{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Data and Making FOMs\n",
    "\n",
    "Here I will go through an example of how to fit data to a transfer function. Later this TF will be used to make a full system model.\n",
    "\n",
    "First imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-iirrational/src/wield/iirrational/_version.py:35: UserWarning: git base version 0.1 is different than the stored version 2.9.5\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import control\n",
    "import scipy.signal as signal\n",
    "from scipy import interpolate\n",
    "import scipy.linalg\n",
    "from scipy.stats import gmean\n",
    "import gwinc\n",
    "import yaml\n",
    "from wield.iirrational.v2 import data2filter\n",
    "from wield.control.AAA import tfAAA\n",
    "from wield.utilities.file_io import load, save\n",
    "from wield.control.ss_bare.ss import BareStateSpace\n",
    "from scipy.signal import cheby2\n",
    "from wield.control import SISO\n",
    "from buzz import ssutil\n",
    "from physunits import m, s, kg, Hz\n",
    "from scipy.constants import c, G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define a function to grab the BNS waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://github.com/wield/wield-control/blob/main/src/wield/control/AAA/test/test_AAA_present.py\n",
    "def gen_waveform(**params):\n",
    "    \"\"\"Generate frequency-domain inspiral waveform\n",
    "\n",
    "    Returns a tuple of (freq, h_plus^tilde, h_cross^tilde).\n",
    "\n",
    "    The waveform is generated with the lalsimulation\n",
    "    SimInspiralChooseFDWaveform() function.  Keyword arguments are\n",
    "    used to update the default waveform parameters (see DEFAULT_PARAMS\n",
    "    macro).  The mass parameters ('m1' and 'm2') should be specified\n",
    "    in solar masses and the 'distance' parameter should be specified\n",
    "    in parsecs**.  Waveform approximants may be given as string names\n",
    "    (see `lalsimulation` documentation for more info).\n",
    "\n",
    "    For example, to generate a 20/20 Msolar BBH waveform:\n",
    "\n",
    "    >>> hp,hc = waveform.gen_waveform('m1'=20, 'm2'=20)\n",
    "\n",
    "    **NOTE: The requirement that masses be specified in solar masses\n",
    "    and distances in parsecs is different than that of the underlying\n",
    "    lalsimulation method which expects mass and distance parameters to\n",
    "    be in SI units.\n",
    "\n",
    "    \"\"\"\n",
    "    import lalsimulation\n",
    "    from inspiral_range import waveform\n",
    "    from inspiral_range import const\n",
    "\n",
    "    iparams = dict(waveform.DEFAULT_PARAMS)\n",
    "    iparams.update(**params)\n",
    "\n",
    "    # convert to SI units\n",
    "    iparams[\"distance\"] *= const.PC_SI\n",
    "    iparams[\"m1\"] *= const.MSUN_SI\n",
    "    iparams[\"m2\"] *= const.MSUN_SI\n",
    "    iparams[\"approximant\"] = lalsimulation.SimInspiralGetApproximantFromString(\n",
    "        iparams[\"approximant\"]\n",
    "    )\n",
    "\n",
    "    m = iparams[\"m1\"] + iparams[\"m2\"]\n",
    "\n",
    "    # calculate delta F based on frequency of inner-most stable\n",
    "    # circular orbit (\"fisco\")\n",
    "    fisco = (const.c ** 3) / (const.G * (6 ** 1.5) * 2 * np.pi * m)\n",
    "    df = 2 ** (np.max([np.floor(np.log(fisco / 4096) / np.log(2)), -6]))\n",
    "\n",
    "    # FIXME: are these limits reasonable?\n",
    "    if iparams[\"deltaF\"] is None:\n",
    "        iparams[\"deltaF\"] = df\n",
    "    # iparams['f_min'] = 0.1\n",
    "    # iparams['f_max'] = 10000\n",
    "\n",
    "    hp, hc = lalsimulation.SimInspiralChooseFDWaveform(**iparams)\n",
    "    \n",
    "    freq = hp.f0 + np.arange(len(hp.data.data)) * hp.deltaF\n",
    "\n",
    "    print(\"f0\", hp.f0)\n",
    "    select = abs(hp.data.data) > 0\n",
    "\n",
    "    return freq[select], hp.data.data[select], hc.data.data[select]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define the parameters of our BNS model and plot its PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/user_conda/mcculler/py312/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n",
      "\n",
      "SWIGLAL standard output/error redirection is enabled in IPython.\n",
      "This may lead to performance penalties. To disable locally, use:\n",
      "\n",
      "with lal.no_swig_redirect_standard_output_error():\n",
      "    ...\n",
      "\n",
      "To disable globally, use:\n",
      "\n",
      "lal.swig_redirect_standard_output_error(False)\n",
      "\n",
      "Note however that this will likely lead to error messages from\n",
      "LAL functions being either misdirected or lost when called from\n",
      "Jupyter notebooks.\n",
      "\n",
      "To suppress this warning, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n",
      "import lal\n",
      "\n",
      "  import lal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f0 0.0\n",
      "F_Hz max 14501.800000000001\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = dict()\n",
    "params[\"m1\"] = 1.4\n",
    "params[\"m2\"] = 1.4\n",
    "# rest of params from first BNS detection see https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.119.161101\n",
    "params[\"f_min\"] = 0.01\n",
    "params[\"f_max\"] = 1000000\n",
    "params[\"deltaF\"] = 0.01\n",
    "params[\"distance\"] = 1e6\n",
    "F_Hz, hp, hc = gen_waveform(**params) # assuming that this is giving an ASD I.M. Jan 2024\n",
    "\n",
    "BNS_intrp = interpolate.interp1d(F_Hz, abs(hp)**2) ## we add interpolation to put all of our data with the same FQ spacing\n",
    "\n",
    "print(\"F_Hz max\", max(F_Hz))\n",
    "\n",
    "plt.loglog(F_Hz, abs(hp)**2, label = \"hp\")\n",
    "plt.loglog(F_Hz, abs(hc)**2, linestyle = \":\", label = \"hc\")\n",
    "plt.title(str(params[\"m1\"]) +\"/\"+str(params[\"m1\"])+ \"Solarmass BNS Spec\")\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"Power spectral density [strain^2/Hz^2]\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirpmass: \t 2.423423336413827e+30  kg\n",
      "\t\t 1.2187707886145691  solar masses\n"
     ]
    }
   ],
   "source": [
    "# # compare the waveform with part of equation 3 from https://arxiv.org/abs/1709.08079\n",
    "m1 = (params[\"m1\"] * 1.988416e30) * kg\n",
    "m2 = (params[\"m2\"] * 1.988416e30) * kg\n",
    "chirp_mass = (m1*m2)**(3/5)/(m1+m2)**(1/5)\n",
    "print('chirpmass: \\t', chirp_mass, ' kg\\n\\t\\t', chirp_mass/1.988416e30, ' solar masses')\n",
    "\n",
    "\n",
    "# N = kg* m/(s**2)\n",
    "#c = c #* m/s\n",
    "# G = G * N * m**2 / (kg**2)\n",
    "# F_Hz = F_Hz * Hz\n",
    "# eq3 = ((5/24)**0.5 * c * ( G * chirp_mass * (c**3))**(5/6)* 1/(np.pi**(-2/3)))**2 * F_Hz**(-7/3)\n",
    "# m2Mpc = 1/3.086e22\n",
    "# eq3_Mpc = eq3\n",
    "\n",
    "# print(\"eq3[0]\", eq3[0])\n",
    "# print(\"eq3_Mpc[0]\", eq3_Mpc[0])\n",
    "\n",
    "# print(\"BNS hp[0]\", abs(hp[0])**2)\n",
    "\n",
    "# plt.loglog(F_Hz, eq3_Mpc, label=\"eq3\")\n",
    "# plt.loglog(F_Hz, abs(hp)**2, label=\"BNS Strain from LAL Simulation\")\n",
    "# plt.title(str(params[\"m1\"]) +\"/\"+str(params[\"m1\"])+ \"Solarmass BNS Spec\")\n",
    "# plt.xlabel(\"Frequency [Hz]\")\n",
    "# plt.ylabel(\"Power spectral density [strain^2/Hz^2]\")\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the LIGO Sensitivity Curve\n",
    "\n",
    "Now I must multiply this by the GWINC curve to get our final FOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power Spectral Density [strain/hz]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F_bug = np.logspace(np.log10(min(F_Hz)), np.log10(max(F_Hz)-1), 10000)\n",
    "budget = gwinc.load_budget('aLIGO', freq=F_bug)\n",
    "trace = budget.run(freq=F_bug)\n",
    "\n",
    "plt.title(\"ALIGO BUDGET\")\n",
    "plt.loglog(F_bug, trace.psd)\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"Power Spectral Density [strain/hz]\")\n",
    "# plt.xlim([6, max(F_Hz)])\n",
    "# plt.ylim([1e-20, 1e-17])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eq3:  4.867082672729032e+92  Mpc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_969275/2399436929.py:2: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
      "  I7 = np.trapz(F_bug**(-7/3)/trace.psd, F_bug)\n"
     ]
    }
   ],
   "source": [
    "prefactor = (2*(5/96)**0.5 * c * ( G * chirp_mass * (c**3))**(5/6) * 1/(np.pi**(-2/3))* 2 / 2.26)**2 \n",
    "I7 = np.trapz(F_bug**(-7/3)/trace.psd, F_bug)\n",
    "eq3 = prefactor * I7**0.5\n",
    "print(\"eq3: \", eq3/3.086e22, ' Mpc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from inspiral_range import inspiral_range as ir\n",
    "from inspiral_range import waveform\n",
    "from inspiral_range import const\n",
    "\n",
    "DETECTION_SNR = 8.0\n",
    "def ian_sensemon_range(freq, m1=1.4, m2=1.4, horizon=False, integrate=False, detection_snr=DETECTION_SNR):\n",
    "    \"\"\"Detector inspiral range from closed form expression\n",
    "\n",
    "    Masses `m1` and `m2` should be specified in solar masses (default:\n",
    "    m1=m2=1.4).  If the `horizon` keyword is specified the \"horizon\"\n",
    "    range will be returned, which differs from the angle-averaged\n",
    "    range by ~2.26.\n",
    "\n",
    "    @returns distance in Mpc as a float\n",
    "\n",
    "    \"\"\"\n",
    "    if horizon:\n",
    "        theta = 4\n",
    "    else:\n",
    "        theta = 1.77\n",
    "    theta /= 1e6 * const.PC_SI\n",
    "    M_chirp = waveform.M_chirp(m1, m2) * const.MSUN_SI\n",
    "\n",
    "    i73 = freq ** (-7/3)\n",
    "    val = theta / detection_snr \\\n",
    "        * waveform.habs_nsp_prefactor(M_chirp) \\\n",
    "        * np.sqrt(i73) / 2\n",
    "\n",
    "    return val\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range:  194.97198453267936\n",
      "relative difference:  15.834426264720078\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irdata = ian_sensemon_range(F_bug, integrate=False, horizon=True)**2\n",
    "\n",
    "range_ir = ir.sensemon_range(F_bug, psd=trace.psd, m1=1.4, m2=1.4, horizon=False, integrate=True, detection_snr=DETECTION_SNR)\n",
    "print(\"range: \", range_ir)\n",
    "\n",
    "#np.savetxt('FBNS_Current_range.txt', [range])\n",
    "\n",
    "BNS_PSD = BNS_intrp(F_bug)\n",
    "IR_interp = interpolate.interp1d(F_bug, irdata)\n",
    "\n",
    "midpoint = len(F_bug)//2\n",
    "print('relative difference: ', BNS_PSD[midpoint]/irdata[midpoint])\n",
    "\n",
    "\n",
    "plt.loglog(F_bug, BNS_PSD)\n",
    "plt.loglog(F_bug, irdata)\n",
    "plt.scatter(F_bug[midpoint], BNS_PSD[midpoint])\n",
    "plt.scatter(F_bug[midpoint], irdata[midpoint])\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"Power spectral density\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose which inspiral waveform PSD to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BNS_PSD = BNS_intrp(F_bug) # us the custom inspiral equatin from LAL\n",
    "BNS_PSD = IR_interp(F_bug) # use the inspiral range aproximation and calibration "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will put the BNS waveform in the Fq space of the GWINC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BNS_PSD [2.45847163e-35 2.45034592e-35 2.44224707e-35 ... 1.03982333e-49\n",
      " 1.03638652e-49 1.03296107e-49]\n",
      "irdata [2.45847163e-35 2.45034592e-35 2.44224707e-35 ... 1.03982333e-49\n",
      " 1.03638652e-49 1.03296107e-49]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power spectral density')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"BNS_PSD\", BNS_PSD)\n",
    "print(\"irdata\", irdata)\n",
    "plt.loglog(F_bug, BNS_PSD)\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"Power spectral density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly for this step we will combine the twu curves to make a FOM. we divide the BNS PSD by the LIGO Noise spectrum without the controls noise. From the paper:\n",
    "$$\\mathrm{SNR}^2 = 4\\int_{0}^{\\infty} \\frac{\\left |h_\\mathrm{gw}(f)\\right |^2}{S_h(f)} df$$\n",
    "\n",
    "where $S_h(f)$ is the noise spectrum of the detector. This can be written as:\n",
    "\n",
    "$$ S_h(f) = S_{\\mathrm{det}}(f) + |C(f)|^2 S_{\\mathrm{p}}(f)$$\n",
    "\n",
    "where $S_{\\mathrm{det}}(f)$ is the detector noise spectrum without controls noise, $S_{\\mathrm{p}}(f)$ is the control noise spectrum, and $C(f)$ is the coupling function from the controls noise in the control loop to DARM. By using the taylor series of A/(B+x) to linearize the integral. i.e.:\n",
    "\n",
    "$$\\frac{A}{B+x}=\\frac{A}{B} - \\frac{Ax}{B^2} + \\frac{Ax^2}{B^3} - \\frac{Ax^3}{B^4} + \\frac{Ax^4}{B^5} - \\frac{Ax^5}{B^6} + O(x^6)$$\n",
    "\n",
    "taking the first two terms of the taylor series and applying it to the SNR equation we get:\n",
    "\n",
    "$$\\mathrm{SNR}^2 = 4\\int_{0}^{\\infty} \\frac{\\left | h_{\\mathrm{signal}}(f)\\right |^2}{S_{\\mathrm{det}}(f)} \\ df - 4\\int_{0}^{\\infty} \\frac{\\left |C\\right |^2\\left | h_{\\mathrm{signal}}(f)\\right |^2 }{S_{\\mathrm{det}}(f)^2}  S_{\\mathrm{p}}(f) \\ df$$\n",
    "\n",
    "To get range we need to use the $\\mathrm{SNR}$ not the $\\mathrm{SNR}^2$ so we take the square root of the above equation which has a different linearization. as $A \\rightarrow \\infty$ \n",
    "\n",
    "$$\\sqrt{A+B} = \\sqrt{A} \\sqrt{1+\\frac{B}{A}} = \\sqrt{A} (1+\\frac{B}{2 \\sqrt{A}})$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PSD of FOM for BNS Signal')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Div_PSD = BNS_PSD / (trace.psd)**2 # divide the BNS PSD by the aLIGO noise psd squared. this will give a PSD of LIGO's sensitivity to a BNS signal\n",
    "#Div_PSD = Div_PSD * 4000**2 # to put it in units of displacement\n",
    "F_Div = F_bug\n",
    "plt.loglog(F_Div, Div_PSD)\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"Power spectral density\")\n",
    "plt.title(\"PSD of FOM for BNS Signal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the Data to a Transfer Function\n",
    "\n",
    "First we will Define a function to reduce the number of points to fit to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edited from https://git.ligo.org/wield/wield-ligo-mcculler/-/blob/main/src/wield/LIGO/mcculler/filter_cavity/test_FC1_SUSPOINT_fits.py\n",
    "def reduce(F_Hz, PSD):\n",
    "    F_groups = np.logspace(-2, 4, 50) # originally 200 length\n",
    "    F_groups = np.geomspace(min(F_Hz), max(F_Hz), 50) # originally 200 length\n",
    "    \n",
    "    idx_groups = np.searchsorted(F_Hz, F_groups)\n",
    "    idx_pairs = list(zip(idx_groups[:-1], idx_groups[1:]))\n",
    "\n",
    "    lPSDs_min = []\n",
    "    lPSDs_max = []\n",
    "    lPSDs_med = []\n",
    "    lPSDs_mean = []\n",
    "    lF_Hz = []\n",
    "    for idx1, idx2 in idx_pairs:\n",
    "        if idx1 == idx2:\n",
    "            continue\n",
    "        lPSDs_max.append(\n",
    "            np.nanmax(PSD[idx1:idx2])\n",
    "        )\n",
    "        lPSDs_med.append(\n",
    "            np.nanmedian(PSD[idx1:idx2])\n",
    "        )\n",
    "        lPSDs_min.append(\n",
    "            np.nanmin(PSD[idx1:idx2])\n",
    "        )\n",
    "        lPSDs_mean.append(\n",
    "            np.nanmean(PSD[idx1:idx2])\n",
    "        )\n",
    "        lF_Hz.append(np.mean(F_Hz[idx1:idx2]))\n",
    "\n",
    "    return np.asarray(lF_Hz), np.asarray(lPSDs_min), np.asarray(lPSDs_med), np.asarray(lPSDs_max), np.asarray(lPSDs_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will reduce the LIGO sensitivity curve and its reduced form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0410dec380>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F_FOM_dwn, PSD_min, PSD_med, PSD_max, PSD_mean = reduce(F_bug, Div_PSD)\n",
    "FOM_PSD = PSD_mean\n",
    "# The current form of FOM_PSD is the ASD\n",
    "FOM_ASD = FOM_PSD**0.5\n",
    "\n",
    "plt.loglog(F_bug, Div_PSD**0.5, label = 'FOM ASD')\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = 'Downsampled FOM ASD')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will define some functions to work with these transfer functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-control/src/wield/control/SISO/zpk.py:164: NumericalWarning: StateSpace is large (>50 states), using reduced response fiducial auditing heuristics. TODO to make this smarter\n",
      "  warnings.warn(f\"StateSpace is large (>{self.N_MAX_FID} states), using reduced response fiducial auditing heuristics. TODO to make this smarter\", util.NumericalWarning)\n",
      "/opt/conda/user_conda/mcculler/py312/lib/python3.12/site-packages/control/freqplot.py:435: FutureWarning: bode_plot() return value of mag, phase, omega is deprecated; use frequency_response()\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f04112dbfe0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def FBNSsimpSS(gain=1, return_name=False, lo_ord=4):\n",
    "    if return_name:\n",
    "        name = \"FOM BNS Simple\"\n",
    "        return name\n",
    "    F_Fq_lo = 10 * 2 * np.pi\n",
    "    F_Fq_hi = 120 * 2 * np.pi\n",
    "    hp_order = lo_ord\n",
    "    lp_order = 2\n",
    "    F_p = []\n",
    "    F_z = []\n",
    "    for ord in range(hp_order):\n",
    "        F_p.append(-F_Fq_lo + 0*1j*F_Fq_lo)\n",
    "        F_p.append(-F_Fq_lo - 0*1j*F_Fq_lo)\n",
    "        F_z.append(0)\n",
    "        F_z.append(0)\n",
    "\n",
    "    for ord in range(lp_order):\n",
    "        F_p.append(-F_Fq_hi)\n",
    "    F_k = 1\n",
    "    c_zpk = cheby2(8, 170, 1.25*2*np.pi, btype='high', analog=True, output='zpk')\n",
    "    F_z.extend(c_zpk[0])\n",
    "    F_p.extend(c_zpk[1])\n",
    "    F_k *= c_zpk[2]\n",
    "\n",
    "    ian_lfq = -0.7\n",
    "    ian_lfq2 = 2\n",
