24_ions_low_trap_coherence (new)_20241205.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "898b4977",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "# from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 200\n",
    "    for n in range(0, N):\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "t = np.linspace(0, 100, 1000)\n",
    "plt.figure(figsize=(4.25, 4.25 / 2))\n",
    "nbar_opt = 43\n",
    "plt.plot(t, 1 - thermal_carrier(t, 1 / 16, nbar_opt), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt}$', zorder=1)\n",
    "plt.title('9 ions carrier rabi oscillation (approximation)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'$S_{1/2}$ state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2b43ce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005288124084472656\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq,root,brentq,newton,curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "def Eqposition3(N):    \n",
    "    def func(u): #the equations\n",
    "        p=np.empty(shape=(N))\n",
    "        for m in range(N):\n",
    "            p[m]=u[m]-np.sum(1/(u[m]-u[:m])**2)+np.sum(1/(u[m]-u[m+1:])**2)\n",
    "        return p\n",
    "    def dfunc(u): # first order derivative\n",
    "        p=np.empty(shape=(N))\n",
    "        for m in range(N):\n",
    "            p[m]=1+2*np.sum(1/(u[m]-u[:m])**3)-2*np.sum(1/(u[m]-u[m+1:])**3)\n",
    "        return p\n",
    "    ni = np.arange(0,N)\n",
    "    guess = 3.94*(N**0.387)*np.sin(1/3*np.arcsin(1.75*N**(-0.982)*((ni+1)-(N+1)/2)))\n",
    "    x0=newton(func,guess,fprime=dfunc,maxiter=100000) # newton method\n",
    "    return(np.round(x0,5))\n",
    "\n",
    "def Axialfull(N, wz): # N is the ion number, wz is the axial frequency, in MHz\n",
    "    t0 =time()\n",
    "    u=Eqposition3(N)\n",
    "    u_1=np.empty(shape=(N))\n",
    "    for i in range(N):\n",
    "        u_1[i]= np.sum(1/np.absolute(u[i]-u[:i])**3) + np.sum(1/np.absolute(u[i]-u[i+1:])**3)\n",
    "    A=np.empty((N,N))\n",
    "    for i in range(N):\n",
    "        for j in range(N):\n",
    "            if j==i:\n",
    "                A[i,j]=1+2*u_1[i] # A_mn, m==n\n",
    "            else:\n",
    "                A[i,j]=-2/np.absolute(u[i]-u[j])**3 #A_mn, m!=n\n",
    "    print (time()-t0)\n",
    "    w,v = np.linalg.eig(A)\n",
    "    idx = np.argsort(w)\n",
    "    w = w[idx]\n",
    "    v = v[:,idx]\n",
    "    return ([w,v])\n",
    "    # return(np.sqrt(([w,v])[0])*wz) / 2\n",
    "\n",
    "Axialfull(9, 0.18)\n",
    "\n",
    "def Axialmodes(N, wz):\n",
    "    return(np.sqrt(Axialfull(N, wz)[0])*wz)\n",
    "\n",
    "#L-D parameter matrix in i，m\n",
    "def eta_z(N, wz):\n",
    "    A = Axialfull(N,wz)\n",
    "    #print(A[1])\n",
    "    eta = np.zeros((N,N))\n",
    "    for i in range(N):\n",
    "        eta[i] = 2*np.pi/(729e-9)*np.sqrt(1.0545718e-34/(2*40*1.66054e-27*2*np.pi*np.sqrt(A[0])*wz*1e6)) *  np.transpose(A[1])[:,i]\n",
    "    #print(time()-t0)\n",
    "    return eta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f2398ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005908012390136719\n",
      "BSB #0 mode real AOM freq:  233.535 MHz\n",
      "BSB #1 mode real AOM freq:  233.46984754089905 MHz\n",
      "BSB #2 mode real AOM freq:  233.40896485098978 MHz\n",
      "BSB #3 mode real AOM freq:  233.3511872257002 MHz\n",
      "BSB #4 mode real AOM freq:  233.29574464645654 MHz\n",
      "BSB #5 mode real AOM freq:  233.2421074718405 MHz\n",
      "BSB #6 mode real AOM freq:  233.18989889524028 MHz\n",
      "BSB #7 mode real AOM freq:  233.13886479251076 MHz\n",
      "BSB #8 mode real AOM freq:  233.08882945969782 MHz\n",
      "\n",
      "RSB #0 mode real AOM freq:  233.713 MHz\n",
      "RSB #1 mode real AOM freq:  233.77815245910094 MHz\n",
      "RSB #2 mode real AOM freq:  233.83903514901021 MHz\n",
      "RSB #3 mode real AOM freq:  233.8968127742998 MHz\n",
      "RSB #4 mode real AOM freq:  233.95225535354345 MHz\n",
      "RSB #5 mode real AOM freq:  234.00589252815948 MHz\n",
      "RSB #6 mode real AOM freq:  234.05810110475971 MHz\n",
      "RSB #7 mode real AOM freq:  234.10913520748923 MHz\n",
      "RSB #8 mode real AOM freq:  234.15917054030217 MHz\n"
     ]
    }
   ],
   "source": [
    "N = 9\n",
    "wz = 0.178\n",
    "Axialmodes_1 = Axialmodes(N,wz) / 2\n",
    "carrier_freq = 233.624\n",
    "for i in range(len(Axialmodes_1)):\n",
    "    print(f\"BSB #{i} mode real AOM freq: \", carrier_freq - Axialmodes_1[i], \"MHz\")\n",
    "\n",
    "print(\"\")\n",
    "\n",
    "for i in range(len(Axialmodes_1)):\n",
    "    print(f\"RSB #{i} mode real AOM freq: \", carrier_freq +Axialmodes_1[i], \"MHz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472e9977",
   "metadata": {},
   "source": [
    "<div align = \"left\">\n",
    "    <img src=\"IMAGE_multiple_mode_rabi.png\" style=\"zoom:80%\">\n",
    "    <br>\n",
    "    <div align = \"center\"\n",
    "          style=\n",
    "           \"color:orange;\n",
    "            border-bottom: 1px solid #d9d9d9;\n",
    "            display: inline-block;\n",
    "            color: #999;\n",
    "            padding: 2px;\n",
    "            font-size: 15px\"> Multi-ion carrier rabi </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a367f2df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "# from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# Define a function to calculate equilibrium positions of N ions in a trap\n",
    "def Eqposition3(N):    \n",
    "    def func(u): # Equations for equilibrium positions\n",
    "        p = np.empty(shape=(N))\n",
    "        for m in range(N):\n",
    "            p[m] = u[m] - np.sum(1/(u[m] - u[:m])**2) + np.sum(1/(u[m] - u[m+1:])**2)\n",
    "        return p\n",
    "    \n",
    "    def dfunc(u): # Derivative of equilibrium equations for optimization\n",
    "        p = np.empty(shape=(N))\n",
    "        for m in range(N):\n",
    "            p[m] = 1 + 2*np.sum(1/(u[m] - u[:m])**3) - 2*np.sum(1/(u[m] - u[m+1:])**3)\n",
    "        return p\n",
    "    \n",
    "    ni = np.arange(0, N) # Initial guess for positions\n",
    "    guess = 3.94*(N**0.387)*np.sin(1/3*np.arcsin(1.75*N**(-0.982)*((ni+1)-(N+1)/2)))\n",
    "    x0 = newton(func, guess, fprime=dfunc, maxiter=100000) # Solve using Newton's method\n",
    "    return np.round(x0, 5)\n",
    "\n",
    "# Define a function to calculate the full matrix of axial mode frequencies and eigenvectors\n",
    "def Axialfull(N, wz): \n",
    "    u = Eqposition3(N) # Get equilibrium positions\n",
    "    u_1 = np.empty(shape=(N))\n",
    "    for i in range(N): # Loop to calculate elements of the A matrix\n",
    "        u_1[i] = np.sum(1/np.absolute(u[i] - u[:i])**3) + np.sum(1/np.absolute(u[i] - u[i+1:])**3)\n",
    "    A = np.empty((N, N))\n",
    "    for i in range(N): # Fill the A matrix with calculated values\n",
    "        for j in range(N):\n",
    "            if j == i:\n",
