obj, param_names):\n",
    "    def wrapperDE(params):\n",
    "        param_dict = dict(zip(param_names, params))\n",
    "\n",
    "        #bayes_opt is a maximizer but differential_evolution is a minimizer... hence the inversion\n",
    "        return -1*obj.optimizerWrapper(**param_dict)\n",
    "    \n",
    "    return wrapperDE\n",
    "\n",
    "wrapperDE = create_DE_wrapper(problem, param_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a7368a0-9973-4abc-b98a-3cab01e60b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "defaultSettingsVector = [getQuadkG(tao, name) for name in quadNames]\n",
    "#defaultSettingsVector = [ 139, 111, -156, 150 ] \n",
    "\n",
    "\n",
    "# importedDefaultSettings = loadConfig(\"setLattice_configs/defaults.yml\")\n",
    "# defaultSettingsVector = [importedDefaultSettings[name] for name in quadNames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c10f957-cc4f-4ba4-a189-04723baa5cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Total Num Evals: 889'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n",
      "/var/folders/3j/4b_6gm4j44z6mpdlh3f3ggjc0000gn/T/ipykernel_58001/1806273417.py:23: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Q4ER_1      Q3ER_1        Q2ER        Q1ER        betaX     alphaX  \\\n",
      "0   132.289416  110.234706 -154.252921  161.329752    62.118396  -2.566001   \n",
      "0   138.903887  110.234706 -154.252921  161.329752   100.995843  -6.439859   \n",
      "0   132.289416  115.746442 -154.252921  161.329752  1042.198623 -58.296523   \n",
      "0   132.289416  110.234706 -161.965568  161.329752   159.281162  -6.634261   \n",
      "0   132.289416  110.234706 -154.252921  169.396239    59.791888  -2.647784   \n",
      "..         ...         ...         ...         ...          ...        ...   \n",
      "0   149.698540  108.759771 -150.352630  105.000003    34.605720  -0.034895   \n",
      "0   149.698442  108.759791 -150.352634  105.000003    34.605660  -0.034896   \n",
      "0   149.698414  108.759796 -150.352634  105.000003    34.605671  -0.034897   \n",
      "0   149.698461  108.759790 -150.352636  105.000003    34.605597  -0.034894   \n",
      "0   149.698451  108.759792 -150.352637  105.000003    34.605577  -0.034894   \n",
      "\n",
      "         betaY     alphaY  alphaPenalty     betaError    maximizeMe  \n",
      "0   108.808154  -3.733519  2.039774e+04  2.731569e+01 -2.036293e+04  \n",
      "0   104.419501  -3.135363  5.111098e+04  1.666700e+00 -5.101165e+04  \n",
      "0   263.421464  -9.175794  3.481331e+06  5.964807e+01 -3.481127e+06  \n",
      "0   457.357033 -21.470161  5.044193e+05  4.833886e+01 -5.043084e+05  \n",
      "0   142.160624  -4.942471  3.128717e+04  4.078619e+01 -3.126816e+04  \n",
      "..         ...        ...           ...           ...           ...  \n",
      "0    34.605720   0.016958  6.682026e-01  2.000616e-07  3.393752e+01  \n",
      "0    34.605660   0.016953  6.681424e-01  4.856373e-08  3.393752e+01  \n",
      "0    34.605671   0.016951  6.681529e-01  2.451867e-08  3.393752e+01  \n",
      "0    34.605597   0.016954  6.680795e-01  1.173596e-08  3.393752e+01  \n",
      "0    34.605577   0.016954  6.680594e-01  5.077042e-08  3.393752e+01  \n",
      "\n",
      "[889 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "#### #Optional: Define list of starting points\n",
    "totalPopSize = 100  # Total population size\n",
    "numDimensions = len(bounds)  # Number of parameters\n",
    "\n",
    "# #Uniformly distributed\n",
    "populationDE = np.random.rand(totalPopSize, numDimensions)\n",
    "for i in range(numDimensions):\n",