    "    ian_lfq3 = 0.5\n",
    "    F_z.append(0)\n",
    "    F_p.append(ian_lfq)\n",
    "    F_z.append(0)\n",
    "    F_p.append(ian_lfq)\n",
    "\n",
    "    F_z.append(ian_lfq3)\n",
    "    F_z.append(ian_lfq3)\n",
    "    F_p.append(ian_lfq2)\n",
    "    F_p.append(ian_lfq2)\n",
    "    \n",
    "    F_p = F_p\n",
    "    F_z = F_z\n",
    "\n",
    "    #F_mod = ws_tf2ss(F_z, F_p, F_k)\n",
    "    #F_mod = ssutil.normalize_gain(F_mod) * gain \n",
    "    #F_iod = {\"FBNS.in\": 0, \"FBNS.out\": 0}\n",
    "    F_mod = SISO.zpk(F_z, F_p, F_k, angular=True, fiducial_rtol=1e-5, fiducial_atol=1e-10)\n",
    "    F_mod = F_mod * F_mod\n",
    "    F_mod = F_mod.asSS\n",
    "    F_mod = F_mod / abs(F_mod.asSS.Linf_norm()[0]) \n",
    "\n",
    "\n",
    "    F_mod = F_mod * gain\n",
    "    F_mod.balance_and_truncate()\n",
    "\n",
    "    #F = ssutil.wieldSS(F_mod, F_iod)\n",
    "    \n",
    "    return F_mod.mimo(\"FBNS.in\", \"FBNS.out\")\n",
    "\n",
    "plt.loglog(F_bug, Div_PSD**0.5, label = 'FOM ASD')\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = 'Downsampled FOM ASD')\n",
    "mag, phase, omega = control.bode(FBNSsimpSS(gain=4 * 100**2).mod, Hz=False, plot=False, omega=F_FOM_dwn*2*np.pi, label = \"BNS Simple\")\n",
    "#plt.loglog(omega/(2*np.pi), mag, label=\"BNS Simple\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0410abcd70>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('BNS_FOM_handfit.yml', 'r') as file:\n",
    "    filt_dict = yaml.safe_load(file)\n",
    "p_hand = np.array(filt_dict['p'], dtype=np.complex128)\n",
    "z_hand = np.array(filt_dict['z'], dtype=np.complex128)\n",
    "k_hand = np.float64(filt_dict['k'])\n",
    "\n",
    "hand_zpk = SISO.zpk(z_hand, p_hand, k_hand)\n",
    "hand_xfr = hand_zpk.fresponse(f=F_FOM_dwn)\n",
    "\n",
    "plt.loglog(F_bug, Div_PSD**0.5, label = 'FOM ASD')\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = 'Downsampled FOM ASD')\n",
    "plt.loglog(F_FOM_dwn, hand_xfr.mag, label = 'Handfit ASD')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f04107e3aa0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Divide the FOM ASD by the hand fit to get the reduced ASD\n",
    "reduced_ASD = FOM_ASD/hand_xfr.mag\n",
    "\n",
    "plt.loglog(F_FOM_dwn, reduced_ASD, label = 'reduced ASD')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we form a guess of the TF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results = tfAAA(F_Hz=F_FOM_dwn, xfer=FOM_ASD)\n",
    "results = tfAAA(F_Hz=F_FOM_dwn, xfer=reduced_ASD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we play with the poles and zeros to get a good fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2π·[-0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -0.3958406743523140, -0.3958406743523140, -0.3958406743523140,\n",
      " -5.305505757497158e-10, -0.005431630862136328, -0.005431630295139218,\n",
      " -0.04216327861663196, -0.04216327868321049, -3.049643520855844,\n",
      " -3.049643519812004, -2707.242418662691, -2707.242418662458,\n",
      " -2.957619742415325e-12 ± 92.58406532411048j,\n",
      " -14.98934980857282     ± 21.76848865621670j,\n",
      " -14.98934980875151     ± 21.76848865620463j,\n",
      " -2.069960897503006e-10 ± 10.00487816878042j,\n",
      " -1.178440698489668e-11 ± 2.829913488939144j,\n",
      " -1.029654952340069e-11 ± 1.332579977525743j,\n",
      " -0.01709138866381974   ± 0.2654485123713641j,\n",
      " -0.01709138868276358   ± 0.2654485123375496j,\n",
      " -6.152212595327936e-12 ± 0.3571372397086687j,\n",
      " -0.4720203451684382    ± 2.629597995989603j,\n",
      " -0.4720203452849284    ± 2.629597996216710j]\n"
     ]
    }
   ],
   "source": [
    "#print(\"poles\", results.poles)\n",
    "#print(\"zeros\", results.zeros)\n",
    "#print(\"gain\", results.gain)\n",
    "\n",
    "all_z = results.zeros\n",
    "all_p = results.poles\n",
    "k = results.gain\n",
    "\n",
    "for i in range(0, len(all_z)):\n",
    "    if all_z[i] >= 0:\n",
    "        all_z[i] = -all_z[i]\n",
    "        \n",
    "for i in range(0, len(all_p)):\n",
    "    if all_p[i] >= 0:\n",
    "        all_p[i] = -all_p[i]\n",
    "\n",
    "AAA_zpk = SISO.zpk(all_z, all_p, k, angular=False, fiducial_rtol=1e-5, fiducial_atol=1e-10)\n",
    "recalibrated_zpk =  hand_zpk * AAA_zpk \n",
    "# recalibrated_zpk =  AAA_zpk \n",
    "print(recalibrated_zpk.zeros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f041050c380>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(F_FOM_dwn, reduced_ASD, label = 'Reduced ASD')\n",
    "plt.loglog(F_bug, AAA_zpk.fresponse(f=F_bug).mag, label = 'AAA Fit', linestyle = '--')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 states have been removed from the model\n",
      "0 states have been removed from the model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/user_conda/mcculler/py312/lib/python3.12/site-packages/control/xferfcn.py:1087: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  den[j, :maxindex+1] = poly(poles[j])\n",
      "/opt/conda/user_conda/mcculler/py312/lib/python3.12/site-packages/control/xferfcn.py:1117: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get the ZPK and the minreal ZPK for all poles and zeros\n",
    "B_res_all = control.zpk(all_z, all_p, k)\n",
    "B_res_all_minreal = control.minreal(B_res_all)\n",
    "minreal_all_z, minreal_all_p, minreal_all_k = signal.tf2zpk(B_res_all_minreal.num[0][0], B_res_all_minreal.den[0][0])\n",
    "\n",
    "# Get only the real poles and zeros\n",
    "z = [lz for lz in all_z if lz.real < 0]\n",
    "p = [lp for lp in all_p if lp.real < 0]\n",
    "\n",
    "# Get the ZPK for the real poles and zeros and the minreal ZPK for only the real poles and zeros\n",
    "B_res = control.zpk(z, p, k)\n",
    "B_res_minreal = control.minreal(B_res)\n",
    "minreal_z, minreal_p, minreal_k = signal.tf2zpk(B_res_minreal.num[0][0], B_res_minreal.den[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will plot our guess and our othe tfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f041029c080>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s7e1NXRwhRBV1Wd6FrKIsAKzMrGieYsUri1OxKNUHltgmGhp09qftlOVgUz/6sNzJq9te5a+EvwA4MeaEiUtTOySkZrN+aQT223+hZdxVzK91AypVQnSQDqsm+XTz98Sx2WBo9hh4hEI9rZGr6yr7+W2y+J6YmEiPHj1o1qwZLVu2ZOXKlQDk5OQQHh5O69atadGiBd98842piiiEMBGdTkdOcQ4AblZuOKTmM/EHfWhJaaCBwdkMnPEP2r725wMTWoB62wR2L/zc7Zn0z1d5Zt1uspb+xsb+fTnvboWZFlqeURD0mw0HVuSw6OfFHPrmEXTzWsGf/9JP/aCtn01v9Z3JalySk5O5fPkyrVu3JjU1lbZt23LmzBksLS0pKirC2tqa/Px8QkNDOXToEC4ulfvlJDUuQtR9ucW5dPypIwDv+Ubg8tpkPHJzUTsX4zfMDvNnfwDPUBOX8v77x/Z/8OeFPwGpcbmdtNwi1v2ynrzV39D1/AUsrk15la+GqGY6nBvl0Uedg42tFzQfCqGPQ4O2UhNjYpX9/DZZHxcvLy+8vPSdp9zd3XF2dubq1av4+vpiba3voFVYWIhGo6n9o0UKIapVWW2LTakZVjPexCM3F3ObUvwGWmH+wgaw9zZxCU2jvvZxqW6uthY8N/ZxCkcOYcvBc5xZ9hWtDu/AO6uQTlEKiLLlT19bskOK6ZL/NY32fwmO/hA6TB9ipDmpVqvyT8HOnTsZNGgQ3t7eKBQK1qxZU+6Y+fPnExgYiKWlJWFhYezatavCax0+fBitVovvtRlYMzMzadWqFT4+Prz22mu4urpWtZhCiDoouzgblUbHK7+WEpiRhMpCg+8ABeYvrn1gQwtIU9HdsjRX8Wjnpkz/ci6N1u9ky/OvcyDAG40CmibCQ3+qSVrnzsKLnmzLuYJm96fwVRf48iHY/h+4ctbUb0FUoMrBJS8vj1atWvHFF19UuH/FihVMmzaNGTNmcPToUbp27Ur//v1JSEgwOi49PZ3Ro0ezcOFCwzZHR0eOHTtGXFwcy5Yt4/Lly7csR1FREdnZ2UYvIUTdllOUzfMbtbSOLUWh0uL9SAkWU1brJ/V7gEmNS9UFuNkx5R9jeXLtJv7+aCnr2z1EppUZrjnQdbcSpxWOLDrjyy9aJ/LTz8H2OfBlOCzoAnvmQXayqd+CuKZa+rgoFApWr17NkCFDDNvat29P27ZtWbBggWFbSEgIQ4YMYc6cOYA+dPTu3Zvnn3+eUaNGVXjtl156iYcffpgnn3yywv3vvfceM2fOLLdd+rgIUXcdev9VbH/ciFahw6trLs7vrAGfdqYulsm9uetN1seuB6SPy73S6XTsO53Mnm+/pfn+3whMzzXsO95QRXG4LX3Nz+NeWqzfqFBCw57Q6mlo+qgMdlcDTPpUUXFxMZGRkfTp08doe58+fdi7dy+g/08zduxYHn74YaPQcvnyZUOtSXZ2Njt37iQ4OPiW93rzzTfJysoyvBITE2vgHQkh7peMFSuw/XEjAFt7ajn5xFwJLddIjUv1USgUdArx5v8+fofQddvZOvFfHPT3Rgu0jNXQbkUWx/5wY1FhOOc824BOC+e3wKoJ8FEQrJkMcTvlySQTqJHOuWlpaWg0Gjw8PIy2e3h4kJKSAsCePXtYsWIFLVu2NPSP+f777ykuLmb8+PHodDp0Oh1TpkyhZcuWt7yXhYUFFhYWt9wvhKjdSjRacgpLcbI2J3fbdlLeew+AXzor2BLSiJGdHzNtAUW95+diw+RpI8meOJxV6/eSufwLup05iU+6Dp81l7hqBd90DqNV9yaEX9qDIjMBon7Qvxx8oeVwaD3ygW/KvF9q9KmimzuS6XQ6w7YuXbqgvUVSjYqKqsliCSFqAY1Wx1c7zvPVjvPkFJbyqPlVpqz5L+ggObiEn7taoi7ww9nm9iOqPkikxqVm2VuaM/aJ7pQM7crGA+c4vvhzukTtwj2nmC5/JVO8PZklbZxpOOJ1umqSUUavhaxE2PWx/uXfGdqOhhCZN6km1UhwcXV1RaVSGWpXyqSmpparhRFCPHi0Wh3Tf45ibVQSoKVr9q+M2XcASuB4IMwf7ASKQjxsnExd1FpFgsv9Ya5SMqhTMAM7fs7BmFRWRUTQYs9qgq7k0uHQVbSHvmdlUxucx7xKj8Y+mB//GWL+ggt79K8N/wctntSHGO/Wpn479U6N/BSo1WrCwsLYvHmz0fbNmzfTqVOnmrilEKIO+fSvs6yNSsJDd47/nnmft7YewK4AYrzgw2Eqrqr08xS52TqatqC1zDNNnwGgh28P0xbkAaFQKGgf5MGM/7xF81+38vvz/8chf1eUQMvTefi8uZA/p83m94IQiiYdgp7/0o8HU5QNhyNgYXf949UHv4GCDFO/nXqjyk8V5ebmEhMTA0CbNm345JNP6NmzJ87Ozvj5+bFixQpGjRrFV199RceOHVm4cCHffPMNJ0+exN/fv1rfxI1k5FwharfICxkM/2obA1OXMvrQGayL9fPKpLcsIq5hKN80KyJHkwTAAM/J/LfvRBOXuHbJLs7GztxOxnQxkat5xaxYtRnNqnl0PZuA+trcSKmOSjKHPcwjL76LbfrfcOR7OLUONNeeSjKzhGaDod1z4NteBrirQGU/v6scXLZv307Pnj3LbR8zZgxLliwB9APQ/e9//yM5OZnQ0FA+/fRTunXrVpXbVZoEFyFqL61Wx7Oz/8PwbT8RnFwCwOUGZjRpnYKdVwPC097BtfF2ssy3ATC+yQymdXzKlEUWokKFJRpW/nWYi8v+xyMnTmFXqP8ozbJWkNy3HT2mzcbJzhZOrITI7yD15PWT3ZtD+HPQYjhYyudUmRoPLrWVBBchaqfM7FRWvjGODttjMdNCgYWCksfb8VDpWhQqFZee+I3O32di6bIPc/e1AMwI+y9PhQ4wccmFuDWNVsf6g2c4smQOPY8cxiNb/9BJoTnEdQ2h8z9m4dGwOVw6ApGL4MSvUFqgP1ltq+8L0+458Lr107MPilo/O7QQ4sGxf0MEUf0fpstWfWg5E+JJw+URtFdvR6EEur+GR9OOmKsUlBbbGs5zsbEyXaGFqASVUsHgDk15b8ESVEv/5MeBvYl1M8eyBEK2nuLKwCdZO6ofian5MPhL+Mcp6PdfcG0CxbkQuRi+7grf9oKoZVBSYOq3VOtJcBFC1BidTsfKpTNwmP4RHukartoo+fThx+i7/E8cD/8PCrPAuy10/QdmKiW+ztbotJaG823Vlre5uhC1h0KhoGvTBsz+aB5+y3bww8gnOeZniUoHTQ4nkPvMC/w+uBsx+3aha/8iTD4IY3+H5sNAaQ4XD8Gal+CTEPjzbci4YOq3VGtJcBFC1JiIvyMo/Wk1AJFBbrzQ41+0eHwCVseWwPmt+g6LQ78GlTkADRyt0GmuhxXza9uFqEta+Drx77dn0emn3fz00vPsaWKHVgENz1yhZNLrbO7Tgb9XLUXn1wmeXAzTo+GRd8DBT//00d558Fkr+Olp/c9J/erRcc8kuAghasShlEP8uG0uLeP0v3S/CJiAxtqRUU1K9X9RAvSeBW5NDOe42KhBe33AObVKBp8TdZefiw2zXpnO0GW7WPXma2xq4UyxGfgmZqOa8R92dGvH4W8/Rad2hK7/gFei4Onl0OhhQAdnNsD3Q+GLcDjwNRTKJMIgwUUIUQNKtCX8e/+/6XFchxJIDGhGio0LT7bxxHHjZH3nxIY9IPx5o/NcbC3Q6VSGdXOl1LiIus/F1oK3R4/jhR93sG32v1kT7kmeBXikFWDz0UL2dW7H3o/epTS/AIL7w6jVMOUwPPQiqO0g/Rz88Zq+Gen3f0DqaVO/JZOS4CKEqHab4zcTmxHDI9cmMF7h1gaAqRa/waVIsHDQd1RUGv8KcrZRgwQXUU9Zq82YNmQY/1yyheOfzmd5twCu2oJTTglO3/5MVJf27Hh7GiXp6eAaBAP+p+/MO+AjcA3Wd+Y99C3Mbw9Lh8CZjQ/kJI8SXIQQ1Uqn0/F99Pe0iNfhkqWlxNqWnZ6hDPNMxS3yM/1Bj34EDj7lzrWzNOPGX0vSx0XUR+YqJc893JP3vt5A8oKlfNe3KUnOCmwKtbiv3MTJ7l3Z+spzFF28CBZ28NDzMPkAjF4LwY+CQgmx2+CnEfBFGOz/6oFqRpLgIoSoVtFXo/k7/W96HdePDLo3sB1KlY53S+aBthSaDdGPXVEBa7WZUY2LmbJG54EVwqQUCgVPhIfzn89Wo/n2V74d2pYYTwUWpTq8Nu3jbJ/e/PXcCHLPnNKPtNuwBzy9DKYehY5T9DWXV2Nh4+vwSTP44w1IP2/qt1XjJLgIIarVxriN2OXrCD+rr8L+2a0Nb6l/xiEvFmw9YOCntxzu3EatQqeTX0viwdOnWQgfz/kRt8V/8PUzXTjur8JMCw32Hidx8DD+GjGQrMMH9Qc7BUDff+ufRnr042tjwuTAgQXweRgsGwHnt9Xbp5HkN4QQotpodVo2xm+k6986VBodad4NcXHKYYxyg/6AwV+CtfMtz7e2MG4qUsqvKPGA6RDoz9x3viF0yRa+mtCP/U3M0AINjp0n6dkxbB34CFe2/olOpwMLWwifAJMOwLO/QlAfQAdnN8L3Q2B+R/10A/VsUDv5rSCEqDbHrhwjJTeZXsf162u82vC2+ff6lbajIaj3bc+3UauM1mUiQfGgau7lwWf//JRHluzi21eeYGsLC0qV4BWTRNqkV9jZqwsXV/+CTqPRd3Jv3AtGroQpkfDQC2BuA1dOwW9T9c1IW96H7GRTv61qIcFFCFFt/oz/k8ZJ4HNFi1ZtgdKrgJbKOHRqO3jk3Tueb6VWAdertxVIcBEPNj9nRz596X2GL9rD96+P5fcwawrNwf3SVXLefJu93Tpy/rtFaIuvzULt2hgGfKhvRurz72uD2l2FXR/B3Baw6gVIOmraN3WPJLgIIarNrku7ePi4vm/LuSZtecVmDQCKrq+CjesdzzdXGf9KkhoXIfTc7Gz435jXeTFiL6vee5lfOtqRawnO6TkUz/mQQ53bE/35J2jz8vQnWDlCpyn6jrzDl4JfR9CWwPEVsLAHLOoP0etAqzHl26oSCS5CiGpxIfsCKWnxdI7W15ikuRbgo0ijxMYLOkyq1DXMlArQXX8E2tbc9jZHC/HgsbO04P3HJ/HaN3vZPOdNfujhzFVbsM8pRPHlNxzp0oHI99+mNCNDf4LKDJoNhuc2wvPboMVwUJpBwl74eRTMaw375tepx6kluAghqsWui7voeFqHVTEUu3vypPt2AMx6vQ3mlZvl2VylBJ0ZeXFTGBfwKdbm1jVYYiHqLgszM97qP5pZX+4i8qMPiOjrRZIT2BSUYv3jL/zdrQt7/m8KxSkp109q0BYe/wam/Q1d/wlWzpCZAJve1PeD2fhWnZjcUYKLEKJa7Ly4k4eP6ZuJ8v3NcVDmc8WmCYpWT1X6GmYqfdOQttAHH5vgGimnEPWJSqVkao+hfDh3CylffM1Xg4OI9QCLEi3Ov23hzMM92TpxFAVxcddPsveCR96GV0/CwLnXH6fe/6W+BmbFKEg4UGsfp5bgIoS4Z/kl+SSdPEjTi6BTKmnldQQATa9ZoFTd4ezrbuzjopT+LUJUmkKhYGRYNz777zrMvv6ZBcPbEu2rwEwLXtsPEztgAH+OGkrW38eun6S2hnbj9I9Tj/wFGvYEnRZOrYNFfeDbR+DEL6ApMd0bq4AEFyHEPduXvI+uUfqnGlQBNlhblxBpHoZnm/53dR3zG+Yu0tbSv/aEqO36Nm3BvFk/4r9oIwvGdieyoQqlDnwPnSbpiafY9HgfUvftvH6CUqkfqmD0GnhpL7R5FlQW+nnFfh0Pn7WG/KumejvlSHARQtyzPfHb6XZCHzS8G8Sj0SmIb/v6XV+nrKkIQKuV4CLEvWjn68e8N76i49IdfDtpMHuC1WgV4HcykfRxL7J5QFfiNq3VD2ZXxqO5fqDIV09CjzfB2hWc/G87cOT9JsFFCHFPdDodmVu34JgPGhsFtl5F/KLpTqdO3e76WkbBRXKLENWikasLH0/9D4/9sJcf/zmOLaE2lCrBJzaNwlfeYGuv9vz9y2J0N840besGPd7QB5ihX5mu8BWQ4CKEuCdnMs4QdigTABe/bAoUFvzp8RxeDpV7kuhGNzYVaaSpSIhq5W5nwwfjX2P093tZ8/Y/+b2NM0Vm4H0pB9W//seO7mEcWPTJ9cHsAMwtwdHPdIWugAQXIcQ92X/sd1rH6kOGS8N8vtEMoH2r0Cpd68b+uNI1V4iaYW+l5u2nxzP5u51snf1vVj/kRZ4FeFwpxP5/37CnWzu2zp1BcUGeqYtaIQkuQoh7UrBuA0od5HmWkm1rw8LSgfRr7lWla904Uq48VCREzbJSq5g+ZBj/XPQXRz9awM+dG5JlDa6ZJXh9tYrDXR9i/fuTyM1KM3VRjUhwEUJU2dX8dEL2JQHgFpjHp6VP4O/lgZ/LvQ8cJ/MUCXF/qM2UPN+7B//6Zj3xX/zEjz1acMVOgVOulkY/biO6Rzf+t2k2qfmppi4qIMFFCHEPjvzxHR6ZUKjWYebvxHJNT/qHelbLtaXGRYj7S6VU8FSn1ry/YAW5C3/jh96dSHJUcc4Lvk9ZQa+VfZi25Q1iMmJMWk4JLkKIKstftx6AK41LmVX6FBpU9G9RTcGlWq4ihLhbCoWCAW0aMXvet6iXbGZtvwmU5gegQ8OWi78zdN1Q3t8912Tlk+AihKiSktJiPE4kA2AT7Mqm0jY0drelsbtdtVxfalyEMC2FQkHXpl78PHU6PwxYSjPdDEqyQ9HpFORmme5JIzOT3VkIUaf9vWkx9vmQr4Zd/s9DvKLamolA+rgIUZuE+TuxYuxTRCcN4POdB3mjR1eTlUVqXIQQVXLpt6UAXA40Y8klbwD6Nq++4CK5RYjap5m3PQue6oWTjYXJymDS4DJ06FCcnJx44oknjLZ/9NFHNG/enNDQUH744QcTlU4IcUt5aVhG6+cuyWvdlsISLb7OVjT3tq+2W0huEUJUxKTBZerUqSxdutRo24kTJ1i2bBmRkZEcPnyYBQsWkJmZaZoCCiEqdOn32TRIBS1w2G84AL1CPIzGYblX1XktIUT9YdLg0rNnT+zsjDvynTp1ik6dOmFpaYmlpSWtW7dm48aNJiqhEKKcwiyit/4OQLKPBVuS1YA+uFQniS1CiIpUObjs3LmTQYMG4e3tjUKhYM2aNeWOmT9/PoGBgVhaWhIWFsauXbvueN3Q0FC2bdtGZmYmmZmZbN26lUuXLlW1mEKI6nYoguJE/a+OjLYtSMstxs7CjPCA6p09VipchBAVqXJwycvLo1WrVnzxxRcV7l+xYgXTpk1jxowZHD16lK5du9K/f38SEhJue91mzZoxdepUHn74YYYOHUp4eDhmZvLwkxC1QnE+hbu/xOtacIlvqJ8BuluwG2qz6q3AleAihKhIlX/T9O/fn9mzZzNs2LAK93/yySeMHz+eCRMmEBISwty5c/H19WXBggV3vPaLL77IkSNH2LZtG2q1msaNG9/y2KKiIrKzs41eQogacvQHjqUUYlUMWbZKthTofzZ7hbhX+62UklyEEBWokT4uxcXFREZG0qdPH6Ptffr0Ye/evXc8PzVVPx/CmTNnOHjwIH379r3lsXPmzMHBwcHw8vX1vbfCCyEqpimBvfNITdbPQ3S5pR+nL+ejVECPJtUfXKRzrhCiIjXSBpOWloZGo8HDw7iznoeHBykpKYb1vn37cuTIEfLy8vDx8WH16tWEh4czZMgQMjMzsbGxYfHixbdtKnrzzTeZPn26YT07O1vCixA14cRKdFmJ2Cfqx2xJaRoOV6GdvzNONupqv53EFiFERWq088jNfzHpdDqjbZs2barwvMrUypSxsLDAwsJ0A+EI8UDQamDXJ5wvsMA9A0qVsM+uM1wt5ZEaaCYC8HW+9xmmhRD1T40EF1dXV1QqlVHtCuibgG6uhRFC1AGn10P6OWLSPfAHLjV2YM9FDQCPVPNj0Mtf6EBCej6tfR2r9bpCiPqhRvq4qNVqwsLC2Lx5s9H2zZs306lTp5q4pRCipuh0sOtj/WKSFQBpLVpQotER4GJNIzebar1dh4YuDA+X5l4hRMWqXOOSm5tLTEyMYT0uLo6oqCicnZ3x8/Nj+vTpjBo1inbt2tGxY0cWLlxIQkICEydOrJaCCyHuk/NbIPkYWQobfOKLATjj2x3ioUewu3SiFULcV1UOLocPH6Znz56G9bIOsmPGjGHJkiWMGDGC9PR0Zs2aRXJyMqGhoWzYsAF/f/97L7UQ4v7Z9QkAx1VhuGpjuOJiztZMb6CA7k3cTFs2IcQDp8rBpUePHuh0utseM2nSJCZNmlTVWwghTC1hP1zYA0pzMmMLcQXSWjXiUmYBapWS9g2rd7RcIYS4E5POVSSEqOWu1bZoWozA7XgSAJeb6vuphQc6Ya2WUa2FEPeXBBchRMWSj8O5TaBQctqqDQ65WgrN4YDlQwDSTCSEMAkJLkKIiu2Zq/+3+VAu7NkNQFIzd/Yn5gPQTYKLEMIEJLgIIcrLuggn1+iXO09Dvf+EfnOrMApLtHjYWxDsYWe68gkhHlgSXIQQ5R38BnQaCOhKqtYKr2u1LHF+jwDQNchNHoMWQpiEBBchhLHiPIhcol/u8BJ/r1+KEkhqYMnOK/paFunfIoQwFQkuQghjx1dAYSY4+kOTfhTs0PdvyW3XnDOXcwDo3NjVhAUUQjzIJLgIIa7T6eDA1/rl9i9SXFKE50n9nGM5LfQDTjb1tMO5BmaDFkKIypDgIoS4LnYbXDkNalto8yx/b/0F6yLItlYQbdkWgI6NXExcSCHEg0yCixDiuv0L9P+2HgmWDiRvXg/AlVY+7L+QBUDHhhJchBCmI8FFCKGXFgPn/gQU0P5FAGwOnwFA1akrsVfyUCigfaAEFyGE6UhwEULoHbzWtyWoD7g0IuH0ITxSi9EoICd4AADNvOxxsDY3YSGFEA86CS5CCCjIhKM/6pc7vATAmd+XAXCpoR1HrujDijQTCSFMTYKLEAKO/gAleeAWAg17AFC65yAA2o5t2B+bDkjHXCGE6UlwEeJBp9VcbyZq/yIoFOQXZON17ioArt0eIz49H6UCwgOdTVhQIYSQ4CKEOPMHZCaAlRO0HAFA1PaVWJRAjo2SNMfWADTxsMPeUvq3CCFMS4KLEA+6skegw8aC2hqApJ1/ApDZzIcjifrHoMP8nUxROiGEMCLBRYgHWfJxuLAbFCoInwCATqdDfUz/GLRDx85EXsgAJLgIIWoHCS5CPMjKhvdv9hg4+AAQf+Uc/heKAGjYcxh/X8oGJLgIIWoHCS5CPKhyr8CJlfrlDpMMm49t+xm1BnLtzUmw8qZYo8XVVo2fs7WJCiqEENeZmboAQggTiVwMmiLwbgs+4YbNmft2AVDYMojTCZkAtPFzQqFQmKKUQghhRGpchHgQlRbDoW/1yx1egmuhpLC0EMeTFwHw6Powxy/pO+a29nU0RSmFEKIcCS5CPIii10DuZbD1hGZDDJsPxe2m0SUtAP49BxGdpA8uzb3tTVBIIYQoT4KLEA8ane76I9DhE8BMbdh1dudazLSQ52JNqYcXsWl5ADT3djBFSYUQohwJLkI8aC4dgaQjoFJDu3FGuwoPHgZA0bYFMal56HTgamuBm52FKUoqhBDlSHAR4kFT1rel+TCwcTVsTsxOxOdcJgA+3fqRlFkAgJ+z1f0uoRBC3JIEFyEeJPlX4e9f9cvXBpwrszdmC42S9cvOnbuRkl0IgKeD5f0soRBC3JYEFyEeJEd/0D8C7dUKfNoZ7UrYtRGVDgo8HDH39iYjvwQAZxt1RVcSQgiTkOAixINCq4XDEfrl8AmGR6ABijRFqI5GA2D1UDharY55W84BoJTxW4QQtYgEFyEeFOe3QEY8WDhA6BNGu45cPkJwvL6Gxatbb6KTsw37Eq7m389SCiHEbZk0uAwdOhQnJyeeeML4l6iZmRmtW7emdevWTJgw4RZnCyHuSlmn3DYjDbNAlzlwZgsBKfplm/btOZ2SY9hXXKq9XyUUQog7MmlwmTp1KkuXLi233dHRkaioKKKiovj2229NUDIh6pmMC3B2EwCZLZ/krV1vcSjlkGF36t5tKIESX3fM3d05e/l6cNFodfe7tEIIcUsmDS49e/bEzs7OlEUQ4sEQuRjQQcOe/O/8L/wW+xvPbXoOgOTcZFxO6R8ncujYBcCoxkWCixCiNqlycNm5cyeDBg3C29sbhULBmjVryh0zf/58AgMDsbS0JCwsjF27dlXq2tnZ2YSFhdGlSxd27NhR1SIKIQBKCuHItZrN8Akk5iQa7d6dtJvmF/ThxKlTVwDOpFzv4yKdc4UQtUmVg0teXh6tWrXiiy++qHD/ihUrmDZtGjNmzODo0aN07dqV/v37k5CQcMdrx8fHExkZyVdffcXo0aPJzs6+5bFFRUVkZ2cbvYQQN4heC/npYN8AmvRDh3ENyuFTW/G/ol+2fughcgpLuJxdZNgvuUUIUZtUObj079+f2bNnM2zYsAr3f/LJJ4wfP54JEyYQEhLC3Llz8fX1ZcGCBXe8tre3NwChoaE0a9aMs2fP3vLYOXPm4ODgYHj5+vpW7Q0JUV+VdcoNGwcqM3S668GlRFtC3qGDAOga+WHm7FzuKSKpcRFC1CY10seluLiYyMhI+vTpY7S9T58+7N2797bnZmRkUFSk/2vv4sWLREdH07Bhw1se/+abb5KVlWV4JSYm3vJYIR44ycfg4kFQmkPb0QBGNS4nrpyg8Xn90P5OnboBkHhzcJFBE4QQtYhZTVw0LS0NjUaDh4eH0XYPDw9SUlIM63379uXIkSPk5eXh4+PD6tWrKSkp4cUXX0SpVKJQKPjss89wdna+5b0sLCywsJAJ4ISo0KFrA841ewzs9D+PWt31x5v3Ju2leYI+yNi2bw+UH7dFgdS4CCFqjxoJLmUUN1Ux63Q6o22bNm2q8LwTJ07UZLGEeDAUZMKJlfrlG+YlMqpxOb2DR9JBp1BgHR4OlA8uSqUEFyFE7VEjlcCurq6oVCqj2hWA1NTUcrUwQogacuwnKMkH92bg19Gw+cY+LqqoUwCYBTdG5eAAQMLVgvtbTiGEuAs1ElzUajVhYWFs3rzZaPvmzZvp1KlTTdxSCHEjne56p9zw8UaPBt1Y49IsXt9sVDZ+C5Tv43Jj0BFCCFOrclNRbm4uMTExhvW4uDiioqJwdnbGz8+P6dOnM2rUKNq1a0fHjh1ZuHAhCQkJTJw4sVoKLoS4jbgdkB4DaltoOcJo141BpGz8Fuv2Dxn2pWQV3r9yCiHEXapycDl8+DA9e/Y0rE+fPh2AMWPGsGTJEkaMGEF6ejqzZs0iOTmZ0NBQNmzYgL+//72XWghxe2W1La2eAgvj0ak1Og0ALlk6PDNBp1Ri3a4dALlFpRSUaIyOv7mvmhBCmFKVg0uPHj3uWIU8adIkJk2aVNVbCCGqIusSnN6gXw4vP0lpQam+D0votdoWdbMQVLa2AFzJ0Q9FYGthRm5R6X0orBBC3B0ZoUGI+iZyCeg04N8F3EPK7c4u1o8uXfYYtH3H6/3OUq8FFzc7GWJACFE7SXARoj4pLYYj3+mXw8dXeEhOcQ7odDf0b2lv2CfBRQhR20lwEaI+Ob0eci+DrQc0HXjLwzwywS0btGZKrNu2MWxPzdZ3zHWX4CKEqKUkuAhRnxxepP+37WgwU5fbnVGYAVx/mijO3YPQ/+zkzVXHKdFoSc8rBsDVVoKLEKJ2qtGRc4UQ99GVsxC/CxRKaDumwkMOpBwArgeXg47+5Bdr+OlgIv4uNmQXlADgaG1+f8oshBB3SWpchKgvympbgvqCY8WzpO+6uAulVmd4oiiveQum924CwJI98WReCy72lhJchBC1kwQXIeqD4nw4tky/fItOuaXaUnZd3EWfIzqc8iDHEiZMfJQXuzfERq0iJbuQw/FXAbC3kuAihKidJLgIUR+cXAWFWeDoB40eqfCQvUl7Kc24yvBd+mH+l3dXYu9gi4WZirb+TgBcztY/VWRnKa3IQojaSYKLEPXBoQj9v2HjQFnxj/XamLU8s12LbSHEesBfrRWoFCoAQrzsjY6VpiIhRG0lwUWIui7pKCQdAaU5tBlV4SEZhRnE799Cz2P6vi2L+6jQKRUo0A/nH+BiY3S8vZXUuAghaicJLkLUdWWdcpsNBlu3Cg9ZcWo5YzYVoQT+btmCMz76wKJS6mtc/JytjY6XGhchRG0lwUWIuqwwC078ol9u91yFhxRpiohbvpigZMg3N8fs5ZGGfWU1Li62xmO+WKlVNxwjhBC1hwQXIeqyYyugJB/cQsC/U4WH/H58JUM25wCwq8NgfIK8DfvK+rg42xgHFwsz+dUghKid5LeTEHWVTgeHr3XKbfccKMrXjRRpikiZNxfHfLjoYEvTSc8bzequuHaOk/XNwUVFGz9HAEaEVzwmjBBCmIL0wBOirrqwF66cBnNraDWiwkPW/fkF3fbnAfBju5F829KHyNRkw/6yGhf1TTUs5ioFPz3fgYSr+TTxsKuhNyCEEHdPalyEqKvKOuW2eAIsHcrtzi3Oxeyz71DpYJ9fA5o82hczlRId12tclIqKfwUoFAoszVUSWoQQtY4EFyHqotwrEL1Wv3yLTrkbF71D0/gSiszgq+BnGda2AUCFTUVCCFFXSHARoi46+j1oS8C7LXi3Kbf7ytVEPBdtBODXZi1xadyIpp76QeZurHEpayoSQoi6QoKLEHWNVguRi/XLt5iXaNec6bhk67hib8YK/xH0buZxfef13CI1LkKIOkeCixB1zfktkJmg79fSfFj53X/vodGGvwFY2KoXxSoLnKyvDygnNS5CiLpMgosQdU3ZvEStngG1dbndZ2a+gVoD54Mc2e2sn3AxI6/YsP/G4KKQ4eWEEHWMBBch6pLMRDi3Sb9cQafcyN+XEHgijVIl7O7zomFsF6XyekC5VedcaTUSQtQFElyEqEuOfAc6LQR0BbcmRrs0Wg1p8z4HIL5nEzZmXB8h1+zG4HJjJ5cbqCS5CCHqAAkuQtQVmhI4slS/XEFty46Vn+J3IZ8iM3Aa+w6Z+SWGfSrlnX/Ub6yVEUKI2kqCixB1xenfIfcy2LhD04FGuwpKCij5Wh9q0vq342S+o9H+Zt72huUbm4puJDUuQoi6QIb8F6KuOPSt/t+2o8HMeG6hTd+/T3BSCYVqBR3/77/8+PtFAB5t6UX3IDe6N3EzHHurpiKpcBFC1AUSXISoC66cgfhdoFBCu3FGu7ILs7BZsg6AvKE9sHDx5FD8CQBe6t6I0AbG0wHcqsZFmoqEEHWBNBUJUReU1bYEDwAHH6Ndmxa9g0+qhgJLJQ9Nm010cja5RaXYW5oR4mVf7lI25jYV3kJiixCiLpAaFyFqu6JciPpJv3zTSLlXc6/g8uNfABQP74fayZljp+MBaOPnhKqCWpRwz3BGBI+goUPDGi22EELUBAkuQtR2J36G4hxwbgSBPYx2/bnwX7RK15JnraLdy+/pD7+UBUBLn/IzRoN+7JZ/dfhXDRZYCCFqjkmbioYOHYqTkxNPPPFEpbYL8cDR6a6PlBs+AW54rPlyVhJeP+/Srzw7FDM7OwBOXMoGKNe3RQgh6gOTBpepU6eydOnSSm8X4oGTsB8u/w1mVtD6aaNdfy14E/dMHbl25rSZ+CYAhSUazl3OAaCFBBchRD1k0uDSs2dP7K79lViZ7UI8cMo65bZ4AqycDJsvpsfh/+tBAMzHjkBlrZ+z6OzlHEq1Olxs1Hg5WN734gohRE2rcnDZuXMngwYNwtvbG4VCwZo1a8odM3/+fAIDA7G0tCQsLIxdu3bdS1mFeLDkpkL0Wv1y+ASjXTvnv41LDuQ4qmk54Z+G7TGpuQAEedgazUNUGRU/JC2EELVLlYNLXl4erVq14osvvqhw/4oVK5g2bRozZszg6NGjdO3alf79+5OQkFDlwlakqKiI7Oxso5cQ9cKRpaAtAZ9w8G5t2JycFk/gmkgALMY/i9LCwrAv9koeAA3dbO9rUYUQ4n6pcnDp378/s2fPZtiwYRXu/+STTxg/fjwTJkwgJCSEuXPn4uvry4IFC6pc2IrMmTMHBwcHw8vX17dary+ESWhK4fBi/fJNtS17vngbxzzIdLGg5ZhXjPbFpulrXBq6VjxWixBC1HU10seluLiYyMhI+vTpY7S9T58+7N27t1rv9eabb5KVlWV4JSYmVuv1hTCJc5sg+yJYOUOzIYbNqWkJ+K09DIB6/EgUauOh/6/XuEhwEULUTzUyjktaWhoajQYPDw+j7R4eHqSkpBjW+/bty5EjR8jLy8PHx4fVq1cTHh5+y+0VsbCwwOKGqnIh6iqdTkeRpghLM8sb5iUaBebXO9lu/+gNWhRAirM5R70eJTCvGGcbteH8uLRrwcVVmoqEEPVTjQ5Ad3PnQJ1OZ7Rt06ZNFZ53q+1C1GdTt07leNpxfuj4Ab7ntwIKaPccABqtjk/X7KT7H0cBWBrSmW074lgZlcyvL3XC29GKjPwSikq1AHg7WpnqbQghRI2qkaYiV1dXVCqVUe0KQGpqarlaGCGE3vaL27laeJVF++foNwT1AacAiku1TI3YTcBnb2FbBIlu5jw08lUCXW1Izirk9V+PA5CcVQCAq60atdnd/2jLXEVCiLqgRoKLWq0mLCyMzZs3G23fvHkznTp1qolbClFvaNLP6RfCJ1Cq0fL6kl30WvQ+zS9fJd8CePNFXnq4CYvHhmOuUrDrXBpHEzJIySoEwFPGbxFC1GNVbirKzc0lJibGsB4XF0dUVBTOzs74+fkxffp0Ro0aRbt27ejYsSMLFy4kISGBiRMnVkvBhahPSjQlhmWdphgc/aHxI8z75SCPLJxFo+wkcizhy7GufNv3BQACXG14tIUXa6KSWBuVRJCHvl+Lp700Ewkh6q8qB5fDhw/Ts2dPw/r06dMBGDNmDEuWLGHEiBGkp6cza9YskpOTCQ0NZcOGDfj7+997qYWoZ/JL8w3LOoDw8WzZHU3z/72OX24q2bYqZj4FQx4ehbnK3HDsgGvBZcfZK9hZ6n+cZcRcIUR9VuXg0qNHD3S624+1OWnSJCZNmlTVWwjxwMgryTMsFynNuGzTFdWEl/DOTSfXwYG3R+Ry1d2SJ5s8aXTeQ4HOAMSl5XHusn4MF1dbecpOCFF/mXSuIiGEXk5xjmFZWerGhRem4pGbTrq9K3++0YFkFwUDAgfgZOlkdJ6jtZrAa4PN7T2fdm2bOVUxc3BzAF7q0ahK5wshxP1Qo49DCyEqJy0jFgCfKzqGLs/CLldLoq0bzgu+YO1p/ci5Q4OGVnhuQ1cb4tLyyC4sBaoeXIa28aF7E3fDuDBCCFEbSXARohZIPfMbgSk6ZizXYF8AcfaenP7HBwTan6egtIAA+wBau7Wu8NzAm4b3t7eqWnABJLQIIWo9aSoSwtQ0pWQc2sE7y/ShJcYTPug3gecHt2Nj/EYAHmv02C1ne/ZxMn6KyPEegosQQtR2UuMihKmd+Z2A7aXYFCk57QNznlQxtLErCmURB1MOAtDLv9ctT3e5qTOuo7XUmggh6i8JLkKYWMHqz/BKVlKqhI+HmFFgCU18C9iTtIdSbSkB9gEEOgTe8vybnyKyt5QfayFE/SW/4YQwpaQoknbFAlbsCVGQpm2NOVFcyI4lp0T/pFFXn663vYSrrXENi7VafqyFEPWX/IYTwoRK/vyMoguWKID1LT2xKG2ElijOZ53nct5lANq4t7ntNZxu6lBrUYV5ioQQoq6Q33BCmErOZTJ+24pCpyDaF87aNqdvUCsA/k77m3OZ+jmLWri2uO1lrNUqo3WlUqZLFELUXxJchKiCrKKsO44cfSfavQvJOKfvn7IhXIkmrwnj2ncA4GrhVbQ6La5WrnjaeN72OpZmqtvuF0KI+kSCixB3afel3XRZ3oX/Hfpf1S9SWkTWyqVoi1WkOsDBAAc6NmhHiLsXzpbOhsP87e88t5fUsAghHiQSXIS4Sx8f/hiAH079UOVr6E78QsYJfY3NH+2UFOe2Yng7fUhp7NjYcJy3jfc9lFQIIeofCS5C3G86Hfkr51GUZU6hOWxrqUCR257eIR4A+Nr5Gg71svUyVSmFEKJWkuAixP2WsI+r+1MBfWjJLg2ia0AoVtc62d7YVORh7WGSIgohRG0lwUWI+6xo/SfkJlkC15qJMjrRp9n1gOJi5WJYtlPb3ffyCSFEbSbBRYj7KeMCGRv1w/hHNlaQZOuKJrcpPZu6Gw6xMbepcFkIIYQEFyHu2r08Bq3Z8SWZcfpJEX8PV1CU1pMmHvZGw/ZbqK4vS3ARQghjElyEuF+Kcsn85Rd0pUoS3CA20IPSrDZ0aOhidJhadX0kXAkuQghhTIKLEPeJ7siPpJ/Sd8Dd0E6JRV4fQFUuuBjVuJhJcBFCiBtJcBHiftBqyVkxH02eGdlWENfel6TEZgC09nU0OtRcaW5YtlFLcBFCiBtJcBHiLumoQh+X81u4HJkLwOY2CgY3eYlSrQona3O8HCyNDtXoNIZlaSoSQghjElyEuAeV7ahbsPoTSq+oKVVCUp+W2JbqZ3xu5m2PQmE8ZH+pttSwrFYaz/wshBAPOjNTF0CIuiIlL4Ufon8gKTfJsK1YW2zUJ+VmiVfzOX3iEA22n0aBJftCFEx85C1W7csBoJmXfblzbhw59+ZQI4QQDzoJLkJU0j+2/4PjaceNthVpim4ZXJbui+f99dHMLFqAV7wFKiBzcBdaurXk4zT9WC6N3W3LnRfoEMj8R+bjbu1ebp8QQjzoJLgIUUk3hxaAYk1xhcf+GnmRd9aexIUs3C+dQqU150wDJX17vQ1AwtV8APycK+7D0tWnazWVWggh6hfp4yLEPagouMSn5TFjzQnQ6Zib8SNeUfqnhNY0DmfDsVw0Wh0XM64FFxfr+1peIYSo6yS4CFFJSkX5H5ciTZHRuk6n441Vx9EUFvPfmF9w3RGLUgeH2tiww3Eom/5OISW7kBKNDnOVAk97y3LXFEIIcWsSXISopIqe8MkqyjJa/+tUKqdOXeC/e7+m5ckDaBXw4yNK2n22DIXSjPj0fI5cyADA29EKlVI63wohxN2Q4CJEJZmrzMtti8uKMyxrtDp+XPYXn23/jJD0OPIsYM5wJS0fH0gz9yaGjrhbT6cC4GEntS1CCHG3al1wOXPmDK1btza8rKysWLNmjamLJUSFNS7nMs8Zlrd+u4JXVv8Xj4JMUp2VvDVGhZOPkie7zQYg1NsBgD0xaQA428gYLUIIcbdq3VNFwcHBREVFAZCbm0tAQAC9e/c2baGEAEq0JYbl3v692XxhM5GXI9HpdKTNX4DP558DcKqhNf97rAg3VSkftHwFhZm+pqasI25qjr5fjIutBBchhLhbtS643GjdunU88sgj2NjIsOfCtHQ6HXkleQBseXILKoWKLQlbiEk5yZkpL6DbshuA39vY8X2ffBx0Wj7P0mHXdpzhGr5Oxk8QuUiNixBC3LVqbyrauXMngwYNwtvbG4VCUWEzz/z58wkMDMTS0pKwsDB27dpV4bV+/vlnRowYUd1FFOKuFZQWGOYQsjW3xcXKhYcb9OBfP2nQbdlNqVLBgr6WfNevADsUfJOSSkD7KWB+vR+Lj5OV0TVdbG894q4QQoiKVXtwycvLo1WrVnzxxRcV7l+xYgXTpk1jxowZHD16lK5du9K/f38SEhKMjsvOzmbPnj0MGDCguosoxF3LLs4GwExhhpWZPoC8UtqDppcgXw2znlGyrW0pQRaeLL90iWClDYSNM7rGzU1DTlLjIoQQd63am4r69+9P//79b7n/k08+Yfz48UyYMAGAuXPnsmnTJhYsWMCcOXMMx61du5a+fftiaXn7Jy+KioooKro+lkZ2dvY9vgMhyksr0HeodbFyMcwfZPbHTgCOtm7EcRsvWipb81PmMsxKNdDlJbAwHs7fydo4qNhaqO5DyYUQon65r08VFRcXExkZSZ8+fYy29+nTh7179xptq2wz0Zw5c3BwcDC8fH1973iOEHfrSv4VAFytXAEozcggd8sWAFY5DKXo8hDeD/DELOVvMLeBh14odw0HK+PHqa3Ma3UXMyGEqJXua3BJS0tDo9Hg4eFhtN3Dw4OUlBTDelZWFgcPHqRv3753vOabb75JVlaW4ZWYmFjt5RbiSoE+uLhZuQGQvf53dCUl5Ps3JtrGk4ZuNgSf+0Z/cLtxYO1c7hpmKiX2ltfDio3UuAghxF0zyZ98ZVXtZXQ6ndE2BwcHLl++XKlrWVhYYGEhnRxFzUovSAfA1Vpf45K5ahUAf/qFA/BqUBqKI/tApYaOU255HQdrc7ILSwGwVktwEUKIu3Vfa1xcXV1RqVRGtSsAqamp5WphhKhNympcXK1cKYyOpujUKXTm5vxoF4JapaRvxo/6A1uPBHuvW17H+obmISu1NBUJIcTduq/BRa1WExYWxubNm422b968mU6dOt3PoghxV5LzkgHwsPYg81d9bcuFkHBy1dZMaJyNOm4rKJTQ+ZXbXsfc7HrNorW51LgIIcTdqvY/+XJzc4mJiTGsx8XFERUVhbOzM35+fkyfPp1Ro0bRrl07OnbsyMKFC0lISGDixInVXRQhqk1Ctv5xfX8LL7LWfwTAD44tAHhOt1p/UOgT4Bx42+vodNeXraWPixBC3LVqDy6HDx+mZ8+ehvXp06cDMGbMGJYsWcKIESNIT09n1qxZJCcnExoayoYNG/D396/uoghRLUq0JSTlJgHgcSSBvKwsSpzd2OfUiM6O6bgkbNQf2OXVu7quWlXrpgoTQohar9qDS48ePdDd+GdlBSZNmsSkSZOq+9ZC1IiU3BRKdaVYqCzQ/a5/BPpQcEe0CiVv2m1CUaiD4AHg0eyO1zK7Iazc3EldCCHEncmffELcwfms8wC01HqTv0c/3tAiu+YEKJJpnl5W2zK9UtdSSlYRQoh7Io81CHEHp6+eBqD3STPQ6cgKbsklaxeW2X2JorgUGvcG33ATl1IIIR4MUuMixB2cuXoGhU5H0/36J4t+9wkjTHGGTsV79E8S9Z5V6WtJhYsQQtwbCS5C3MGZjDOEJOiwvJwJ1jb8bNWYGebL9DvbPFupvi1laqpfy+v9mgLw7qDKl0UIIeoiaSoS4jZyi3NJzElk8nF9h/PIhmE8rI6irfIcmFtDzxl3db2aqnF5qUcjRoT74iwzTgsh6jmpcRHiNs5mnMWqSEfHM/r15c6hvG62XL/S6WWw8zRd4W4ioUUI8SCQ4CLEbZxMP0mnUzrUJTp0fgGEu54mQHkZrY07dJp619eTJ6CFEOLeSHAR4jaOph6l5zEtANGh7ZlqvgYAZc+3wML2rq+nkO65QghxTyS4CHELOp2O5JOHaJIEOqWSUstzOClyybVrBG1Gmbp4QgjxQJLgIsQtXMy9SKuD6QDowtow1HwTAOb9ZoOqiv3apcJFCCHuiQQXIW4hKimS7n9fm77CLQ0LRQlnrFpj0ax/la8puUUIIe6NBBchbiH5r99xzIMiOzXNbPfpt7WfcU89bLV3mMdLCCHE7UlwEaICOp0Ox78iASgKNkOhhLXaLoR16HmHM29Po5XgIoQQ90KCixAVSIg/TrPTBQAEu1+gSGfObr+XsLM0v6frSm4RQoh7I8FFiAqcWf8jKh0keypwtC9hsaYf4a1b3fN1palICCHujQQXISpQsvsAAMU+hVzV2TJf8xgPN3W/5+tKbhFCiHsjwUWImxQX5uN9KhUAX498Pi8dSmADb1xtLe752jJyrhBC3BsJLkLc5NS2VVgWQ5YNODk58YOmNz2C7722RQghxL2T4CLETZJ/+xGAy34aZpS+QAlm9Ah2M3GphBBCgAQXIYzlpWMVFQ+AonlDthU2wcnanJY+jiYtlhBCCD0JLkKU0em4uHQC7ldBo4AzrV8FoFsTN1RK6ZwihBC1gQQXIcqc+IXoQ0cBuBRgzb5U/ezPXRq7VtstJP4IIcS9keAiBEDWJdjwD0qS9E8OaTq24/jFLAA6NHQxZcmEEELcQIKLEFotrJ1Edl42Phf1PxKatgMo1epo4GiFj5NV9d1LnocWQoh7YmbqAghhcocjIHY7R3Kc8SiFTAczzpgFA+dpH+iMQsLGA0er1VJcXGzqYghRr5ibm6NSqe75OhJcxIMtLQb+fBuA9CwvPMggOyyII4mZAIQHOlfr7SQC1X7FxcXExcWh1WpNXRQh6h1HR0c8PT3v6Q9CCS7iwaUphdUvQmkBxQHdcN0UC4BnrwH8fVzfv6VFAwdTllDcZzqdjuTkZFQqFb6+viiV0pouRHXQ6XTk5+eTmqofldzLy6vK15LgIh5cez6FS4fBwoGogCG4Z3xEiQrswweRffAwapWSJh52pi6luI9KS0vJz8/H29sba2trUxdHiHrFykrfXzA1NRV3d/cqNxvJnxPiwZR8DLb/R7884EPid2wHIC3Yg9OZ+iaCYE871GbV+yMi3WVqN41GA4BarTZxSYSon8r+ICgpKanyNSS4iHpHo9VwKv0UGq2m4gNKCmHVC6AthZDH0LV4EvODJwCw7d6Nv5P0zUShDezvV5FFLSMdsoWoGdXxs1XrgktOTg7h4eG0bt2aFi1a8M0335i6SKKO+TjyY4avH86XUV+W36nVwNpJcOU02LjDwE85fSmKxnFFAIQ8OpKzl3MBaOpZ/cFFPg6FEOLe1LrgYm1tzY4dO4iKiuLAgQPMmTOH9PR0UxdL1CHfR38PwDcnbgq9Oh1s+Cf8/SsozWHY12DjyvFNP2KmhSw3a2wbNSH2ij64NHSzud9FF8IkevTowbRp0wzrAQEBzJ0712TlEeJ2al1wUalUhjawwsJCNBoNOp3OxKUS9cLW9+HwIkABwxZCo4cBKNq1FwBdhzZotDoSruYD0NDN1lQlFeKujB07FoVCUe4VExNTI/d77733KrzfX3/9ZTjm6tWrTJs2jYCAANRqNV5eXowbN46EhIQKyz5x4sRy95k0aRIKhYKxY8dWqlzBwcGo1WouXbpUbl9sbCxPP/003t7eWFpa4uPjw+DBgzl79qzhmBvfi42NDUFBQYwdO5bIyMhKfmXE/VDtwWXnzp0MGjQIb29vFAoFa9asKXfM/PnzCQwMxNLSkrCwMHbt2mW0PzMzk1atWuHj48Nrr72Gq2v1zRUjHlB75sGuj/XLg+ZC6DAAErMTCYzOBKBh/ye4mFFAiUaHpbkSL3tL05RViCro168fycnJRq/AwMAau1/z5s3L3a9bt26APrR06NCBv/76i/nz5xMTE8OKFSs4f/484eHhxMbGGl3L19eX5cuXU1BQYNhWWFjITz/9hJ+fX6XKs3v3bgoLC3nyySdZsmSJ0b7i4mJ69+5NdnY2q1at4syZM6xYsYLQ0FCysrKMjl28eDHJycmcPHmSL7/8ktzcXNq3b8/SpUur8FUSNaHaH4fOy8ujVatWjBs3jscff7zc/hUrVjBt2jTmz59P586d+frrr+nfvz/R0dGG/6COjo4cO3aMy5cvM2zYMJ544gk8PDwqvF9RURFFRUWG9ezs7Op+S6KuO7IUNusHmaPXTAgba9h1YM/PNM+BEnMlbp16sCNe//8nwMUGpcwI/cDT6XQUlNyik3cNszJX3VVHRgsLCzw9PcttHzt2LJmZmUZ/RE6bNo2oqCi2b99e5fKZmZlVeD+AGTNmkJSURExMjOEYPz8/Nm3aRFBQEJMnT+aPP/4wHN+2bVtiY2NZtWoVI0eOBGDVqlX4+vrSsGHDSpUnIiKCZ555hu7duzN58mTeeustw9cvOjqa2NhYtm7dir+/PwD+/v507ty53HXKBkgDfZNZnz59GDNmDFOmTGHQoEE4OTlV8iskakq1B5f+/fvTv3//W+7/5JNPGD9+PBMmTABg7ty5bNq0iQULFjBnzhyjYz08PGjZsiU7d+7kySefrPB6c+bMYebMmdX3BkT9cnIN/PaKfrnzNOgyzWj3lS2bAMhv2RClpSWxackABLrWTP+Wf/QJZuS3B3j6ocr9FSlMq6BEQ7N3Npnk3tGz+mKtrntDbWm1WpYvX87IkSPLBRsrKysmTZrEv/71L65evYqz8/WRqceNG8fixYsNwWXRokU899xzlQpXOTk5rFy5kgMHDtC0aVPy8vLYvn07PXv2BMDNzQ2lUskvv/zCtGnT7nr8kFdffZWlS5eyefNmhg8fflfniup3X/u4FBcXExkZSZ8+fYy29+nTh7179f0MLl++bKg1yc7OZufOnQQHB9/ymm+++SZZWVmGV2JiYs29AVH3/DoBdFpoOwZ6vWe0K6MwA/djFwHw6DUAgKRMfVW1r3PNDD7WubErx9/rwwdDQ2vk+uLBtX79emxtbQ2vW/2xV11OnDhhdL+HHnoIgCtXrpCZmUlISEiF54WEhKDT6cr1vxk1ahS7d+8mPj6eCxcusGfPHp599tlKlWX58uUEBQXRvHlzVCoVTz31FBEREYb9DRo0YN68ebzzzjs4OTnx8MMP8/7775drsrqVpk2bAhAfH1+p40XNuq9xPi0tDY1GU67Zx8PDg5SUFAAuXrzI+PHj0el06HQ6pkyZQsuWLW95TQsLCywsLGq03KIO05ZA86Ew8NNyo7/tPbOZ4Iv6jt8Neg8CICWrEADPGuzfYm9pXmPXFtXLylxF9Ky+Jrv33ejZsycLFiwwrNvY1OxTccHBwaxbt86wXtnfw2UPW9zcDObq6sqjjz7Kd999h06n49FHH610/8aIiAijkPPss8/SrVs3MjMzcXR0BGDy5MmMHj2abdu2ceDAAVauXMkHH3zAunXr6N27d5XKLEzDJPWQN3/zdTqdYVtYWBhRUVEmKJWoj3SNHkExdCEoy38IXNz2Ow11kOvtiNrHB4CkLH2Ni7ejdMwV+t9VdaW5xsbGhsaNG5fbrlQqyz2ZeS+jlpZRq9UV3s/NzQ1HR0eio6MrPO/06dMoFAoaNWpUbt9zzz3HlClTAPjyywrGYapAdHQ0Bw4c4NChQ7z++uuG7RqNhp9++omXXnrJsM3Ozo7HHnuMxx57jNmzZ9O3b19mz559x+By6tQpgBrt7Cwq7742Fbm6uqJSqQy1K2VSU1Nv2flWiLuSft5oNX/YV2BWfvh2nU6H+oB+tFzzrh0M2w01Lg5WNVhIIe4fNzc3kpOTjbbV5B+HSqWS4cOHs2zZsnK/6wsKCpg/fz59+/Y16t9Spl+/fhQXF1NcXEzfvpWr6YqIiKBbt24cO3aMqKgow+u1114zai66mUKhMPSHuZO5c+dib29Pr169KlUmUbPua3BRq9WEhYWxefNmo+2bN2+mU6dO97Mooj46uZqShT2MNuWgrfDQ2MzzND2rr13x76t/NLpUoyU1R/+EmpeD1LiI+uHhhx/m8OHDLF26lHPnzvHuu+/y999/1+g9//3vf+Pp6Unv3r35448/SExMZOfOnfTt25eSkpJb1qaoVCpOnTrFqVOnKtWBtqSkhO+//56nn36a0NBQo9eECROIjIw0BJrBgwfzyy+/EB0dTUxMDBERESxatIjBgwcbXTMzM5OUlBQuXLjA5s2beeKJJ1i2bBkLFiwwNDsJ06r2OtDc3FyjTldxcXFERUXh7OyMn58f06dPZ9SoUbRr146OHTuycOFCEhISKhx8SIhKKc6HjW/Ake/IVyoAR8OunOIcPG3KP7J5fM9amuZBkYUKx3btAUjLLUaj1aFSKnC1lX5Ton7o27cvb7/9Nq+99hqFhYU899xzjB49mhMnTtTYPV1dXdm/fz+zZs3ixRdfJDk5GRcXF/r168cPP/xw27FZ7O0rP9XGunXrSE9PZ+jQoeX2BQUF0aJFCyIiInjnnXcICAhg5syZxMfHo1AoDOuvvvqq0Xnjxo0DwNLSkgYNGtClSxcOHjxI27ZtK10uUbMUumoelvbGR9BuNGbMGMOgQPPnz+d///sfycnJhIaG8umnnxoGLrpX2dnZODg4kJWVdVc/AKKOuhwNv4zTzz2EgpSOL9E7Zb1h99L+S2nj3qbcad/9cyAPrT9PWvsgun6n72D496UsBn6+Gzc7Cw7NkCrhB1FhYSFxcXGGATKFENXrdj9jlf38rvYalx49etxxiP5JkyYxadKk6r61eJDodBC5GDa+CaWFYOsBwxaS7+wHa68Hl5zinHKnanVaXI7GA7DFrgn7N51mWq8mZObrOyw6WctTP0IIUVvVje7yQtyoIBN+mwrRa/XrjXvBkK/A1o38NOO2+4qCy66o/QRe0o+Guk7VmqvbzlOq1dGigQMAjlblO/MKIYSoHWrdJItC3FbiIfiqqz60KM2g9/vwzEqwdQMgvyTf6PDc4lyj9dTsQnZ99DlKIN5dzTMDwgFYsieexKv6zrqOUuMihBC1ltS4iLohLx32fQF7PgOdBpwC4PFF4BNmdFh+qXFwySm5XuNSUFzKykn/4vHIKADSerXgH32a8MffyZy/ksfaKP2Msk7WUuMihBC1lQQXUbtlJsC+LyHyOyi9NnNs82H6GZ4tHcodfnONS1lTkba0lD+ee5WHD/8FwE/dlLR+aigKhYKuQW6cv5LH6RT9sY42UuMihBC1lQQXUTulntLXrpxYCdpS/Tav1tD1HxAyqNzw/WUKysLNNQeTD5Kbl8HR5/+PkCN70KBg+UAH1rbIZZijfuTOlj7GAUj6uAghRO0lwUXULokHYfencGbD9W2B3aHLq9Cwxy0DS5mypqLmLs1JyE4gJukEfwzrQugFLSVKJedfeIMNLp+CFtyt3QHwdzGe08XW4u7miBFCCHH/SHARVVai0ZKSVUhRqRbQ4WxjgZO1+d1PRFaUA+e3wYGv4MKeaxsV+pqVLtOgQdjtzjZS1lTU1LkprwdNJPWlV/BLKqZADf97XMGIR20p2at/7NnVSj+Bm6+z8fD+VnVkbhohhHgQyW9oUSnZBYX8dnIfJw9vRXvuOB6X0vBPzcXnagl5lnDFXskVO3PS7CzIc3JG5emHV6PmdA1/mNZ+zYzDjE6nHzDu3GaI2QwX9ulncQZQmkOrp6DzK+AadNflLKtxccnSYjPzP/glFZOltuKzEY056XOKz458BoCThRNqlb5JyM3WAnOVghKNfvyhu52VVwghxP0jwUVUSFNawv7t6ziyYz3KmNP4pGQRekVH29Lyx9oVgmemBtAAhUAWEAfsAOaz11ZBiocdJY3caOhnTivOYJF7yfgiToH6GpYOL4G9d5XLnV+Sj88VHV2+2oAmI4/LVk7M6/cysyZ04umNA0kvTAfAzdrNcI5CocDBSk1arn6eIiu1jBIgxN1SKBSsXr2aIUOGAPpZoMeOHUtUVBRNmzZlzZo1BAYGcvToUVq3bm3Ssoq6TYKLMMjNSGPLioXkbd9KozNJOBfouHng+wK1knQfNzSNA7BqEYJ7aEusirQUJl2kIDGR3AsXKbyYiGVaOvbZRahLwTlXh3NuNpzPBuCkGpK9vSj2tcEjLJx2g6ei9mp2z+XXFhfjvvs0s37QYFmYR7ydB3N6vMRXL/enmYc9LV1bcjztOGAcXEA/dktZcLGUGhdRx4wdO5bMzEzWrFljtL1sCpaMjIz7PkHgu+++i42NDWfOnMHW1hZHR0eSk5NxdXWtctmCg4OJi4sjLi6OBg0aGO2LjY1lxowZ7Nixg6tXr+Lq6kpYWBgffvghTZo0ATCq+bW2tsbb25vOnTvz8ssvExZW+SZpYVoSXB5w8aePsuenCKwOHaLxhWyaaK7vy1dDvKc9RYFBBLbvSpsevbEICEChVEJxHuSkQHYSpJ0Bh3NQfBoUp8A9DdC3CBUWKTlbbElShiWlly3xugQ2RdAwXgHx+bBrB3/P30Gsnz0FLZoTMnAYrTr0xVxV+UeSi+LiyPx5JVmrV9MlMxOAU27OfNBpCgteephm3vo5L3r7974eXKyMg4uD1fX7WUsfFyHu2fnz53n00Ufx9/c3bPP0LD/haWXt3r2bwsJCnnzySZYsWcKMGTMM+4qLi+nduzdNmzZl1apVeHl5cfHiRTZs2EBWVpbRdRYvXky/fv0oLCzk7NmzLFy4kPbt27No0SJGjx5d5fKJ+0d+Qz9gSgoL2P/Xr8Ru/A3PY6fxu1LMjXOepjgqiW3sim2bUB55qDVhijzIvQw5+2HHWlifol+/aUTachz9ULiFYOXelFZuIbTyfQhcGpGbX8i2zeu5uGsDDqdPEZSYiW0RNI/Jhph9sHof++1f43wTNyw7d6Dlo08S7NMGldK4FkRbXEzOn5vJ/Pln8g8eNGxPt1WypTWs9X2Sec/0IDzA2bCvd0BvPo78GCgfXBxvCC7Sx0UY6HRw09hA94259R2fortb6enpTJkyhV27dnH16lUaNWrEW2+9xdNPP204pkePHrRs2RJLS0u+/fZb1Go1EydO5L333jMcc+7cOcaPH8/Bgwdp2LAhn332mdF9ymo2IiMjmTVrFu+++y5jx441NBU5OjoaJuN1cnICjCfirUhERATPPPMM3bt3Z/Lkybz11luG+0RHRxMbG8vWrVsNQcnf35/OnTuXu46jo6MhQAUEBNCnTx/GjBnDlClTGDRokKE8ovaS4HIvko9BaZH+lxvXJpYsWzZMNKm7YZtWv6zTXlvnhmWt/qXVXFvWgFZ7fblsn7b02r8l+mVNSfl1TYl+4sHSIrQFeZyNTyUuNh1FYiGeSVpcS8H1Wum0Ckj01qHxLaaRex7drQrpqbgIOVGw5Yfbv39za/3khi6Nwb0puIXo/3UNBgvbCk+xtbZk0OAnYPATAGTm5vPnxrUk7dqI55lThFzMwTVbh+vhVDi8jtLP17HW14z8ts0JfexpmnqEkrXyV7JWr0ZzrXYFpZKUpm351imYv7ssR6tUENFjOA/5uxrdu4FtA5q7NOdk+kk8bYz/8rO2uP6jIMFFGJTkwwdV73N1T95KArXNnY+7C4WFhYSFhfH6669jb2/P77//zqhRo2jYsCHt27c3HPfdd98xffp0Dhw4wL59+xg7diydO3emd+/eaLVahg0bhqurK/v37yc7O5tp06YZ3Sc5OZlevXrRr18//vnPf2Jra0taWpphv6+vL7/++iuPP/44Z86cwd7eHisr46f7bpSTk8PKlSs5cOAATZs2JS8vz9DUBODm5oZSqeSXX35h2rRpqFR39zP86quvsnTpUjZv3szw4cPv6lxx/0lwuRcrntWP7FqLFGngXI41qWmWcFmNS7ISdSkE3HBMjhWk+miw9C6ipVsOzc2utQ+pbcHCCyzsrr3s9cHE1h3sPPXLZf/aeuiPuce/CB1trRn+xNPwhP4vvpiLqWz/dRkl+7fSJO4C3pnFhFwohQvHYPUx4m58r44uHAntxrf2oSSpHVDZnMVaqcDH1o+H/Cv+sHm347usiVnDwIYDjbarVdc75FpK51xRB61fvx5bW+M/GDQajdF6gwYN+Oc//2lYf/nll9m4cSMrV640Ci4tW7bk3XffBSAoKIgvvviCLVu20Lt3b/766y9OnTpFfHw8Pj4+AHzwwQf079/fcL6npydmZmbY2toaajduDC4qlQpnZ31tqLu7+x37uCxfvpygoCCaN28OwFNPPUVERIQhuDRo0IB58+bx2muvMXPmTNq1a0fPnj0ZOXIkDRs2vOPXrmnTpgDEx8ff8VhhehJc7oWDLyiUgOLaB/i1D/GyZcOHukJ/nEJxw/HcsHzDPoVK/69SdW3bjetK/cSCSjN0CjOy04s4d+kql1Oy0aTmYZtWjFuGFjMteN1QzGwrOO9jSU6gF96t2tK1QzcesnO4IaBceylNX9PQ2Medxq9Mg1emkZpdyJath4jfvBzfc5GEJmVhroGjjRT8GWrPPqthlBQEA9DMy55WoQrWX4RQ11t39A1xCSHEJaTcdrXZ9QBmrpTgIq4xt9bXfJjq3nehZ8+eLFiwwGjbgQMHePbZZw3rGo2G//znP6xYsYJLly5RVFREUVERNjbGNTstW7Y0Wvfy8iI1NRWAU6dO4efnZwgtAB07dryrst6tiIgIo/fx7LPP0q1bNzIzMw2hZ/LkyYwePZpt27Zx4MABVq5cyQcffMC6devo3bv3ba+vu1ZDftdjUAmTkOByL8ZtuPMx1eBifAIn9mwj/cQRlPGxOCZfxistF8tSHTbAzX9PZFspOONnT2rjhjg89Ai9ew2jvUvda7d1t7fk6SFdYUhXsgpK2HI8lt9Or+aY7ndKFJlYspjO9iOYHj6ZFg0c+eeONQAVBpM7MbshrCiV8stLXKNQVHtzTU2xsbGhcePGRtsuXrxotP7xxx/z6aefMnfuXFq0aIGNjQ3Tpk2juLjY6Dhzc+PO8QqFAq1WC1z/kL95f02Jjo7mwIEDHDp0iNdff92wXaPR8NNPP/HSSy8ZttnZ2fHYY4/x2GOPMXv2bPr27cvs2bPvGFxOnToFQGBgYM28CVGtJLjUEhqthovp8Rw/uIuUY0fgfCyOSak0SM3DKU9LAMbNPQDFKrjkoiTZzZ5c7wZYBLUkuF1POoV3pL15/frWOliZM6x9MMPav0Feyct8GvkpK86sIDJ7BSviC/Bx/Qf7kvcB+uH+75bqhrBiJsFF1FO7du1i8ODBhtoLrVbLuXPnCAmpfNhv1qwZCQkJJCUl4e2tb5Ldt2/fXZdFrdYPAHlzc9bNIiIi6NatG19++aXR9u+//56IiAij4HIjhUJB06ZN2bt37x3LMnfuXOzt7enV6+YBIERtVL8+3WoxjVbDlYIrpOQmc+niWVKi/ybvfCyqS8nYp2bhkV6A11UdjbXQ+KZztcBlJwVJbtake7miCQjAqXkrwtv1pJ9XE5SKB6tpw8bchn91+BfNXJoxa98s1p1fx7rz6wAIcgqinUe7u76m2uz611AlwUXUU40bN+bXX39l7969ODk58cknn5CSknJXwaVXr14EBwczevRoPv74Y7Kzs40eTa4sf39/FAoF69evZ8CAAVhZWZXro1NSUsL333/PrFmzCA0NNdo3YcIE/ve//3Hs2DF0Oh3vvvsuo0aNolmzZqjVanbs2MGiRYuMamkAMjMzSUlJoaioiLNnz/L111+zZs0ali5det/HuhFVI8GlmhSWFpKUfZHkpPMkXTxHWnIC2alJFF9NxSwjE+f0PLyuavHKgIaF5Zt3yuRaKLjoZkOGlxvaho1wa9mGNp160swzQNpfbzIsaBgOFg68vvN1ijT6weOmh00v9+h0ZdxYy6KUr7Oop95++23i4uLo27cv1tbWvPDCCwwZMqTcWCe3o1QqWb16NePHj+ehhx4iICCAefPm0a9fv7sqS4MGDZg5cyZvvPEG48aNY/To0eUeh163bh3p6ekMHTq03PlBQUG0aNGCiIgI3nnnHQICApg5cybx8fEoFArD+quvvmp03rhx4wCwtLSkQYMGdOnShYMHD9K2bdty9xC1k0JXUYNlHZadnY2DgwNZWVnY29tX23WXR55hV9xZsktSySm9Qr42jafX7cclMw+bgmLs8rXYFEJl6j60QJqdmitO9uS4u6Nr4IdrkxaEtO9Kw5BGqFQPVg3KvUrJS2H56eU4WjgypvmYKgW8Dzed5stt5wGI/WCA9HN5QBUWFhIXF0dgYCCWlpamLo4Q9c