    "                A[i, j] = 1 + 2*u_1[i] # Diagonal elements\n",
    "            else:\n",
    "                A[i, j] = -2/np.absolute(u[i] - u[j])**3 # Off-diagonal elements\n",
    "    w, v = np.linalg.eig(A) # Calculate eigenvalues and eigenvectors\n",
    "    idx = np.argsort(w) # Sort eigenvalues and corresponding eigenvectors\n",
    "    w = w[idx]\n",
    "    v = v[:, idx]\n",
    "    return [w, v]\n",
    "    \n",
    "# Calculate axial modes frequencies based on the full matrix calculation\n",
    "def Axialmodes(N, wz):\n",
    "    return np.sqrt(Axialfull(N, wz)[0])*wz\n",
    "\n",
    "# Function to calculate L-D parameter matrix for N ions and axial frequency wz\n",
    "def eta_z(N, wz):\n",
    "    hbar = 1.0545718e-34 # Planck's constant over 2*pi\n",
    "    m_ca = 1.67*10**(-27) * 40 # Mass of Ca ion\n",
    "    A = Axialfull(N, wz) # Get full axial matrix\n",
    "    eta = np.zeros((N, N)) # Initialize eta matrix\n",
    "    for i in range(N): # Calculate eta values\n",
    "        eta[i] = 2*np.pi/(729e-9)*np.sqrt(hbar/(2*m_ca*2*np.pi*np.sqrt(A[0])*wz*1e6)) * np.transpose(A[1])[:, i]\n",
    "    return eta\n",
    "\n",
    "N = 9 # Number of ions\n",
    "wz = 0.177 # Axial frequency in MHz\n",
    "eta = eta_z(N, wz) # Calculate L-D parameters\n",
    "\n",
    "# Set constants and initial conditions for the simulation\n",
    "m_ca = 1.67*10**(-27) * 40 # Mass of Ca ion\n",
    "Z = 1 # Charge of the ion\n",
    "e = 1.6*10**(-19) # Charge of an electron\n",
    "epsilon = 8.85*10**(-12) # Vacuum permittivity\n",
    "l = np.power((Z**3*e**2/(4*np.pi*epsilon*m_ca*(wz*2*np.pi*10**6)**2)), 1/3)*10**6 # Characteristic length\n",
    "\n",
    "Axialmodes_1 = Axialmodes(N, wz) # Calculate axial modes\n",
    "M = 16 # Number of phonon states considered\n",
    "nbar = 10 # Average number of phonons\n",
    "Population = np.zeros((N, M)) # Initialize phonon state population matrix\n",
    "mean_phonon = np.zeros(N) # Mean phonon number for each mode\n",
    "# Assume nbar limited by cooling/heating, proportional to mode frequency\n",
    "for i in range(5): # Calculate mean phonon numbers\n",
    "    if i == 0:\n",
    "        mean_phonon[i] = nbar\n",
    "    else:\n",
    "        mean_phonon[i] = mean_phonon[0]*Axialmodes_1[0]/Axialmodes_1[i]\n",
    "\n",
    "for i in range(N): # Calculate phonon state population for each mode\n",
    "    for j in range(M):\n",
    "        Population[i, j] = (mean_phonon[i])**j/((mean_phonon[i]+1)**(j+1))\n",
    "\n",
    "coupling_1 = np.zeros(N) # Coupling coefficients for each ion\n",
    "for i in range(N): # Calculate coupling coefficients\n",
    "    coupling_1[i] = np.prod(np.exp(-eta[i, :]**2/2))\n",
    "\n",
    "# Pre-calculate all the special functions needed for the simulation\n",
    "eta_2 = eta**2\n",
    "laguerre_fun = np.zeros((M, 24, 24))\n",
    "for m in range(M):\n",
    "    for j in range(N):\n",
    "        for k in range(N):\n",
    "            laguerre_fun[m, j, k] = laguerre(m)(eta_2[j, k])\n",
    "\n",
    "intensity_distribution = np.ones(N) # Assume uniform intensity distribution across ions\n",
    "\n",
    "def P_i(t, I):\n",
    "    # Calculate the Rabi frequencies modified by coupling and intensity distribution once for each ion\n",
    "    modified_Rabi = 1 / 5.05 * coupling_1[I] * intensity_distribution[I]\n",
    "    \n",
    "    # Pre-compute the product of Laguerre polynomials for all combinations of phonon states\n",
    "    laguerre_products = np.ones((M, M, M, M, M))\n",
    "    for m_1 in range(M):\n",
    "        for m_2 in range(M):\n",
    "            for m_3 in range(M):\n",
    "                for m_4 in range(M):\n",
    "                    for m_5 in range(M):\n",
    "                        laguerre_products[m_1, m_2, m_3, m_4, m_5] = laguerre_fun[m_1][I][0] * laguerre_fun[m_2][I][1] * laguerre_fun[m_3][I][2] * laguerre_fun[m_4][I][3] * laguerre_fun[m_5][I][4]\n",
    "    \n",
    "    # Compute the final Rabi frequencies considering Laguerre polynomial products\n",
    "    final_Rabi = modified_Rabi * laguerre_products\n",
    "    \n",
    "    # Calculate the population product for all combinations of phonon states\n",
    "    population_products = np.outer(np.outer(np.outer(np.outer(Population[0], Population[1]), Population[2]), Population[3]), Population[4]).reshape(M, M, M, M, M)\n",
    "    \n",
    "    # Calculate probability matrix using vectorized operations\n",
    "    P = population_products * np.cos(final_Rabi * t)**2\n",
    "    \n",
    "    return np.sum(P)\n",
    "\n",
    "# Simulation parameters\n",
    "times = np.linspace(0, 100, 200) # Time array for simulation\n",
    "time_evo = np.zeros((N, len(times))) # Initialize array for time evolution data\n",
    "\n",
    "# # Plot the time evolution of ground state probability\n",
    "# fig, ax = plt.subplots()\n",
    "# for i in range(N):\n",
    "#     ax.plot(times, time_evo[i], label=f\"Spin {i+1}\")\n",
    "# plt.title(\"Spin up population at ground state\")\n",
    "# plt.xlabel(\"Time\")\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37506e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate time evolution of ground state probability for each ion\n",
    "for n in range(N):\n",
    "    for i, time in enumerate(times):\n",
    "        time_evo[n, i] = P_i(time, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "122f34ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py as h5\n",
    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
    "from scipy.interpolate import interp1d\n",
    "import scipy as sc\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import quad\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Setting matplotlib style parameters\n",
    "matplotlib.rcParams.update({\n",
    "    \"font.family\": \"STIXGeneral\",\n",
    "    \"xtick.labelsize\": 12,\n",
    "    \"xtick.direction\": \"in\",\n",
    "    \"xtick.major.pad\": 3,\n",
    "    \"xtick.top\": True,\n",
    "    \"ytick.labelsize\": 12,\n",
    "    \"ytick.direction\": \"in\",\n",
    "    \"ytick.right\": True,\n",
    "    \"axes.labelsize\": 12,\n",
    "    \"axes.labelpad\": 3,\n",
    "    \"axes.grid\": True\n",
    "})\n",
    "\n",
    "# Initialize the figure with a specific size and aspect ratio\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# Example plotting commands (commented out since `rabi_t`, `pmt_counts_avg_thresholded`, `errorbar`, `t_fine`, and `fitted_curve` are undefined in your snippet)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded, yerr=errorbar, fmt='o', c='black', lw=0.5, markersize=4, zorder=2, label=r'Raw Data')  # Adjust markersize as needed\n",
    "# plt.plot(t_fine, fitted_curve, 'C0', zorder = 1, label='Fitted Curve')\n",
    "# Plot the time evolution of ground state probability for N spins\n",
    "for i in range(N):\n",
    "    if(i < 5):\n",
    "        plt.plot(times, time_evo[i] / np.max(time_evo[i]), label=f\"Spin {i+1}\")\n",
    "# Setting title, labels, and legend with specific font settings\n",