    "    low, high = bounds[i]\n",
    "    populationDE[:, i] = low + populationDE[:, i] * (high - low)\n",
    "\n",
    "#Normally distributed\n",
    "# populationDE = np.zeros((totalPopSize, numDimensions))\n",
    "# for i in range(numDimensions):\n",
    "#     low, high = bounds[i]\n",
    "\n",
    "#     #Define mean based on bounds\n",
    "#     #mean = (high + low) / 2\n",
    "\n",
    "#     #Optional: Define mean based on defaultSettingsVector\n",
    "#     mean = defaultSettingsVector[i]\n",
    "    \n",
    "#     std_dev = (high - low) / 1000\n",
    "#     populationDE[:, i] = np.random.normal(mean, std_dev, totalPopSize)\n",
    "\n",
    "#Optional: Add specific points to initial evaluation list\n",
    "#populationDE = np.vstack([[ np.mean(i) for i in bounds ], populationDE])\n",
    "populationDE = np.vstack([[ defaultSettingsVector ], populationDE])\n",
    "\n",
    "# result = differential_evolution(\n",
    "#     wrapperDE, \n",
    "#     bounds,\n",
    "#     maxiter=1000, \n",
    "#     disp=True,\n",
    "#     polish = False, \n",
    "#     init = populationDE\n",
    "# )\n",
    "\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "\n",
    "#Optional: Manually define starting simplex\n",
    "# initial_simplex = np.tile(defaultSettingsVector, (numDimensions + 1, 1))\n",
    "# for i in range(1, numDimensions + 1):\n",
    "#     initial_simplex[i][i - 1] *= 1.1\n",
    "\n",
    "result = minimize(\n",
    "    wrapperDE, \n",
    "    defaultSettingsVector,\n",
    "    method = \"Nelder-Mead\",\n",
    "    bounds = bounds,\n",
    "    #options={'initial_simplex': initial_simplex}\n",
    "    options = {'adaptive': True, 'maxiter': 10000},\n",
    ")\n",
    "\n",
    "# result = minimize(\n",
    "#     wrapperDE, \n",
    "#     defaultSettingsVector,\n",
    "#     method = \"L-BFGS-B\",\n",
    "#     bounds = bounds,\n",
    "#     #options = {\"eps\": [0.001*x for x in defaultSettingsVector]}\n",
    "#     #options = {\"eps\": [0.1 for x in defaultSettingsVector]}\n",
    "# )\n",
    "\n",
    "# result = minimize(\n",
    "#     wrapperDE, \n",
    "#     defaultSettingsVector,\n",
    "#     method = \"COBYQA\",\n",
    "#     bounds = bounds,\n",
    "#     options = {\n",
    "#         \"scale\": True, \n",
    "#         #\"initial_tr_radius\": 1e-1, #1e-1\n",
    "#         #\"final_tr_radius\": 1e-8, \n",
    "#         \"disp\": True \n",
    "#     } #\"*_tr_radius\" appears to constrain the scaled, not raw, variables (if scaling is enabled)\n",
    "# )\n",
    "\n",
    "print(problem.history) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7609ee6-86ee-4ea2-8754-c212e14d63fd",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92f80610-6064-4201-ae41-9a08d793be87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution for MFFF\n",
    "# Q18801 : 9.1118762703\n",
    "# Q18901 : -26.8202925872\n",
    "# Q19851 : -66.7286045338\n",
    "# Q19871 : 38.2151615835\n",
    "# betaX : 70.711628558\n",
    "# alphaX : -0.0007707405\n",
    "# betaY : 70.7116282515\n",
    "# alphaY : 0.0092241649\n",
    "# alphaPenalty : 0.\n",
    "# magnetError : 1.8817831143\n",
    "# betaError : 2.1677e-6\n",
    "# maximizeMe : 998.1182147181\n",
    "\n",
    "# defaultSettingsVector = [\n",
    "#     9.1118762703,\n",
    "#     -26.8202925872,\n",
    "#     -66.7286045338,\n",
    "#     38.2151615835,\n",
    "# ]\n",
    "\n",
    "\n",
    "#Solution for BEGBC20\n",
    "# Q18601 : 11.0828855439\n",
    "# Q18701 : -14.6111079829\n",
    "# Q18801 : 29.1074584516\n",
    "# Q18901 : -28.2598209214\n",
    "# betaX : 29.9999745112\n",