7tfsYq+/ktNS6V9N2ZuVws2W20LeBSKZ6Z5Y/NsVCRY2lOvpU1RTZ2aOxdwDsAm8AQnIKD8AsNIsTDST4cq4mnjSfTwqZV2/Xk+yKEELWXBJdKau4WQFrKCezN3HEwd8NR7c7Jx/NIMHPAxtUXD+9GeDTwx9HDjUZ2VkZ9JkTtV7/qHYUQov6S4FJJH/Z6DYXi9TsfKOokyS1CCFE3SLVAJUnH2PpNalyEEKJukOAiBKCTOhchhKgTJLgIIYQQos6Q4CIESCcXIYSoIyS4CIHkFiGEqCskuAhBxRPHCSGEqH0kuAiBPFUkRHUICAhg7ty5hnWFQsGaNWsADEPxR0VFmaRsd+Pm9yFql1oZXIYOHYqTkxNPPPGEqYsihBC13tixY1EoFCgUCszMzPDz8+Oll14iIyPDpOVKTk6mf//+9+VepggbFy9eRK1W07Rp09set3fvXlQq1R3nc1q2bBkqlYqJEydWZzHrnVoZXKZOncrSpUtNXQzxAJFhekRd169fP5KTk4mPj+fbb7/lt99+Y9KkSSYtk6enJxYWFlU+X6fTUVpaWo0lql5Llixh+PDh5Ofns2fPnlset2jRIl5++WV2795NQkLCbY977bXXWL58Ofn5+TVR5HqhVgaXnj17YmdnZ+piiAeIDDAoKqLT6cgvyTfJ6277XVlYWODp6YmPjw99+vRhxIgR/Pnnn0bHLF68mJCQECwtLWnatCnz58832n/x4kWeeuopnJ2dsbGxoV27dhw4cACA8+fPM3jwYDw8PLC1tSU8PJy//vrrtmW6samozOnTp+nUqROWlpY0b96c7du3G/Zt374dhULBpk2baNeuHRYWFuzateuO9+7RowcXLlzg1VdfNdQ8ldm7dy/dunXDysoKX19fpk6dSl5enmF/amoqgwYNwsrKisDAQH788cdKfb11Oh2LFy9m1KhRPPPMM0RERFR4XF5eHj///DMvvfQSAwcOLDcDdpn4+Hj27t3LG2+8QdOmTfnll18qVY4H0V0P+b9z504+/PBDIiMjSU5OZvXq1QwZMsTomPnz5/Phhx+SnJxM8+bNmTt3Ll27dq2uMgshxH1RUFpA+2XtTXLvA88cwNrcukrnxsbGsnHjRszNzQ3bvvnmG959912++OIL2rRpw9GjR3n++eexsbFhzJgx5Obm0r17dxo0aMC6devw9PTkyJEjaLVaAHJzcxkwYACzZ8/G0tKS7777jkGDBnHmzBn8/PwqXbb/+7//Y+7cuTRr1oxPPvmExx57jLi4OFxcXAzHvPbaa3z00Uc0bNgQR0dHLl68eNt7r1q1ilatWvHCCy/w/PPPG65z4sQJ+vbty/vvv09ERARXrlxhypQpTJkyhcWLFwP6ZrbExES2bt2KWq1m6tSppKam3vF9bNu2jfz8fHr16oWPjw/t27fns88+K/dH94oVKwgODiY4OJhnn32Wl19+mbfffrvcH0uLFi3i0UcfxcHBgWeffZaIiAhGjx5d6a/rg+Sug0teXh6tWrVi3LhxPP744+X2r1ixgmnTpjF//nw6d+7M119/Tf/+/YmOjjb85w4LC6OoqKjcuX/++Sfe3t53VZ6ioiKja2VnZ9/lOxJCiLpv/fr12NraotFoKCwsBOCTTz4x7H///ff5+OOPGTZsGACBgYFER0fz9ddfM2bMGJYtW8aVK1c4dOgQzs7OADRu3NhwfqtWrWjVqpVhffbs2axevZp169YxZcqUSpdzypQphs+OBQsWsHHjRiIiInjttdcMx8yaNYvevXsb1l1cXG57b2dnZ1QqFXZ2dnh6ehqO+/DDD3nmmWeYNm0aAEFBQcybN4/u3buzYMECEhIS+OOPP9i/fz/t2+sDakREBCEhIXd8HxERETz11FOoVCqaN29O48aNWbFiBRMmTCh33LPPPgvom/Nyc3PZsmULvXr1Mhyj1WpZsmQJn3/+OQBPPfUU06dPJyYmxuh7IPTuOrj079//tp2tPvnkE8aPH2/45s2dO5dNmzaxYMEC5syZA0BkZGQVi1venDlzmDlzZrVdTwghyliZWXHgmQMmu/fd6NmzJwsWLCA/P59vv/2Ws2fP8vLLLwNw5coVEhMTGT9+vFGNRGlpKQ4ODgBERUXRpk0bQ2i5WV5eHjNnzmT9+vUkJSVRWlpKQUHBbftsVKRjx46GZTMzM9q1a8epU6eMjmnXrl213DsyMpKYmBij5h+dTodWqyUuLo6zZ88aylCmadOmODo63va6mZmZrFq1it27dxu2PfvssyxatMgouJw5c4aDBw+yatUqw/sdMWIEixYtMgouf/75J3l5eYbPVldXV/r06cOiRYv44IMPbluWB1G1zg5dXFxMZGQkb7zxhtH2Pn36sHfv3uq8lcGbb77J9OnTDevZ2dn4+vrWyL2EEA8WhUJR5eaa+83Gxsbw1/m8efPo2bMnM2fO5P333zc093zzzTeGmoUyKpUKACur2wel//u//2PTpk189NFHNG7cGCsrK5544gmKi4vvuew3N5vY2NhUy721Wi0vvvgiU6dOLbfPz8+PM2fOVHj/O1m2bBmFhYVGX8uyQBQdHU2zZs0AfW1LaWkpDRo0MDrO3NycjIwMnJycAH0z0dWrV7G2vv5/TavVcvToUd5//33D90joVWtwSUtLQ6PR4OHhYbTdw8ODlJSUSl+nb9++HDlyhLy8PHx8fFi9ejXh4eEVHmthYXFPvdaFAJCuuaK+effdd+nfvz8vvfQS3t7eNGjQgNjYWEaOHFnh8S1btuTbb7/l6tWrFda67Nq1i7FjxzJ06FBA3+clPj7+rsu1f/9+unXrBuhrfCIjI+/Y1FSZe6vVajQajdG2tm3bcvLkyVs2t4SEhFBaWsrhw4d56KGHAH0tSWZm5m3LExERwT/+8Q/Gjh1rtH3q1KksWrSIjz76iNLSUpYuXcrHH39Mnz59jI57/PHH+fHHH5kyZQrp6emsXbuW5cuX07x5c8MxWq2Wrl278scffzBw4MDbludBU63BpczN6VWn091Vot20aVN1F0kIIR4oPXr0oHnz5nzwwQd88cUXvPfee0ydOhV7e3v69+9PUVERhw8fJiMjg+nTp/P000/zwQcfMGTIEObMmYOXlxdHjx7F29ubjh070rhxY1atWsWgQYNQKBS8/fbbhpqcu/Hll18SFBRESEgIn376KRkZGTz33HO3Pacy9w4ICGDnzp089dRTWFhY4Orqyuuvv06HDh2YPHmyoSPyqVOn2Lx5M59//jnBwcH069eP559/noULF2JmZsa0adNuW/sUFRXFkSNH+PHHH8uN3/L0008zY8YM5syZw++//05GRgbjx483NMeVeeKJJ4iIiGDKlCl8//33uLi48OSTT6JUGj/oO3DgQCIiIiS43KRaH4d2dXVFpVKVq11JTU0tVwsjhBCiZk2fPp1vvvmGxMREJkyYwLfffsuSJUto0aIF3bt3Z8mSJQQGBgL6Gos///wTd3d3BgwYQIsWLfjPf/5jaKb49NNPcXJyolOnTgwaNIi+ffvStm3buy7Tf/7zH/773//SqlUrdu3axdq1a3F1db3tOZW596xZs4iPj6dRo0a4ubkB+lqkHTt2cO7cObp27UqbNm14++238fLyMpy3ePFifH196d69O8OGDeOFF17A3d39lmWJiIigWbNmFQ46N2TIEK5evcpvv/1GREQEvXr1KhdaQF/jUhaAFi1axNChQ8uFlrLj1q9fz+XLl2/79XnQKHT3MEmLQqEo9zh0+/btCQsLMxofoFmzZgwePNjQObcmZWdn4+DgQFZWFvb29jV+P1E/zNlwiq93xgIQ/59HTVwaYSqFhYXExcURGBiIpaWlqYsjRL1zu5+xyn5+33VTUW5uLjExMYb1uLg4oqKicHZ2xs/Pj+nTpzNq1CjatWtHx44dWbhwIQkJCTKEsRBCCCHu2V0Hl8OHD9OzZ0/DetkTPWPGjGHJkiWMGDGC9PR0Zs2aRXJyMqGhoWzYsAF/f//qK7UQQgghHkh3HVx69Ohxx6GoJ02aZPI5MoQQQghR/9TKuYqEEEIIISoiwUUIIYQQdYYEFyGEEELUGRJchBBCCFFnSHARQgghRJ0hwUUIIYQQdYYEFyFAZlkU4gGzfft2FArFHSdUvJOAgADmzp1bLWUSlSPBRQiAKk98IYTpjR07FoVCgUKhwNzcHA8PD3r37s2iRYuqNBGiqD7vvfee4Xtz4+uvv/4yHHP16lWmTZtGQEAAarUaLy8vxo0bR0JCgtG1yr7PFY1EP2nSJBQKRbkZq28lODgYtVrNpUuXyu2LjY3l6aefxtvbG0tLS3x8fBg8eDBnz541HHPje7GxsSEoKIixY8cSGRlZya9M1UlwEUKIeqBfv34kJycTHx/PH3/8Qc+ePXnllVcYOHAgpaWlpi7eA6158+YkJycbvbp16wboQ0uHDh3466+/mD9/PjExMaxYsYLz588THh5ObGys0bV8fX1Zvnw5BQUFhm2FhYX89NNP+Pn5Vao8u3fvprCwkCeffJIlS5YY7SsuLqZ3795kZ2ezatUqzpw5w4oVKwgNDSUrK8vo2MWLF5OcnMzJkyf58ssvyc3NpX379ixdurQKX6XKk+AihBC3oNPp0Obnm+R1t/PfWlhY4OnpSYMGDWjbti1vvfUWa9eu5Y8//jD6cEpISGDw4MHY2tpib2/P8OHDDbMPZ2VloVKpDH8163Q6nJ2dCQ8PN5z/008/GWZXjo+PR6FQsGrVKnr27Im1tTWtWrVi3759huMvXLjAoEGDcHJywsbGhubNm7NhwwYANBoN48ePJzAwECsrK4KDg/nss8+M3tfYsWMZMmQIH3zwAR4eHjg6OjJz5kxKS0v5v//7P5ydnfHx8WHRokWGc8rKtXz5cjp16oSlpSXNmzdn+/btt/0a7t27l27dumFlZYWvry9Tp04lLy/PsD81NZVBgwZhZWVFYGAgP/74Y6W+N2ZmZnh6ehq91Go1ADNmzCApKYm//vqLAQMG4OfnR7du3di0aRPm5uZMnjzZ6Fpt27bFz8+PVatWGbatWrUKX19f2rRpU6nyRERE8MwzzzBq1CgWLVpk9H8tOjqa2NhY5s+fT4cOHfD396dz5878+9//Nvp/AODo6IinpycBAQH06dOHX375hZEjRzJlyhQyMjIqVZaquOsh/4UQ4kGhKyjgTNswk9w7+EgkCmvre7rGww8/TKtWrVi1ahUTJkxAp9MxZMgQbGxs2LFjB6WlpUyaNIkRI0awfft2HBwcaN26Ndu3bycsLIzjx48DcPz4cbKzs7G3t2f79u10797d6D4zZszgo48+IigoiBkzZvD0008TExODmZkZkydPpri4mJ07d2JjY0N0dDS2trYAaLVafHx8+Pnnn3F1dWXv3r288MILeHl5MXz4cMP1t27dio+PDzt37mTPnj2MHz+effv20a1bNw4cOMCKFSuYOHEivXv3xtfX13De//3f/zF37lyaNWvGJ598wmOPPUZcXBwuLi7lvlYnTpygb9++vP/++0RERHDlyhWmTJnClClTWLx4MaAPUYmJiWzduhW1Ws3UqVNJTU2t8vdHq9WyfPlyRo4ciaenp9E+KysrJk2axL/+9S+uXr2Ks7OzYd+4ceNYvHgxI0eOBGDRokU899xzdwxmADk5OaxcuZIDBw7QtGlT8vLy2L59u2EOQjc3N5RKJb/88gvTpk1DpVLd1Xt69dVXWbp0KZs3bzb6HlYnqXERAqRzrqi3mjZtSnx8PAB//fUXx48fZ9myZYSFhdG+fXu+//57duzYwaFDhwD9fHRlH4Dbt2/nkUceITQ0lN27dxu29ejRw+ge//znP3n00Udp0qQJM2fO5MKFC8TExAD6Gp7OnTvTokULGjZsyMCBAw3NJObm5sycOZPw8HACAwMZOXIkY8eO5eeffza6vrOzM/PmzSM4OJjnnnuO4OBg8vPzeeuttwgKCuLNN99ErVazZ88eo/OmTJnC448/TkhICAsWLMDBwYGIiIgKv04ffvghzzzzDNOmTSMoKIhOnToxb948li5dSmFhIWfPnuWPP/7g22+/pWPHjoSFhREREWHUZHMrJ06cwNbW1vB66KGHALhy5QqZmZmEhIRUeF5ISAg6nc7wtSwzatQodu/eTXx8PBcuXGDPnj08++yzdywHwPLlywkKCqJ58+aoVCqeeuopo69JgwYNmDdvHu+88w5OTk48/PDDvP/+++WarG6ladOmAIb/czVBalyEEOIWFFZWBB+p+c6Gt7p3ddDpdCgU+mR+6tQpfH19jWolmjVrhqOjI6dOnSI8PJwePXoQERGBVqtlx44dPPLII/j5+bFjxw7atm3L2bNny9W4tGzZ0rBc1oyUmppK06ZNmTp1Ki+99BJ//vknvXr14vHHHzc6/quvvuLbb7/lwoULFBQUUFxcTOvWrY2u37x5c5TK639ne3h4EBoaalhXqVS4uLiUq/3o2LGjYdnMzIx27dpx6tSpCr9OkZGRxMTEGDX/6HQ6tFotcXFxnD171nCNMk2bNsXR0bHC690oODiYdevWGdYtLCzueE7Z/QHD96+Mq6srjz76KN999x06nY5HH30UV1fXSl0zIiLCKOQ8++yzdOvWjczMTMN7mTx5MqNHj2bbtm0cOHCAlStX8sEHH7Bu3Tp69+5dpTJXJ6lxEUKIW1AoFCitrU3yqq5f/KdOnSIwMBAwDjE3unF7t27dyMnJ4ciRI+zatYsePXrQvXt3duzYwbZt23B3dy9XQ2Bubm70NQMMTzNNmDCB2NhYRo0axYkTJ2jXrh2ff/45AD///DOvvvoqzz33HH/++SdRUVGMGzeO4uLiW16/7B4VbavME1S3+rpqtVpefPFFoqKiDK9jx45x7tw5GjVqdE8fyGq1msaNGxteZcHRzc0NR0dHoqOjKzzv9OnTKBQKGjVqVG7fc889x5IlS/juu+947rnnKlWO6OhoDhw4wGuvvYaZmRlmZmZ06NCBgoICfvrpJ6Nj7ezseOyxx/j3v//NsWPH6Nq1K7Nnz77jPcqCYdn/uZogwUUIwNLs7tpxhagLtm7dyokTJ3j88ccBfe1KQkICiYmJhmOio6PJysoyhJGyfi5ffPEFCoWCZs2a0bVrV44ePcr69evL1bZUhq+vLxMnTmTVqlX84x//4JtvvgFg165ddOrUiUmTJtGmTRsaN27M+fPnq+Gd6+3fv9+wXFpaSmRkpKEp42Zt27bl5MmTRgGj7KVWqwkJCaG0tJTDhw8bzjlz5sw9jQOjVCoZPnw4y5YtIyUlxWhfQUEB8+fPp2/fvkb9W8r069eP4uJiiouL6du3b6XuFxERQbdu3Th27JhRQHvttddu2YQG+rBW1h/mTubOnYu9vT29evWqVJmqQoKLEMD4roG08nVkxoCK25qFqO2KiopISUnh0qVLHDlyhA8++IDBgwczcOBARo8eDUCvXr1o2bIlI0eO5MiRIxw8eJDRo0fTvXt3oyaQHj168MMPP9C9e3cUCgVOTk40a9aMFStWlOvfcifTpk1j06ZNxMXFceTIEbZu3WoISY0bN+bw4cNs2rSJs2fP8vbbbxv62lSHL7/8ktWrV3P69GkmT55MRkbGLWsnXn/9dfbt28fkyZOJiori3LlzrFu3jpdffhnQN/f069eP559/ngMHDhAZGcmECROwuscmvX//+994enrSu3dv/vjjDxITE9m5cyd9+/alpKSEL7/8ssLzVCoVp06d4tSpU5XqQFtSUsL333/P008/TWhoqNFrwoQJREZGGgLN4MGD+eWXX4iOjiYmJoaIiAgWLVrE4MGDja6ZmZlJSkoKFy5cYPPmzTzxxBMsW7aMBQsWVKoJraokuAgB2Fuas3ZyZ57v1tDURRGiSjZu3IiXlxcBAQH069ePbdu2MW/ePNauXWv4YFMoFKxZswYnJye6detGr169aNiwIStWrDC6Vs+ePdFoNEYhpXv37mg0mruucdFoNEyePJmQkBD69etHcHAw8+fPB2DixIkMGzaMESNG0L59e9LT05k0adK9fSFu8J///If//ve/tGrVil27drF27dpb9gVp2bIlO3bs4Ny5c3Tt2pU2bdrw9ttvG/rsgH7cEl9fX7p3786wYcN44YUXcHd3v6cyurq6sn//fnr27MmLL75Iw4YNGT58OA0bNuTQoUM0bHjr30n29vbY29tX6j7r1q0jPT2doUOHltsXFBREixYtiIiIwMfHh4CAAGbOnEn79u1p27Ytn332GTNnzmTGjBlG540bNw4vLy+aNm3KSy+9hK2tLQcPHuSZZ565uy/CXVLo7nawgFouOzsbBwcHsrKyKv0NFUII0A/kFRcXR2BgIJaWlqYujqii+Ph4AgMDOXr0aLmOvsK0bvczVtnPb6lxEUIIIUSdIcFFCCGEEHWGjOMihBCiXgkICLjrKRNE3SE1LkIIIYSoMyS4CCHETeSvdSFqRnX8bElwEUKIa8oeG7555FYhRPXIz88Hyo+GfDekj4sQQlxjZmaGtbU1V65cwdzc3Gh+HCFE1el0OvLz80lNTcXR0fGuZ52+kQQXIYS4RqFQ4OXlRVxcHBcuXDB1cYSodxwdHfH09Lyna0hwEUKIG6jVaoKCgqS5SIhqZm5ufk81LWUkuAghxE2USqWMnCtELSUNuEIIIYSoMyS4CCGEEKLOkOAihBBCiDqj3vVxKRvcJjs728QlEUIIIURllX1u32mQunoXXHJycgDw9fU1cUmEEEIIcbdycnJwcHC45X6Frp6Nba3Vav+/vTsKaeqL4wD+XQsNw1VTknQ6emiG6RxbE4qE7EFJSIp6tYQIBkVEiAhC0ZMv9lY9WC8VBVZgQQThw8gwKLMMeigMDJfWpKI2FYu283/485e/unau827n3u37AaF7du726+vZ4efdpnC5XBgZGYHFYll0m9/vx/DwcNKxvx1HIhGUl5cjFArBZrPpWnOiuvQ6J9k8LXloGfv/v82Y00ozSjTOjOQZJRrLpucbM2JGepwjm5Pqvp3s2Cg5CSEQjUZRWlqa9Jc/Zt0VlzVr1iAvLy9ht2a1Wpd9U5aOyY5tNpvu39hEdel1TrJ5WvLQMpbodjPltNKMEo0zI3lGicay6fnGjJiRHufI5qS6b2vZs4yQU7IrLf/Jyjfnnjx5UvP40jHZcTqk8hhaz0k2T0seWsYykVGqj6PlnJVmlGicGWkbz+bnGzNiRnqcI5uT6r6tIqN0PU7WvVSULpFIBBs2bMDPnz9170izCXOSY0ZyzEiOGckxI23MllNWXnFJh/z8fJw/fx75+fmqSzE05iTHjOSYkRwzkmNG2pgtJ15xISIiItPgFRciIiIyDTYuREREZBpsXIiIiMg02LgQERGRabBxISIiItNg45IGoVAIe/fuRVVVFdxuN+7evau6JEM6dOgQNm3ahCNHjqguxTAePnyIyspKbNu2DdeuXVNdjiFx3chxD5KLRqPw+/3weDyoqanB1atXVZdkWHNzc3A6nWhvb1ddCgB+HDotPn/+jHA4DI/Hg+npaXi9Xrx//x7r169XXZqhBINBzMzM4Pr167h3757qcpT78+cPqqqqEAwGYbPZ4PV68fz5c9jtdtWlGQrXjRz3ILlYLIZfv36hoKAAc3NzqK6uxvDwMIqKilSXZjhdXV0YGxtDRUUFenp6VJfDKy7psGXLFng8HgDA5s2bYbfb8f37d7VFGVBDQwMKCwtVl2EYL168wI4dO1BWVobCwkI0Nzfj8ePHqssyHK4bOe5BclarFQUFBQCA+fl5xGIx8Of45cbGxvDu3Ts0NzerLmVBTjYug4ODOHDgAEpLS2GxWHD//v1lc65cuYKtW7di3bp18Pl8ePr0aUqP9fLlS8TjcZSXl6+y6szKZEbZYrWZTU1NoaysbOHY4XBgcnIyE6VnDNeVNnrmZNY9SEaPjH78+IHa2lo4HA50dHSguLg4Q9Vnhh4Ztbe3o7u7O0MVa5OTjcvs7Cxqa2tx6dKlhLf39fXhzJkz6OrqwuvXr1FfX4/9+/djYmJiYY7P50N1dfWyr6mpqYU53759w9GjR9Hb25v2/5PeMpVRNlltZol+2rNYLGmtOdP0WFe5QK+czLwHyeiR0caNG/HmzRuMj4/j9u3bCIfDmSo/I1ab0YMHD+ByueByuTJZtpzIcQBEf3//orG6ujoRCAQWjW3fvl10dnZqvt/5+XlRX18vbty4oUeZSqUrIyGECAaD4vDhw6st0XBSyWxoaEgcPHhw4bbTp0+LW7dupb1WVVazrrJ13SSSak7ZtAfJ6LFHBQIBcefOnXSVqFwqGXV2dgqHwyGcTqcoKioSNptNXLhwIVMl/1VOXnFJ5vfv3xgZGUFjY+Oi8cbGRjx79kzTfQgh0NbWhn379qG1tTUdZSqlR0a5RktmdXV1ePv2LSYnJxGNRvHo0SM0NTWpKFcJritttOSU7XuQjJaMwuEwIpEIgH//OvLg4CAqKyszXqsqWjLq7u5GKBTCx48f0dPTgxMnTuDcuXMqyl1kreoCjObr16+IxWIoKSlZNF5SUoIvX75ouo+hoSH09fXB7XYvvKZ48+ZN1NTU6F2uEnpkBABNTU149eoVZmdn4XA40N/fD7/fr3e5hqAls7Vr1+LixYtoaGhAPB5HR0dHTn3CQeu6yqV1k4iWnLJ9D5LRktGnT59w/PhxCCEghMCpU6fgdrtVlKuEXvu4Cmxc/mLpewuEEJrfb7Bnzx7E4/F0lGUoq8kIQE5+YkaWWUtLC1paWjJdlqHIMsrFdZNIspxyZQ+SSZaRz+fD6OiogqqMRes+3tbWlqGK5PhS0RLFxcWwWq3LOs7p6ellnWmuYkYrx8zkmJE2zEmOGcmZOSM2Lkvk5eXB5/NhYGBg0fjAwAB2796tqCpjYUYrx8zkmJE2zEmOGcmZOaOcfKloZmYGHz58WDgeHx/H6Ogo7HY7KioqcPbsWbS2tmLnzp3YtWsXent7MTExgUAgoLDqzGJGK8fM5JiRNsxJjhnJZW1Gij7NpFQwGBQAln0dO3ZsYc7ly5eF0+kUeXl5wuv1iidPnqgrWAFmtHLMTI4ZacOc5JiRXLZmxL9VRERERKbB97gQERGRabBxISIiItNg40JERESmwcaFiIiITIONCxEREZkGGxciIiIyDTYuREREZBpsXIiIiMg02LgQERGRabBxISIiItNg40JERESmwcaFiIiITOMfqkW4W6VRx1YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, TF_all = scipy.signal.freqs_zpk(all_z, all_p, k, worN = F_Hz)\n",
    "_, TF_minreal = scipy.signal.freqs_zpk(minreal_all_z, minreal_all_p, minreal_all_k, worN = F_Hz)\n",
    "\n",
    "recalibrated_zpk_xfr = recalibrated_zpk.fresponse(f=F_Hz)\n",
    "\n",
    "plt.loglog(F_bug, Div_PSD**0.5, label = 'Full FOM ASD')\n",
    "plt.loglog(F_FOM_dwn, hand_xfr.mag, label = 'Handfit ASD')\n",
    "plt.loglog(F_Hz, recalibrated_zpk_xfr.mag, label = 'Recalibrated AAA')\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = 'Downsampled FOM ASD')\n",
    "#plt.loglog(F_Hz, abs(TF_all), label = 'ALL ZPK Fit Bary. (order {})'.format(results.order))\n",
    "#plt.loglog(F_Hz, abs(TF_minreal), label = 'Minreal ALL ZPK Fit Bary.', linestyle = '--')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fit for the poles and zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-0.395840674352314), np.float64(-5.305505757497158e-10), np.float64(-0.005431630862136328), np.float64(-0.005431630295139218), np.float64(-0.04216327861663196), np.float64(-0.042163278683210494), np.float64(-3.0496435208558443), np.float64(-3.049643519812004), np.float64(-2707.2424186626913), np.float64(-2707.242418662458), np.complex128(-2.957619742415325e-12+92.58406532411048j), np.complex128(-2.957619742415325e-12-92.58406532411048j), np.complex128(-14.989349808572818+21.768488656216697j), np.complex128(-14.989349808572818-21.768488656216697j), np.complex128(-14.989349808751511+21.768488656204628j), np.complex128(-14.989349808751511-21.768488656204628j), np.complex128(-2.0699608975030063e-10+10.004878168780424j), np.complex128(-2.0699608975030063e-10-10.004878168780424j), np.complex128(-1.1784406984896683e-11+2.8299134889391437j), np.complex128(-1.1784406984896683e-11-2.8299134889391437j), np.complex128(-1.0296549523400693e-11+1.3325799775257434j), np.complex128(-1.0296549523400693e-11-1.3325799775257434j), np.complex128(-0.017091388663819738+0.2654485123713641j), np.complex128(-0.017091388663819738-0.2654485123713641j), np.complex128(-0.017091388682763577+0.2654485123375496j), np.complex128(-0.017091388682763577-0.2654485123375496j), np.complex128(-6.152212595327936e-12+0.3571372397086687j), np.complex128(-6.152212595327936e-12-0.3571372397086687j), np.complex128(-0.4720203451684382+2.629597995989603j), np.complex128(-0.4720203451684382-2.629597995989603j), np.complex128(-0.47202034528492837+2.6295979962167104j), np.complex128(-0.47202034528492837-2.6295979962167104j))\n",
      "(np.float64(-200.0), np.float64(-200.0), np.float64(-1999.9999999999998), np.float64(-1.1743521290869434e-10), np.float64(-0.011159044867910854), np.float64(-0.011159044751801689), np.float64(-1302.7841836435434), np.float64(-1302.7841836432744), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-1.276683601197908e-11+91.63193908076074j), np.complex128(-1.276683601197908e-11-91.63193908076074j), np.complex128(-18.709468807770158+25.224614971813303j), np.complex128(-18.709468807770158-25.224614971813303j), np.complex128(-18.709468807798835+25.224614971785446j), np.complex128(-18.709468807798835-25.224614971785446j), np.complex128(-3.5224484988923e-11+10.490717255985867j), np.complex128(-3.5224484988923e-11-10.490717255985867j), np.complex128(-7.755934678337948e-12+2.81092371100195j), np.complex128(-7.755934678337948e-12-2.81092371100195j), np.complex128(-3.1553850493935663e-12+1.2246948964676j), np.complex128(-3.1553850493935663e-12-1.2246948964676j), np.complex128(-8.200117750311727e-13+0.29012748368774854j), np.complex128(-8.200117750311727e-13-0.29012748368774854j), np.complex128(-0.03924083547004946+0.07982846579002265j), np.complex128(-0.03924083547004946-0.07982846579002265j), np.complex128(-0.0392408354694464+0.07982846579497013j), np.complex128(-0.0392408354694464-0.07982846579497013j), np.complex128(-4.26914787276891e-11+0.5783185011865986j), np.complex128(-4.26914787276891e-11-0.5783185011865986j), np.complex128(-4.995095343193837e-11+0.6196917699694076j), np.complex128(-4.995095343193837e-11-0.6196917699694076j), np.complex128(-0.7658207288552369+6.119579611317334j), np.complex128(-0.7658207288552369-6.119579611317334j), np.complex128(-0.7658207289076394+6.119579611415145j), np.complex128(-0.7658207289076394-6.119579611415145j))\n",
      "TEE_LOGFILE None\n",
      "A [-2.00000000e+02+0.00000000e+00j -2.00000000e+02+0.00000000e+00j\n",
      " -2.00000000e+03+0.00000000e+00j -1.17435213e-10+0.00000000e+00j\n",
      " -1.11590449e-02+0.00000000e+00j -1.11590448e-02+0.00000000e+00j\n",
      " -1.30278418e+03+0.00000000e+00j -1.30278418e+03+0.00000000e+00j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -1.27668360e-11+9.16319391e+01j -1.27668360e-11-9.16319391e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -3.52244850e-11+1.04907173e+01j -3.52244850e-11-1.04907173e+01j\n",
      " -7.75593468e-12+2.81092371e+00j -7.75593468e-12-2.81092371e+00j\n",
      " -3.15538505e-12+1.22469490e+00j -3.15538505e-12-1.22469490e+00j\n",
      " -8.20011775e-13+2.90127484e-01j -8.20011775e-13-2.90127484e-01j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -4.26914787e-11+5.78318501e-01j -4.26914787e-11-5.78318501e-01j\n",
      " -4.99509534e-11+6.19691770e-01j -4.99509534e-11-6.19691770e-01j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j]\n",
      "B [-2.00000000e+03+0.00000000e+00j -3.90376294e-03+0.00000000e+00j\n",
      " -1.30278418e+03+2.64289979e-05j -1.30278418e+03-2.64289979e-05j\n",
      " -1.11590448e-02+1.16415322e-10j -1.11590448e-02-1.16415322e-10j\n",
      " -2.00000000e+02+3.81469727e-06j -2.00000000e+02-3.81469727e-06j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -4.64082010e-04+6.19691770e-01j -4.64082010e-04-6.19691770e-01j\n",
      " -4.33097913e-04+5.78318501e-01j -4.33097913e-04-5.78318501e-01j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -9.04325228e-03+2.90127484e-01j -9.04325228e-03-2.90127484e-01j\n",
      " -8.84127110e-02+1.22469490e+00j -8.84127110e-02-1.22469490e+00j\n",
      " -2.88099878e-03+2.81092371e+00j -2.88099878e-03-2.81092371e+00j\n",
      " -3.07907738e+00+1.04907173e+01j -3.07907738e+00-1.04907173e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -2.69399238e+01+9.16319391e+01j -2.69399238e+01-9.16319391e+01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j]\n",
      "------------:Q-ranked order reduction:\n",
      "4P   0.17    order reduced annealing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-iirrational/src/wield/iirrational/fitters_ZPK/codings_s/cplx_sos_NL.py:176: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  V_D_c1 = -pD_c1 * c1 / disc * V_D\n",