    "plt.title('Spin up population at ground state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'$S_{1/2}$ state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# plt.ylabel(r'$D_{5/2}$ state probability', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.legend()\n",
    "# Enabling grid\n",
    "plt.grid(True)\n",
    "# Save the figure with tight bounding box\n",
    "plt.savefig('Spin_up_population_at_ground_state.pdf', format='pdf', bbox_inches='tight')\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Initialize the figure with a specific size and aspect ratio\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "time_evo_in_total = np.zeros(len(times))\n",
    "for i in range(N):\n",
    "    time_evo_in_total += time_evo[i]\n",
    "plt.plot(times, time_evo_in_total / np.max(time_evo_in_total), color = \"C0\", label=f\"Total spin with $\\Omega_{0} = {1/5.05:.2f}$ and $\\\\bar{{n}} = {nbar}$\")\n",
    "\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / 3\n",
    "print(η)\n",
    "t = np.linspace(0, 100, 1000)\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    return a * thermal_carrier(times, b, nbar) + c\n",
    "b = 1 / 16\n",
    "# b = 1 / (5.05 * 3)\n",
    "plt.plot(t, 1 - fit_function(t, 1, b, 0, 10), color = \"C1\", label=f\"Single ion with $\\Omega_{0} = {b:.2f}$, $\\eta = {η:.2f}$ and $\\\\bar{{n}} = {nbar}$\")\n",
    "# plt.plot(t, 1 - fit_function(t, 1, 1/16, 0, 10), color = \"C1\", label=f\"Sinmulation with single ion $\\eta = {η:.2f}$\")\n",
    "\n",
    "# Setting title, labels, and legend with specific font settings\n",
    "plt.title('Spin up population at ground state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'$S_{1/2}$ state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# plt.ylabel(r'$D_{5/2}$ state probability', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.legend()\n",
    "# Enabling grid\n",
    "plt.grid(True)\n",
    "# Save the figure with tight bounding box\n",
    "plt.savefig('Spin_up_population_at_ground_state_total.pdf', format='pdf', bbox_inches='tight')\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72f7fd8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_samples = 50\n",
    "\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240214/results/\"\n",
    "date = \"2024-03-07/\"\n",
    "number = 7497\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "\n",
    "# Plotting\n",
    "nbar = 6.076080795652779\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "plt.title('9 ions rabi oscillation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# Plot the smooth curve with a higher zorder to ensure it's on top\n",
    "# plt.plot(rabi_t, pmt_counts_avg_thresholded, c='C0', lw=2, zorder=1, label='Smoothed Curve')\n",
    "pmt_counts_avg_thresholded_renew = pmt_counts_avg_thresholded[2:]\n",
    "plt.errorbar(rabi_t[2:], (pmt_counts_avg_thresholded_renew - 0.14595767082374253) * (1/0.8809862132150643), yerr=errorbar[2:], fmt='o', c='black', lw=1, zorder=2)\n",
    "# plt.plot(times, scaled_array, zorder=0)\n",
    "times = np.linspace(0,100,500)\n",
    "plt.plot(times, thermal_carrier(times, 0.0622167267179297, nbar), zorder=1, label = f'Simulation with $\\\\bar{{n}} = {nbar:.2f}$')\n",
    "# Scatter plot of the original data\n",
    "# plt.scatter(rabi_t, pmt_counts_avg_thresholded, c='black', s=10, lw=1, zorder=2)\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.grid(True)\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "pdf_filename = 'Rabi_9_ions.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9589f9e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 0.8746129787341189, b = 0.062794562906589, c = 0.14783338177001276, nbar = 5.231787854596411\n",
      "Optimal parameters with their standard errors:\n",
      "a = 0.8746129787341189 ± 0.047491474611936854\n",
      "b = 0.062794562906589 ± 0.0002520133365431911\n",
      "c = 0.14783338177001276 ± 0.025251734978180188\n",
      "nbar = 5.231787854596411 ± 0.9944125064161446\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 200\n",
    "    for n in range(0, N):\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-15/\"\n",
    "# number = 8591\n",
    "# file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240214/results/\"\n",
    "date = \"2024-03-07/\"\n",
    "number = 7497\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "times = np.linspace(0, 100, 200) # Time array for simulation\n",
    "\n",
    "# /Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/2024-03-15/23\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:-1]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:-1]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [0.8809862132150643, 0.0622167267179297, 0.14595767082374253, 6]  # Example initial guesses for a, b, c, and nbar\n",
    "# 0.8809862132150643, b = 0.0622167267179297, c = 0.14595767082374253, nbar = 6.076080795652779\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, 1, b_opt, 0, nbar_opt - nbar_err)\n",
    "fit_upper_bound = fit_function(times, 1, b_opt, 0, nbar_opt + nbar_err)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "plt.plot(times, fit_function(times, 1, b_opt, 0, nbar_opt), lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / a_opt, yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "number_drop = 2\n",
    "plt.errorbar(rabi_t[number_drop:], (pmt_counts_avg_thresholded[number_drop:] - c_opt) * (1/a_opt), yerr=errorbar[number_drop:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation with filters', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.05, 1.05)\n",
    "pdf_filename = 'Rabi_9_ions_v2.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d11f6fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal parameters: a = 0.8748082396338182, b = 0.06214954439763648, c = 0.1478999892518834, nbar = 5.821031342904066\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    return a * thermal_carrier(times, b, nbar) + c\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [1, 0.062, 0.1, 7]  # Example initial guesses for a, b, c, and nbar\n",
    "\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "\n",
    "# # Plotting the data and the fit\n",
    "# plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.title('9 ions rabi oscillation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# legend_fontsize = 12\n",
    "# plt.legend(fontsize=legend_fontsize)\n",
    "# plt.grid(True)\n",
    "# # pdf_filename = 'Rabi_9_ions.pdf'\n",
    "# # plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57084012",
   "metadata": {},
   "source": [
    "### with 300 us wait time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "084e2104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 1.1596274537560862, b = 0.02334033070639091, c = -0.03603551642667138, nbar = 41.1067232102636\n",
      "Optimal parameters with their standard errors:\n",
      "a = 1.1596274537560862 ± 0.08036730379538339\n",
      "b = 0.02334033070639091 ± 0.00018653675197359567\n",