    "# alphaX : 0.006501117\n",
    "# betaY : 30.000134506\n",
    "# alphaY : -0.0010495807\n",
    "# alphaPenalty : 0.\n",
    "# magnetError : 0.\n",
    "# betaError : 1.87e-8\n",
    "# maximizeMe : 999.9999999813\n",
    "\n",
    "# defaultSettingsVector = [\n",
    "#     11.0828855439,\n",
    "#     -14.6111079829,\n",
    "#     29.1074584516,\n",
    "#     -28.2598209214,\n",
    "# ]\n",
    "\n",
    "\n",
    "#MFFF solution with BC20 quads\n",
    "# Q4ER_1 : 140.8988557651\n",
    "# Q3ER_1 : 109.0853610373\n",
    "# Q2ER : -153.2846988707\n",
    "# Q1ER : 133.0038356884\n",
    "# betaX : 16.6482155283\n",
    "# alphaX : -0.0079475915\n",
    "# betaY : 16.6482174646\n",
    "# alphaY : -0.0009889492\n",
    "# alphaPenalty : 0.\n",
    "# betaError : 5.8152e-6\n",
    "# maximizeMe : 16.6482097131\n",
    "\n",
    "# bestSettings = {\n",
    "# \"Q4ER_1\" : 140.8988557651,\n",
    "# \"Q3ER_1\" : 109.0853610373,\n",
    "# \"Q2ER\" : -153.2846988707,\n",
    "# \"Q1ER\" : 133.0038356884,\n",
    "# }\n",
    "\n",
    "#PENT solution with BC20 quads (full BOOST range)\n",
    "# Q4ER_1 : 149.698406205\n",
    "# Q3ER_1 : 108.7598054393\n",
    "# Q2ER : -150.3526427039\n",
    "# Q1ER : 105.0000034129\n",
    "# betaX : 34.6054312443\n",
    "# alphaX : -0.0348912443\n",
    "# betaY : 34.6054312505\n",
    "# alphaY : 0.0169526787\n",
    "# alphaPenalty : 0.6679137845\n",
    "# betaError : 9.1e-9\n",
    "# maximizeMe : 33.9375174507\n",
    "\n",
    "bestSettings = {\n",
    "    \"Q4ER_1\" : 149.698406205,\n",
    "    \"Q3ER_1\" : 108.7598054393,\n",
    "    \"Q2ER\" : -150.3526427039,\n",
    "    \"Q1ER\" : 105.0000034129,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f48f54ce-61c4-4ae6-91e4-382bf47bb686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Base Tao lattice\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    }
   ],
   "source": [
    "#Start from golden lattice\n",
    "tao = initializeTao(\n",
    "    loadDefaultLatticeTF = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b97e6459-ae8e-416c-b332-ec121ae6e150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 0.500113514905197,\n",
       " 'alpha_a': -8.61413026751329e-05,\n",
       " 'gamma_a': 1.99954605827816,\n",
       " 'phi_a': 73.1003856252223,\n",
       " 'eta_a': -4.29100544671707e-07,\n",
       " 'etap_a': -7.60864677687215e-06,\n",
       " 'beta_b': 0.499955681369933,\n",
       " 'alpha_b': 0.000116201966385052,\n",
       " 'gamma_b': 2.00017731724298,\n",
       " 'phi_b': 60.4082301501082,\n",
       " 'eta_b': -6.77061438266207e-18,\n",
       " 'etap_b': -1.31862568529426e-17,\n",
       " 'eta_x': -4.29100544671707e-07,\n",
       " 'etap_x': -7.60864677687215e-06,\n",
       " 'eta_y': -6.77059836943936e-18,\n",
       " 'etap_y': -1.31862402801866e-17}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tao.ele_twiss(\"PENT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "940e6206-f418-40b7-9fcb-ab83b06bd614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(len(quadNames)):\n",
    "#     setQuadkG(tao, list(quadNames)[i], defaultSettingsVector[i])\n",
    "\n",
    "for name in quadNames:\n",
    "    setQuadkG(tao, name, bestSettings[name])\n",
    "\n",
    "if \"Q3ER_1\" in quadNames:\n",
    "    setQuadkG(tao, \"Q3ER_2\", bestSettings[\"Q3ER_1\"])\n",
    "\n",
    "if \"Q4ER_1\" in quadNames:\n",
    "    setQuadkG(tao, \"Q4ER_2\", bestSettings[\"Q4ER_1\"])\n",
    "    setQuadkG(tao, \"Q4ER_3\", bestSettings[\"Q4ER_1\"])\n",
    "\n",
    "\n",
    "# #BC20 quads\n",
    "# setQuadkG(tao, \"Q1EL\", 0)\n",
    "# setQuadkG(tao, \"Q2EL\", 0)\n",