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-iirrational/src/wield/iirrational/fitters_ZPK/codings_s/cplx_sos_NL.py:176: RuntimeWarning: invalid value encountered in scalar multiply\n",
      "  V_D_c1 = -pD_c1 * c1 / disc * V_D\n",
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-iirrational/src/wield/iirrational/fitters_ZPK/codings_s/cplx_sos_NL.py:177: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  V_D_c2 = -pD_c2 / (2 * disc) * V_D\n",
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-iirrational/src/wield/iirrational/fitters_ZPK/codings_s/cplx_sos_NL.py:177: RuntimeWarning: invalid value encountered in scalar multiply\n",
      "  V_D_c2 = -pD_c2 / (2 * disc) * V_D\n",
      "3W   0.09  Fitter_checkpoint improvement succeed, None\n",
      "3W   0.69    Fitter_checkpoint improvement succeed, None\n",
      "3W   1.06  Fitter_checkpoint improvement succeed, None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5P   1.06  zero flipping, maxzp 44, residuals=-1.04e-01, -1.04e-01, reldeg=-3\n",
      "5P   1.12  zero flipped, maxzp 44, residuals=-1.02e-01, reldeg=-3\n",
      "5P   1.21  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.28  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.36  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.40  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.44  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.48  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.53  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.58  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.63  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.66  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.69  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.73  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.77  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.81  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.85  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.88  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.91  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.94  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   1.98  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.02  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.06  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.09  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.13  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.17  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.21  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.24  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.28  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.32  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "5P   2.36  zero flipped, maxzp 44, residuals=-1.01e-01, reldeg=-3\n",
      "2A  10.36  Baseline fit residuals: -1.01e-01, at order 44\n",
      "BASELINE:  44\n",
      "------------:investigations:\n",
      "2I  10.39    max(z, p)       ChiSq.\n",
      "                   order    avg. res.    med. res.    max. res.\n",
      "             -----------  -----------  -----------  -----------\n",
      "                      42    -0.132534    0.0284283      1.05963\n",
      "                      52    -0.136876    0.011756       4.5478\n",
      "C [-2.00000000e+03+0.00000000e+00j -3.90376294e-03+0.00000000e+00j\n",
      " -1.30278418e+03+2.64289979e-05j -1.30278418e+03-2.64289979e-05j\n",
      " -1.11590448e-02+1.16415322e-10j -1.11590448e-02-1.16415322e-10j\n",
      " -2.00000000e+02+3.81469727e-06j -2.00000000e+02-3.81469727e-06j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -7.65820729e-01+6.11957961e+00j -7.65820729e-01-6.11957961e+00j\n",
      " -4.64082010e-04+6.19691770e-01j -4.64082010e-04-6.19691770e-01j\n",
      " -4.33097913e-04+5.78318501e-01j -4.33097913e-04-5.78318501e-01j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -3.92408355e-02+7.98284658e-02j -3.92408355e-02-7.98284658e-02j\n",
      " -9.04325228e-03+2.90127484e-01j -9.04325228e-03-2.90127484e-01j\n",
      " -8.84127110e-02+1.22469490e+00j -8.84127110e-02-1.22469490e+00j\n",
      " -2.88099878e-03+2.81092371e+00j -2.88099878e-03-2.81092371e+00j\n",
      " -3.07907738e+00+1.04907173e+01j -3.07907738e+00-1.04907173e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -1.87094688e+01+2.52246150e+01j -1.87094688e+01-2.52246150e+01j\n",
      " -2.69399238e+01+9.16319391e+01j -2.69399238e+01-9.16319391e+01j\n",
      " -8.48230016e+00+8.48230016e-01j -8.48230016e+00-8.48230016e-01j]\n",
      "(np.float64(-200.0), np.float64(-200.0), np.float64(-1999.9999999999998), np.float64(-1.1743521290869434e-10), np.float64(-0.011159044867910854), np.float64(-0.011159044751801689), np.float64(-1302.7841836435434), np.float64(-1302.7841836432744), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-8.482300164692441+0.8482300164692441j), np.complex128(-8.482300164692441-0.8482300164692441j), np.complex128(-1.276683601197908e-11+91.63193908076074j), np.complex128(-1.276683601197908e-11-91.63193908076074j), np.complex128(-18.709468807770158+25.224614971813303j), np.complex128(-18.709468807770158-25.224614971813303j), np.complex128(-18.709468807798835+25.224614971785446j), np.complex128(-18.709468807798835-25.224614971785446j), np.complex128(-3.5224484988923e-11+10.490717255985867j), np.complex128(-3.5224484988923e-11-10.490717255985867j), np.complex128(-7.755934678337948e-12+2.81092371100195j), np.complex128(-7.755934678337948e-12-2.81092371100195j), np.complex128(-3.1553850493935663e-12+1.2246948964676j), np.complex128(-3.1553850493935663e-12-1.2246948964676j), np.complex128(-8.200117750311727e-13+0.29012748368774854j), np.complex128(-8.200117750311727e-13-0.29012748368774854j), np.complex128(-0.03924083547004946+0.07982846579002265j), np.complex128(-0.03924083547004946-0.07982846579002265j), np.complex128(-0.0392408354694464+0.07982846579497013j), np.complex128(-0.0392408354694464-0.07982846579497013j), np.complex128(-4.26914787276891e-11+0.5783185011865986j), np.complex128(-4.26914787276891e-11-0.5783185011865986j), np.complex128(-4.995095343193837e-11+0.6196917699694076j), np.complex128(-4.995095343193837e-11-0.6196917699694076j), np.complex128(-0.7658207288552369+6.119579611317334j), np.complex128(-0.7658207288552369-6.119579611317334j), np.complex128(-0.7658207289076394+6.119579611415145j), np.complex128(-0.7658207289076394-6.119579611415145j))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3W  10.36  Fitter_checkpoint improvement succeed, None\n"
     ]
    }
   ],
   "source": [
    "snr = np.ones_like(F_FOM_dwn)\n",
    "snr[:-1] = (F_FOM_dwn[1:] - F_FOM_dwn[:-1])**-0.001\n",
    "from wield.iirrational import fitters_ZPK\n",
    "\n",
    "all_z = (np.array(recalibrated_zpk.zeros))\n",
    "all_p = (np.array(recalibrated_zpk.poles))\n",
    "k = float(recalibrated_zpk.k)\n",
    "\n",
    "z_iir = tuple(np.array(all_z) / (np.pi * 2))\n",
    "p_iir = tuple(np.array(all_p) / (np.pi * 2))\n",
    "print(z_iir)\n",
    "print(p_iir)\n",
    "\n",
    "iir_results = data2filter(\n",
    "    F_Hz=F_FOM_dwn,\n",
    "    xfer=FOM_ASD,\n",
    "    #xfer=reduced_ASD,\n",
    "    mode='reduce',\n",
    "    zeros=z_iir, \n",
    "    poles=p_iir,\n",
    "    gain=k * (2*np.pi)**(len(z_iir) - len(p_iir)),\n",
    "    SNR_phase_rel=0,\n",
    "    SNR=snr,\n",
    "    #relative_degree=-4,\n",
    "    # resavg_RthreshOrdDn=1.01,\n",
    "    baseline_only=True,\n",
    "    #coding_map=fitters_ZPK.codings_s.coding_maps.RI\n",
    "    # trust_SNR = True,\n",
    ")\n",
    "print(\"C\", iir_results.fit_aid._fitters[0].fitter.poles.fullplane)\n",
    "print(tuple(np.array(all_p) / (np.pi * 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041074d8b0>,\n",
       "     log_idx = 0,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410dc8560>,\n",
       "     log_idx = 1,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff1040>,\n",
       "     log_idx = 12,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff2000>,\n",
       "     log_idx = 14,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410037860>,\n",
       "     log_idx = 36,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04100370b0>,\n",
       "     log_idx = 38,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041083d9d0>,\n",
       "     log_idx = 62,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410037920>,\n",
       "     log_idx = 73,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410102b10>,\n",
       "     log_idx = 75,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff2810>,\n",
       "     log_idx = 100,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04104b4770>,\n",
       "     log_idx = 101,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008e270>,\n",
       "     log_idx = 126,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041083f890>,\n",
       "     log_idx = 127,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff3020>,\n",
       "     log_idx = 152,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041022a1b0>,\n",
       "     log_idx = 153,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410848740>,\n",
       "     log_idx = 178,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff3b30>,\n",
       "     log_idx = 179,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410177620>,\n",
       "     log_idx = 204,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410272930>,\n",
       "     log_idx = 205,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008ede0>,\n",
       "     log_idx = 230,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008edb0>,\n",
       "     log_idx = 231,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008d1f0>,\n",
       "     log_idx = 256,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410100aa0>,\n",
       "     log_idx = 257,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008d7c0>,\n",
       "     log_idx = 282,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041071aff0>,\n",
       "     log_idx = 283,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee0bef0>,\n",
       "     log_idx = 308,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410101a30>,\n",
       "     log_idx = 309,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04106baed0>,\n",
       "     log_idx = 334,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee0b7a0>,\n",
       "     log_idx = 335,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee4e810>,\n",
       "     log_idx = 360,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff2c30>,\n",
       "     log_idx = 361,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff3d70>,\n",
       "     log_idx = 386,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee4e360>,\n",
       "     log_idx = 387,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee4e780>,\n",
       "     log_idx = 412,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041029fdd0>,\n",
       "     log_idx = 413,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04102c0170>,\n",
       "     log_idx = 438,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee81610>,\n",
       "     log_idx = 439,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf6e40>,\n",
       "     log_idx = 464,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff1a90>,\n",
       "     log_idx = 465,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041071b020>,\n",
       "     log_idx = 490,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040fff08f0>,\n",
       "     log_idx = 491,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041053faa0>,\n",
       "     log_idx = 516,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041053e150>,\n",
       "     log_idx = 517,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf4950>,\n",
       "     log_idx = 542,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040eebfb60>,\n",
       "     log_idx = 543,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee0a600>,\n",
       "     log_idx = 568,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee09d60>,\n",
       "     log_idx = 569,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04100374a0>,\n",
       "     log_idx = 594,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040eebd970>,\n",
       "     log_idx = 595,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee0b710>,\n",
       "     log_idx = 620,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410583710>,\n",
       "     log_idx = 621,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf5e50>,\n",
       "     log_idx = 646,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410037b60>,\n",
       "     log_idx = 647,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ee0a6f0>,\n",
       "     log_idx = 672,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040eebd7c0>,\n",
       "     log_idx = 673,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf6f30>,\n",
       "     log_idx = 698,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf40e0>,\n",
       "     log_idx = 699,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04100ecd70>,\n",
       "     log_idx = 724,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040eebd5e0>,\n",
       "     log_idx = 725,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f0410422f30>,\n",
       "     log_idx = 750,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ecf46b0>,\n",
       "     log_idx = 751,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ede2810>,\n",
       "     log_idx = 776,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ede2240>,\n",
       "     log_idx = 777,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ed4e720>,\n",
       "     log_idx = 802,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04101af530>,\n",
       "     log_idx = 803,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008ed20>,\n",
       "     log_idx = 828,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f041008f410>,\n",
       "     log_idx = 829,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040ed2aae0>,\n",
       "     log_idx = 854,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04104b7cb0>,\n",
       "     log_idx = 855,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f040edb2000>,\n",
       "     log_idx = 988,\n",
       "     valid = True,\n",
       "     ),\n",
       " Bunch(\n",
       "     checkpoint_idx = -1,\n",
       "     fitter = <wield.iirrational.fitters_ZPK.MRF.MultiReprFilterS at 0x7f04101af6b0>,\n",
       "     log_idx = 990,\n",
       "     valid = True,\n",
       "     )]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iir_results.fit_aid._fitters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f040ee4c950>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#_, TF_all = scipy.signal.freqs_zpk(all_z, all_p, k, worN = F_Hz)\n",
    "#_, TF_minreal = scipy.signal.freqs_zpk(minreal_all_z, minreal_all_p, minreal_all_k, worN = F_Hz)\n",
    "\n",
    "\n",
    "plt.loglog(F_bug, Div_PSD**0.5, label = \"Full FOM ASD\")\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = \"Downsampled FOM ASD\")\n",
    "plt.loglog(F_FOM_dwn, hand_xfr.mag, label = 'Handfit ASD')\n",
    "plt.loglog(F_Hz, recalibrated_zpk_xfr.mag, label = 'Recalibrated AAA')\n",
    "plt.loglog(F_FOM_dwn, reduced_ASD, label = \"reduced ASD\")\n",
    "#plt.loglog(F_Hz, abs(TF_all), label = 'ALL ZPK Fit Bary. (order {})'.format(results.order))\n",
    "#plt.loglog(F_Hz, abs(TF_minreal), label = 'Minreal ALL ZPK Fit Bary.', linestyle = '--')\n",
    "plt.loglog(F_Hz, np.abs(iir_results.fitter.xfer_eval(F_Hz)), label = \"data2filter Fit FOM\", ls='--', linewidth=4)\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.98039622e-01-1.74495439e+00j,  1.12787318e+00-3.58889136e+00j,\n",
       "        3.36453718e+00-4.56701571e+00j, ...,\n",
       "       -7.24288676e+18-3.58464631e+19j, -7.24286678e+18-3.58463869e+19j,\n",
       "       -7.24284680e+18-3.58463107e+19j])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iir_results.fitter.xfer_eval(F_Hz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 10, 'b': 8, 'd': 6, 'c': 4}\n",
      "{'a': 10, 'b': 8}\n",
      "{'d': 6, 'c': 4}\n"
     ]
    }
   ],
   "source": [
    "# Python code to merge dict using a single \n",
    "# expression\n",
    "def Merge(dict1, dict2):\n",
    "\tres = {**dict1, **dict2}\n",
    "\treturn res\n",
    "\t\n",
    "# Driver code\n",
    "dict1 = {'a': 10, 'b': 8}\n",
    "dict2 = {'d': 6, 'c': 4}\n",
    "dict3 = Merge(dict1, dict2)\n",
    "print(dict3)\n",
    "print(dict1)\n",