      "c = -0.03603551642667138 ± 0.040836810055157886\n",
      "nbar = 41.1067232102636 ± 4.650731943905143\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 500\n",
    "    for n in range(0, N):\n",
    "        # res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * (n + 0.5) + η**4 * ((1/8) * (1 + 2 * n * (1 + n)))) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-15/\"\n",
    "# number = 8591\n",
    "# file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8652\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "times = np.linspace(0, 200, 200) # Time array for simulation\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [1, 0.03, 0.1, 20]  # Example initial guesses for a, b, c, and nbar\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, *popt[:3], nbar_opt - nbar_err) / a_opt\n",
    "fit_upper_bound = fit_function(times, *popt[:3], nbar_opt + nbar_err) / a_opt\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "plt.errorbar(rabi_t, pmt_counts_avg_thresholded / a_opt, yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation with 300 $\\mu$$s$ delay time', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-5, 170)\n",
    "pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat_no_shuttling_300us_delay.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "800ef47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal parameters with their standard errors:\n",
      "a = 1.1596274537560862 ± 0.08036730379538339\n",
      "b = 0.02334033070639091 ± 0.00018653675197359567\n",
      "c = -0.03603551642667138 ± 0.040836810055157886\n",
      "nbar = 41.1067232102636 ± 4.650731943905143\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "times = np.linspace(0,200,500)\n",
    "fit_lower_bound = fit_function(times, *popt[:3], nbar_opt - nbar_err) / a_opt\n",
    "fit_upper_bound = fit_function(times, *popt[:3], nbar_opt + nbar_err) / a_opt\n",
    "\n",
    "# Plotting\n",
    "nbar = nbar_opt\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "plt.title('9 ions rabi oscillation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "# Plot the smooth curve with a higher zorder to ensure it's on top\n",
    "# plt.plot(rabi_t, pmt_counts_avg_thresholded, c='C0', lw=2, zorder=1, label='Smoothed Curve')\n",
    "pmt_counts_avg_thresholded_renew = pmt_counts_avg_thresholded[2:]\n",
    "plt.errorbar(rabi_t[2:], (pmt_counts_avg_thresholded_renew - c_opt) * (1/a_opt), yerr=errorbar[2:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, scaled_array, zorder=0)\n",
    "# plt.plot(times, fit_function(times, 1, b_opt, 0, nbar), zorder=1, lw=2, label = f'Simulation with $\\\\bar{{n}} = {nbar:.2f}$')\n",
    "plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "# Scatter plot of the original data\n",
    "# plt.scatter(rabi_t, pmt_counts_avg_thresholded, c='black', s=10, lw=1, zorder=2)\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.grid(True)\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "# pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat.pdf'\n",
    "# plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600e9110",
   "metadata": {},
   "source": [
    "### 9 ion flopping without shuttling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad6edb1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 0.9437525027029401, b = 0.019531803970010052, c = 0.10928143419717275, nbar = 28.647854952627178\n",
      "Optimal parameters with their standard errors:\n",
      "a = 0.9437525027029401 ± 0.10223206171236525\n",
      "b = 0.019531803970010052 ± 0.0002860164843889301\n",
      "c = 0.10928143419717275 ± 0.052824027944253525\n",
      "nbar = 28.647854952627178 ± 5.86648846202098\n"
     ]
    },
    {
     "data": {
      "image/png": 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AOp2u3D0z3YPKeOWVVxAZGYkFCxYAAP7v//4Pjx49MvMrVHTtbm5uePjwIQwGA27cuMHea1Nb0/9N6615/oKCgpCSkoJz584hOzsbO3bsAAB2xFe6vx4+fFhuAqdUKsWkSZOwY8cOVskfP34cDMPg+vXryMjIwGuvvYakpCS88MILOHnyJI4cOYKZM2eyowGTvKZ7WNUza2LJkiU4evQohg0bVmmfW3PPLYXRlv09mdqY7vuiRYtw4MABvPbaa4iKisL27dvxzTffWG15aBAaxq/e/Ll79y5ZsWIFGTlyJLly5UqlbRMSEsjrr79OAJBx48aRmzdvEkKMUVILFy4kzz33HFmxYgWZM2cOG4mSlZVFXnrpJQKA/Pvf/yZ5eXnku+++IwBIr169yL1798iOHTuIi4sLWbduHfnvf/9L3nnnHWIwGMqdPyoqinTp0oUwDEO++OILkpmZSQgxRvaEhIQQJycnsmjRInLq1Cni7OxMpkyZQtRqNTl79iwZMmQImThxIlm8eDGZM2cOiYuLq/SayqLVaskHH3xAhgwZQj744AMyZ84cEhkZyW6fNWsW6dChA9myZQt57733yC+//MJu++uvv0i7du2Ivb09GTZsGHn48CEhhJB79+6RadOmEQBk3rx5JDY2lpw6dYq0a9eOcLlc8v333xO5XE42bdpEGIYhPXv2JOfOnSN6vZ5MmDCBiEQi0q9fP3Ljxg3SvXt30qZNG3Ljxg0il8tJ9+7diZ+fH/n5559JREQE6dq1K2EYhmzZsoXk5+cTQgi5du0aGTx4MJk3bx557733yFtvvUWUSiUhhJCLFy8Sf39/4urqSo4cOULS0tLI+PHjCQDy9ttvE4VCUeFzolarydy5c83WLVq0yCyKKjMzk6xbt44AIOHh4eTChQtsXzk7O5Pw8HBy+/Zt9t5MnDiRJCYmkhMnThAfHx/i5OTERvBU9vwRQohKpSIvvvgisbe3JwMHDiS3b98m3bp1I5999hnJzs4mBoOBDB8+nEilUvLpp59avKaCggIya9YsIpFISGhoKNmxYwfp2bMn+fjjj0lKSgohhJCvvvqK+Pr6EgcHBzJ8+HB2Yuvdu3fJ2LFjCQDy5ptvkqSkpCqfWRMajYZIJBKzqChLWHPPP/30UwKAvPjii+Thw4ckOjqa9O/fn3A4HLJ9+3aSl5dH3nrrLQKAzJ49mzx9+pSoVCqycOFC4uzsTCQSCZkzZw77/DRWGELqeL5/C0Sr1eLUqVNmUTEUCqXxUFhYiFmzZuHgwYMNLUqTgSoLCoXS4vjyyy/h7e3NBkxQqoaGzlIolBbBvXv3sHTpUnTq1AkXLlxoMhmeGwtUWVAolBaBRqNBdHQ0cnJysHPnTqsjEylGqBmKQqFQKFVCQ2cpFAqFUiVUWVAoFAqlSpq1z8JgMCA1NRWOjo7VTnJGoVAozR1CCGQyGXx8fMolhixLs1YWVSUpo1AoFIoxB1jZMrxladbKwtHREYCxI0y1mK0lNzcXQUFBiI+PZ6f8U2wH7d+6hfZv3dJc+rewsBD+/v7su7IyGo2yOH/+PD788ENERERU2i42NhYff/wxOnTogOjoaEybNg2jR4+22NZkenJycqq2sjDlzHF0dKz2vpSqof1bt9D+rVuaW/9aY6ZvcGUhl8vxxx9/4D//+Q8ePnxYadusrCwMHToUv/32G8LDw5Gbm4u2bdvi6NGj6NOnTz1JTKFQKC2PBo+GEovFmDZtGiZNmlRl2//+978QCAQIDw8HYMwIOWrUKLz//vt1LSaFQqG0aBpcWZiwZjblgQMHylUE69WrFyIjI6tV1McahEIhpkyZYnUhH0r1oP1bt9D+rVtaYv82GmVRFSqVCo8fPy7nsffz84PBYMDt27dtej6hUIhp06a1qIehPqH9W7fQ/q1bWmL/NrjPwlry8vJACClXr1ksFgMAMjMzK9w3NzfXrMiLUCis8iab2qtUKqjVaptWyqIYi9bweDzI5XLweE3mMWwy0P6tWxp7/zIMAx6PV85io1arzYpAyWQyq4/Z+K6yAkxF7ct67U3Lpu2WCAoKMlueMmUKpk2bVuU5HR0dcf/+fZpwrI7w8vJCXFxcQ4vRbKH9W7c09v7V6/WQy+VmCmHXrl34/fffa3S8JqMs3NzcwOPxypVmNC1XVC4UMJaDLB1HbM3IIjc3FykpKfDz84ODgwOdAW5jCCFQKBS0b+sI2r91S2PvX0IIlEolsrKy0KVLFzg7OwMAnnnmGXz99ddsO5lMVu5juiKajLJgGAZdunRBSkqK2fqUlBTweDy0bdu2wn3d3NyqFQtNCEF+fj6cnZ0hlUqrnAZPqT4GgwFarRZ2dna0f+sA2r91S1PoXwcHB2g0GuTm5kIikYBhGPD5fNZ0DwB8Pt/q4zXOq6yAsWPHIjo62mzdtWvXMHToUJvOotTr9dDr9bC3t7fZMRsCU96XnJwcyGQy6nehUFoYTk5O7PustjQaZWFyuhgMBnbdqVOn0Lp1a9y9excAMH/+fGRnZ7MVrgoKCnD48GG8/fbbNpVFp9MBsC6ct7GSl5eHmJgYPHz4EPHx8Xj48CFiYmKQl5fX0KJRKJR6wuR8N73TakODKwutVoudO3di165dAICVK1fizp07AIx5S7Kysli/hKenJ06fPo1Nmzbhk08+wWuvvYZvvvkGw4cPbzD5GyN5eXl48uQJNBqN2XqNRoMnT55QhUGhtBBs6U9p1pXyCgsL4ezsjIKCgmr5LFQqFeLi4iCVSpucz4IQgpiYmHKKojR8gQBdOnduUMecwWBAYWEhnJycmlT/NhbkcjnkcrnZOmdnZ9jZ2QGg/VvXNJX+ValUiI+PR1BQEEQiUbnt1XlHNt6rpNQIuVxeqaIAAK1Gg7yCwnqSiGIt1fExffbZZ/D29jb7q2lIpDVcvXoVixcvRseOHeHg4IBWrVph8uTJePTokcX2sbGxmDp1Kvz8/GBvb4927dphzZo1UCqVVp8zOjoaY8aMgZubG+zt7dGpUyd88cUX7PbIyEgwDGPx79KlS7W+5sqIjIyEq6sruFxujc9tTR+9/PLLFV4jwzDlAn7qkiYTDUWxjqoUBWC0X7773vu4dvkSxGIHXL9+HUqlEhs2bMDy5csBAEuXLkVkZCRu3bpVZ7JevHgRx44dw9GjR5GQkFCjY9SHnDWhKrkOHz6MX3/9FcHBwfjkk0+Ql5eHpKQks/snEAjg7+8PV1fXcvvPnDkTAwYMMFvXsWNH215