    "# setQuadkG(tao, \"Q3EL_1\", 0)\n",
    "# setQuadkG(tao, \"Q3EL_2\", 0)\n",
    "# setQuadkG(tao, \"Q4EL_1\", 0)\n",
    "# setQuadkG(tao, \"Q4EL_2\", 0)\n",
    "# setQuadkG(tao, \"Q4EL_3\", 0)\n",
    "# setQuadkG(tao, \"Q5EL\", 0)\n",
    "# setQuadkG(tao, \"Q6E\", 0)\n",
    "# setQuadkG(tao, \"Q5ER\", 0)\n",
    "# setQuadkG(tao, \"Q4ER_1\", 0)\n",
    "# setQuadkG(tao, \"Q4ER_2\", 0)\n",
    "# setQuadkG(tao, \"Q4ER_3\", 0)\n",
    "# setQuadkG(tao, \"Q3ER_1\", 0)\n",
    "# setQuadkG(tao, \"Q3ER_2\", 0)\n",
    "# setQuadkG(tao, \"Q2ER\", 0)\n",
    "# setQuadkG(tao, \"Q1ER\", 0)\n",
    "\n",
    "# #BC20 sextupoles\n",
    "# setSextkG(tao, \"S1EL\",   0)\n",
    "# setSextkG(tao, \"S2EL\",   0)\n",
    "# setSextkG(tao, \"S3EL_1\", 0)\n",
    "# setSextkG(tao, \"S3EL_2\", 0)\n",
    "# setSextkG(tao, \"S3ER_1\", 0)\n",
    "# setSextkG(tao, \"S3ER_2\", 0)\n",
    "# setSextkG(tao, \"S2ER\",   0)\n",
    "# setSextkG(tao, \"S1ER\",   0)\n",
    "\n",
    "#IP quads\n",
    "setQuadkG(tao, \"Q5FF\", 0)\n",
    "setQuadkG(tao, \"Q4FF\", 0)\n",
    "setQuadkG(tao, \"Q3FF\", 0)\n",
    "setQuadkG(tao, \"Q2FF\", 0)\n",
    "setQuadkG(tao, \"Q1FF\", 0)\n",
    "setQuadkG(tao, \"Q0FF\", 0)\n",
    "setQuadkG(tao, \"Q0D\", 0)\n",
    "setQuadkG(tao, \"Q1D\", 0)\n",
    "setQuadkG(tao, \"Q2D\", 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4dc3c3e-97e5-4fb1-a7a2-b311a3eb4468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 34.6054312434826,\n",
       " 'alpha_a': -0.0348912442786957,\n",
       " 'gamma_a': 0.0289323774607166,\n",
       " 'phi_a': 71.3929134224581,\n",
       " 'eta_a': 0.25722980889581,\n",
       " 'etap_a': 0.00703810570674548,\n",
       " 'beta_b': 34.6054312497198,\n",
       " 'alpha_b': 0.0169526787588886,\n",
       " 'gamma_b': 0.0289055028992075,\n",
       " 'phi_b': 55.7841566930006,\n",
       " 'eta_b': -5.23981051661064e-17,\n",
       " 'etap_b': 3.09315269520948e-19,\n",
       " 'eta_x': 0.25722980889581,\n",
       " 'etap_x': 0.00703810570674548,\n",
       " 'eta_y': -4.99881538335302e-17,\n",
       " 'etap_y': 2.51440024990579e-19}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tao.ele_twiss(\"PENT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58f13df2-68c2-4335-b965-77cac1516e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mode_flip': False,\n",
       " 'beta_a': 40.6986521573231,\n",
       " 'alpha_a': 0.421317892283347,\n",
       " 'gamma_a': 0.0289323774607166,\n",
       " 'phi_a': 70.9592885868486,\n",
       " 'eta_a': 0.146252128894701,\n",
       " 'etap_a': 0.00703810570674548,\n",
       " 'beta_b': 42.3269324194407,\n",
       " 'alpha_b': 0.472738054068742,\n",
       " 'gamma_b': 0.0289055028992075,\n",
       " 'phi_b': 55.3595065736466,\n",
       " 'eta_b': -5.12876508087603e-17,\n",
       " 'etap_b': 3.09315269520948e-19,\n",
       " 'eta_x': 0.146252128894701,\n",
       " 'etap_x': 0.00703810570674548,\n",
       " 'eta_y': -4.79651158016705e-17,\n",
       " 'etap_y': 2.51440024990579e-19}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tao.ele_twiss(\"MFFF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f2358e1f-8c6c-4d6f-a40a-15929e39aea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pandas.io.formats.style.Styler at 0x31f9dca90>"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "getMatrix(tao,\"MFFF\",\"PENT\",print = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d872551e-56e7-42bb-91dd-fefa0a277f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1687.5x675 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "floorplanPlot(tao,\n",
    "              zmin = 800,\n",