    "print(dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f041029f110>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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48RnxvLLjFY5ePwrAgLQM/pt0k1NKM7Z1n8WohzpgpZEumgem1uDdfARPNB/BE9fPcHPf52w+v5b1tlZE2NqAfQxZxPBtzK98c7oeLd27M6XTUFoFeps7ciHumSRAQogK7ej1o7y0eQLJuUk46g28k5hElwyFQ0HT6DD4RWyt5WOsTFRvgNuAzxiW/T7DIn8m/uCXbNTdYL2jPSdtbFA7nOFozhme2rwIV6UNjzV6mOfa9cDaSsZcicpBPjmEEBXWmnN/8O6edzCQR73cXD69dh21bSOUcUsI9q5n7vAsg60LdHgB73bP8fTZjTy9dz4XL+9jg6M9ax0diNFCCnv48twevjrlQZtqvXm9y5PUrxZg7siFuCNJgIQQFdLsvd/w/dl5AHTPyGTW9UT0rV/CKfRd0Mjt2eVOrYGGYdAwjJqxR3hh7wLGnVjFYa2GNU4O/OngQLbVDQ6m/MzDfyzF16YZz7YczoB6vbHR2Jg7eiEKkU5zIUSFkpWbx5MrZpqSnydTUpmdmIHdI0tw6v++JD8VgW9LePhrVBP/pnWbF3gvXc+OmCu8fz2Rltk6UCnE5h5l+v436PRTMO/t+ZCLKRfNHbUQBUgCJISoMPZfSKTb129zNHMZAC/cTOFlvTPWz25G1XiweYMThbn4Qa/pMPkk9qGzGaT15Pu4ONZfjuXZm6m46DTkKun8cu4nBqwZwFPrn2HTxU3oDDpzRy6EdIEJIczPYFB4b91JfjyxDFuftQBMTrrJMw714IkV4OBh5gjFHdk4Qrtnoc0zcPZPAvbOZ8Klv3gxOZm/7GxZ6OTLcfs8jlw/yJEdB3G3qUZTmtAiowU1XGuYO3phoaQFSAhhdj/tv8QPx9Zg670GgOdupvBM9Q4waq0kP5WJWgMN+8Ho9fDsNjSNh9IlO5elCRfYeOUqg5NUWOfZkJSTyI6cHQz4bQAvbnmR3Vd3Y1AM5o5eWBhJgIQQZnXxRgYfbP0TB5/loILHU9J4qUY/eHwpWFethUstil8rGLYY1YQj0O45fLDmvZRL7Lt8jqnxuVTPcEdBYeeVnbyw+QUG/zaY5aeXk6nLNHfkwkJIAiSEMBu9QWHiym04eH+NQa3QPSOT1+s9hmrwAhnsXFW41YSw2agmn4DgqVjZuTMiK56tCZH8GJNC0E0vVAYbolOieX//+/Rc2ZNPDn1CbHqsuSMXVZwkQEIIs/lq91mSVDPJscqlfk4us2oPQ913FshyFlWPQzUIfh3V5BPoe39IhrUHzfUpLE8+yJZLlwlJ8MJW705abhpLTiwhdFUoU7ZPIeJaBIqimDt6UQXJIGghhFnEZcKWS5NJdknHTa/n04AB2PeR5KfKs7bH0HYsWxK8CKupQ7X3c6pf+5tPMw6Sm67mf7bN2eTtRIL6IuGXwgm/FE7jao15pskz9AjsgUYtM02L0iEtQEKIcqfTGzh99QcuuMSjUhQ+8uqOX9/ZkvxYEEWlQWk8FM24nTByDbk1umGtMvDfnCNsubSTty470VzdDGu1DScST/DyjpcZ9NsgVpxdQY4+x9zhiypAEiAhRLn7c+l/2VftFACjHILoEPaZJD+WSqWCOiFYj/4dnt9JWt1BGFAzPO8EP55fy/+iVfSx64yLtQuXUi8xY+8M+qzswzfHviE1N9Xc0YtKTBIgIUS5urr2fVbmridDraahyp0JD/8syY8w8mmO05Pfo554hCv1niQXLV0NZ/n45FI+P5/FKLdeeNt7k5idyLzD8+i9sjefHPqEhMwEc0cuKiFJgIQQ5SZvxyf8ee4LjtnaYGPQ8MmAH7BSy1BEcRu3mvg/MR/VpL85HvgkWVjTIi+Klw8v4stzibzuM4i6LnXI0GUYB0z/GsoH+z8gPiPe3JGLSkQSICFE+dj1CTG7P2CBqysAPWwH4ePoY96YRIWmdfWlyTPzyRkfyS7PJ0lXbKmde54n9nzGl2cv8b96o2hRvQW5hlyWnl5K2Kow3t/3PnHpceYOXVQCkgAJIcqWosDOj9FvmcE7HtXIVauo79SGjnYtzR2ZqCRcPf3oMn4+1545yGrnJ0hV7PHMvEDPTTP434VYvm75Gq29WqMz6Fh+Zjlhq8OYsXeGzCUk7kgSICFE2TEYYOObsPU9ljk7ctTWBg22zO0+A5WM+xH3qE6NQAZPns+BQdv5UTOEHEWLx41DdFj1Ep9dz+PbzrNo692WPEMeK86uoN+qfkzbM40raVfMHbqogCQBEkKUjbxcWP0c7JvPDbWaT1yrAzCp1WS8HbzNHJyorFQqFT1bNeDh/y7i+7arWGXoikFR4XT+D5r/PIaFNo1Z3PNr2vu0J0/J49dzvzJg9QDe3/e+DJYWBVT6BOjy5csEBwcTFBREs2bNWLFixR23CyHKQU4aLH0Mjq1Ap2gY4doBnUZPHeeGjGw83NzRiSrAzlrDs/270mbicmb4fcl+Q0OsDdnYbJ9B/WXj+bre03wf+j0dfDqQp+QZu8ZWhTHn0BySs5PNHb6oACp9AmRlZcXcuXM5efIkmzdvZvLkyWRkZBS7XQhRxlLjUBaHwvktZCo2PKYZRZyLsQti+kNvyUy+olQFVrNn2nPDyRzxO7NsJnJDccY57Tyq7/pTc8divg6ey7d9vqVF9Rbk6HNYfGIxoatC+eLoF2To5DvBklX6BMjHx4cWLVoA4Onpibu7O0lJScVuF0KUoeQYlEW9UMUf47rizPDcN7hWKwqAIXWH0Lx6czMHKKqqkIZeTHn1XdZ0/o1lhp4AuJ34jqRP2tI4PYfvQ79nfo/5NHBrQLounfmR8wn9NZTvT3wvM0tbKLMnQDt37mTAgAH4+vqiUqlYs2ZNoWMWLFhArVq1sLW1pXXr1uzatavIsg4dOoTBYCAgIKBE24UQpSjrJnnfDUaVcpkLBm+G6WYQ1M2JFEM0TlonJraaaO4IRRVnY6VhbO9WPDT5B/7n8xFXlWq451zF9sf+nP9pCl282vHLgF+Y3XU2NZxrcDPnJrMPzabfqn78evZX8gx55q6CKEdmT4AyMjJo3rw5n3/+eZH7ly9fzqRJk3jzzTc5cuQIXbp0ITQ0lJiYmALHJSYm8tRTT/HVV1+VaLsQonQlr5yE1c3zXFE8eF49jWmj+nA4fRkAzzZ7lmp21cwboLAY/m72TH7+ec4/Es56qx6oUagb9S3RHz3E5agT9K3VlzWD1jC903S8Hby5lnmNaXunMeyPYey8slNWn7cQZp+CNTQ0lNDQ0GL3z5kzhzFjxjB27FgA5s6dy8aNG1m4cCGzZs0CICcnhyFDhjB16lQ6depkOre47UXJyckhJ+ffZtDUVOMaMzqdDp1Od9/1u11+WaVZZmVj6degKtb/2rFt+J9fQ56i5gP711jwdCi7ElYRnxGPl70Xj9R5pEB9q+I1uBdS//Kpf8eGgeTU+Zk1v39Pt1PTqa07R/qPvfitzusEP/w8A2oOoHdAb1acW8Gi44uISo7ixS0v0sarDZNaTCKoWlCZxSbvgbKrf0nLVCkVKNVVqVSsXr2awYMHA5Cbm4u9vT0rVqxgyJAhpuMmTpxIZGQkO3bsQFEURowYQYMGDZg2bZrpmOK2F2fatGlMnz690Paff/4Ze3v7B62aEFVao2MfUD/vNGtUPchq8jQqdRZz0uaQpWQxxG4IrW1amztEYeEy05Joen4hTZQzAKxRunEm8EkaetgAkGXIYmfOTvbm7CUPY1dYM20zetn2wk3jZra4xb3LzMxkxIgRpKSk4OzsXOxxFToBio2Nxc/Pj7/++qtAC84HH3zAd999x5kzZ9i9ezddu3alWbNmpv0//PADKSkpRW5v2rRpka9dVAtQQEAAN27cuOMFvFc6nY7w8HB69eqFVqsttXIrE0u/BlWu/gmn0H7dBZ2iYX3IBsI6t+LTyE9ZcnIJdVzqsCx0WaE7v6rcNbhHUn/z1F/R67i0ZgZ1Tn+BGoVzBj++93uXZ4aE4u9mB0BsRiwLjy5k3cV1AGjVWh6r/xjPNH4GFxuXUotF3gNlV//U1FQ8PDzumgCZvQusJG6fMVZRFNO2hx56CIPBUOR5xW0vio2NDTY2NoW2a7XaMnlzllW5lYmlX4OqUv+Mw0vRAlsNLenUugUpeSksO2Mc+zOp9SRsbWyLPbeqXIP7JfUv5/prtdR77EOyz/ZBt2Is9XRXeTP2RWZ+Pgrv4Od5tmsdarjW4MNuH/J0k6f5JOIT9sft54fTP/Dbhd94rtlzPN7wcaw11qUYkrwHSrv+JS3P7IOg78TDwwONRkN8fMEVfhMSEvDy8jJTVEIIE4MB9fFfAIhwC6W6kw3fnfiObH02zTya0c2/m5kDFKIw2/ohOE3aT0ZgCLYqHe+pvyZg2394eO6f7Im6AUCjao34utfXLOy5kLqudUnNTeXjQx8zcM1A/oz+UwZKVwEVOgGytramdevWhIeHF9geHh5+10HNQohykHACu5wbZCg2uDQNIyk7ieVnlgPwfPPnZb0vUXE5eOAwahVKzxkYVBoGavbyWeokPli0lInLjpCQlo1KpeIhv4dYOWAlMzrNwNPOk6vpV3l156s8teEpjl0/Zu5aiAdg9gQoPT2dyMhIIiMjAYiOjiYyMtJ0m/uUKVP45ptv+Pbbbzl16hSTJ08mJiaGcePGmTFqIQSA7vxOAA4ZGtAtyI/vT3xPVl4WQdWC6OLXxczRCXEXajWqhyaifuZPDM7+1FRfY5X1u7gd+5Yen2znh70X0RsUNGoNQ+oNYe3QtYxvMR47Kzsir0cyYv0Ipu6aSnxG/N1fS1Q4Zk+ADh06RMuWLWnZsiVgTHhatmzJO++8A8Dw4cOZO3cuM2bMoEWLFuzcuZP169dTo0YNc4YthABSTm4F4Jh1M/zcDSw9vRSAcc3GSeuPqDwC2qEetwsa9sdapWea9ns+1s9m9m/7GbrgL45dSQHAzsqOF5q/wB+D/2BgnYEArL2wlgGrB7AgcgGZukxz1kLcI7MnQMHBwSiKUuixZMkS0zHjx4/n4sWL5OTkEBERQdeuXc0XsBDCyKDHIX4/AKpaXfnx9I9k5mXSwK0BwQHB5o1NiHtl7w7Df4TQj1A01vTRHGKDzZuorx5i0PzdTPv9BKnZxvllvBy8mPnQTJb1W0Yrz1Zk67NZeHQhA9YM4I/zf2BQSn4DjjAfsydAQojKSYn/Gzt9GmmKHTWbtmLpKWPrj4z9EZWWSgXtn0c1ZhO41cJPdZ2VNjMYq/6D7/ZcoOcnO/jjaKxpAHRjj8Ys6buEj7t9jJ+jHwmZCbyx+w1GrBvBkYQjZq6MuBtJgIQQ9+X6sS0ARCgNuabaQ5oujZrONekR2MPMkQnxgHxbwvM7ofFQNOh5Q7uUpQ7/Iy/tOv9ZeoSnvj3AxRvGleRVKhV9avbht8G/MbHVRBy0DpxIPMFTG57ilR2vcDX9qpkrI4ojCZAQ4r5kntkGQJx7a5ad/QmApxs/jVolHyuiCrB1hke+hQHzwMqWDvoIdjq/TWer0+w6d4Pec3fy6ZZz5OTpAbDR2DC26VjWDlnLw/UeRoWKjRc3MnD1QOYdnkeGLsPMFRK3k08qIcS90+dR/eZhAKJrehCXEYe7rTsD6gwwc2BClCKVClqPgme3gkd9HHOv86P2feZ6rsWQl8uc8LOEztvFnvM3TKd42HkwrdM0VgxYQTvvduQacvnm2Df0W9WP1edWy/igCkQSICHEPUuJjsBBySRZsecgEQA81vAxbDSFZ1MXotLzagzPboPmI1ApBgan/swh7/+jjcN1LlzPYMTX+5myPJIb6f8up9TAvQHf9P6GeSHzCHQKJDE7kXf2vMPj6x4nMiHSfHURJpIACSHu2eXDfwKwxrE+USlnsNHY8FiDx8wclRBlyMYRhiyERxaDrSuuySdYofov8+scQK0ysOrIVXp8soOlB2IwGIyDpFUqFd0Du7Nm0BpeafMKjlpHTiaeZOSGkbz515ukGFLMXCnLJgmQEOKeqS7uBmBDdeOaSIPrDsbNVlbMFhagyVAYvxfqdEeVl02/q3M5WusLunjlkpKlY+qqYzzyxR5Ox6eaTtFqtDzd+Gn+GPIHQ+sNRYWKDZc2MDd1Lt8c/4Ycfc4dXlCUFUmAhBD3RJebQ63Mo1yysuKkKh4VKkYGjTR3WEKUH2dfeHIVhH0MVnY4xe7m+5yJLG4Tg4O1hsMxyfT7dDez1p8iMzfPdJqHnQfTO01naf+ltKjeAh06Fvy9gEFrBrH50mZZX6ycSQIkhLgnpw/vxJ4cvnd2B+Ahv4eo4SwzswsLo1JBu2dh3C7wbYUqO4WQ469zqMEPDGtgg96g8OXOC/Sas5Mtp64VOLVxtcYs6rmIYfbDTOuLTd4+mWc3PcvZm2fNVCHLIwmQEOKe3Di2mUyVirVO9oBx8LMQFsujHozZBMFTQaXB7txaZic8y9rgOPxcbLmanMWY7w7xwo8RxKdkm05TqVQ0t27O6gGreb7Z81irrdkfv59hfwxj5r6ZJGcnm69OFkISICHEPXGK38d6R3sy1Qb8Hf15yO8hc4ckhHlptBD8Ojy3DbybQlYSTfa9zI7Ar3i5gxMatYoNx+Pp8cl2vt0djd7wb1eXnZUdL7V8id+H/E6vGr0wKAaWnVlG/zX9WXp6KXmGvDu8sHgQkgAJIUos+tpNGuWdYpmTE2Bs/ZGJD4X4h09z4+3yIW+BWovVuT/5z+kn2dU7llYBLmTk6pmx9iSD5u/m+NXUAqf6OfoxJ3gOi3ovop5bPVJyUvhg/wcM+2MY++P2m6lCVZt8cgkhSuzEwW2ctVU4Y2ONjcaGwXUHmzskISoWjRa6vWpcSsO3FWSn4Lv9ZX51/h9z+1bH2daK41dTefjLfay6qCY9p2ALTzufdvzS/xfeav8WLjYuRCVHMXbTWCZtm8SVtCtmqlTVJAmQEKLEss5uY6mzsfUnrFYYLjYuZo5IiArKKwjGhEPP6aCxQXV+M4P3DOWv7ucZ3NwLgwI74tSEfvoXm07EFzjVSm3F8IbDWTdkHSMajkCj0rAlZguD1gzi08OfkqnLNFOlqhZJgIQQJZKWrcMp7SDhDjL4WYgS0VjBQ5Pghb8goD3kpuG0dSpz0//LLwPtqWajEJ+aw3M/RPDc94eITc4qcLqLjQtT209l5YCVdPDpQK4hl6+Pfc2A1QNYe2Gt3Db/gCQBEkKUyF+nY4l2jiNPpaKZa32CqgWZOyQhKgePejD6T+j3Cdg4w9VDtA1/mJ88f+alzj5YqVVsOnmNXnN2FBokDVDXrS5f9fqKeSHz8Hf0JyErgam7pjJyw0hO3DhhpkpVfpIACSFKJOrINv5wsgNgmEx8KMS9Uauh7Vh48QAEDUKl6Gl4fR0vnx/FtiF6WtdwMw2SHjz/L45fLbhMhmlZjcFrmNhqInZWdhy9fpTH1z3O23+9zY2sG8W8sCiOJEBCiLvSGxSuJaznslaLvaKmd83e5g5JiMrJ2Qce/Z68R38iU+uOKvkSAeueZGX1RcwJ88HJ1opjV1M4eiW5yNNtNDaMbTqWtUPWMqD2ABQU1kStof/q/iw5vgSdXle+9anEJAESQtzV0SvJxDkYZ6gNdWuMvdbezBEJUbkp9fqwtdGH6NuNA5Ua1fGVDN0zmL96xTCxex0ebxt4x/M97T35oMsH/Bj2I009mpKhy+CTiE8Y+vtQdl3ZVU61qNwkARJC3NWOU6eJdDDervtIY+n+EqI06DW2GHq9D89uNc4hlJ2Cc/grTL48EfX1kyUqo3n15vwY9iPvdX6ParbVuJh6kfFbxvPSlpeISY0p4xpUbpIACSHu6vilH8lRq6irM9C4dh9zhyNE1eLbEsZuhT6zQOsAl/fBF11g45uQk37X09UqNYPrDmbtkLWMbjwaK7UVO67sYPBvg/lfxP/I0GWUQyUqH0mAhBB3pNcbuMBBAMKsA1Gp5WNDiFKnsYKO4+GlA9BoACh62Ps5zG8HJ3+HEtzy7mjtyJQ2U1g1cBWd/TqjM+j49vi3DFg9gD/O/yG3zd9GPsmEEHe0+cJhbthkYW1QGFp/kLnDEaJqc/GH4T/CiBXgWgNSr8IvI+GveSUuopZLLRb2WMjn3T8n0CmQ61nXeWP3G3Lb/G0kARJC3NGyk8sA6JGZSbX60v0lRLmo3xte3A9dXwU7d2g2/J5OV6lUdAvoxupBq5nUalKB2+bf3fMuiVmJZRR45SEJkBCiWNl52fydvA2APtnW4FbTvAEJYUm0dtD9LZh0zHj7/H2w1lgzpumYArfNrzq3igGrB/DDyR/QGSz3tnlJgIQQxdp2eRu55OKny6O+eztQqcwdkhCWx8bxgYvIv23+h9AfCKoWRJoujY8OfsQjvz/Cntg9pRBk5SMJkBCiWKvP/Q5Av/QMXBr2MHM0QogH1cKzBUv7LWV6p+m427pzIeUCz4c/z8StE7mcdtnc4ZUrSYCEEEW6kXWD/XHGvwwHpGfg3CjEzBEJIUqDWqVmaL2h/DHkD0YGjcRKZcXWy1sZvGawRa02LwmQEKJIG6I3YMBAs+wcHFXVwfXOM9MKISoXZ2tnXmv7GisHrqSjT8d/V5tfM4D1F9ZX+dvmJQESQhTpj/N/ANA/PYNkz/ZmjkYIUVbquNbhy15fMi9kHn6OfiRkJvDfXf9l1J+jOJV4ytzhlRlJgIQQhZy7eY5TSafQKNA3IxObesHmDkkIUYbyV5v/bfBv/Kflf7CzsuNwwmGGrx3OjL0zuJl909whljpJgIQQhfxxwdj60yUzEzeDAZ/mPc0ckRCiPNhobHiu2XP8Pvh3QmuFoqCw4uwK+q3ux0+nfiLPkGfuEEuNJEBCiAL0Bj3rLqwDYFB6BnEaX6zcAswclRCiPHk7ePNR149Y0ncJDdwakJabxocHPmTYH8PYH7ff3OGVCkmAhBAFHLx2kITMBOwVDV0zs0io1tbcIQkhzKS1V2uW91/O2x3extXGlajkKMZuGsuU7VO4mn7V3OE9EEmAhBAF5A9+fijdgDVgVaebeQMSQpiVRq3h0QaPsnbIWkY0HIFGpSH8UjiD1gxifuR8svKyzB3ifZEESAhhkqnLJPxSOABPpMYD4NeilzlDEkJUEC42LkxtP5VfBvxCO+925Ohz+OLoFwxcM5CNFzdWutvmJQESQphsv7ydrLwsPDWutMzN4ZLaH1cvmf9HCPGv+m71+ab3N8wJnoOPgw/xGfG8suMVxmwaw9mbZ80dXolViQRoyJAhuLm58cgjjxTY/r///Y/GjRsTFBTEhAkTKl12KkR523BxAwDtsx1RAVddWps3ICFEhaRSqehVoxe/Df6N8c3HY6Ox4WD8QYb9MYyZ+2aSkpNi7hDvqkokQBMmTOD7778vsO369et8/vnnREREcOzYMSIiIti3b5+ZIhSi4kvJSWH31d0A9L0RC4ChxkPmDEkIUcHZWdnxQosX+H3w7/Su0RuDYmDZmWX0W92P5aeXozfozR1isapEAhQSEoKTk1Oh7Xl5eWRnZ6PT6dDpdHh6epohOiEqh60xW8kz5FHXuRZdsy8C4N1cxv8IIe7O19GXT4I/4ds+31LPrR4pOSm8v/99hq8dzqH4Q+YOr0hmT4B27tzJgAED8PX1RaVSsWbNmkLHLFiwgFq1amFra0vr1q3ZtWvXXcutXr06r7zyCoGBgfj6+tKzZ0/q1KlTBjUQomrYEG3s/upoZRzzc04JoHaNmmaMSAhR2bT1bssv/X/hjfZv4GztzJmbZxi9cTSv7niV+Ix4c4dXgJW5A8jIyKB58+aMHj2ahx9+uND+5cuXM2nSJBYsWEDnzp358ssvCQ0N5eTJkwQGFj848+bNm6xdu5aLFy9iZ2dHaGgoO3fupGvXrkUen5OTQ05Ojul5amoqgKn1qLTkl1WaZVY2ln4NKmL9k7KT2B9vnNys7TVj3320Y0tq6vPQl0ELdkW8BuVJ6m/Z9Yeqfw0eqfMIPfx68MWxL/g16lf+vPgn2y9vZ3TQaEY2GolG0QBlU/+SlqlSKtDIYJVKxerVqxk8eLBpW/v27WnVqhULFy40bWvUqBGDBw9m1qxZpm3bt2/n888/Z+XKlQCsWLGC7du3M3/+fABmz56Noii89tprRb72tGnTmD59eqHtP//8M/b29qVRPSEqrP05+/kj6w/8NH58deEygYYrfOE0EZ+6MghaCPFg4vLiWJu1lkv6SwC4ql0Jsw2jkbYRKpWq1F8vMzOTESNGkJKSgrOzc7HHmb0F6E5yc3OJiIjg9ddfL7C9d+/e7Nmz547nBgQEsGfPHrKzs9FqtWzfvp3nnnuu2OOnTp3KlClTTM9TU1MJCAigd+/ed7yA90qn0xEeHk6vXr3QarWlVm5lYunXoCLW/9fwXyELhjfsR2DUWwA07vEYHRrXK5PXq4jXoDxJ/S27/mB51+AZ5Rk2xWxi7pG5XMu8xs+ZP1PbqjYzu8+kgUeDUn2t/B6cu6nQCdCNGzfQ6/V4eXkV2O7l5UV8/L99iX369OHw4cNkZGTg7+/P6tWr6dChA2FhYbRs2RK1Wk2PHj0YOHBgsa9lY2ODjY1Noe1arbZM3pxlVW5lYunXoKLUPz4jniPXj6BCRRedHQCnDIE0b1ivzOOrKNfAXKT+ll1/sKxr0L9uf7rX6M6i44tYcnwJF/IucDTpKE18mpTq65T0elboBCjf7U1kiqIU2LZx48Yiz5s5cyYzZ84s09iEqOw2XjT+/rTyaoX1uYMAnLJtTiNby/hQFkKUH3utPf9p+R8G1BzArI2zGFp3qNliMftdYHfi4eGBRqMp0NoDkJCQUKhVSAhxf/Lv/gqtGYp97F4A0n06mjMkIUQV5+foR5hdGFZq87XDVOgEyNramtatWxMeHl5ge3h4OJ06dTJTVEJUHTGpMZxIPIFGpaGnRws8si9hUFQ4N5AFUIUQVZvZu8DS09OJiooyPY+OjiYyMhJ3d3cCAwOZMmUKI0eOpE2bNnTs2JGvvvqKmJgYxo0bZ8aohaga/rz4JwDtfdrjHHsCgDNKAE3r1TRjVEIIUfbMngAdOnSIkJAQ0/P8O7GefvpplixZwvDhw0lMTGTGjBnExcXRpEkT1q9fT40aNcwVshBVhqn7q1YoKRFb8ACOqBvzuIeDeQMTQogyZvYEKDg4+K6LlI4fP57x48eXU0RCWIbzyeeJSo5Cq9bSPbA7+jXvAZBUvW2ZzM0hhBAVidkTICGEeWy6tAmATr6dcNbpIN3YFW1bt+jZ0oUQoiqp0IOghRBlJ/yS8eaCXjV6QYxxYtEzBn+C6tQyZ1hCCFEuJAESwgJFp0Rz7uY5rFRWBAcEk3FmOwAHlEY0D3A1a2xCCFEeJAESwgJtvrQZgPa+7XGxcUEfvRuAK86tcLCRnnEhRNUnCZAQFii/+6t3jd6QdROnlDPGHTVkfi0hhGWQBEgIC3M59TKnkk6hUWkICQiBS3tRoXDe4EOdWnXMHZ4QQpQLSYCEsDDhMcbWn7bebXGzdUO5aOz+2m9oRJCvszlDE0KIciMJkBAWJvziLXd/AboLuwA4RBD1vZzMFpcQQpQnSYCEsCCx6bEcTzyOWqWmR2APyE5Bm3AcgESPNlhbyUeCEMIyyKedEBYkf/BzG682VLOrBjH7UWHgosELnwAZ/yOEsBySAAlhQfJnf87v/uKScfzPPkMjGvu5mCssIYQod5IACWEh4jPi+fv636hQGbu/AOWScQboA4aGNJEB0EIICyIJkBAWIn/yw5aeLaluXx10WRAbCUAEjWjkIwmQEMJySAIkhIUwTX5Ys7dxw9XDqAw6rimu2HrUwlarMWN0QghRviQBEsICJGQmcCThCICp+4vL+wA4ZKhPY38Z/yOEsCySAAlhAbbEbEFBoXn15ng7eBs3xuwHIMLQgMa+kgAJISyLJEBCWID87i/T3V8GA1w2JkCHDPVlALQQwuJIAiREFZeUnUTEtQjglgToxlnITiZTseGkUkOWwBBCWBxJgISo4rZf3o5BMRBULQhfR1/jxn/G/xw11CHAwwUnW635AhRCCDOQBEiIKm5LzBYAugd0/3fjP+N/Din1aSytP0IICyQJkBBVWIYug32xxtYe091fYGoBijDUp4nMAC2EsECSAAlRhe2+uptcQy41nGtQx/Wftb4yEiHpAgCHDXVpIneACSEskCRAQlRhpu6vwO6oVCrjxljjfEDnDT6k4ihdYEIIiyQJkBBVlE6vY9eVXcBt439iDwNwTKmFn6sdbg7W5ghPCCHMShIgIaqoA/EHSNelU92uOs2qN/t3xz8tQH8b6kjrjxDCYkkCJEQVld/9FRIQglp1y6+6KQGqJQOghRAWSxIgIaogg2Jg2+VtgHH8j0lqHKTFoUfNCaUmTfykBUgIYZkkARKiCvr7+t/cyLqBk9aJdt7t/t3xT+tPlMGPLGzlDjAhhMWSBEiIKmhrzFYAuvh3Qau5ZZbn+L8B4wDo6k42eDrbmiM8IYQwO0mAhKhiFEUpcPt7AQknAThtCJAFUIUQFk0SICGqmKjkKGLSYrBWW9PFr0vBnQmnATir+MsAaCGERZMESIgqJr/7q6NvR+y19v/uyMuBpPMAnDX401jG/wghLJgkQEJUMfndXwXW/gJIjAJDHmmKHfG4yx1gQgiLJgmQEFVIbHosp5JOoVap6RbQreDO6/92f7nYWePnameGCIUQomK4rwRIURRu3LhBYmJiaccjhHgA+XP/tPRsibute8GdSdEARCs+NPFz/ndtMCGEsED3lADFx8fz1FNP4ebmhpeXF56enri5ufHMM89w7dq1sopRCFFCxXZ/ASTHAHBF8ZD5f4QQFs+qpAempqbSqVMn0tPTGT16NA0bNkRRFE6ePMnSpUvZvXs3hw8fxtHRsSzjLdKQIUPYvn07PXr0YOXKlQX2ZWZm0qhRI4YNG8bHH39c7rEJUV5uZt8k4loEUMTt7wDJlwC4olSnq9wBJoSwcCVOgObNm4dGo+HEiRNUr169wL633nqLzp078+mnn/LGG2+UepB3M2HCBJ555hm+++67QvtmzpxJ+/btyz0mIcrb9svbMSgGGro3xM/Rr9B+JTkGFXDZ4ClzAAkhLF6Ju8DWrVvHG2+8USj5AfD09GTq1Kn88ccfpRpcSYWEhODk5FRo+7lz5zh9+jRhYWFmiEqI8rX1svH29yJbfwwGSL4MQKLWi5rVHMozNCGEqHBKnACdPXuWTp06Fbu/U6dOnDlz5p4D2LlzJwMGDMDX1xeVSsWaNWsKHbNgwQJq1aqFra0trVu3ZteuXSUq+5VXXmHWrFn3HJMQlU2mLpO9sXsB6B5QRAKUlYTKoAOguk9N1GoZAC2EsGwlToBSU1NxdXUtdr+rqyupqan3HEBGRgbNmzfn888/L3L/8uXLmTRpEm+++SZHjhyhS5cuhIaGEhMTc8dyf/vtN+rXr0/9+vXvOSYhKpu/Yv8iR5+Dv6M/9d2KeM9n3AAgWXGgob974f1CCGFhSjwGSFEU1Ori8yWVSoWiKPccQGhoKKGhocXunzNnDmPGjGHs2LEAzJ07l40bN7Jw4cI7tu7s27ePZcuWsWLFCtLT09HpdDg7O/POO+8UeXxOTg45OTmm5/nJnE6nQ6fT3XO9ipNfVmmWWdlY+jUoi/qHXwwHIMQ/hLy8vEL7VanxWAGJijONvBzNfu3lPSD1v/VfS2Tp16As61/SMlVKCbMWtVqNi4tLsXOHKIpCamoqer2+5FHeHoxKxerVqxk8eDAAubm52Nvbs2LFCoYMGWI6buLEiURGRrJjxw7Ttu3bt/P5558XugsMYMmSJRw/fvyOd4FNmzaN6dOnF9r+888/Y29vX8QZQlQMekXPrJRZZJPNs47PUsOqRoH9rpkXqJZ2iiaxyzloqM+hhm/hK0OAhBBVVGZmJiNGjCAlJQVn5+Jv+ChxC9DixYtLJbB7cePGDfR6PV5eXgW2e3l5ER8fb3rep08fDh8+TEZGBv7+/qxevZq2bdve02tNnTqVKVOmmJ6npqYSEBBA796973gB75VOpyM8PJxevXqh1WpLrdzKxNKvQWnXf1/cPrK3ZeNu685z/Z9Do9aY9qn3formyAzT82RcGDW0L1Ya804CL+8Bqb8l1x/kGpRl/Us6HKfECdDTTz9938E8qNtbnRRFKbBt48aNdzx/1KhRd30NGxsbbGxsCm3XarVl8uYsq3IrE0u/BqVV/x2xxpbQkIAQbG1s/92Rdg12fFjgWMXeHTvbwu9zc5H3gNTfkusPcg3Kov4lLa/ECVBRsrOzWb58ORkZGfTq1Yt69eo9SHGFeHh4oNFoCrT2ACQkJBRqFRLCEhkUA9tijMtfFJr9+dC3oM8tsMnGufA0FkIIYYlK3A7+6quvMnHiRNPz3NxcOnbsyLPPPssbb7xBy5Yt2bt3b6kGZ21tTevWrQkPDy+wPTw8/I635AthKY7fOE5CVgIOWgfa+9wy4aeiwNGfAchrOcq02dPTu5wjFEKIiqnECdCGDRvo0ePfvzB/+uknLl26xLlz57h58ybDhg3j/fffv+cA0tPTiYyMJDIyEoDo6GgiIyNNt7lPmTKFb775hm+//ZZTp04xefJkYmJiGDdu3D2/lhBVTf7aX138umCtsf53R/zfxrW/rOz40+lh0+b6AZIACSEE3EMXWExMDEFBQabnmzZt4pFHHqFGDeMdJxMnTryvGZcPHTpESEiI6Xn+QOSnn36aJUuWMHz4cBITE5kxYwZxcXE0adKE9evXm15XCEulKApbY4yzPxfq/jr5u/GYuj343+E8+v+zWZOXWY4RCiFExVXiBEitVheY52ffvn28/fbbpueurq7cvHnzngMIDg6+6/xB48ePZ/z48fdcthBVWXRKNBdTL6JVa3nI76GCO08Zl6U54RLM+cgsyB8b7V67fIMUQogKqsRdYA0bNjSt9XXixAliYmIKtNxcunRJBiYLUY7yu7/a+7TH0drx3x3Xz8CNM6DW8vHFWgAsafwt9P0/aNDPHKEKIUSFU+IWoFdffZXHH3+cdevWceLECcLCwqhVq5Zp//r162nXrl2ZBCmEKCw/ASrU/XXK2P2V6tuZ7VE5WKlV9O3dD1xsby9CCCEsVolbgB5++GHWr19Ps2bNmDx5MsuXLy+w397eno4dO5Z6gEKIwuIz4jmReAIVKoIDggvu/Kf76w9dGwAGNvfFW5IfIYQo4J7mAerZsyc9e/YssC0lJYWffvqJ3377jaNHjzJ58uRSDVAIUVj+4OcWni3wsPP4d8fNSxB3FEWlZu5l47xcY7vIuB8hhLjdfc+Hv3XrVp588kl8fHz47LPPCAsL49ChQ6UZmxCiGMXe/XV6HQCXHJpz3eBEl3oeBPmW3lIuQghRVdxTC9CVK1dYsmQJ3377LRkZGTz66KPodDp+/fXXArfICyHKTnJ2MoeuGf/Y6B7QveDOfxKgZalNAWn9EUKI4pS4BSgsLIygoCBOnjzJZ599RmxsLJ999llZxiaEKMLOqzvRK3rqudUjwDng3x0ZNyBmDwBrda1p4OVE13oexZQihBCWrcQtQJs2bWLChAm88MILpb7mlxCi5LZcKuburzMbQDFwWlWLK0p1Pu5au9BCwkIIIYxK3AK0a9cu0tLSaNOmDe3bt+fzzz/n+vXrZRmbEOI2WXlZ7Ik1tvIUHv+zFoB1ua3xdLJhYHPf8g5PCCEqjRInQB07duTrr78mLi6O559/nmXLluHn54fBYCA8PJy0tLSyjFMIAeyJ3UO2PhtfB18auDX4d0dOGsp546rwGw1tGdW5JtZW932PgxBCVHn3/Alpb2/PM888w+7duzl27Bgvv/wyH374IZ6engwcOLAsYhRC/CP/7q/ugd0Ldm+d24RKn8NFgxdXtDV4op2slSeEEHfyQH8iNmjQgI8++ogrV66wdOnS0opJCFGEPEMeO67sAIwJUAHHfgVgraEDj7YJxMVeW97hCSFEpVIqbeQajYbBgwfz+++/l0ZxQogiRFyLICUnBTcbN1p6tvx3R9ZNlHObAPjD0JkxD9UqpgQhhBD5ZJCAEJVEfvdXt4BuWKlvuYHz1B+oDDpOGQKo26QtAe72ZopQCCEqD0mAhKgEFEVh6+WiZ3/OO/oLAL/rO/NM55rlHZoQQlRKkgAJUQmcTDpJfEY8dlZ2dPDp8O+OtHg0l3YDcNy9B60C3cwUoRBCVC6SAAlRCeRPfviQ30PYWt2ysvuxlahQOGSoT3D7tjLxoRBClJAkQEJUAtsuG+f4CQkI+XejopB9YAkAvytdGNrSzwyRCSFE5SQJkBAV3KXUS0QlR2GlsqKrf9d/d1w5iG3yObIUa7IbDMHNwdp8QQohRCUjCZAQFVz+3V9tvNvgYuNi2p536DsA1hk6MLhDI7PEJoQQlZUkQEJUcFtiilj8NCcNjhsnP9zu0JcOtauZIzQhhKi0JAESogK7nnmdv6//Ddw2/uf4Kqz0WZw3+NCoXW/Uahn8LIQQ90ISICEqsG2Xt6Gg0NSjKV4OXqbtWf8Mfl5hCGFYmwAzRSeEEJWXJEBCVGD5kx8WWPsr/hh21w6jUzQk1B6Cp7NtMWcLIYQojiRAQlRQablp7I/bDxRMgPT7vgTgT0Nb+ndqbpbYhBCispMESIgKavfV3eQZ8qjlUovaLrWNGzOT4NgKANbaDKBbfU8zRiiEEJWXJEBCVFD5d391D7il++vIj2j02Zw01KBB255oZPCzEELcF0mAhKiAcvW57LqyC7jl9neDnrz9XwPwnb43w9oGmis8IYSo9CQBEqIC2he3j8y8TDztPGns0di48dwmrFJjSFYcuFFzAAHu9uYNUgghKjFJgISogPJnfw4JDEGtMv6aGvZ/BcAyfQgPd6hvttiEEKIqkARIiApGb9CbFj813f114xzqC1sxKCrWWYfSs5HXHUoQQghxN5IACVHBHL1+lKTsJJy0TrT1bmvceMA49meLoSUd27TG2kp+dYUQ4kHIp6gQFUx+91fXgK5o1VrIScMQ+RMA3+n7MLytzPwshBAPShIgISoQRVEKL34auRR1bjrnDT7oArpQp7qjGSMUQoiqQRIgISqQc8nnuJJ+BWu1NZ19O4NBj+6v+QAs0fdhePsaZo5QCCGqBkmAhKhAtlwytv508u2EvdaerONr0aZeJFlxINpvEAOa+5o5QiGEqBqqRAI0ZMgQ3NzceOSRRwpsX7t2LQ0aNKBevXp88803ZopOiJILjwkHoGeNnhgMCpfXzwZgjaYP/xvZGa2mSvzKCiGE2VWJT9MJEybw/fffF9iWl5fHlClT2Lp1K4cPH+b//u//SEpKMlOEQtzdpdRLnLt5DiuVFcEBwaz4/TfqZx8jV9HQ+tH/Ut3JxtwhCiFElVElEqCQkBCcnJwKbDtw4ACNGzfGz88PJycnwsLC2Lhxo5kiFOLuwi8ZW3/a+bQjIjob2wjjqu+x/mE0bdjQnKEJIUSVY/YEaOfOnQwYMABfX19UKhVr1qwpdMyCBQuoVasWtra2tG7dml27dt213NjYWPz8/EzP/f39uXr1ammGLkSp2nxpMwAtqnXlg2Wb6afeB0DN/q+ZMywhhKiSzJ4AZWRk0Lx5cz7//PMi9y9fvpxJkybx5ptvcuTIEbp06UJoaCgxMTF3LFdRlELbVCpZOVtUTLHpsZxIPIEKFSt2OPNI3nqsVAYMNbuCTzNzhyeEEFWOlbkDCA0NJTQ0tNj9c+bMYcyYMYwdOxaAuXPnsnHjRhYuXMisWbOKPc/Pz69Ai8+VK1do3759scfn5OSQk5Njep6amgqATqdDp9OVuD53k19WaZZZ2Vj6NSiq/hujjd2zjtTj2rUcnrA1ToZoaDcOfRW8TvIekPrf+q8lsvRrUJb1L2mZZk+A7iQ3N5eIiAhef/31Att79+7Nnj177nhuu3btOH78OFevXsXZ2Zn169fzzjvvFHv8rFmzmD59eqHtmzZtwt6+9FfdDg8PL/UyKxtLvwa31n9l2koArsc3YKTVdpzIJM3Gh61nc+HcenOFWObkPSD1t3SWfg3Kov6ZmZklOq5CJ0A3btxAr9fj5VVw4UcvLy/i4+NNz/v06cPhw4fJyMjA39+f1atX07ZtWz755BNCQkIwGAy89tprVKtWrdjXmjp1KlOmTDE9T01NJSAggN69e+Ps7FxqddLpdISHh9OrVy+0Wm2plVuZWPo1uL3+17Ou8/bqtwEwpAUx2ekDyAL77i8T1qq/maMtG/IekPpbcv1BrkFZ1j+/B+duKnQClO/2sTuKohTYVtzdXQMHDmTgwIEleg0bGxtsbArfZqzVasvkzVlW5VYmln4N8uu/PWoHCgr6rABm1E7EKeYq2LmjafkEmip+feQ9IPW35PqDXIOyqH9Jy6vQCZCHhwcajaZAaw9AQkJCoVYhISqr1Wf+BMAqqxmP86txY7tnwbr0u16FEEIYmf0usDuxtramdevWhfoIw8PD6dSpk5miEqL03My+yZmUSABGulZHHX8UtPbQ7nnzBiaEEFWc2VuA0tPTiYqKMj2Pjo4mMjISd3d3AgMDmTJlCiNHjqRNmzZ07NiRr776ipiYGMaNG2fGqIUoHVsubUXBgD7bh7GGfxL9Vk+DQ/Hj1YQQQjw4sydAhw4dIiQkxPQ8fyDy008/zZIlSxg+fDiJiYnMmDGDuLg4mjRpwvr166lRQ1bFFpXfr2c2AOCb5Y9b8q+gtoKOL5o5KiGEqPrMngAFBwcXOWnhrcaPH8/48ePLKSIhykdabhonbh4C4BXVP3NWNX0UXAPMGJUQQliGCj0GSIiqbMeVnSjo0eS40femcdkLOk80b1BCCGEhJAESwkx+PWOcvuGh/Dm7GvQDT1n0VAghyoPZu8CEsES5Si7Hkg8A8ELmGePGhyabMSIhhLAs0gIkhBmc1p3FQC6uuVYE5WZD7RAIaGvusIQQwmJIAiSEGezPPA7A4MwkVAAhb5g1HiGEsDSSAAlRzjJ1mcQoxm6vsIwMqNMDAtqZOSohhLAskgAJUc62X9mBotIRoNPRMFcHwVPNHZIQQlgcSYCEKGcrTq8HoG9GJkqdHjL2RwghzEASICHKUXpuOsdv7gegT3omahn7I4QQZiEJkBDlaOulrejJo2auDlevYPBvY+6QhBDCIkkCJEQ5Wn3kOwB6ZmTj3H+mmaMRQgjLJQmQEOUkNSuJo5lnAbDPbY1VtdpmjkgIISyXJEBClJOtO6ejU0GNXD3p1R81dzhCCGHRJAESojxkJrHxonHtL8eMBtRwtTVzQEIIYdkkARKiHKRsfY99Nsal96p5P4NGfvOEEMKs5GNYiLJ2/Qxbz6wgT6XCNseVh5vLnV9CCGFukgAJUdY2vcVGe2OXV25mRzrUcjdzQEIIISQBEqIsRW3m5vnN7LMzJkBd/Hqglf4vIYQwO/kkFuJ+3bwI6deL36/Pg41vssnBHr1KhT7bl+EtWpVbeEIIIYonCZAQ9yMjEeY1h4/rFn9MxGK4fprfHJ0BcNa3o0PtauUUoBBCiDuRBEiI+3HjzL//1+cV3p+VjLLtA65aaThmawWKioWDxkj3lxBCVBDyaSzEfVH9+9+c1EJ787b/H6qsJH6w9wGgSbVWtPSrWU6xCSGEuBtJgIS4H/rcf/9/WwKkvx4F+79CAVY5uQEwrOHAcgxOCCHE3UgCJMT9yE3/9//Z/yZAiqJw+oeJWJHHT1ZNyLJOQavW0rNGTzMEKYQQojiSAAlxP3Iz/v1/Tprpv8t++YnGqbvJU9RENO0AQDf/bjhbO5d3hEIIIe5AEiAh7sctSU9+F9jXO87S4sT/ARBVczjHdX8D0K92v3IPTwghxJ1JAiTE/bitC2z5wRjOb/qSRuoYsq2cSev+KPEZ8Thpneji38V8cQohhCiSlbkDEKJSuqULLDXpGp+E/8VG7TIAbHu+wbqrOwHoWaMnNhobs4QohBCieJIACXE/cv5tAUo8t493NddxU6WjeDVB1+opwn/tDUj3lxBCVFSSAAlxP3JSTP+tFbuOWhowoEE96HN2xe8jNTcVTztP2njJyu9CCFERyRggIe5Hxo1Cm+K7zgLflvwW9RsAYbXD0Kg15R2ZEEKIEpAWICHuxz8J0Jl2M1n+12li7eqzMOQ5ErMS2XVlFwCD6gwyZ4RCCCHuQFqAhLgfGcZV4Hcme/CtPhS3oGBUKhXro9eTp+TRpFoT6rrdYaFUIYQQZiUJkBD3458WoC0xBgBCGngCmLq/BtWV1h8hhKjIJAES4l7lZoLOeBv88WQt1ho1net6cDrpNGdunkGr1hJaK9TMQQohhLgTSYCEuFepVwHI1diTjh3ta7vjYGNlav3pHtgdFxsXc0YohBDiLqp0AhQdHU1ISAhBQUE0bdqUjIyMu58kxN3cvARAvMoLUNG9oSc6vY51F9YBMvhZCCEqgyp9F9ioUaN4//336dKlC0lJSdjYyIy8ohQkGxOgc7lugHH8z84rO7mZc5PqdtXp6NvRnNEJIYQogSrbAnTixAm0Wi1duhjXYXJ3d8fKqkrne6K8/JMAxRiqU9vDgZoeDqw5vwaA/nX6Y6WW95kQQlR0FTYB2rlzJwMGDMDX1xeVSsWaNWsKHbNgwQJq1aqFra0trVu3ZteuXaZ9586dw9HRkYEDB9KqVSs++OCDcoxeVGnXzwJwQfEhpKEnN7JusPvKbkC6v4QQorKosAlQRkYGzZs35/PPPy9y//Lly5k0aRJvvvkmR44coUuXLoSGhhITEwOATqdj165dzJ8/n7179xIeHk54eHh5VkFUUcr1UwBEKX50b+jJmqg15Cl5NK/enDqudcwcnRBCiJKosG31oaGhhIYWfyvxnDlzGDNmDGPHjgVg7ty5bNy4kYULFzJr1iz8/f1p27YtAQEBAISFhREZGUmvXr2KLC8nJ4ecnBzT89TUVMCYSOl0utKqlqms0iyzsqnU10CXidU/g6CvWNWgma8D761fCcDQOkNLVKdKXf9SYunXQOpv2fUHuQZlWf+SlllhE6A7yc3NJSIigtdff73A9t69e7Nnzx4A2rZty7Vr17h58yYuLi7s3LmT559/vtgyZ82axfTp0wtt37RpE/b29qVbAZDWKCrnNXDJvEgwCkmKI24Odny5fj5XM65iiy36U3rWn15f4rIqY/1Lm6VfA6m/Zdcf5BqURf0zMzNLdFylTIBu3LiBXq/Hy8urwHYvLy/i4+MBsLKy4oMPPqBr164oikLv3r3p379/sWVOnTqVKVOmmJ6npqYSEBBA7969cXZ2LrXYdTod4eHh9OrVC61WW2rlViaV+Rqojv0CZ+Cc4s9j3ZqyP3MLZMCg+oMY3GZwicqozPUvLZZ+DaT+ll1/kGtQlvXP78G5m0qZAOVTqVQFniuKUmDb3brRbmVjY1PkbfJarbZM3pxlVW5lUhmvQWbcKayAKIMfrWprmf3nTgAebfjoPdelMta/tFn6NZD6W3b9Qa5BWdS/pOVV2EHQd+Lh4YFGozG19uRLSEgo1CokRGlKiz4EwE2XhuyO32Aa/Fzfrb6ZIxNCCHEvKmUCZG1tTevWrQv1HYaHh9OpUyczRSWqPIMBp6S/AXCu3Y5fz/0KwLD6w8wZlRBCiPtQYbvA0tPTiYqKMj2Pjo4mMjISd3d3AgMDmTJlCiNHjqRNmzZ07NiRr776ipiYGMaNG2fGqEVVprsRhb0hg2xFi1JLy9VjV3HSOtG7Zm9zhyaEEOIeVdgE6NChQ4SEhJie5w9Qfvrpp1myZAnDhw8nMTGRGTNmEBcXR5MmTVi/fj01atQwV8iiirt4dBf1gLOqWhxIMt7tNaDOAOys7MwbmBBCiHtWYROg4OBgFEW54zHjx49n/Pjx5RSRsHTJUfsAOFutHruuGmcdf7zh4+YMSQghxH2qlGOAhDAH+xvG8T97q6tRUHjI7yFqutQ0b1BCCCHuiyRAQpTA5esp1Mk7T4ZKxV95xrXAnmj0hJmjEkIIcb8kARKiBP6O2IWtSscKJ3cy9FnUdK5JJ1+541AIISorSYCEKIH009sxAMtcXQHj2B+1Sn59hBCispJPcCHuIitXj1fSIfbZ2XJVo8NB68CguoPMHZYQQogHIAmQEHdxKDqB1qrTfOfsBMDguoNx0DqYOSohhBAPosLeBi9ERXH15D48bPLYY2+HRqXhyUZPmjskUYEYDAZyc3Pv6RydToeVlRXZ2dno9foyiqzisvT6g1yDB6m/VqtFo9E8cAySAAlxF6pLu1ns4gxA75q98XfyN3NEoqLIzc0lOjoag8FwT+cpioK3tzeXL18utKizJbD0+oNcgwetv6urK97e3g907SQBEuIODAYF6/QDbPS3B2B049FmjkhUFIqiEBcXh0ajISAgALW65CMKDAYD6enpODo63tN5VYWl1x/kGtxv/RVFITMzk4SEBAB8fHzuOwZJgIS4gwsJKRx1jsWgsqeDe1MaVWtk7pBEBZGXl0dmZia+vr7Y29vf07n53Wa2trYW++VnyfUHuQYPUn87O+PyQwkJCXh6et53d5jlXXUh7sHxyE2sc7IFYGyr/5g5GlGR5I9bsLa2NnMkQlie/D86dDrdfZchCZAQd7DtylKy1Wrq5FnTzreDucMRFZAljt8QwtxK4/dOEiAhipGcncxfmvMADHZqL190QghRhUgCJEQxvj66iBy1QoOcXPq3ksHPQliq7du3o1KpSE5OfqByatasydy5c0slJvHgJAESogg3s2/yy5mlAIxKzsGjdmszRyRE6Rg1ahQqlQqVSoVWq8XLy4tevXrx7bff3vPt/KJ0TZs2zfSzufWxefNm0zFJSUlMmjSJmjVrYm1tjY+PD6NHjyYmJqZAWfk/53HjxhV6nfHjx6NSqRg1alSJ4mrQoAHW1tZcvXq10L4LFy7w+OOP4+vri62tLf7+/gwaNIizZ8+ajrm1Lg4ODtSrV4/Ro0cTGRlZsgtTRiQBEqIIS04sIVvJoVFOLv7qILDAuzRE1dW3b1/i4uK4ePEiGzZsICQkhIkTJ9K/f3/y8vLMHZ5Fa9y4MXFxcQUeXbt2BYzJT4cOHdi8eTMLFiwgKiqK5cuXc/78edq2bcuFCxcKlBUQEMCyZcvIysoybcvOzmbp0qUEBgaWKJ7du3eTnZ3NsGHDWLJkSYF9ubm59OrVi9TUVFatWsWZM2dYvnw5TZo0ISUlpcCxixcvJi4ujhMnTjB//nzS09Pp2bMn33///X1cpdIhn+pC3CYpO4mlp42tPy8kp5DjJ6u+i6rFxsYGb29v/Pz8aNWqFW+88Qa//fYbGzZsKPAlFxMTw6BBg3B0dMTZ2ZlHH32Ua9euAZCSkoJGoyEiIgIwzs/i7u5O27ZtTecvXbrUNE/LxYsXUalUrFq1ih49euDr60vLli3Zu3ev6fhLly4xYMAA3NzccHBwoHHjxqxfvx4w3nU3ZswYatWqhZ2dHQ0aNGDevHkF6jVq1CgGDx7MBx98gJeXF66urkyfPp28vDxeffVV3N3d8ff359tvvzWdkx/XsmXL6NSpE7a2tjRu3Jjt27ff8Rru2bOHrl27YmdnR0BAABMmTCAjI8O0PyEhgQEDBmBnZ0etWrX46aefSvSzsbKywtvbu8Aj/07DN998k9jYWDZv3kxYWBiBgYF07dqVjRs3otVqefHFFwuU1apVKwIDA1m1apVp26pVqwgICKBly5YlimfRokWMGDGCkSNH8u2336IoimnfyZMnuXDhAgsWLKBDhw7UqFGDzp07M3PmzALvA/h34sKaNWvSu3dvVqxYwbBhw5gwYQI3b94sUSylTRIgIW7z5dEvycrLomGOjuDMLFwadTd3SKISUBSFzNy8Ej+ycvX3dPydHrd+Kd2v7t2707x5c9OXpaIoDB48mKSkJHbs2EF4eDjnz59n+PDhALi4uNCiRQtTovD333+b/k1NTQWMY2e6detW4HXefPNNpkyZws6dO6lXrx6PP/64qdXpxRdfJCcnh507d3Ls2DH+7//+D0dHR8A4b4y/vz+//PILJ0+e5J133uGNN97gl19+KVD+1q1biY2NZefOncyZM4dp06bRv39/3Nzc2L9/P+PGjWPcuHFcvny5wHmvvvoqL7/8MkeOHKFTp04MHDiQxMTEIq/VsWPH6NOnD0OHDuXvv/9m+fLl7N69m5deesl0zKhRo7h48SJbt25l5cqVLFiwwDR53/0wGAwsW7aMJ554Am9v7wL77OzsGD9+PBs3biQpKanAvtGjR7N48WLT82+//ZZnnnmmRK+ZlpbGihUrePLJJ+nVqxcZGRkFEsPq1aujVqtZuXLlfS3nMX78eNLS0ggPD7/nc0uDTIQoxC0upV7ilzPGD9SXk5JIURyo3bidmaMSlUGWTk/QOxvN8tonZ/TB3vrBP84bNmxoSmQ2b97M33//TXR0NAEBAQD88MMPNG7cmIMHD9K2bVuCg4PZvn07L7/8Mtu3b6dHjx5cuHCB3bt3ExYWxvbt25k8eXKB13jllVfo168fqampTJs2jaZNmxIVFUXDhg2JiYnh4YcfpmnTpgDUrl3bdJ5Wq2X69Omm57Vq1WLPnj388ssvPProo6bt7u7ufPrpp6jVaho0aMBHH31EZmYmb7zxBgBTp07lww8/5K+//uKxxx4znffSSy/x8MMPA7Bw4UL+/PNPFi1axGuvvVboOs2ePZsRI0YwadIkAOrVq8enn35Kt27dWLhwITExMWzYsIF9+/bRvn17wNiS0qjR3SdSPXbsmCnpAwgKCuLAgQNcv36d5OTkYsto1KgRiqIQFRVFu3b/fmaNHDmSqVOnmlq6/vrrL5YtW3bXFi6AZcuWUa9ePRo3bgzAY489xqJFiwgJCQHAz8+PTz/9lNdee43p06fTpk0bQkJCeOKJJwr87IpTr149wNgKZw7SAiTELeYdnkeekkdztRcdsnM4Y9sMa638nSAsg6IopukeTp06RUBAgCn5AeOXsaurK6dOnQIgODiYXbt2YTAY2LFjB8HBwQQHB7Njxw7i4+M5e/ZsoRagZs2amf6f3z2W3zIyYcIE3n//fTp37sy7775rSsbyffHFF7Rp04bq1avj6OjI119/XWjwb+PGjQvMLOzl5WVKqAA0Gg3VqlUr1BrTsWNH0/+trKxo06aNqZ63i4iIYMmSJTg6Opoeffr0wWAwEB0dzalTp0xl5GvYsCGurq5FlnerBg0aEBkZaXr8+uuvdz0HMLUC3j5dh4eHB/369eO7775j8eLF9OvXDw8PjxKVuWjRIp588t/Fn5988klWrVpV4G64F198kfj4eH788Uc6duzIihUraNy4cYladYqLubzIJ7sQ/4hMiCT8UjhqlZqnE41N8ime7c0clags7LQaTs7oU6JjDQYDaalpODk7lcoyCHbaB18ZG4xJT61atYCCydCtbt3etWtX0tLSOHz4MLt27eK9994jICCADz74gBYtWuDp6VmoxUKr1Zr+n19O/t1nY8eOpU+fPqxbt45NmzYxa9YsPvnkE/7zn//wyy+/MHnyZD755BM6duyIk5MTs2fPZv/+/cWWn/8aRW0ryR1vxX0xGwwGnn/+eSZMmFBoX2BgIGfOnLnj+XdibW1N3bp1C22vXr06rq6unDx5ssjzTp8+jUqlok6dOoX2PfPMM6buufnz55cojpMnT7J//34OHjzIf//7X9N2vV7P0qVLeeGFF0zbnJycGDhwIAMHDuT999+nT58+vP/++/Tq1euOr5F/p1j+e668SQuQEBg/1D859AkAg2oP4KFk419+9g1CzBmWqERUKhX21lYlfthZa+7p+Ds9SuMv6K1bt3Ls2DFTN1BQUBAxMTEFxsqcPHmSlJQUU1KTPw7o888/R6VSERQURJcuXThy5Ahr164t1PpTEgEBAYwbN45Vq1bx8ssv8/XXXwOwa9cuOnXqxPjx42nZsiV169bl/PnzD1zvfPv27TP9Py8vj4iICBo2bFjksa1ateLEiRPUrVu30MPa2ppGjRqRl5fHoUOHTOecOXPmgeYRUqvVPProo/z888/Ex8cX2JeVlcWCBQvo06cP7u7uhc7t27cvubm55Obm0qdPyZL0RYsW0bVrV44ePVqgReq1115j0aJFxZ6nUqlo2LBhgQHhxVm4cCHOzs707NmzRDGVNkmAhAD+uPAHkdcjsbOyY0y1TtiRzU3FkbpNZPyPqHpycnKIj4/n6tWrHD58mA8++IBBgwbRv39/nnrqKQB69uxJs2bNeOKJJzh8+DAHDhzgqaeeolu3bgW6doKDg/nxxx/p1q0bKpUKNzc3goKCWL58OcHBwfcU16RJk9i4cSPR0dEcPnyYrVu3mpKtunXrcujQITZu3MjZs2d5++23OXjwYKldk/nz57N69WpOnz7Niy++yM2bN4sdLPzf//6XvXv38uKLLxIZGcm5c+f4/fff+c9/jOsFNmjQgL59+/Lss8+yf/9+IiIiGDt2rGkRz/s1c+ZMvL296dWrFxs2bODy5cvs3LmTPn36oNPpim3d0Wg0nDp1ilOnTpVo4VCdTscPP/zA448/TpMmTQo8xo4dS0REhCkxGjRoECtXruTkyZNERUWxaNEivv32WwYNGlSgzOTkZOLj47l06RLh4eEMGzaMlStXMn/+/BJ1DZYFSYCExUvNTTW1/jzf7Hk4exiAo5rGeLve2yrfQlQGf/75Jz4+PtSsWZO+ffuybds2Pv30U3777TfTF6RKpWLNmjW4ubnRtWtXevbsSe3atVm+fHmBskJCQtDr9QWSnW7duqHX6++5BUiv1/Piiy/SqFEj+vbtS4MGDViwYAEA48aNY+jQoQwfPpz27duTmJjI+PHjH+xC3OLDDz/k//7v/2jevDm7du3it99+K3asTLNmzdixYwfnzp2jS5cutGzZkrfffts0pgmM894EBATQrVs3hg4dynPPPYenp+cDxejh4cG+ffsICQnh+eefp3bt2jz66KPUrl2bgwcP3nHgsbOzM87OziV6nd9//53ExESGDBlSaF+9evVo2rQpixYtwt/fn5o1azJ9+nTat29Pq1atmDdvHtOnT+fNN98scN7o0aPx8fGhYcOGvPDCCzg6OrJlyxZGjBhxbxehFKmU0rh/sgpKTU3FxcWFlJSUEr9pSkKn07F+/XrCwsIK9Utbiop2DWbum8myM8uo7VKblQNWEvv5AGrc3MuK6i8x7MWZpf56Fa3+5lAVrkF2djbR0dHUqlULW1vbezrXYDCQmpqKs7NzqYwBqmwqUv0vXrxIrVq1OHLkCC1atCi3161I18AcHrT+d/r9K+n3t+VddSFucTLxJL+cNd72/kb7N9ACXslHANDU6mLGyIQQQpQlSYCExdIZdLy7510MioHQmqG092mPEncUWyWbZMWBmo3b3r0QIYQQlZLcBi8s1jd/f8PppNO42rjyWjvjZGc3T+/EHTisNKCzn6tZ4xNClL2aNWuWykzaovKRFiBhkU4nnearv78CjF1fHnbGwY5Z5/8C4IpTc2ysSmduFSGEEBWPJEDC4uTqc3lr91vkKXn0COxB35p9jTsUBZfrxoUd9f4yAaIQQlRlkgAJizMnYg5nbp7B1caVtzq89e8kckkXcMy7SY5ihVfDDuYNUgghRJmSBEhYlC0xW/jp1E8AzHxopqnrCyA3eg8Afyu1aVHLu8jzhRBCVA2SAAmLEZsey9t/vQ3AqMaj6OrftcD+5NM7AThl1Rhf1websVUIIUTFJgmQsAiZukwmbptIWm4azTyaMaFl4UUMtVcPAJDm1abQPiGEEFWLJECiyjMoBt766y1OJ53G3dad2d1mo9XcNvtwRiJuWRcBcKrbufyDFMKMgoODmTRpkrnDMPnrr79o2rQpWq2WwYMHs337dlQqlWkx0SVLlpht/ajbVaRYxL2RBEhUeQsiFxB+KRytWsu8kHn4OvoWOkaJNa7/dd7gQ1CdGuUdohCVyu0JSUldvHiRMWPGUKdOHXx8fKhXrx7vvvsuubm5BY6bMmUKLVq0IDo6miVLltCpUyfi4uJwcXEpstxp06aV6TIW+fW9/fHWW28xfPhwzp49e8+xTJ8+HTc3NzQaTYEyN2/ebDomKSmJSZMmUbNmTaytrfHx8WH06NHExMQUKGvUqFGoVCrGjRtX6HXGjx+PSqVi1KhR913/qkomQhRV2tLTS/ny7y8BeLfju7TwbFHkcSkXInAFTlGTnn5Ff8gKIR7M6dOnMRgMLFy4EG9vby5evMjzzz9PRkYGH3/8sem48+fPM27cOPz9/U3bvL3L/sYEnU53x7Xpzpw5U2BtKUdHR+zs7O57lfeGDRuyZcuWAmthubu7A8bkp0OHDlhbW7NgwQKaNGnCxYsXeeutt2jbti179+4tsPhpQEAAy5Yt43//+58pnuzsbJYuXUpgYOB9xVfVVdkWoMuXLxMcHExQUBDNmjVjxYoV5g5JlLO1F9bywf4PABjXfByD6g4q9tiMS8YWoBtODbHVygSIourKyMjgqaeewtHRER8fHz755JNCx/z444+0adMGJycnvL29GTFiBAkJCYCxFSckJAQANze3Aq0Lf/75Jw899BCurq5Uq1aN/v37c/78eVO5ffv2ZfHixfTu3ZuaNWsycOBAXnnlFVatWmUqW6VSkZiYyDPPPINKpWLJkiV3bHFasmQJ06dP5+jRo6ZWlCVLlgCQkpJiWoXd2dmZ7t27c/ToUdO5+a013377LbVr18bGxuaOs0J7enri7e1tejg6OhboArtTLEWxsrIqUJ63tzfW1tYAvPnmm8TGxrJ582bCwsIIDAyka9eubNy4Ea1Wy4svvligrFatWhEYGGi6lgCrVq0iICCAli1bFhuDJauyCZCVlRVz587l5MmTbN68mcmTJ5ORkWHusEQ52XRxE2/tfguAEQ1HML75+Dseb5t4AgC1T/Myj01UUYoCuRklf+gy7+34Oz3uYSmHV199lW3btrF69Wo2bdrE9u3biYiIKHBMbm4u7733HkePHmXNmjVER0ebkpyAgAB+/fVXwNgiEhcXx7x58wBjcjVlyhQOHjxoatkYMmQIBoOh2HhSUlJMrR4BAQHExcXh7OzM3LlziYuLY/jw4Xesz/Dhw3n55Zdp3LgxcXFxpnMURaFfv37Ex8ezfv16IiIiaNWqFT169CApKcl0flRUFL/88gu//vorkZGRJb6O9xLLvTIYDCxbtownnniiUMuXnZ0d48ePZ+PGjQXqATB69GgWL15sev7tt9/yzDPP3F9lLECV7QLz8fHBx8cHMGbt7u7uJCUl4eDgYObIRFn7Leo33tnzDgbFwIDaA/hvu//+O9lhUXLSqJZzBQDP+rIAqrhPukz4oPD4sqKoAdfSfO03YsH67p9t6enpLFq0iO+//55evXoB8N133xXoagIKfGnWrl2bTz/9lHbt2pGeno6jo6MpYfH09CwwAPjhhx8uUM6iRYvw9PTk5MmTNGnSpFA858+f57PPPjO1Qmk0Gry9vVGpVLi4uJSo28vOzg5HR0dTa0q+rVu3cuzYMRISErCxsQHg448/Zs2aNaxcuZLnnnsOMCZ7P/zwA9WrV7/ra91+nS5dulSiWIpz8uTJAl1qQUFBHDhwgOvXr5OcnEyjRo2KPK9Ro0YoikJUVBTt2rUzbR85ciRTp041taT99ddfLFu2jO3bt981FktUYVuAdu7cyYABA/D19UWlUrFmzZpCxyxYsIBatWpha2tL69at2bVrV5FlHTp0CIPBQEBAQBlHLcztp1M/8dZfb2FQDAytN5T3Or+HWnXnt3nO1b8BiFPcaVKv9h2PFaIyO3/+PLm5uXTs2NG0zd3dnQYNGhQ47siRIwwaNIgaNWrg5OREcHAwQKHBt0WVP2LECGrXro2zszO1atUq9ry4uDjCwsIYNmwYY8eOfcCaFRYREUF6ejrVqlXD0dHR9IiOji7QLVejRo0SJT8Au3btIjIy0vRwc3N7oBjr1avH4cOHTeXlt6zdTX433e1/2Hl4eNCvXz++++47Fi9eTL9+/fDw8CiqCEEFbgHKyMigefPmjB49utBfFQDLly9n0qRJLFiwgM6dO/Pll18SGhrKyZMnCwz4SkxM5KmnnuKbb7654+vl5OSQk5Njep6amgoYB8XpdLpSqhWmskqzzMqmLK6BzqDj44iPWXHOONZrRIMRvNzqZQx6AwZ98c3vABf+/otGQJS6Fu0drMr8ZyPvgapxDXQ6HYqiYDAYjF08Glt4/UqJzlUUhbT0dJwcHe/cOllSGlu4QzdTPr1eD/BvzLfFZDAYyMjIoHfv3vTq1Yvvv/+e6tWrExMTQ2hoKNnZ2QXOvb2cAQMG4O/vz5dffomvry8Gg4FmzZqZzst39epVBg4cSIcOHfjiiy+K7CK7/XVu3Xb79vyE4NZy9Ho9Pj4+bN26tVDZrq6uGAwGFEXBwcHhjl10t5Zbo0aNQre8lySWoiiKglarpU6dOgXeAwaDgWrVquHq6sqJEyeKLOfUqVOoVCpq1aplqkf+z2/UqFFMmGCc5+yzzz4rtL+iyL9O9xtXfr10Oh0aTcFxmyX9XKmwCVBoaCihoaHF7p8zZw5jxowx/eUwd+5cNm7cyMKFC5k1axZgTGqGDBnC1KlT6dSp0x1fb9asWUyfPr3Q9k2bNmFvb/8ANSlaeHh4qZdZ2ZTWNcgwZLAscxnRedGoUNHLtheN4huxYcOGu56blAOux3fRSA3XrQNKdE5pkfdA5b4G+d0c6enphW7jLhGtPWk5pfSFlJ1WosM8PT3RarVs27aNIUOGAJCcnMzZs2fp0KEDqampREZGcuPGDd544w1Tl09+63pGRgapqammL5jk5GTTHUxJSUmcOnWKjz/+mLZtjV3Je/fuBSArK8v0R2VsbCwDBw6kefPmzJ07l/T09EJxKopCdna26ZzMzEwA0tLSUKvVZGdnoyiKab/BYCA3N9f0HKBBgwbEx8eTnZ1d5F1Qqamp5OTkoNfrC5xXlNtf/1YliaUo+e+ZtLSif3aDBg1i6dKlvPLKK3h5eZm2Z2VlsWDBArp3746VlZXp55GXl0dqaiqdOnUy/THfsWNHUlNTycvLQ6fT3TUmcyiu/neTm5tLVlYWO3fuJC8vr8C+/J/X3VTYBOhOcnNziYiI4PXXXy+wvXfv3uzZY1zPSVEURo0aRffu3Rk5cuRdy5w6dSpTpkwxPU9NTSUgIIDevXsX6KN9UDqdjvDwcHr16nXH2y2rstK8Bnvj9jJ371xu5N3A3sqemZ1m0s2/W4nOTc/J47GvD/AZxubwPmFDsA4Ke6B4SkLeA1XjGmRnZ3P58mUcHR2xtbW9p3MVRSEtLQ0nJ6fSaQEqIWdnZ5555hmmTZuGv78/Xl5evPXWW6jVaqytrXF2dqZRo0ZYW1vz3Xff8fzzz3P8+HHmzJkDgIODA87OzgQFBaFSqdixYwdhYWHY2dkRGBhItWrV+Pnnn6lbty4xMTG8++67gHFsjLOzM7GxsQwaNIjAwEDee+89srOzTfW/dcyMSqXC1tbW9Nmb/0eok5MTzs7O2NraolKpTPsbNGhATEwMFy5cwN/fHycnJwYOHEjHjh156qmnmDVrFg0aNCA2NpYNGzYwaNAg2rRpg42NDRqN5q6f8be//q1KEkv+GKRb5d/tVdx74KOPPmL37t088sgjfPjhhzRp0oTo6Gjeeecd8vLy+OKLL0yvqdVqsbKyMj0/efKk6ecNxmRdq9WW6nfZg3rQ34Hs7Gzs7Ozo2rVrod+/kiZ6lTIBunHjBnq9vkBWDODl5UV8fDxgnEl0+fLlNGvWzDR+6IcffqBp06ZFlmljY1Pkm1Sr1ZbJB3RZlVuZPMg1yNRl8tmRz/jx1I8A1HGpw+xus6nnVq9E5xsMCq/+epSr1xKoaxsLgEOdTlCOPxN5D1Tua6DX61GpVKjV6kKtAneT3+Sff355+vjjj8nIyGDw4ME4OTnx8ssvk5qaaorFy8uLJUuW8MYbb/DZZ5/RqlUrPv74YwYOHGiqa0BAANOnT+eNN95gzJgxPPXUUyxZsoRly5YxYcIEmjVrRoMGDfj0008JDg42nbd582aioqKIioqicePGBeK6/fbzW6/rrf8WtX3YsGGsWbOGHj16kJyczOLFixk1ahTr16/nzTffZOzYsVy/fh1vb2+6du2Kj48ParXa9MV7t5/B7a9f3L47xXK7/Ncu7j3g6enJvn37mDFjBi+88AJxcXFUq1aNvn378uOPPxZo1cq/5T6/nNu76W7fXxE86O9A/s+vqM+Qkn6mqJQ7TXpQQahUKlavXs3gwYMBYxOqn58fe/bsKTCYb+bMmfzwww+cPn36gV8zNTUVFxcXUlJSSr0FaP369YSFhVXaD/4H9aDXYGvMVmYdmEV8hjHZHd5gOK+0eQVbqxL+FZ4aS8SPb2EdH0Et1TUcVVngEgiTj91zLPdD3gNV4xpkZ2cTHR1tuhHjXhgMBlJTU3F2dq5QX0rlxdLrD3INHrT+d/r9K+n3d6VsAfLw8ECj0Zhae/IlJCQUahUSFUxuJiRdxEqfdc+nnrhxgnmH57E3zjiuwM/Rjzfbv0kX/y4lLyR6Fzk/P0lrXXLBeyDrBN9zPEIIISqvSpkAWVtb07p1a8LDw00D+cA4oHLQoOJn+xVmkJsJJ9dA5M+QcAoyb6AFwlChXP/CmHi0eQZci5+q/cSNE3xz7Bs2xxjXyLFSWzG68WiebfYsdlb3MAV91GYMS0dgo8/hmKEmV+uNpK/NMXD2g+D/PlA1hRBCVC4VNgFKT08nKirK9Dw6OprIyEjc3d0JDAxkypQpjBw5kjZt2tCxY0e++uorYmJiilwMTpS/lCunuLntM7yif8POUPAuD53aFq0hG1XcEYg7AnvnG5OgLq+Ao3E+Dp1ex7bL2/jx1I8cSTgCgAoVA+oM4IXmL+Dv5F/oNe/o7EaUZU+iNuQSrm/Fb3U/4NMnOoC6/AagCiGEqDgqbAJ06NAh03ozgOkOraeffpolS5YwfPhwEhMTmTFjBnFxcTRp0oT169dTo4as5G0Oqdk6DlxI4sLxvTQ49w0P5e7GRWUcXnbZUJ2l+hB2GFpwWalOKg74kEhH9QkettpFZ07A/i/IifiJwx2eY4uDmo2XNpKckwwYW3xCa4byTJNnqOtW996DOxeOsvxJVIZcNujb8oXHGyx9vC1qSX6EEMJiVdgEKDg4+I6L0gGMHz+e8ePvvMaTKDs5eXr+PB7PLwcuob24nVGaP3lO889CgyrYZ9WGszWfxKtFH4ZUd6JVYiZR19M5dy2ViLMKm/O6syq3HU0ct1DbaRfH7fO4fvXfRWvdbarxSIOHGd5gOJ72nvcXZNRmlGVPoNLnsl7fjunWL7P66Y7YW1fYt74QQohyIN8C4p5FJaSx9MBlNkWcoWfuFt7ThFPb2jgg3YCaWP9Q7Lu/Qofarehwy3m1q9tTzz8bn/gYUvR/cNP+JlHJUVzCgHFFHSvsDAZ6ZmQRmp5FZHoL9t1sgkNGBn2bZOHreg/jfQDDqfUoK0ahMeTwp74trygT+Onp9vdcjhBCiKpHEiBRIlm5etYdi+PX/VE4XtlBP80+pqgjcNAaZxw1WDuhbjUSVdux2Du4EZsey6GLm7icdpnzyeeJSo7iQsoFcvT/LjfCP5Pn1nCuQRe/LnTx74Jvjiusm0rN7O10sfqdgXH7ePvyaGasbU6H2u483MqfsKY+ONgU/9ZVDAYu/DaTWkc/QYPCJn1r3rKazJyHW9My8MHW7hFCCFE1SAIkipWj07EzKpbw42e4cXYLrZTDPK85TY6rjpsaDd9rbLjp4MdNj9rctHUiIfsEcRseI1ufXWyZNhob6rnWwyXdhcFtB9PKp1Xh7q2XfoNTa9Gve4Ua6XF8b/1/7DUE8enFIbx6IYh3fjtBaBNvHm7tT8fa1QqM5Tly4hS5v0+hfY5xRvBlSi+uPTSDrd3q4WxbOeebEUIIUfokASpnh64dIiIngtzzuajVagzcslAdBgzKPwvrKYX/r/DPcYoBAwZQMJ2jKAoKSoHj9IqePEMeOoMOnV5n/Pefx63bTf836MjKyyYlO43svAzyuGV9FX84CIDrbTXKg9SzcNvM49XtquPj6IOfgx91XOtQ160udV3r4u/oj0FvYP369fQI7FH8JHiN+qOp3Q22fQAHvqYjJ+lofZJLKn9W69qxK7IpLxzxw9nZjUebu9LVMY6kg7/QKW0jtiodOkXDlpqT6TXsNao5Fp7hWwghhGWTBKicrTi3gvCscFbvX23uUEpMoyjYK2BvZYe9rStujj642brjZuuGm42b8V9bNzzsPPB18MXbwRtrjXWx5d1tdXYTGyfoOws6jIe/5sLRZdTIvcIkqytMslplPCaX/MzMSAWX7JtiP3Qefeu2vt8qCyHKkEaj4ccff+Txxx8vcr+iKDz//POsXLmSmzdvcuTIESZNmkSLFi2YO3du+QYrqixJgMpZQ7eGXIq9hJenFxqNBhUq1Co1atU/68mo1KhRg+rf/6tUKtNxBf6PqsDzAmX8c6xWrS340OvQJV4hK/4iVjcv4Zp5BXdDKlpFQaso2CgK9gYFldoJlU87fBv1xK5OCKpqdaAcF20swDUA+n0CPafByd/h7J9w5SCkxZkOuYY7F13aUSPkGWo0722+WIWo4EaNGkVycrJpjcR827dvJyQkhJs3bxZaS6q8/fnnnyxZsoTt27dTu3ZtPDw8WLVqVYEW45o1azJp0iQmTZp0x7JuX0rpfk2bNo3p06cDxgTO1dWVoKAghg4dygsvvFDkWpLPPfccixYt4qeffuKxxx4rsG/69OnMmDHDFKO3tzchISF8+OGHBAQEmI4LDg5mx44dzJo1q9AC4GFhYWzYsIF3332XadOmFdgXHBzM9u3bi6zL7ft0Oh1vvfUW69ev58KFC7i4uNCzZ08+/PBDfH19iyzj4sWL1KpVq8h9YPz5REdHm+qXz9HRkQYNGvD666/Ts2dPwHht16xZQ2RkpOm4Xbt2MWDAAEaOHMmnn35aJosGSwJUzkY3Ho3XJS/CgsthDaTsFAxXj3Dj7H6yLh3CMfEY1XRxhQ4zKCouqf1IdWuCa70OBLTsg9qrUcVLImycoOUTxgdAbgbossHKBi8bR2QRFCGqhvPnz+Pj40OnTp1M29zd3c0YkVHjxo3ZvHkzBoOBxMREtm/fzvvvv88PP/zA9u3bcXJyMh2bmZnJ8uXLefXVV1m0aFGhBAigYcOGbNmyBTDW+cUXX+TRRx9l7969BY4LCAhg8eLFBRKg2NhYtm7dio+Pj2nb2rVr8fX1pVWrVqZty5Yto1WrVpw9e7bYfV5eXhw+fJi3336b5s2bc/PmTSZNmsTAgQM5dOhQkdciICCAuLjC3yeHDh1i8ODBvPjiiwW2L168mL59+5KcnMzs2bMZPnw4GzZsMCVBt1q3bh3Dhg3j1VdfNSWdZUESoKoiJx3i/0Z3OYKU8wfRxEfilnUJNXD7DDrRBi8u2jQg06MZDrXaUrNJR2r6eJZJhl2mrB2MDyFEqUtMTOSll15i165dJCUlUadOHd54440C3VbBwcE0a9YMW1tbvvnmG6ytrRk3blyB1ohz584xZswYDhw4QO3atZk3b94dX3fUqFF89913gLHloEaNGly8eJHg4GBTF1hwcDCXLl1i8uTJTJ48GSi8mjwYWyEA05JJ+WUBLFy4kI8//pjLly9Tq1Yt3nrrLUaOHHnH2KysrPD29gbA19eXpk2b0qtXL5o3b87//d//8f7775uOXbFiBUFBQUydOhUfHx8uXrxoiuf28tRqNb6+vjz77LNMmDDBtEhovv79+/PLL7/w119/0blzZwCWLFlC7969iYmJMR1Xu3ZtJk+eTIcOHUhOTubRRx+lWrVq9OnTh7y8vGL3ubi4EB4eXiC2zz77jHbt2hETE1Ng5fl8Go3GdC3yXbt2jRdeeIHHHnuMV155pcA+V1dXvL298fb25osvvmDZsmVFJkA///wzo0ePZvbs2UyYMOGOP48HJQlQZZSbCdeOo1w9TNalQxiuHsEh9TwqFLSAxy2HXjZU56SqNkkujbHyb4VPow40rVuTWnZyR5QQpUlRFLLySrbIr8FgICsvCyudVamsBG5nZVfqf8BkZ2fTunVr/vvf/+Ls7My6desYOXIktWvXpn379qbjvvvuO6ZMmcL+/fvZu3cvo0aNonPnzvTq1QuDwcDQoUPx8PBg3759pKam3rXLat68edSpU4evvvqKgwcPotFoCh2zatUqmjdvznPPPcezzz5bbFkHDx7E09PT1PqQX9bq1auZOHEic+fOpWfPnqxdu5bRo0fj7+9fYAWCkmjYsCGhoaGsWrWqQAK0aNEinnzySVxcXAgLC2Px4sV3bM2Ij49n1apVaDSaQnW2trbmiSeeYPHixQUSoI8++qhAshkUFMTGjRsZMWIER48e5cUXXzRdHzc3t2L3FSUlJQWVSlXi7lCdTsfDDz+Mt7c333zzzR2P1Wq1WFlZkZeXV2D7/PnzmTJliunalTVJgCq6vJx/kp0jZFw8iOHqERxTolCjRwXY33JorOLOMUNtzmvrofdugXvddjSrX4cePk5YaR78Q1YIUbysvCza/9z+7geWgf0j9mOvtb/7gf9Yu3Ytjo6OBbbp9foCz/38/Ar8Ff+f//yHP//8kxUrVhRIgJo1a8a7774LQL169fj888/ZsmULvXr1YvPmzZw6dYqLFy/i729cv++DDz4gNDS02NhcXFxwcnIqsoUhn7u7OxqNBicnp2KPAahe3bi2YH7rQ76PP/6YUaNGmVYSmDJlCvv27ePjjz++5wQIjEnQpk2bTM/PnTvHvn37WLXKeLPGk08+yYQJE3j33XcLJLwnT57E2dnZmBBnGZPnCRMm4OBQuGV7zJgxPPTQQ8ybN4+IiAhSUlLo169fgQTozJkzTJo0ibZt29K8eXPCw8M5cuQIM2fOJCEhodh9bm4F50fLzs7m9ddfZ8SIEQVaou7kpZdeIioqikOHDmFra1vscTk5OcyePZvU1FS6du1q2n7q1Cleeumlckt+QBKgCiNbp+fazTTSYo6hvxKBdcLfuCYfp3rmeazIQwXc+nF1XXHhqKE2x5XaJDo3xiawFQ3r1aNtTTd6u9tXvu4sIUS5CQkJYeHChQW27d+/v8AXj16v58MPP2T58uVcvXqVnJwccnJyCn05N2vWrMBzHx8fEhISAOOXWmBgoCn5AejYsWNpV+eenTp1iueee67Ats6dO9+1e644iqIU+MxdtGgRffr0wcPD2B4fFhbGmDFj2Lx5M7179zYdV69ePX7//Xd0Oh2//fYbK1asYObMmUW+RrNmzahXrx4rV65k27ZtjBw5stA40rNnzzJz5kxatWrFzp07+eWXX1i6dCnXr1+/475bEyCdTsdjjz2GwWBgwYIFJar/F198wZIlS9i2bVuBn/WtHn/8cTQaDVlZWbi4uDB79mx69epl2u/v74+rqysfffQRoaGhBcY2lRVJgMzFkAfXzrJ585/cOLePhobzNFLFUEOlK3RokuLI34Y6nFDVJtmlMWr/1gQE1qaJvyvjvJ2w1RZuIhZClC87Kzv2j9hfomMNBgNpaWk4OTmVWhfYvXBwcKBu3YILC1+5cqXA808++YT//e9/zJ07l6ZNm+Lg4MCkSZPIzc0tcNztX8IqlQqDwTjVRVHjcirKH2e3x3F7EnMvTp06ZbojSq/X8/333xMfH4+V1b9fsXq9nkWLFhVIgLRaLXXr1kWtVtO4cWPOnTvHCy+8wA8//FDk6zzzzDPMnz+fkydPcuDAgUL7BwwYUGhb/pit+vXrF7svn06n49FHHyU6OpqtW7eWqPVn9+7dTJgwgQULFhQYtH67//3vf/Ts2RNnZ2c8PT0xGAykpv47gZyTk5MpQQwODmbbtm3F3oFWWiQBKmfqPZ/y0NmlWB17HvKyMA3/+uczMB17LljX55pjI1Jcm5Dn3QJ7z1rU83Kis6cjWunKEqJCUqlUJe6GMhgM5FnlYa+1L5UEqCzs2rWLQYMGmVqFDAYD586do1GjRiUuIygoiJiYGGJjY01fZrff4XS/rK2tC3XbFUWr1RY6rlGjRuzevZunnnrKtG3Pnj33VLd8p0+f5s8//2Tq1KkArF+/nrS0NI4cOVJgLM/p06d54oknSExMpFq1akWW9fbbb1O/fn0mT55c4G6tfCNGjOCVV16hefPmBAUF3TGu4m6BL25ffvJz7tw5tm3bVmyMt7p8+TIPP/wwzz33HGPHjr3jsd7e3oWS7tu5ubmxefNm+vTpY0qC/Pz87hrH/ZIEqJypEk5QLeOc8Ym1I7mezcio1hSbwNbY1WyDo3ttmlWQv5CEEJarbt26/Prrr+zZswc3NzfmzJlDfHz8PSUJPXv2pEGDBjz11FN88sknpKam8uabb5ZKfDVr1mTnzp089thj2NjYmLqbijpuy5YtdO7cGRsbG9zc3Hj11Vd59NFHadWqFT169OCPP/5g1apVbN68+Y6vmZeXR3x8fKHb4Fu0aMGrr74KGLu/+vXrR/PmzQuc27hxYyZNmsSPP/7IxIkTiyy/du3aDBo0iHfeeYe1a9cW2u/m5kZcXFypT6GSl5fHI488wuHDh1m7di16vZ74eOMC1+7u7lhbF57YNjs7myFDhuDn58frr79uOv5WdxqfVRwXFxc2bdpE3759TUlQcd1qD6pi/ulRhRlajiSixvPont8Lr1/GeuwG3IZ8hH3r4eadbFAIIW7x9ttv06pVK9Nf497e3vc8maBarWb16tXk5OTQrl07xo4dW+wYl3s1Y8YMLl68SJ06dUyDnYvyySefEB4eTkBAAC1btgRg8ODBzJs3j9mzZ9O4cWO+/PJLFi9eTHBw8B1f88SJE/j4+BAYGEhwcDC//PILU6dOZdeuXTg6OnLt2jXWrVvHww8/XOhclUrF0KFDWbRo0R1f4+WXX2bdunXs3190d6qrq2uRg6QfxJUrV/j999+5cuUKLVq0wMfHx/TYs2dPkefs37+fiIgIjhw5QkBAQIFz8h/3y9nZmY0bN+Ll5UVwcDCXL1++77LuRKUU1UkrSE1NxcXFhZSUlBKPgi8JnU7H+vXrCQsrh4kQKyhLvwaWXn+oGtcgOzub6OhoatWqdce7XoqSP/7B2dm5wnaBlSVLrz/INXjQ+t/p96+k39+Wd9WFEEIIYfEkARJCCCGExZEESAghhBAWRxIgIYQQQlgcSYCEEOIByH0kQpS/0vi9kwRICCHuQ/4kd7fPjCyEKHuZmZlA4ZnI74VMhCiEEPfBysoKe3t7rl+/jlarvadbeQ0GA7m5uWRnZ1vsLdCWXH+Qa3C/9VcUhczMTBISEnB1dS0w2/a9kgRICCHug0qlwsfHh+joaC5dunRP5yqKQlZWFnZ2dhVmbazyZOn1B7kGD1p/V1fX+5pp+laSAAkhxH36//buPaap+/0D+LsUuRZQQLHKxW0ozmtD1UWjCMYpsnmZQ822gA1oRqaZxnhbnFu2mJhtGPdd3HRu3mbc1M3AEjIvxFRwLFNBWSQ6hw4iCGK8sFYQ0Pbz++MXGksLLfS0Bc77lTTxfM6Hc57n6Tnl8Zy2+Pn5YeTIkd2+Dfb06VMUFxcjKSmpz34RpCvknj/AGriS/4ABA1y68tOODRARkQt8fHy6/U3QSqUSz549Q0BAgCx/+ck9f4A16A35y+/GIxEREckeGyAiIiKSHTZAREREJDt8D1An2r9kyWAwSLrdp0+form5GQaDQZb3fQHWQO75A6wB85d3/gBr4M78239vO/qyRDZAnTAajQCAmJgYL0dCRERE3WU0GhEWFtbpeoXg97jbZTabUVdXh5CQECgUCkyePBmXLl2ymtNxrKvl9n8bDAbExMSgpqYGoaGhksRqL7aezu1svbPj3qhBd/J3Zn53atAb8u8q5p7MdSX/jmOd1cObx0BP8+9sXW84BvrKOdC+fPbs2X5zDtgb5zHg3ddBIQSMRiOGDRvW5Zcs8gpQJ3x8fBAdHW1ZViqVNk9Sx7GuljuuCw0NlexJtxdbT+d2tt7ZcW/UoDv5OzO/OzXoDfl3FXNP5rqSf8cxR/XxxjHQ0/w7W9cbjoG+cg50XO4P54C9cR4D3n8d7OrKTzu+CdpJq1atcjjW1bK9n5dKd7btaG5n650d90YNurtdKWvQG/Lv7rbdmX/HMUf1kYon8u9sXW84BvrKOeDMvnuKr4N94xjoLa+DAG+BeZzBYEBYWBj+++8/ybrevkbuNZB7/gBrwPzlnT/AGvSG/HkFyMP8/f3x8ccfw9/f39uheI3cayD3/AHWgPnLO3+ANegN+fMKEBEREckOrwARERGR7LABIiIiItlhA0RERESywwaIiIiIZIcNEBEREckOG6BerKamBsnJyRgzZgwmTJiAn3/+2dshedwbb7yBQYMGIT093duheExBQQESEhIwcuRIfP/9994Ox+Pk+Jw/T+7nvdFoxOTJk6HRaDB+/Hh899133g7JK5qbmxEXF4f169d7OxSv8PX1hUajgUajwYoVK9yyD34Mvherr69HQ0MDNBoN7t27h8TERNy4cQPBwcHeDs1j9Ho9Hj9+jEOHDuGXX37xdjhu9+zZM4wZMwZ6vR6hoaFITEzEhQsXEB4e7u3QPEZuz3lHcj/vTSYTWltbERQUhObmZowbNw6XLl1CRESEt0PzqC1btqCyshKxsbHIzc31djgeFxkZifv377t1H7wC1Iup1WpoNBoAwJAhQxAeHo6HDx96NygPS0lJQUhIiLfD8JiLFy9i7NixGD58OEJCQpCWlobTp097OyyPkttz3pHcz3ulUomgoCAAQEtLC0wmE+T2//TKykr8/fffSEtL83Yo/RobIBcUFxdj/vz5GDZsGBQKBfLz823mfPPNN3jhhRcQEBAArVaL8+fP92hfpaWlMJvNiImJcTFq6Xgy/77C1ZrU1dVh+PDhluXo6GjcuXPHE6FLgseEtDXojee9I1Lk39jYiIkTJyI6OhobN25EZGSkh6J3nRT5r1+/Htu3b/dQxNKTogYGgwFarRbTp09HUVGRW+JkA+SCpqYmTJw4Ebt27bK7/tixY1i7di22bNmCK1euYMaMGZg3bx5u375tmaPVajFu3DibR11dnWXOgwcPkJmZib1797o9p+7wVP59ias1sfc/XYVC4daYpSTFMdHXSVWD3nreOyJF/gMHDsRff/2Fqqoq/Pjjj2hoaPBU+C5zNf9ff/0Vo0aNwqhRozwZtqSkOAaqq6tRVlaGPXv2IDMzEwaDQfpABUkCgMjLy7MamzJlisjJybEaGz16tNi8ebPT221paREzZswQP/zwgxRhuo278hdCCL1eL958801XQ/S4ntSkpKRELFq0yLLu/fffF0eOHHF7rO7gyjHRV5/zjnpag75y3jsixetCTk6OOH78uLtCdKue5L9582YRHR0t4uLiREREhAgNDRWffPKJp0KWnBTHQGpqqrh06ZLksfEKkJu0tbWhrKwMc+bMsRqfM2cO/vjjD6e2IYSATqfDrFmzkJGR4Y4w3UaK/PsbZ2oyZcoUVFRU4M6dOzAajfjtt98wd+5cb4QrOR4TztWgL5/3jjiTf0NDg+V/+waDAcXFxUhISPB4rO7gTP7bt29HTU0NqqurkZubi5UrV+Kjjz7yRrhu4UwNHj16hNbWVgBAbW0trl27hhdffFHyWHwl3yIBAO7fvw+TyYSoqCir8aioKNy9e9epbZSUlODYsWOYMGGC5R7q4cOHMX78eKnDlZwU+QPA3LlzcfnyZTQ1NSE6Ohp5eXmYPHmy1OF6hDM18fX1xY4dO5CSkgKz2YyNGzf2m0+/OHtM9KfnvCNnatCXz3tHnMm/trYW2dnZEEJACIHVq1djwoQJ3ghXclK9LvZlztTg+vXrePfdd+Hj4wOFQoH//e9/bvkkLBsgN+v4/g0hhNPv6Zg+fTrMZrM7wvIYV/IH0C8/AeWoJgsWLMCCBQs8HZbHOMq/Pz7nHXVVg/5w3jvSVf5arRbl5eVeiMpznH1d1Ol0HorI87qqwbRp03D16lW3x8BbYG4SGRkJpVJp09Xfu3fPpvPtj+Sevz1yr4nc8wdYA+Yv7/yB3lUDNkBu4ufnB61Wi8LCQqvxwsJCTJs2zUtReY7c87dH7jWRe/4Aa8D85Z0/0LtqwFtgLnj8+DFu3rxpWa6qqkJ5eTnCw8MRGxuLdevWISMjA5MmTcLUqVOxd+9e3L59Gzk5OV6MWjpyz98euddE7vkDrAHzl3f+QB+qgeSfK5MRvV4vANg8li9fbpnz9ddfi7i4OOHn5ycSExNFUVGR9wKWmNzzt0fuNZF7/kKwBsxf3vkL0XdqwL8FRkRERLLD9wARERGR7LABIiIiItlhA0RERESywwaIiIiIZIcNEBEREckOGyAiIiKSHTZAREREJDtsgIiIiEh22AAREbmBTqeDQqGAQqFAfn6+pNs+d+6cZduLFi2SdNtEcsEGiIic8vwv9Ocfz//NH7KWmpqK+vp6zJs3zzLWWUOk0+mcbmamTZuG+vp6LF26VKJIieSHfwyViJyWmpqKAwcOWI0NHjzYZl5bWxv8/Pw8FVav5e/vj6FDh0q+XT8/PwwdOhSBgYFobW2VfPtEcsArQETktPZf6M8/lEolkpOTsXr1aqxbtw6RkZF49dVXAQDXrl1DWloaVCoVoqKikJGRgfv371u219TUhMzMTKhUKqjVauzYsQPJyclYu3atZY69KyYDBw7EwYMHLct37tzBsmXLMGjQIERERGDhwoWorq62rG+/upKbmwu1Wo2IiAisWrUKT58+tcxpbW3Fxo0bERMTA39/f4wcORL79u2DEALx8fHIzc21iqGiogI+Pj64deuW64XtoLq62u7VtuTkZMn3RSRXbICISBKHDh2Cr68vSkpK8O2336K+vh4zZ86ERqNBaWkpTp06hYaGBqvbNhs2bIBer0deXh7OnDmDc+fOoaysrFv7bW5uRkpKClQqFYqLi/H7779DpVIhNTUVbW1tlnl6vR63bt2CXq/HoUOHcPDgQasmKjMzE0ePHsVXX32F69evY8+ePVCpVFAoFMjKyrK58rV//37MmDEDL730Us8K1oWYmBjU19dbHleuXEFERASSkpIk3xeRbHn8788TUZ+0fPlyoVQqRXBwsOWRnp4uhBBi5syZQqPRWM3funWrmDNnjtVYTU2NACBu3LghjEaj8PPzE0ePHrWsf/DggQgMDBRr1qyxjAEQeXl5VtsJCwsTBw4cEEIIsW/fPpGQkCDMZrNlfWtrqwgMDBSnT5+2xB4XFyeePXtmmbNkyRKxbNkyIYQQN27cEABEYWGh3dzr6uqEUqkUFy5cEEII0dbWJgYPHiwOHjzYZb0WLlxoMw5ABAQEWNUxODhY+Pr62p3/5MkT8corr4jXX39dmEwmp/ZBRI7xPUBE5LSUlBTs3r3bshwcHGz596RJk6zmlpWVQa/XQ6VS2Wzn1q1bePLkCdra2jB16lTLeHh4OBISEroVU1lZGW7evImQkBCr8ZaWFqvbU2PHjoVSqbQsq9VqXL16FQBQXl4OpVKJmTNn2t2HWq3Ga6+9hv3792PKlCkoKChAS0sLlixZ0q1Y2+3cuROzZ8+2Gtu0aRNMJpPN3OzsbBiNRhQWFsLHhxftiaTCBoiInBYcHIz4+PhO1z3PbDZj/vz5+Oyzz2zmqtVqVFZWOrVPhUIBIYTV2PPv3TGbzdBqtThy5IjNzz7/Bu0BAwbYbNdsNgMAAgMDHcaxYsUKZGRkYOfOnThw4ACWLVuGoKAgp3LoaOjQoTZ1DAkJQWNjo9XYtm3bcOrUKVy8eNGmwSMi17ABIiK3SExMxIkTJzBixAj4+tq+1MTHx2PAgAH4888/ERsbCwB49OgR/vnnH6srMYMHD0Z9fb1lubKyEs3NzVb7OXbsGIYMGYLQ0NAexTp+/HiYzWYUFRXZXJlpl5aWhuDgYOzevRsnT55EcXFxj/blrBMnTuDTTz/FyZMn3fI+IyK54/VUInKLVatW4eHDh3jrrbdw8eJF/Pvvvzhz5gyysrJgMpmgUqmQnZ2NDRs24OzZs6ioqIBOp7O5zTNr1izs2rULly9fRmlpKXJycqyu5rzzzjuIjIzEwoULcf78eVRVVaGoqAhr1qxBbW2tU7GOGDECy5cvR1ZWFvLz81FVVYVz587h+PHjljlKpRI6nQ4ffPAB4uPjrW7dSa2iogKZmZnYtGkTxo4di7t37+Lu3bt4+PCh2/ZJJDdsgIjILYYNG4aSkhKYTCbMnTsX48aNw5o1axAWFmZpcr744gskJSVhwYIFmD17NqZPnw6tVmu1nR07diAmJgZJSUl4++23sX79eqtbT0FBQSguLkZsbCwWL16Ml19+GVlZWXjy5Em3rgjt3r0b6enpeO+99zB69GisXLkSTU1NVnOys7PR1taGrKwsFyrjWGlpKZqbm7Ft2zao1WrLY/HixW7dL5GcKETHm+tERF6UnJwMjUaDL7/80tuh2CgpKUFycjJqa2sRFRXV5VydTofGxkbJ/wyGp/dB1F/xChARkQOtra24efMmtm7diqVLlzpsftoVFBRApVKhoKBA0njOnz8PlUpl943fROQcvgmaiMiBn376CdnZ2dBoNDh8+LBTP/P555/jww8/BPD/n3qT0qRJk1BeXg4Adr9mgIgc4y0wIiIikh3eAiMiIiLZYQNEREREssMGiIiIiGSHDRARERHJDhsgIiIikh02QERERCQ7bICIiIhIdtgAERERkeywASIiIiLZ+T8hcb+W94raswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hand Fit the DARM**2 ZPK\n",
    "shift_sq = 0.9\n",
    "z_hand_sq = np.array([-0.07]*18) * 2 * np.pi * shift_sq\n",
    "mp_sq = -0.75 -0.075j\n",
    "mul_sq = 9\n",
    "p_hand_sq = np.array([mp_sq]*mul_sq + [np.real(mp_sq)-np.imag(mp_sq)*1j]*mul_sq)*2 * 2 * np.pi * shift_sq\n",
    "p_hand_sq = np.append(p_hand_sq, [-200]*2 +[-2000]*1 + [-10000]*0)\n",
    "# k_hand_sq = 9e39\n",
    "k_hand_sq = 7e32\n",
    "\n",
    "#plt.loglog(F_bug, Div_PSD**0.5, label = \"Full FOM ASD\")\n",
    "plt.loglog(F_FOM_dwn, FOM_ASD, label = \"Downsampled FOM ASD\")\n",
    "#plt.loglog(F_Hz, abs(TF_all), label = 'ALL ZPK Fit Bary. (order {})'.format(results.order))\n",
    "plt.loglog(F_Hz, np.abs(iir_results.fitter.xfer_eval(F_Hz)), label = \"data2filter Fit FOM\")\n",
    "fq_plot = np.logspace(-2, 5, 10000)\n",
    "\n",
    "\n",
    "hand_sys = SISO.zpk(z_hand_sq, p_hand_sq, k_hand_sq, convention='IIRrational')\n",
    "fr = hand_sys.fresponse(f=fq_plot)\n",
    "\n",
    "plt.loglog(-np.real(p_hand_sq), np.array([1e27]*len(p_hand_sq)), color='black', linestyle='', marker='o')\n",
    "plt.loglog(-np.real(z_hand_sq), np.array([1e27]*len(z_hand_sq)), color='r', linestyle='', marker='x')\n",
    "plt.loglog(*fr.fplot_mag, label = 'Hand fit to DARM**2 ZPK')\n",
    "plt.xlabel(\"Frequency [Hz]\")\n",
    "plt.ylabel(\"ASD\")\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "#plt.savefig('FOM_BNS_fit.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will save our result so it can be loaded by the make full system example file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 940x680 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_p = iir_results.fitter.poles.fullplane\n",
    "out_z = iir_results.fitter.zeros.fullplane\n",
    "out_k = iir_results.fitter.gain\n",
    "\n",
    "# out_z = z_hand_sq\n",
    "# out_p = p_hand_sq\n",
    "# out_k = k_hand_sq\n",
    "\n",
    "pre_save_sys = SISO.zpk(out_z, out_p, out_k, convention='IIRrational')\n",
    "from wield.utilities.mpl import mplfigB\n",
    "plotting_omega = np.logspace(-2, 4, 1000)\n",
    "plt.close()\n",
    "axB = mplfigB(Nrows = 2)\n",
    "fr = pre_save_sys.fresponse(f=plotting_omega)\n",
    "axB.ax0.loglog(*fr.fplot_mag, label='test')\n",
    "axB.ax1.semilogx(*fr.fplot_deg225, label='test')\n",
    "\n",
    "\n",
    "sys_save = SISO.zpk(out_z, out_p, out_k, convention='IIRrational')\n",
    "zpk_dict = {\n",
    "            'z' : [str(zero) for zero in np.asarray(tuple(sys_save.z)).tolist()],\n",
    "            'p' : [str(pole) for pole in np.asarray(tuple(sys_save.p)).tolist()],\n",
    "            'k' : float(sys_save.k)\n",
    "            }\n",
    "\n",
    "save('BNS_FOM.yml', zpk_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is saved. To open it use the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'>\n",
      "39\n",
      "42\n",
      "-2.6027954830162637e+34\n",
      "[-2.06499544e+01 +0.j         -2.31460520e+00 +0.j\n",
      " -2.36675714e+04 +0.j         -1.29081156e+01 +0.j\n",
      " -1.85249029e+04 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -4.94022086e+00 +0.j\n",
      " -1.29183303e+00 +0.j         -9.89052486e-01 +0.j\n",
      " -3.74963778e-02 +0.j         -7.10782084e-01 +1.50105556j\n",
      " -7.10782084e-01 -1.50105556j -3.98864393e+00+13.37090632j\n",
      " -3.98864393e+00-13.37090632j -3.98864401e+00+13.37090626j\n",
      " -3.98864401e+00-13.37090626j -5.87659068e-01 +1.28119215j\n",
      " -5.87659068e-01 -1.28119215j -5.87664308e-01 +1.28119005j\n",
      " -5.87664308e-01 -1.28119005j -4.05717328e-02 +0.04292108j\n",
      " -4.05717328e-02 -0.04292108j -1.19928970e-01+17.71003385j\n",
      " -1.19928970e-01-17.71003385j -7.03816332e-02 +2.25773904j\n",
      " -7.03816332e-02 -2.25773904j]\n",
      "[-5.07240137e+03+0.00000000e+00j -8.37254095e-02+0.00000000e+00j\n",
      " -1.35956576e-01+0.00000000e+00j -5.63690459e-02+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -8.76206516e+01+0.00000000e+00j -3.15322191e+01+0.00000000e+00j\n",
      " -5.73782567e+00+4.07488474e+01j -5.73782567e+00-4.07488474e+01j\n",
      " -5.73782567e+00+4.07488474e+01j -5.73782567e+00-4.07488474e+01j\n",
      " -9.17494552e-01+4.67729536e+00j -9.17494552e-01-4.67729536e+00j\n",
      " -9.79111001e-03+3.76175886e+00j -9.79111001e-03-3.76175886e+00j\n",
      " -1.89534220e-01+6.05891549e-01j -1.89534220e-01-6.05891549e-01j\n",
      " -1.82100115e+00+1.68934241e+01j -1.82100115e+00-1.68934241e+01j\n",
      " -1.26126746e+00+1.65072231e+00j -1.26126746e+00-1.65072231e+00j\n",
      " -1.89534220e-01+6.05891549e-01j -1.89534220e-01-6.05891549e-01j\n",
      " -6.24505932e+02+8.48909812e+02j -6.24505932e+02-8.48909812e+02j\n",
      " -7.85926170e+03+1.19790252e+04j -7.85926170e+03-1.19790252e+04j]\n"
     ]
    }
   ],
   "source": [
    "with open('BNS_FOM.yml', 'r') as file:\n",
    "    filt_dict = yaml.safe_load(file)\n",
    "p = np.array(filt_dict['p'], dtype=np.complex128)\n",
    "z = np.array(filt_dict['z'], dtype=np.complex128)\n",
    "k = np.float64(filt_dict['k'])\n",
    "print(type(k))\n",
    "print(len(z))\n",
    "print(len(p))\n",
    "\n",
    "print(k)\n",
    "print(z)\n",
    "print(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This File has the sole purpose of creating the ASC DHARD Y Model that is then exported to the buzz repository.\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import contextlib\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import control\n",
    "\n",
    "import pytest\n",
    "import scipy\n",
    "from scipy.signal import cheby2\n",
    "from scipy import signal\n",
    "\n",
    "from wield.bunch import Bunch\n",
    "#from wield.control import MIMO\n",
    "from wield.control import SISO\n",
    "from wield.control.ss_bare.ss import BareStateSpace as RawStateSpace\n",
    "from wield.control.ss_bare.ssprint import print_dense_nonzero\n",
    "from wield.utilities.file_io import load, load_ls, save\n",
    "from wield.utilities.mpl import mplfigB\n",
    "\n",
    "from wield.pytest.fixtures import ( \n",
    "    tpath_join,\n",
    "    plot,\n",
    "    dprint,\n",
    "    tpath,\n",
    "    tpath_preclear,\n",
    "    fpath_join,\n",
    "    test_trigger,\n",
    ")\n",
    "\n",
    "from icecream import ic\n",
    "from buzz import ssutil\n",
    "\n",
    "import scipy.optimize\n",
    "from wield.control import SISO\n",
    "\n",
    "\n",
    "def bode(\n",
    "    sys,\n",
    "    axB=None,\n",
    "    F_Hz=None,\n",
    "    omega_limits=None,\n",
    "    include_zp = True,\n",
    "    label=None,\n",
    "    **kwargs\n",
    "):\n",
    "    \"\"\"\n",
    "    sys should be a wield.control.SISO object\n",
    "    \"\"\"\n",
    "    if axB is None:\n",
    "        axB = mplfigB(Nrows = 2)\n",
    "\n",
    "    if include_zp:\n",
    "        z, p = sys._zp\n",
    "        z = z[z.imag > 0]\n",
    "        p = p[p.imag > 0]\n",
    "        F_include = np.sort(np.concatenate(\n",
    "            [\n",
    "                z.imag,\n",
    "                p.imag,\n",
    "                z.imag - abs(z.real) - 1e-6,\n",
    "                z.imag + abs(z.real) + 1e-6,\n",
    "                p.imag - abs(p.real) - 1e-6,\n",
    "                p.imag + abs(p.real) + 1e-6,\n",
    "            ]\n",
    "        )) / (2 * np.pi)\n",
    "        F_include = F_include[F_include > 0]\n",
    "\n",
    "        if omega_limits is None:\n",
    "            omega_limits = (\n",
    "                (F_include[0] + 1e-6) / 3,\n",
    "                F_include[-1] * 3\n",
    "            )\n",
    "\n",
    "    if F_Hz is None:\n",
    "        F_Hz = np.geomspace(omega_limits[0], omega_limits[1], 1000)\n",
    "\n",
    "    if include_zp:\n",
    "        print(\"F_include\", F_include)\n",
    "        F_Hz = np.sort(np.concatenate([F_Hz, F_include]))\n",
    "\n",
    "    fr = sys.fresponse(f=F_Hz)\n",
    "    axB.ax0.loglog(*fr.fplot_mag, label=label, **kwargs)\n",
    "    if hasattr(axB, 'ax1'):\n",
    "        axB.ax1.semilogx(*fr.fplot_deg225, label=label, **kwargs)\n",
    "    return axB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mcculler/local/projects_sync/wield-project-lab/wield-control/src/wield/control/SISO/zpk.py:164: NumericalWarning: StateSpace is large (>50 states), using reduced response fiducial auditing heuristics. TODO to make this smarter\n",
      "  warnings.warn(f\"StateSpace is large (>{self.N_MAX_FID} states), using reduced response fiducial auditing heuristics. TODO to make this smarter\", util.NumericalWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 940x680 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FBNS_mod = SISO.zpk(z,p,k, convention='IIRrational')#.asSS\n",
    "# FBNS_iod = {'F1.in':0, 'F1.out':0}\n",
    "# FBNS = ssutil.makeSys(FBNS_mod, FBNS_iod)\n",
    "# omega_lim = [min(F_Hz), max(F_Hz)]\n",
    "\n",
    "#control.bode(FBNS.mod, Hz=True, wrap_phase=True, omega_limits=omega_lim)\n",
    "\n",
    "from wield.utilities.mpl import mplfigB\n",
    "plotting_omega = np.logspace(-2, 4, 1000)\n",
    "plt.close()\n",
    "axB = mplfigB(Nrows = 2)\n",
    "fr = FBNS_mod.fresponse(f=plotting_omega)\n",
    "axB.ax0.loglog(*fr.fplot_mag, label='test')\n",
    "axB.ax1.semilogx(*fr.fplot_deg225, label='test')\n",
    "\n",
    "# tol_list = range(10, 16)\n",
    "# for tol in tol_list:\n",
    "#     FBNS_tmp = ssutil.balance_sys_gain(FBNS, nr=tol)\n",
    "#     nstates_tmp = FBNS.A.shape[0]\n",
    "#     K_zpk_tmp = SISO.SISOStateSpace(BareStateSpace(FBNS_tmp.A, FBNS_tmp.B, FBNS_tmp.C, FBNS_tmp.D, None)).asZPK\n",
    "#     control.bode(FBNS.mod, Hz=True, wrap_phase=True, omega_limits=omega_lim, label='sts: {}, tol: {}'.format(nstates_tmp, tol))\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.savefig('postsave.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "controls",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}