EKT755BNcuHABkyZNQpcuXZCeno6vvvoKPXr0wKVLl9CpUye2bVJSEnr37g1nZ2csXrwYbm5uuHjxIlatWoXr16/j8OHDVZ7vxIkTGD16NLp3744PP/wQYrEYT548QXJycrm2r7/+Ov71r3+ZrQsNDa39RVvBkiVL0Lt372qf29o+mj9/PoYNG2a2LyEECxYsQGBgIHx9fW13MVVAlUUzg8Ot+pb+8ssv+PvPP3E08hI6tHJHbk42xowZg/T0dLZN27ZtoVKp6lJU9OnTB5cvX0ZiYmK19lOpVOyQuj7krAmW5Cot98iRI7FixQp4enqyPqaymHxMISEh5RRGcHAwgoOD6+4CyvDmm2/it99+M5v8OmXKFHTu3BkbNmzAr7/+yq7/5ZdfkJ+fj/Pnz7MKbN68eTAYDPj555+Rl5dnUQGaKCwsxMyZMzFq1Cjs27evSjNPeHg4Jk6cWKvrGzx4MAIDA/Hjjz9Wa78BAwZg8uTJ1T6ftX0UFhaGsLAws33Pnz8PpVKJ6dOnV/u8tYGaoZoRBkJA+CIwVSiMyLNn0bpDJ4AvxNNcJdzd3bFv3z7k5+ezbRYtWoTvvvuuTuXlcDho1apVtfa5f/++mSmiPuSsCWXlKiu3QCCARCIBIQRJSUmVHispKYk1SQ0ZMgQDBw5EdHQ0Ro4cCUdHR/j6+mLLli11cyHF9OvXr1yWhNatW6Njx464f/++2frCQqOJ09PT02y9t7c3OBxOpdkWAOC3335DRkYG1q9fDw6HA4VCYRYlaQmZTGaTiJ+aUJNz16aPfvvtNzAMg5deeqn6wtYCqiyaOKXt3Dl5BSgs0oLr6F75TgyDS1GRSIx/Arlah7QCFfz8/PD888+bNbNFbLYtycnJwcSJE8vZvRubnCZMclUkN2CMBqzKdKjRaFhndkxMDPLz8zF69Gj07NmT9V288cYbiImJsbi/VqtFdna2VX9VvZRLQwhBRkYGpFKp2frBgwcDAObMmYObN28iKSkJv//+O7799lu8/vrr5fK7leXUqVNwcnJCSkoK2rZtC7FYDCcnJyxcuNDiKPKVV16Bk5MTRCIRhgwZgmvXrlV6fEv9odVqoVarq90fc+bMqda5TdS0j7RaLfbs2YN+/fohMDDQqnPZDNKMKSgoIABIQUFBtfYrKioid+/eJRkZGUSv19eRdLUnNzeX3Lp1i1y9erXk7/oNciM2mdyITSbXb9ww2xZ94yaJS04nK9d+TAAQVzcJ+e93P5FbSXlEVqRhj3vz5k3y2muvkR49ehBCCDl9+jR56aWXyMyZM8lvv/1GOnfuTMRiMfn4449JWloaefnll4lEIiFDhgwhhYWFhBBC9u/fT3g8Hlm1ahUhhJDLly+Tfv36kYCAAEIIIXq9nuTl5ZHt27eT0o/huXPnyGuvvUa+/fZbMnLkSLJ//352244dO4iTkxMJCwsjb731FomMjDST00RhYSFZunQpWblyJZk7dy4JDw8nFy5cIIQQ8s8//5CZM2eSl156iezZs4eEhoYSqVRKfv311wr7+eDBg4RhGBIWFkZSU1PJuXPniFQqJZ6enuTs2bNsn0mlUnL+/Ply/VdW7pSUFEIIIYMGDSJz5swhr7/+OvHw8CBubm5k7dq15vez+C87O5ukpqYSAMTd3Z0kJSWx8t27d48AID/99BO7ztS/er2eREREEABW/cXHx1f94BXzyy+/EABk+/bt5batXbuW2NnZmR37gw8+sOq4Xbp0Ifb29sTe3p4sWbKE7N+/nyxZsoQAIFOnTmXbXbhwgUyYMIFs376dHD58mHz88cdEIpEQkUhEoqOjKzy+LfojKiqKjBkzhnz//ffVOndt++iPP/4gAMg333xj1TmKiorIvXv3SFFRkcXt1XlHUmVhgaagLHJzcy2+VEx/9+JTSY5cRZ6mZ5MnSWkkJTOHKFRaotLoiEKlIUuWvUk4HA4BQAYNe45E3X5M9HoDIYSQ7OxsMmvWLPbFrtFoyLBhw0hAQAA5ceIE0ev1ZOvWrawyUKvVJCsrizg5OZGtW7eyMvr7+7PKghBCVq5cWamyMBgMxM3NjX1x79+/n4jFYqJUKtljBAQEsMcsK6fpGOHh4WZyfPnll0QkEpFbt24RnU5HRo0aRQICAsiePXuIRqMhixcvJq6urpXe62HDhpERI0awy6tXryY+Pj7EYDCwfTRt2rQK5Sott4lBgwaR0NBQ8uWXX5IrV66QyZMnE2dnZ3LlypVy97OwsJAcP36cACBfffWV2XFiY2MJALJr1y52XWllkZubS06ePGnVX0UvlbLcv3+fVYA6na7c9l9++YWMGDGCbNu2jezfv5/Mnj2bMAxDvvzyyyqPHRwcTACQBQsWmK2fP38+AUAePXpU4b6xsbHEzs7O7F6VxVJ/dOnShTz77LNW90fp/q3OuUtTkz6aNm0a4fP5JDs726pz2FJZUAd3NRn95XlkydRVN6xjtFotnEUMNj4jsbhdXZAJrZsrBHYOcHPhwV7ALTWvgostn3+GSRMnYsHChTh76m+MHzYAB/88gf49O0MikZgNcfl8Pnx9fVFUVMROgBw8eDB0Oh0GDx4MgUAAqVSKDh064N69e+x+ZR2TVc3rYBgGS5cuRf/+/QEYc9vI5XJkZWVZ9G2UlRMATp8+jaioKBw4cIBdN3/+fKxfvx4bNmzAb7/9BqlUisDAQLY64+jRo/HVV18hIyMD3t7eFmWbPXs2Zs6ciYyMDHh6eqJv375YvXo1oqKiMHDgQBw7dgwTJkyoUK6K6N+/PwYOHAiNRoMBAwZgz549bC4fEwKBAGKxmDUzvfjii2bHePDgAQBUmB/N1dW1XERNbUhPT8eoUaPg7OyMffv2lct0sHv3bsybNw+PHj1i68+MHz8eBoMBy5cvx7Rp08yuryymuSJlM0O/9NJL+O6773Dx4kW0bt3a4r6hoaEYO3YsDhw4AL1ebzELQ+n+IIRALpdDLBZDKpXimWeeqfH8I2vObaImfSSXy3H48GGMGDGi0v6rK6jPoppkydRIL1Q1+F9OkR75qortqTqtFmqlHI4iHhyEPIup3cP7hyHqwkVMfflVZGWmY97smdDorLNZW8raKxQKWcddTVm5ciXy8/OxceNGnDlzBgCqZUe/fv06AJjZfPl8Pjp16oQbN25Y3MfkTCyd578s48aNg1gsxs6dOwGAtRubooCOHDmCMWPGWC1nafz9/SuVw9/fHwzD4Pbt2/Dy8ioXLnnr1i3weDx06NDB4vE1Gg3S09Ot+qvK/1NQUICRI0ciPz8ff//9t8Vsz9988w26d+9erlDZmDFjoFQqK7wPJkzHLOv89fDwAIAqMxD4+/tDo9GUy1BdltIpcVQqFQoKCmqdEsfac9ekjw4dOtQgUVAm6Miimrg7NnxlLIPBAJ1eBxdR5bqexxDYC8xv8ZUrV+Dm5sbGgrs52uGT/27Ck0cPcPWfKDxMTEXnED9Lh7MKUsuEAB988AHS0tLw/fffIyoqChs3bqzW/qavuaSkJLRp04ZdL5VKkZGRUWO5RCIRpk6dip9//hmTJ0+Gk5MTlixZgoULF+K9996Du7t7tTJ4lsbV1RUhISHl5mSUnWcRExODrl27ltv/9u3baNOmTYVp9//55x8MGTLEKlni4+MrHBWpVCqMHj0ajx49wqlTpypUThkZGRZDY00FxaqKHOrZsydOnjzJOrhNpKamAgDc3SsP4IiLi4NIJDLLrlqWmoQrW4M15wZq1kc7d+6EWCyu8UdJbaHKopr8sWRA1Y3qGJlMhocPH1bZTmxX/uUhEAiwdu1a/PTTT+w6H2c7dOzcFbevX4WeZweNzjbRRQzDmI0KqvpqvXjxIj766CPExMSAy+VaHFEwDFOpQurbty8AYyx6aWWRmpqKfv36VfcSzHjllVewdetWLFy4EJs3b4avry/mz5+Pl156CT/88EOl+1Ylt6urKztvwt/fH6GhoRCLxeyIUK/X4/79+xbzoN26dQvdu3ev8Nhdu3bFyZMnrblEeHl5WVyv1+sxZcoUXLx4EYcPHy4X+1+aNm3a4MSJE3j06JHZPdi1axc4HA66dOkCAFAqlXj69CmbVsfE5MmTsWHDBmzfvh1Dhw5l1//www/g8XhsJFFWVlY5xXHr1i0cOXIEI0eOrHB+BrEQrlw2/DopKQkuLi4VmqSysrLKKeeKzm3pOq3to9LnO3XqFKZNm9Zg2bCpsmiCiMViCASCyvM/8QVmBZ9MBAcH4+eff0bXrl3xxhtvgGEYaNQq/HP2NCZMnwUOl4uMQnW5kM6yL3rTi7z0F5Berzd7IQYFBeGvv/7CsmXLEBcXh7NnzyI/Px8FBQWsbKb99Xo9++V4+fJlBAcHs36H5ORkODs7w9XVFW5ubnjw4AG0Wi37b2k5BwwYgBEjRmDTpk2YPn06hEIh4uPjcffuXXbClU6nM5PTtH9V5q7evXujQ4cOEAgE8PDwgEKhwLhx4/DgwYNys6fLylVW7s6dO0On05mV+zX1haOjY7l7FxsbC5VKVW5kUVRUhMePH2PWrFkVym0Ln8Vbb72FI0eOYPTo0cjNzTWbhAcAM2bMYP//73//G3/99RfCw8OxePFiSCQSHD16FH/99Rfmzp3LmpmuXLmCIUOGYNWqVVi9ejW7f/fu3TF79mz873//g06nw6BBgxAZGYm9e/fivffeY/efMmUK7Ozs0K9fP3h4eODevXvYtm0b7O3tsWHDhgqvJS4uDocOHarymi9fvoyXXnrJYhjrtGnTwOPxMHDgQHh6elZ6bkvXaW0fmfj999+h0+kazAQFgIbOWqI5REPl5uZWuK+bmxsBQNq2bUvGjx9PwsPDyYoVH5LouExyKymPbN/zB+nStSthGIZs2bKFREZGksDAQCKVSsmxY8dIdnY2WblyJQFApk+fThISEsixY8eIi4sLad26NYmIiCCEGMNlW7duTZycnMi///1vsnXrVjJp0iSyf/9+otfryalTp0j//v0JALJhwwaSnJxMBg4cSEQiERk5ciS5ffs2CQgIIL169SKJiYmEEEJ++OEHIhaLyejRo8n+/ftJ11Jy5ufnE0IIkcvlZOHCheS5554jK1asIHPmzCG3bt0ihBBy8eJF4u/vT1xdXcmRI0dIWloaGT9+PAFA3n77baJQKCrt908//ZT88ssvbD9v376drFu3zqy/IyIiyslVWu6cnBxy5MgR4uTkRHx8fMiJEydIYmIimThxIgFAlixZYhYBRgghe/bsIQDInTt3zNZfuXKFACBHjx41W28pWqc2DBo0qNIQ07JcvnyZjBw5knh5eRE+n0/atGlD1q9fT7RarVk/ASgXJUaIMbps9erVJCAggPD5fBIaGko2bdpk1mbLli2kd+/exM3NjfB4POLt7U1mzJhBYmNjK72WQ4cO1Tp0dvPmzaRnz55Wnbui67Smj0z07duXeHh4WIw8qwxbRkPRrLMWaCpZZxNTM5GVngoYSr7ueTw+/Fv5Q1KDYlBpBUVspJeHkwheTuWzVNqKppK1szQV2blN1NTOXRc0xf6tL6w147Zt29bi6BxoOv1ry6yz1AzVRDEYCDRcOwg8AkE0RbDnGaN+PNxcYC+s2W2VOAhZZZEjV8NDLASH0/jqCzcExMq0HJXZuSmNA2vMuKZwZUoJjVclUiqlSKuHSmv0I3CF9nB0doHY0REifs2r+wl4HDjbGSN69AaC/KKqM9i2FKxJ/V46LQel8cIwDBuuXBGmcGVKCVRZNFHylBqY7If2Ai5I8b+1HQlIxSURHjlyqixMWJP6vTrtKA2LKVy5bMI+gUDQqMyJjQlqhmqC6A0GFBaV+ClEfC54HA6EvNrXDLcXcCHkcaDWGdjRS21GK82FqjKlVrcdpeFxdXWFi4sLO2o0mZ7oiMIyVFk0QeQqHXTFYZ4CHgcMA9jxOeBWY1RBitMclP2RMAwDNwcB0gqM2T2zZWr4uTVMXHdjgtq5mycMw1ToxKaYQ5VFEyS/qCQ2347HBZdhIKzG139VVdlc7QVIL1CBFJ/Ll5AW/7VlsnNXFg1F7dyU5gz1WTQx9AYD5OoSExSfx4GAxwGfa92tNIV/lv1CNqU5yMvLA4/LgVhk/I4wEIJCldbSoVoc1M5NacnQkUUTQ6bSQW8wuraFPC44DKz2KVQn/NPVXgCZyqiU8hRaONtRWzxQezt3ReY/CqWxQ5VFEyNfWfKVL+JzwONyILByVFGd8E8nBzE4DAMDIZCptdAbDOA24slH9UlN7dxVmf8olMYM/fU3IQwGAoXG+LXPAOBzORDxOFaHy1Yn/JPDYeBYbIoixFxJUaqPNeY/CqUxQ5VFPZOWlobVq1cjLS2t2vvK1SUmKAGPCy6HgYBn/S2sbvinq31Je6osao7eYEDi06eVtklKSqp1encKpS6hyqKeSUtLw3/+858aKYuCUlFQQh4HPA5jtWMbKAn/rIzS4Z9iEQ+cYnu6UqODVm99ESKKEY1Oj/TsfOi0lStbOvub0tihPosmhFxd8sLhcxmI+NxqOUcZhsH9+/exceNGREdHw9XVFd27d4dOp0NOTg6CgoLw2muvsbn0OQwDJxEP+UVaEAB5Cg086jC5YHNDpdWjUKWF2krzn1qjAY34pzRWqLKoR/R6Pa5duwYAuHbtGrp27Vppnd7SGL/si01Q3GLHdjVMUCamTJkChmEwZcoUjBs3DgsXLgRgjNLZv38/nn/+ecyfPx9ff/01OBwOXB0E7LyOgiJtOWWhUqksZrNs6Wh0RkVhIIBIaJ35T0s4MBDCjuYolMYENUPVEwcOHEBgYCDmz58PAJg/fz4CAwPZAj9VUVjKBMXnccDnMuDVMA+UqZaxh4cHgoKC0LZtW/Tq1QsbNmzA2rVrsXXrVqxcuRIAIBby2JnhKq0e6jJV9EzKhlKCTm9AoUoHg4FAyOVAZO8AHq/ykqs8Hh/giaBQV15ylEJpKKiyqAcOHDiAiRMnIjk52Wx9SkoKJk6caJXCMM15AIwjCyGveiYoSwiFQkgkEjg6OrLHWr58OYKCgvDZZ58hKysLDMNAXJzynAAoKOXo3rp1K1t9jmKEEAKFWgetzgBBca4uBgykXj6V7if18gGfy4FCo0eRxjZlbSkUW0KVRR2j1+uxdOlSi5EupnXLli2rtD613mBg05FzGKZ4xnbdmCp4PB7GjBkDtVqNM2fO4O7du1i9fBn2/roDb82fhW+//RaEEKSkpODIkSMAgLfffptVGlFRUVi8eDG2bt2K559/3uqRU3NBpdWjSGsAn8eB6Q4REBgEDhC4egOcMpZfDg/2Uh/w7B3B5TBgACjUOuhoMAGlkUGVRR0TFRVVbkRRGtOs6qioqArbyFQ6Nh25oAZRUNUlNDQUAJCYmIgZM2YgJDAAk2e8grmL38Sa99/G47h4+Pr6YvLkyQCAzz77DC+//DIIIXjxxRcRFhaGBQsWYO7cuZg1axaKiorqTNbGhE5vgFytB8MA3OKRmtZgQEahGvlKDSB0gMAjEHw3X/CcvcB384XAIxA6nj0yC1XIU2rA4zDQGgxQaPQ0lJbSqKDKoo6xNkS2snal/RVGExSnTlNE8Hg89t/Zs2dj1KhRsBfyYGdvD4PBgAexcRb3YxgGS5cuRf/+/QEADg4OkMvlyMrKqjNZGxNFWj10BgM7o16l0yOzUG0Wcizic+Hq4gyp1A3OTk5mmYLlah2y5WrwOAyKNHqodXR0YS2EEMhkMuTk5EAmk1FFWwfQaKg6xtvbu9btFKVs2AJezaKgqkNqaioAICAgABMmTMCTJ0/w83dfokBpLLkqU2kr/DGuXLkSN2/exMaNG5GTkwPAWK+4uaPRGVCk0YNXnBJFrTMgW66GqZv4XA7cHATlUrM42fEhV2tRoDSGJxv308DFjg+lRgcBj0Ojo6qAplGpH+jIoo4JDw+Hn59fhSMBU+rr8PBwi9tVWj37ZcovzgPFq0MTFACcOXMGQqEQw4YNw7fffotly5bhraWvY8To8QAAtU5f4QS9Dz74AF988QXeeustjBw5sk7lbEwUaXXQEwIeh4HOYK4o7PhceDoJLebwYgA4CvnwcBKVmK70BhSqtFBpS3xVFMvQNCr1h83fOp9//rmtD9mk4XK52LJlCwCUUxim5c2bN1c436KgjAmqrr80//77b1y4cAHvvvsuZDIZFi9ejPnz58PJ0QGC4qfFQIyjHZP8plHGxYsX8dFHH+HNN98El8ttESMKwDinQqU1gM/hgIAgW66BobhPRHwuJGIBGFR+zwRcDjychKzCUOsMUKh1UGr0bIoXijnWZlGmJinbUCNlERERgR49esDOzg5cLtfs79///retZWzyjB8/Hvv27YOvr6/Zej8/P+zbtw/jx4+vcF956ZDZatStqAylUgkAZhFYhBDs2rULkyZNwqJFi7By5UpkZGTAYDDgypUrUKvVOP3nYQBAVnoaElPS2SH+gwcPEB0dzZqvLl++DKVSyUZCJScnN+svvCKtAQZCwOUwyFNozUaC0jKKgsDo9Fbr9FAV/6l1BugMBvA4HEgdhawSLtLqIVPp6OiiAqqTRZlSe2rks5g5cyZ69eqFhQsXmuUaUqvV2Llzp82Ea06MHz8eY8eOxfbt2zF//nx89913mDNnTqUzuA2EQFmcZZbDMBDaIGT277//xmeffQYA2L59Ox4/fgy1Wo3MzEwEBgbi2LFjGDhwIACge/fumDBhAj799FOcPn0an23agoMH9mHr5o1o0649hgwagu7du+PZZ5/FRx99hPHjx2PgwIFYvHgx9u/fj08++QRHjx7FG2+8gf379zdL+7FWb4Baqwefw4FKpy/JCsww5UYUGr1RqfA4HNgJOGzKd53eAI3eAJVODy6HgZs9HzkK40tQrtLCjs+BiM+tVtnclkB1sijLZDKb1xAx1SbRarUtojZJjR3c+/bts/iiGzZsWK0Eas5wuVz06tULANCrV68qU30o1KVCZrkc8Hm191c899xzeO6556xqy+FwsG/fPnaZEIL9x89BV2wW4QlFiI6ONtvn7NmzZssJCQm1krexo9Lqjb4KLoNcWcnLy8WOD36xMjCAQKMzjhwcRTwIeeVf/Dq9wWh60ujA4TBwEPLY+5+n1MJRxIeDkMajlMbaLMpJSUnQ6UpVl+Tz4e/vDzc3txqfOz8/H0+fPjU7bnN3qtfozfPuu+/i3LlzFreZTBGU2lN61jafy1hd5KiuYBjG7IVVOqS3JWKaLMnlMCgsMq9gaJr1ridGRSHic+Fqz4e9gGdxhMDjcuAg5MHVXgAhjws7PpdN56LVGx3mBuq7MMOaLMoAzF7oAKDVahEXF4e0zGzoa+BXy8vLQ1xcXLnjNneneo0+VZ4+fYpt27ahZ8+eZusJITh//jxiY2NtIlxzxNvbG6tWrbIqpLZ0niAhv+5mbVcHsZDHOt2VGmNUVF1OEGzMqLUG6AzGCChZcZ1yBoCbgzEPlIEQaPUG2At4cBTyrCpSxedy4GzHB5cBdHoem8SxsEgLhYMOjqLKc0y1JEyRhE+ePKnR/umpKeDbieEg5MFOYN2rsDqliZubSapGyiIuLg6EEMTHx5ut1+l0LWYCVk3x9vbG6tWrq2xnIIR1bHIZBgIet1G8lJ3s+EjNL2LnBGh1LVNZEEJQpNWDwzAoKCoxF4pFPPCKo6I0egPs+FyrFYUJLodhlYJKZxy9EAAZhSqIhbxm9xKqDa6urggJCSk3z4LD5cJQSQodANDrtFAqFNAZHKAzEDgIeVVGGlbHqV6T0ruNmRopi7lz52L48OHgWKjJfPz48VoLRTH3V/B5xvkVjeElwedyIORzodLqYSAEcrUO9i3Qlq7RGYpTxpsHITgVv+TVOgOEPA4cRfxqKQoTnGKFoTMQpBaoQAiBUqNHQZEWLvbW2eqbIiancXWc0a6urnBxcUFhoQyFShX04IAhemSlVj4CAACG6MHjMJCrdTAQwFFUucKojlO9uVGjX/mIESMAAI8fP8aNGzcgEAjQp08feHl5sdsotaPQzF/ROExQJsRCHjvqMSW9q+uJgo0Nlc4AAmIW2uxU/KLR6E3ObH6tIpg4HAbOdnwo1DrWHJUpUzVbZVGbmdgGAhh4IvDs+HDgc6BSKqw6px7GGvZ8hmNU+oTAyY5foYKqbmni5kSNfuFarRYzZsxA27Zt2SI6rVq1whtvvGFr+Vospf0VIhvNr7AVjqKSbwyVztDiyq0aI5f0ADHOhQCMpiOxiAc9ISAGAkZbhML8vFrnKeJxOfB0EpWqKWIwJiVsZtRmJjYpHuGqtHoI+BxjuDJfBIZbxbcwh4dCPQ/pBSpois2pSq0ecrWuwntW3dLEzYkavYFWrFiBixcv4osvvsD58+fx4MEDHD9+HDKZDOvXr7e1jA1GQ838NBhK+Ss4DIT8uk/xUR3sBSWhn9rikM+WhEZvgN5A2DkVAIp9DAwKC/KRnvgICXGPER8fj4cPHyImJqZWETJCPhdSsZBdTi9UNatZybWdia3U6KDU6MDncQAC5Cg0yJZrwHV0r/SYPCfjdlN6ljyFBhyGgUKtZz8CymJyqleGv79/ozAZA7Z9h9XIDHXlyhXcunXLTHu2adMGQ4YMwdtvv20z4RoKPt9od24ou6NcbW6CauiQ2bJwORzY8bmsnDKVrtYml6YCKQ48IARskSIOw0As5KKwIB+5aeVfeqav45CQkBrH4Hs4CpGr0ECrN0CjMyBfqYWrQ/MwddTGaazW6aFQG/0OhBBkyTXsSJcjcoBI4gNtQRb0upIwbx6fD4mnD7giMWQqHVv9sUirh0ZvgIsdH3KVDjwOwxawKo2rqyuCg4ObxDwLU7YG0zutNtRIWQwYMKDCYVZzyAfE5XLh5OSErKwsiESiep+ZmVdYBFL8cHO5POi1HKjQuFI+iDh6yHTGH7hCqYecTyDkW1dPHDA+JxqNBiqVymKgRGNFo9NDqdKhSKODofglYyfkQa1SIzej8jlGT58+hUgkqvGzJBEBaQXGPk/N0UHEcajwWE2pf61NxyGXy81eenoDQWGRFjqDARyGQa6iJCcXwzBwEvFgZy8GcRZDo1JCr9ODy+NCILI3zqs3aOEiANQcYw42AyHQAcjRqOEg5EGv5cHJzvJHkEgkgre3N7hcLjuD297eHgzDQKVS2aBXagchBEqlEpmZmXBxcalyArA11EhZZGVlQafTsXUPTOzatQtXr16ttVCNAQ8PDzx69AhCoRDZ2dn1eu6MQlVxpA2gEvGgqiJCoyHQ6g3IKlSDAOAwQFGuAKJqKAtCCIqKimBnZ9dohuzWoC6uhKfUGKNnGAAaez7SVCrI83Oq3N9gMEAkEtX4/PmFKmhMz0aecZKfJZpS/6pUKqt+Y1wuF/n5+SX7aY15tRjGmEPNNGeRyzGO9LQcU6JLYyi6pTmNDACGMSoXhVrH/u4AwI7Pgb2QB5GF+jFNpX9dXFzg5eVlk2PVSFm8+OKLaN++PUaOHAkHBwckJSXh4sWLSE5Oxp9//mkTwRoahmEgk8nQr1+/ej2vUqPD3APnARijaxYNCUXXYNvcbFui0RmwdscVpBYav6Jm9A3A//UJAM/KWhtarRbnzp3DwIEDbTJErg80OgOOxaTiSnwOrsQbfRCdfZ0xqI0TLp09j8PffVLlMT777DO88MILNZYhJyEXq/ffBgD4utjjx1f+ZTE0tyn1r16vx8KFC5GRkWHRxs4wDLy8vHDq1Cn2CzklT4nz9zOgM3Bw6GYqaxL1dhZhSi9/KAw8KFTGYlJchoGrgwB+LiI42QvA53KgNxig1hp9FWkFKhQUaSHkiXA1PgcPMkpGOkPauOOlPgEI8jC3pDSF/uXz+TYZUZiokbJ47rnn8PXXX2Pt2rW4fv06uFwu+vbti59++gkDBgywmXCNAS6XW68Pw6XETKTIjOYNZ7EdvN2cavUlWleIAPhKnXE1xRiieCmhEGN6Ar5i62TlcrnQ6XQQiUSN9sdWlrQsOZIKtDjzKA8FKuM9mh7gjqcFGvh4eiAxMbHKY0il0lrdz/5tveHq+AR3UguRIpPhQkIhhnfwLNeuqfXvO++8g4kTJwIwd8qavtr/+9//wsHBAYBRaUenZCJbRXDkVhrSCoxFufxd7TD+X0EAl4PHuUXQGwjaejqjk68z/F3tK/yQkam0iMuS42ZSPoK9XZFVRHA7pRAAsPNqCtIe30Z/Hx6CA4x1Z0wZtptS/9qCGhszn332WURFRUGpVEImk+HkyZMYMGBAuXwplOpxPrZkOO7rYgd3R2ElrRuWwe082P8/yVYgvfhH21xJyFEiLkvBKoq2XmKIeBw4CHiYPnZErYpcWQvDMHhjeBt2efPpR80iMqo6afzvpxXgSZYcF57kIK3AOLKVigWYNzAYhACPM2Rwc+DjhS7eGNXFG0Hu4kpHvI4iPrr6u2JSL38MaeuB4e09ESJ1gPLhP0jaOgf/e3825rw8E0OGDEFgYCCber+lYXPPF01RXjuuJeSy/2/j6dSolUWvQBfY842PUFp+ER6mFzawRHWHSqNDXJYcjzJLTBThoVJkyFRo6+0EP4m4VkWuqsPQdh4I9TB+Zd9NKcTFJ1X7SpoC48ePR0JCAiIiIvDbb78hIiIC8fHxZopCptLiekIeHqbLcKf461/I42BueDAMBoKnuUp09nXG2G6+aOftVC3nvr2Ah36h7nixux86aB4g69BH0MvMfSkpKSmYOHEiDh48aJuLbkJYZYYaN24cunfvjpUrV8JgMODFF1+0GMGg0Whw+/ZtzJo1y+aCtgS0egPuphYAAByEXLTzdqzQgdkY8HC0Q6iHGLdTCqEnQPTTPDzX0Rtu4uYR0lmalAIVEotHFgDgYseDh5MdNDo9uvi5ACj5Ol66dCmSk5PZff38/LB58+ZKi1xVB4Zh8PozbfD6rhsAgC1nHqFfqNQmx25ouFwuBg8eXOH2mOR83E0rROTDkpf49D6tIOAwSC9UoU+wKwaEetSqTr2PixAHvllncRshBAzD4K233mI/DqqLXq9HVFQU0tLS4O3tzZq2GjtWvYkIIWxVNQ6HA4lEAoVCgVatWpm102q1VU6uoVTM/bRCaIsjj70cRfBztW8QOax9mAU8Dv4V6Mbad2PTC5FeWNQslUVijhz300pGTn2CJciRq9Ar0A2eTiU+CFORq7p+GYzq7I2Nf91Hcr4Kl+PycDs5n1VazZVsuQrXE/MQ+TCTzZs2sLUU/q52yJKp0T9Ugn4h0lqHCkdFRSE1JaXC7YQQJCcn4969exg9enS1jn3gwAGLHxNbtmyx2cdEXWGVsjh06JDZ8ty5c9GmTRu4u5efIXn06FGbCNYSiXpUkrHX380O0mITVH1+iVjzMJeWx1lnB2LggOFwkZSnQlKuEh18nOtEtoZCpdHhcaYC90uZ2dp7O4HDAJ0sXGtVX8e2gMthsGhIa7x/MAYA8E3kY2yd0atOz9nQ3E7OR8TDLOQojHOQvF1EGNzWHZkyFcKCpTZRFACQlpZmVbvqzso/cOAAJk6cWM7HZDJtVVViuaGpkY0jLy/PoqKIjo6GUNh4beyNnUtxJf6KUA8x3B0E9folYs3DDKCcPHwnKVyGzgPa9kNMSiHC27g3avNZdUnOK8Lt5HzI1cbRdXtvR6g0enT1d4G3i12DyTW+hy82/HUfhSod/r6TgdT8Ivg0oDx1SUahChH3MvDP+XPQy/PAE7ti3KAxSC9U4V+BbjZTFACsqjUDoFoztfV6PZYuXWoxGMFk2lq2bBnGjh3baE1SNerdiqrkhYSEYMmSJbUSqKVCCMGtZKO/gs8Fuvi64M+jRzBx4kSzFzNQ8vK2ZVRGVQ8zAMybN8+iPNrCbGQd+gjKh//gXmo+G6HSXEjKK0JMcsmoorufM0QCjsVRRX0i4nPxUp8AdvnriMcNKE3d8tX/duKTV4YhY9f7yP7jU6Tveh/rZz6DgnsXEB4qtXp+jzWEh4dXGtkGAE5ST3To0MHqY0ZFRZX73ZTGlB8rKiqqWrLWJ1b3sFqtxty5cxEcHIzvv/8ewcHBZn9BQUHw9vamobM1JDFHicLiamueTnbwcRFW+fJetmwZ60uqLdY8zDk5OZWGaeae3oaEzAL8efwkdu3ahcjISJvJ11CoNDrcTslDYq4xx46jiAcXewFaudnD17