    "              zmax = 1020,\n",
    "              ymin = 0.1,\n",
    "              ymax = 2e3\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60c767fa-f851-401a-969f-5aefda6c2a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Element # 1468',\n",
       " 'Element Name: MFFF',\n",
       " 'Key: Marker',\n",
       " 'S_start, S:    979.004697,    979.004697',\n",
       " 'Ref_time_start, Ref_time:  3.265625E-06,  3.265625E-06',\n",
       " '',\n",
       " 'Attribute values [Only non-zero values shown]:',\n",
       " '    1  L                           =  0.0000000E+00 m',\n",
       " '    7  OSC_AMPLITUDE               =  0.0000000E+00 m',\n",
       " '   24  TILT_CALIB                  =  0.0000000E+00 rad',\n",
       " '   25  DE_ETA_MEAS                 =  0.0000000E+00',\n",
       " '   53  P0C                         =  1.0000000E+10 eV           BETA                        =  1.0000000E+00',\n",
       " '   54  E_TOT                       =  1.0000000E+10 eV           GAMMA                       =  1.9569512E+04',\n",
       " '   64  REF_TIME_START              =  3.2656252E-06 sec      50  DELTA_REF_TIME              =  0.0000000E+00 sec',\n",
       " '',\n",
       " '       TRACKING_METHOD              =  Bmad_Standard             APERTURE_AT                =  Exit_End',\n",
       " '       MAT6_CALC_METHOD             =  Auto                      APERTURE_TYPE              =  Rectangular',\n",
       " '       SPIN_TRACKING_METHOD         =  Tracking                  OFFSET_MOVES_APERTURE      =  F',\n",
       " '       PTC_INTEGRATION_TYPE         =  Matrix_Kick               LONGITUDINAL ORIENTATION   =       1',\n",
       " '                                                                 REF_SPECIES                =  Electron',\n",
       " '',\n",
       " 'Slave_status: Free',\n",
       " '',\n",
       " 'Lord_status:  Not_a_Lord',\n",
       " '',\n",
       " 'Twiss at end of element:',\n",
       " '                          A              B            Cbar                        C_mat',\n",
       " '  Beta (m)        16.64821553    16.64821747  |   0.00000000   0.00000000      0.00000000   0.00000000',\n",
       " '  Alpha           -0.00794759    -0.00098895  |  -0.00000000   0.00000000     -0.00000000   0.00000000',\n",
       " '  Gamma (1/m)      0.06007029     0.06006655  |   Gamma_c =   1.00000000       Mode_Flip = F',\n",
       " '  Phi (rad)       71.09644721    55.82776897            X              Y              Z',\n",
       " '  Eta (m)          0.05829723    -0.00000000     0.05829723    -0.00000000     0.02154990',\n",
       " '  Etap             0.00372242     0.00000000     0.00372242     0.00000000    -0.00000000',\n",
       " '  dEta/ds          0.00372242     0.00000000     0.00372242     0.00000000    -0.00000000',\n",
       " '',\n",
       " 'Tracking: Electron,   State: Alive',\n",
       " '         Position[mm] Momentum[mrad]        Spin   |',\n",
       " '  X:       0.00000000     0.00000000               | t_particle [sec]:        3.26562516E-06  E_tot: 1.00000E+10',\n",
       " '  Y:       0.00000000     0.00000000               | t_part-t_ref [sec]:      0.00000000E+00  PC:    1.00000E+10',\n",
       " '  Z:      -0.00000000     0.00000000               | (t_ref-t_part)*Vel [m]:  0.00000000E+00  Beta:  0.999999999']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tao.cmd('show ele MFFF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "783b6ff3-7c1a-4858-9a4d-dd146e08cf53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "958d873c-24b1-4002-a337-1e31b82c9a1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}