Xh57/MGRDE/oj3XXtqlq24ubD9l11Y98ZcaAvNI5MKszPx6Tvz8efRIzY9H5fLrTCyzYTDwNnVGgFYa9qytl1DYLWtQCgU4ocffsC7776LqKgoPPvss2bbGYaBm5sbxo0bZ3MhWwIXHpf8EPxc7RB355rVXyK2sI/b4iHVy7Jxbs1kRCgL2HVNxXlXEWkFKna2NgD0DHABOAw6+bo0ipxL7o5CPNfJC3/eSYdaD2w79wRvDG/b0GLZDL1ej3fefquCrQRA3ZhvKopsc3H3Ar//bDi2CwOgh0qjt2pSnrWmLWvbNQTVNixv2LABd+/eRceOHctty8/PtzopGMWc0rHyoe5iFOVbV1fYVl8itnpI9aUUBdB0nHcVkZyrNIuCCpQ4wMdZhECJQwNKZc6CQUE48OcJ6OV5+DbjLhYOXNZsMgAf/vs0cjMrfsZt/dFUGkuRbf36D8DErZfwMD0fALD51CN8OLZLlccymbZSUlIqTGni5+dX60mbdUmNPo06duwItVqN5ORkPH36lP27d+8eJkyYYGsZWwTRScavVwZAjwAXBPj7WbWfrV7y1thpa0JdmMzqC53OgIvxOcgtjr7xdRZCxOegs69zreL4bcmBAwcwql9X1pYf++Ny+PgHNJtJY39dvmtVu7oy35gi26ZNm4bBgwdDwOdh09RuKJ6Lil+vPMX1xKqjoiozbdl60mZdUaMn/uDBg3B3d0dAQACCgoLYv/DwcGRlZVV9ABtiMBgQFxdXr+e0NTlyNVLzjU5hd7EAARJxlS9vW6WPMGHNwyyRSGqkTJqC884SaQUqXC4VodbB2xmu9kIEu9d/FTS9Xo/IyEgzX5Apeq2suTIvKx1Tp07FxYsX611OW5JVqMb5FOtKHtSn+SbEXYzXnylJufLmnhtssbLKqE5Kk8ZIjeIbf/jhB/z000+wt7dHREQERo4cCb1ej4MHD2LBggXVOhYhBJs2bUJGRgb4fD7S09OxadOmckVOTCgUCvj5+ZmlKo6MjERwcHBNLqVRcCW+xATl62oPD7GQfXlPnDgRDMNYTK5m6y+RqmYgA7Aoj7U0ZuedJVLylLifLgNgHPF5u4jQ0cexuCpe/WEpfNrX1xcqVcUV8wiA7du3Y/Xq1U020d13Zx9DKWkNrqO0XNoNEw1lvvm/vgE4cfw+ACAxpwj/PfEQH4yqOjqqviZt1gU1UhZjxoxhHdm3b9/GwIEDwTAMwsLCMGnSpGpNzNuwYQNOnTqF06dPAwBWrlyJ6dOn48gRyxEOP//8MxYvXoyQkBAAgJ2dHQYNGlSTy2g0/PM4G8Sghzr5LjRKBnejVfAZOrje0keUpqqH2ZI87u7uVo0oG7PzriwGgwEn72dCWVwNL9TDAVJHIULcLX/E1BWVzX2pFEKQnZ2N8+fPY9iwYXUoYd2Qmq/EzitPwXC4cHtmHrIOfWQsPFEPH03WYCpzzC0eaH8fFY/RXX2smkVfH5M264IaKYs7d+5g9+7d6NevH5599lm8+uqreOutt3Dp0iVERERYfZzc3FysW7cOW7duZdfNnj0bQUFBiIqKsvi18Mcff+Do0aONIhLFVhw9chgp+7+AXpaNPwH8+aV5FFF9f4lU9jBbdPr164fgkBCkJKcAaJrOu7JkyTW4GFfyNRsodUCgxKFeJ+FVNvfFWpraaM7E5yceoag4902n/sPQPawVfv9iHVJS6uejyVrmDQjG5sh4AMCS36Jx8s3BjcafZWtqpCwmT56McePGYdmyZVixYgU8PT3RsWNHMAxTrRfC33//DaVSiZ49e7LrAgMDIZFIsGfPnnLHOnnyJP766y/4+PjghRdewNtvv4127drV5BIaDbv27MPNH1eWW182iqgxfYlYUiZfbNmCCRMmlmvbVJx3ZUnIluNRhtEExecAQRIHtPN2qlcZqpr7Yg0SiRSRkZFNyuSRmC3HoRvGkRMDY5bdWf37YONbcxud+Wbe4BAcjslAfI4SiblF+Piv+1g1unykaHOgRiowPDwc2dnZWLFiBQBg/fr1uHDhAvbu3VutSnkxMca8Nn5+5pE/fn5+uHnzZrn2dnZ2eP/999G6dWts374dPXr0YM1XTRG9Xo9lS5da3NbUoojGjx+P/277CQIn8+ynXt4+TcJ5V5ajt1OhK/attvF0hK+rXb2Hy9Z2VODo6IhX5szBkCFD8NJLLzWZegzr/3oAXfFgqqOPI4a284C/q325yKSGVhQAwOdy8PmUbuzyjgsJuJrQPFLGl6VGI4uff/4ZM2fONFsXFhaGGzdu4ODBg5g+fbpVx8nNNUaamCpgmRCLxcjMzCzXfsCAAWwlvrNnz2Lq1KmYMWMGEhISKs1JlZubC61Wyy4LhcIqc1iZ2pfez9acPXsWmempFW43RRFFREQ0Cb/M5HEv4rohAOeiLkAnz0XX1gFY/vJYDGjjVa4f66N/a0qeQoOrcdkQco1vrBCpHdp7OoAhemitiHqxFZbyr1UHmUwGmUxmts40Yt29e3etJtDq9XqcP3+e/cofMGCATV7eDzMKcfZBOoRc44huXDcvdPEWN7rnpPTz28lbjP/r7Ys9142jwHf23MDRxf0h4De+/GhqtRpqdUmRsrLPR2UwpAYG0XfeeQcbN24st/7BgwcYMWKEVeUlAWDJkiX46quvoNfrzXwQ4eHhyM/PZ0ceFXHx4kX069cPp06dwjPPPFNue2FhIZydy+fvmTJlCqZNm2aVjHXJuXPn8Pnnn1fZ7s0338TAgQPrQSJKY0Kv12PevHnIyan4S9XR0RECgcCsjUQigUajqfRFIJVK8d1339XoBX/x4kX88MMP5c45d+5chIWFVft4lPpj165d+P3338utLygogJNT5WZWq1WfWq3GwoUL8dNPPwEw1sS1RPfu3a09JDw8jGU5FQqFWaisQqGAj49PlfuHhYWhdevWFkchpYmPjzc7vrUji5MnT2L48OF1FnpoZ2dvlbIYOXJkkxhZAMDJe+lYvu82NAYCAQd4pX8wJvbyg7ezuWO4Pvq3pqw8fAcHim3mXXyc8MqAYIt1ruuDb775BlOnTgVguTb1Dz/8gDFjxph95efIVZg6rvI6C9nZ2XBycqr2c3Xw4EFs3LixnNM9NzcXGzdurNWI5Up8Lmb/dBUAYMfj4JMJXTC0fcP0e1VYen5P3kvHG3tuATD6Wr6d3gMDWtdudGhrnnnmGXz99dfsskwmQ1BQkFX7Vis31P/+9z/07t0bf/zxByZPnmy23ZQbaujQodYeEt26dQNgHBqXdlSnpKRgyJAhVh3DxcWlyuG6m5tblVqzIvh8fp29zLza97QqhnzIkCGNwj5rDX4SR3g4O+BJtgJqPZCQp0KmXIdWUst9WJf9WxPkKh3OxuZCrTe+jEO8nNHWx6XBZJw8eTJ4PF6V4dOlw2N37dpl1bGzsrLA4XCsdhrr9Xq89dZblabZfvvttzFhwoRqP68GA8HaPx+y/f5cJy/0b+MFfiM05ZSm9PP7XGdfHLiZjlP3jR+v7x++jz8Wu0Hq2PAJJ03w+XyIxWKzZWup9p1YsGABxowZU+GXf3Wyzg4bNgz29vaIjo5mlUVSUhIyMzMxcWL5yJqyKJVKpKeno1+/flafszFxLSGvJIa8DE01ishdLESQu1FZAEBGQRHis+XoFejWwJJZx42neUgtTrEuceCjg48zAhqoYqGJ6oZPWzufJTY2FoGBgVbXSqlOmu3qRu8djUnDg+IJkG4OfCwYHAqxqHErirJwOBy8N7IdrifmIU+pRVqBCquP3MPnU7o1i3DaGl1BZSainTt3Wn0cOzs7LFu2jDVtAcCPP/6IAQMGoG/fvvjpp5/QsWNHZGZmQq1WY9KkSfj666/ZL5sPP/wQ69evh719w/6Ya8rF+DzYt+0H9xffh7PUy2xbU0kBUBZ3sRCdfEpGcUl5RcgoUCFb3jRqXBy6YSwLTAx6OObGIunqKZw/f67BI9KqEwkUHh4OnzIpJUrDMAwkEglWr15drVopdZVmW63T46M/77HL0/u0QlvP+k+pYgtCPBwxf2AwiEEP1dPb2PP7bqz9fk+jc9DXBKtU97hx49C9e3esXLkSBoMBL774osXsshqNBrdv38asWbOsFmDt2rVYuXIlXn/9dbi6uiI+Ph6HDx8GwzDIz89nFQWPZxT1nXfewf79+9G7d2+88MILVpurGhuEEEQnGqPBHNv1w/aPFgMZDxtVDHlN4PE46OjrDFd7PvKUWqQWqJGj0CA1XwWpuPEMxy2h1eoRGZsN5cN/kHt6G57KsnG2eFtTSrXO5XKx6fPPMWXKlHLbSqdqqW7VNluk2bZUIviXi4lILzBG6ARK7DC7f1CTnnQrTL6GrO9fR1G+MavBuj+AbWt98O1XXzaJ56cirFIWhBD2y4rD4UAikUChUKBVq1Zm7bRaLZKSkqolAIfDwbp16yxuW7p0KZaWmoewd+/eah27MZOcV1RSS9hZBB83Mdq0H9ywQtkIb2c7+LvaIU9pvL6UfBUSsxVWpUJoSM4/yUHS9bMWzYJNLdX6uHHjsHz5cny1bQcUeSUBIFJPbyxeOB+rVq2qcN+KzEm1TbNtKceVj68vuP1eASe4LwBg2bA2cHVouqWZDxw4gDkzXyrXP5lpqU3q+bGEVcri0KFDZstz585FmzZtLDqWq5MXqiVzNaEkm2mAxAHSJvwDKYu7oxChHmLcTjHWgUgtKEJSfhEKlBo42wsaWLqK2R+diNzT2yxuayp1kksTFhaGNs+9gtXbD6KoIBdcsSsWTRkFadYNq/Yva06yNrklgHKzxg8fPmwxx1VqSiqwdx3cX3wf4c8+jzFdq46CbKxUlZ6FEGDJ60ubzPNTlhqN9fr3719OUaSnpwMAXnjhhdpL1QK4GFcSo97WyxEu9k3LmVcZEnsB2ns7g1ecZC0uQ4aCIg1SCooaVrBK0On0+PNERIWRaUDTTLXeuZUbevcLh0OHQRC16oJLCfkoYKzz8VkyJ1WVZhswpuwpPWs8ICAA8+bNq+AlalyXe3obVjzfrkmbn6pOz0KQmpKMYyfO1JtMtqRGd+b48eNwcnLC/v372XVJSUlYvXo1DAbr8s+3dK6WqpMQFuzWpH8kZeHxOAjxsIdfcRSRUkeQI1MjKUfZwJJVzJmHWZDlVqwoStOUkvMFuNmjX4iUVdz302RQuraBl7dPjWuljB8/HgkJCYiIiMBvv/2GiIgIxMcbk+lZqq+RkpJS6cRCwFiStyC+8km4jR1rn4uT1+6jQKmpY2lsT43eUOvXr8fcuXPN7Jn/+te/4Onpiffff99WsjVb8hQaJOQaX5zuYgF83ZpmNFdleDnZIUBacl1pBSok5CghV1kfWl2f7I9OBlfsalXbppRq3dVBgPbeTuhc7C8iAKKTCzHl9Q8B1LxqW9noLAAtNkOuCWufixvZwMl7GZCpmlaEVI2UxdChQ/H5559DIpGYrff19cWPP/5oC7maNaXLMPq72jcrf4UJDyehWZbW+GwFCpQapDZCU5RGZ8C5h5kQ+nUE11EK4/zb8ti6OmF90cbLET1bubClQO+lFsK+TT9s+HqHzaq22SJDblNSwpawpjQx11GKJGEgjsak4viddOQ3oRFGjZSFQqGwuH737t3VmpTXUrkcX2LuaO0phlTceJ2+NcXNXgB/Vzu4F19baoEaco0OiTmWn52G5PjdNBTpCBgOFx3GLwHDNN06yZYIcLVHoLsDerQyjpwIgDspBXBq3x9Xbj8oZ06qSbRObUYFTVUJl6Wy0sSmDxC3Z+aB4XBx9mE2omKzcPxOGjILm8YcpBopixEjRmDy5MmIjIzEo0ePcPLkSUyYMAG7d+/G7NmzbS1js+PykxJ/Rd8QSbPyV5jgcDgIlNjDv5SJLUumQXyWAkpN4/qg2He95Iv4hTEvYvfve5psnWRL8HgctPd2RnsfJwiKH7WYlEI8zVHgemI+Bg4cWOu03zUfFTRdJWyJigIAPL198NL7mzF4xCgARoV98m46opPycfR2Kp5klZ+31tioUQjOsGHDoFar8frrr+POnTsAjFksP/roI7zzzjs2FbC5UaTR406qMaTUScRDa4/6LdNZn3g42SFQYo/op/kAgKfZcgS5OyA5rwhBbo1jgp5CrcOFWONIT8BlMLabL/oEd8GE8eMaXaGd2hDi7gAvJxF6B0tw/nGOcXSRKoOLWIhWqQ61ngNjzRwMNzc3aMFDYU4Gu97fv+Gr3dkaS+lZ+oT1x7GYdMRmypCr0OJxlhxKHcGx26kQdPeDXJ2K3gGu6BEoabSpQWocrzlq1CiMGjUKhYWF0Gq1rP9Cp9M1yy9lW3EjKQ+meLFWrnZwd2x+/goTno5CBEvFsOcxUOoIYrMU6N9Gj8QcRaNRFn/GpLGFdtp7O6Gtl1F5N9U6yRXhYi9Aa08xMmVqXE/MR5FWj9spBejk64jLT3Lg4yKq1Qx7a+ZgrPl0Cz574Ai7pLswyPOwekp/zJ/6QpNWwhVh6fkJC5UgvVCFF7p6Yd+1ZCTnqyBXG3DwRgom9/JHZGw2kvKL0CvADQES+2q9R1UaHUSCug2/r9FbvXRKcCcnJzNH95o1a2ovVTPmanyJczvEQwxJI56kVluc7QXwdBaildRY3EqjJygs0iEuSwGFunGYovZHl5ighrf3gEszvh9tvJzgZMfDkLYl1QzPPspCllyN87HZ0OhqF/Ze2RyMvXv3IkoTDAPDhahVF4ybOBkLpo1uloqiInxd7NEnyA1FGj1m9QuEt7NROcvVevx6KRFylQ6JOUocvpWCP2+n4XGGrNJ7YjAYkJSnxNmHmfjfPwlIyqvb0PQaKYuPPiqfDiE3NxevvPIK1q9fX2uhmjMX40qc232C3MBrpENOWxEgcYCfS4nfIjFbgXylBin5DR8VlavQ4FLxfBd7AQcjOzftaJyq8HUWoZWbPQIlDvAsHtGm5Btzd91PK0S0DcqBVjQHo8i3Jy7GG/taLORi2fA2LdIC0dXfFe29nZAtV2PRoBAESox1XjR6gl8vP8XNpHyIhTzcS5fh4M1U/HIpASfupONaQi7upBTgXmoBbjzNw5kH6fj18lPsvJSIT48/xGfHH+JJRt36PWo0bjl79ixmz56NTz75BO7u7vj+++/x/vvvg8PhNNl04fWBTm/AzafGkYU9j4OOvi4NK1A94OkkQoi7A04/KI7CSZOheys3JNbTBD1LietMX7NHbqWw7XoGuCLArX5rbNc3HA4HHbyd8ThLgdFdfPDDBeNEuuP3MrBgYDCuJOTBzVGENp6186OVNcE8zZFjzR932eWXejfdrLK1RcDjYGBrD+QqNMhWaLBwcCh2X0nCjaR8AMD5xzmISS7AiE5e6OzjDIVGj1sp+dAbCBiYTHsM9AaC2AwZriTkQqExjj6+jIjFoLbulYbu1oYaKYuTJ0+isLAQkyZNgkKhwM2bNzFv3jysX7++VpNymjt3UwuhKjaQ+7rZQ+rYfE0eJjwdhZCKhQhxd8DjLAVkKh0UGi2SchXwqnr3WmEpcV3p7LH7r5coiwnd/Zv9KA8Agt3F8HEWIVemQScfJ9xJLYRcpcO1xDx09XPBuYdZcBTy4O1iV/XBrMBgIFjwazQ0xRne23s5YkrvVi1yVGHCTSzAwNbu+PNOGvIUWvxfWAACJfb4IyYVOj1QoNJhz7VkHOGloqOvEwIlDnC248NAgDylGonZRbiXVgCNvuRdywHQ2dcFBgJw60ZX1ExZbNmyBSkpKYiKisKwYcPw/PPPY+7cuXBxcbGxeM2L0skDQ9wd4N7IU3bbApGAB19XOwRI7PE4yzjHIj5LAXseU6fK4sCBAxYT15myx379v18Rk2Kszy514GNo+8ZV/rKuEPA46OLngj9j0vBCZ2/cTy+E3gBEPsxCFx8n5Cg1OHU/A6M6+8DZjlvriLCP/7yHe2nGokYOAi5mhQUgSNL8MhZUl1BPR/RX6XDmQSaESgYD23ignbcTjtxMwd3i/lLpDLiemI/rifmVHquzrzO6+Drh/8ICwOXUkaZALdJ9nD59Gnv27MHx48exd+9eHDp0CEuXLsV3331naxmbDZeelPgregW4NtoQOVvj52YPf1d79mGLSS0Ev64+f1B59k/TuvffeQvEYPzcHdbeE052zX+UZyLUQwwPRyF0BoIRHUpU9u6rSfB3FSE5T4n1X+9Aq4AAs4SAgYGBFosiWcJgMGDv1afYfj6BXTe8gyf6BDfPeUU1obu/M/oEuiJTpkaBUg0PRxHmhodg6TOh6BXoUulvxE7ARd8gN/x7RBvM7h8ER7u6L/tbo5HFqFGjsHv3bjg4GG28AoEA//nPf3D79m1MmTIF8+fPt6mQzQFCCK4Vp/ngcxl0b2VdHqLmgKeTCG4OAgRKHRCXrUCeUgttsTkuU6aGr5ttH3Rryn/mZ6VDmHwXolZdMD0swKbnb+zYC3jo6u+Kk/fSMKitFDEpBUjKK0KmXINDN1LhX3gHn7+7EIDlUVlVkxNVGh1OP8jA2mP32TDxjt6OGNbOAwF0VMHC4XDQL0QKPQGuxOdAZwAkYiECJWIESsSY0ssY7ZReoIJMpQOXw0As4MLHxR4+LiJwi5VunkINPpcDXh2OKoBamKFMisLE6tWrwTAMtm2zXA+gpfMkS478ImO4qK+LHTycm78JyoS7WAA3sQDBUnvEFdfmjs9RoJ0LkJSrgK+bbZ2d1qae0Mvz0NHbEZ1bQKBBWdp6OuJWUh6yCjWY3icA/z35EFo9weW4bPz9v5UoqygA62p6pOUX4fzjLGw+FYvC4qSRXk5CDG3via4BrnRUUQYej4PwUCmEPAYX43JRpNXDx1kEDocDHoeDIIkYQRLLvw+DwYCU/CJoDQR9glzh62wbP1NFWHXnuFwuBg0axCoCqVSKwsJC9g8wKguhUNisJjLZkiul5lcESu3h7tByzB6m1B8+pZymd1IKAAAP0yuPJa8J1qae4IpdMaGnn03P3VQQi4yjC7lGCzcHASYV94M6+S5kpWZYl6Wimh7ZchXOx2Zi//Vk/HAuDin5xnxHQh6DIe080MXPGf6udFRhCR6Pg36h7hjR0QsiHgePMmUoLKo8wWC+Uo1HGTI4CHkY2ckLA1p71HmAhlUji1atWuHs2bPs8saNG/Hxxx9j7NixeOGFFzBz5kzweDy8++672LFjR50J25Qp7dzu2cq1zmdbNja8XezhKBIg0M0OCblFyFIYfwzZcg2e5igQWstwzdJUlXoCMGb/dA7shCm9/G123qZGe29H3EnJR3p+Ef4VKEFijhIn7uVVvSOAS3cewy20G4o0eiTlKpFeqEJBkRZX43PxMNM4euQywISefmjl5oDurVzq8EqaB518neHlLMTV+Dw8ypAhrUAFsZAHewEXXA4DrZ5AqdFBodHBQchDzwA39Ap0g1s9JSK16o3VsWNHs+V169Zh37592L9/f7mY3k6dOtlOumaEybnNYYC+QZIqWjc/PB1FcLLjIcTDEQm5JRPyCCF4mFFoU2VRWeoJY+I6Ardn5mFYBx84iOreMdhYsRcYXzhHb6dCpdNhXHdfxN7yhTUloOIUfPwVkwYDIRDxebDjM7gYl4M7xaV0AWBq71ZwFHHR3d+5VqlEWhJSsQgjO3ujk58zErPlbA0YvQ7gchhIHAXoLXVDgMTBZuHN1mKVsrC3Lz98DAoKsjj5o6wvgwKk5hchrVANAPB2FsHLtX5vcmNALOLB11kIffIdKO7dh97JFfqebeHmIEB8thLZcpVNXyim1BNl51nYubhDPHgu7Nv2w+wBwTY7X1OljacjQtwdkJijRIi7I5bNGI3Zv66HuiCrgj0YSDy98ezQIazP4km2HNvPJyJLriluAczoGwBnOx68XURs4SWK9fi7GiMIB7SGUVkYDODzOLBvQItEjc9c0SxBtVpdY2GaK6VNUEEShxblrzBx4MABLFq8BBlpqey6eX9LMHrhCrQJexaP02WQhtr267Ns9s8iniP+c9UAhsOFr4sdegS42PR8TREBj4PeQRKk5BchT6GGq4MQr72/Fp8vX1DhPtOXrUS+SoeEnEJcS8jBg/SSNBMiHgczwwLg7SxCgUqLvsHSBn3BNQfEosbRf1ZJce/ePfzyyy9mw/mUlJRy69RqNc6dO2d7KZs4V+JzQQx6qJPvwgABLv2javIpr6tDRRPkcnJy8OO6N/Dq6q/g6TQSXVq52vzFUjr1xDv7boHhGEcZc/oH1llahKZGgMQBXfyccelJLhzt+Bgw7AVwNm7DtxtWQJFbkjSU6yiF2zPzcDjfD4eP3S93nFZudnipTwBc7QR4ki1H32BXhLi3zLQezRGrlcXLL79sto4QYnEd/QGW54/DB5By4CvoZdnYBWDXJ+ZpJ5ozlU2QM3Hg2/UI7DEQcVkKdPJ1rhM58pUaHCzOMCvicTC1T6s6OU9T5V+BEiTnqZCYo0CIuyP6PfM8+gwegfMXohAR/RAZOjsI/TqC4ZT/wHG152Noe0+EBUvAZRjEZsoQKLFH70CphTNRmipWKYsOHTpg4cKFEIsr/0pQKBT44YcfbCJYc+Hn3/Yg5qfV5dZbO8GpqWNNbeacjDSkPLyJO55OaOfpWCchgPuuJ0NbHKE7oqMXNY2UwVHER3ioFH/cSkWWXAV3scgYMj9wMAYNHIwchRq3k/ORVqBCoUoLIY8LqViINp5ihLqL2QliqflFEIt4CG/j3mjMJxTbYNXd/PDDDzFlyhSrDujp6VkrgZoTer0eb725zOI2ayY4NQesnSDHKcrD07wiPM6So523k01lMBgIfvonnl1ePDTEpsdvLgS5i9E70BVnY7Mh4nHhWCpSTOIgxJC2lf+2c+RqqHR6DO/gSedUNEOs+oSzVlEAwIQJE2osTHMjKioK2RkVvywrmuDUnLB2gpydkwTxt67g2//9hNOnz0Cv19tMhqjH2UjKM04S6+DtiNaetlVGzYlegRL0CHBFcr4SCo3W6v3yFGrkKtQIC3ardYlWSuOEjhPrEGu/qq1t1xSxZoIcR+SIb/7zJhR5RmfqF7CtT2f7+Tj2/7PCAmt9vOaMKf2ETk9wMykP3k4iONtXXvo3o1AFmVqHfqES9A1ueXOIWgo0UUsdInS07odj7dd3U8Q0QQ6oONzaoJKxisKEyadjbZbTiniYLsO5R8ZpZs4irll6D71ej8jISOzatQuRkZE2Hc00ZUQCHp5p74neQW7IVmjxNEdhMSWLUqNDbKYMOoMBQ9t5oF+IlOZ+asbQO1uHGLzagutYcUQIwzDw9/dHeHh4PUpV/1RUm1kikUDgYNkkZBqFLFu2rFYv8U0nH7L/n9SzFXhc4yN/4MABBAYG1jgFd3NHwONgSFsPPN/FCy4OfCTkyBGbWYikHAWe5ijwKKMAaQUqhHo4YGxXX/SkSQKbPdQMVYdcis+H2zPzkHXoI5jSTJgwfWVv3ry52Tq3S1N2glyeQYjsJ3ewatWqCvcp7dOpSYLK+Gw5/r5rTIon4jGYOzAQQNWFkZp7hJq1mMqwBkuMqeXTC4uQK9eCw2Hg4SiAt4s9AlztW0SFQQpVFnUGIQQXYrNg37YfPF58H8zlH81mL/v5+WHz5s0t6qVUeoJccrYMmz6+YNV+NfXprP3jHvv/Ye094eVsX2VhpJYQoVZdRAIeOvg4o4NP3cyBoTQNqLKoI57mKtl8UG36DsWBH1fg7o3LtSpR2ZxwdxTA1dW6AlA18elciM3CmYfG/EZ8DjB3oDEPlDWFkWozmqFQmitUWdQR/zzJYf/fzssJEkda66M0HA4HHTp0gLPUEwXZmbBUbAdg4OrhhbZd/1WtY+crNfj4z5J0FAPbuKODt/GrmEaoUSg1gxob64gLj0sSPYe3plEiluByuZi8+IPipbKRUsbl5+e+i3Ox2chXVl4MxoRcpcN3Z+Nwp7jovZDHYHb/ILbeubWjlOYcoUah1AT6BqsDCCH457HRBMLjAGEhNEdORUwYNw4zPtgMiYeX2XonqSfe+fQ7jB47DnFZCpy6l4FceeUKI1euwZ+3U7Hn2lN23cA2HuhYKt+Uad5HRWG8LSVCjUKpLtQMVQfcTS1ErtJYfzjQzQG+LbB+hbUEuYvRdcAIPDfqBZyJPIsjF++DK3aFV5tu6D6wAwQ8DoLdHfE4Uw6FRoewEClC3R3MRmoGgwGPMuS4HJ+Dv2LSkKMwzjx2c+BjfHdfuNiXpISvrDBSS4tQo1CqA1UWdUDkw5IJZj0CbJ92uznh62IHLxchcuQajBoxHE+FwYjNlCNfZcDR22mY0MMfAh4HIR5iJOUpceRmKgIl9mglsYe9gAelRof4LDmSclXIL9Lgcqla58+080QnCxE8FRVGaokRahSKtdC3WB1w5kGJshjWwaMBJWn8CHgcdPRxxvG7adAbCCb19MPGEw+g0wPnH+egg48z2ns5gcfhIEgihlKjw9M8JR5nydmZK3Y8Llwc+NgXncS6ybv6OiEsxA2+rpYLKpWd90Ej1CiUyqHKwsbkKjSIfpoPAHCz56Orv3XhoS2ZUA8xrsQbRxeeTiK80NkHh24a56T8/E8Clg1rA08n40vfXsBDkMQ8Vb7eYMCOC/FILw5V9hDzERYiQSdfl0oDC0rP+6BQKJVDHdw2Jiq2pHZxe29HuItbXgnV6uIo4qOjjyPylGoYDAaEt3ZHJx9jGhCVzoCtZx8jvbDI4r4avR4//pOAu8XRT3Z8LkZ08kaQuwOCJbQePIViK6iysDGlTVCD27jTkFkraeftBGd7AXKUGnAYBjP6toK3s3E0kV+kw5ZTsbgYlw19sUOagCA+R47Np2NxJ7UQAMBlgJd6+8PJToCufq40DQWFYkOoGcqG6A0EEQ+MuYh4HGBQO1oIylqkYhE6eTvh/JNsSOwFEPJ4WDAoBN+fe4LkfBVUOgP2XEvG0dtp8HYSoVClRVapUFoBl8HsAcHgcoBAiR1Cae1nCsWm0E8vG3IzKR+FKmOG1FB3MVrRkNlq0cnXBW4OQlYJOIn4eG1oKLr5u7BtlBo9nmQrzBSFt7MIS4a2hreTEDwOgx4BbnRUQaHYGDqysCERpUxQfUIkENGQ2WrhJhagu78zzjzIhKu9AAIeByIeD7PCAtE/RIazsVl4nCGHqri2QpDUHr0C3NAnyA0MgEcZMvQMcEMIHVVQKDaHvs1sSESp+RUjOlATVE3o6ueCx1lyJOUpEOLuyK4P9XBEqIcjDIRAa9CDz+GCU2oWdlKuAu6OIvQIdGkAqSmU5g8dq9uI1Pwi3C12tHo6CtGZ1iGuESIBD/2CpRDwOMiSqcpt5zAMhFyemaIoLNJAoyfoFyqBVGx5XgWFQqkdVFnYiL/vpLP/79HKBY4ifgNK07QJchejT5Ab8pRqyFTaStsqNTqkFRaheytntPNyrLQthUKpOdQMZSP+ulOS0npMN58GlKR50LOVGwqUOlx/mgcvsQ7JD24gLzsTrlIPtO/eB1wuFzKVFsn5SnT1c0Z4aw8apkyh1CFUWdiAjEIVriYYcxK5OfDRj2aZrTU8HgeD23ngwuljeGPdChRkl4zc3Dy88eKC99G27zPo0coNA9tI2RTkFAqlbqC/MBtw/G7Ji6xXK1c429NZ27bg6JFDWPvGq2aKAgByM9PxvzWvg5d0Fc928ICQyyAyMhK7du1CZGQk9Hp9A0lMoTRf6MjCBhy+WVJb+8Xufg0oSfOhslrZgLFW9uZ1K9DKzR5vvPFGueyxW7ZsodljKRQbQkcWtSQxR4HriUYTlNSBj/DW1ARlC6ytlT1p0qRy7VJSUjBx4kQcOHCgrsWkUFoMVFnUkoM3Utj/928thaMdjYKyBbWpgW0ajSxbtoyapCgUG0GVRS0ghOBQKWUxvXdAA0rTvKhtDWzTyCMqKspGElEoLRvqs6gFl+JykZCjBAAESuzR1dcJkZGRtJiODTDVyk5JSanAb2EdtRmhUCiUEujIohb8ejmR/X+gLAahocEYMmQIXnrpJQwZMgSBgYHUbl5DTLWygZLa2CbKLldGbUcoFArFCFUWNSRTpsJft41frdonF/HzumXU0WpjTLWyfX19zdb7+flhz5498PPzq1BxMAwDf39/hIeH14eoFEqzh5qhasjuK0kwACAGPfJPf2/RVEKIMcRz2bJlGDt2LDVJ1YDKamVzuVxMnDgRDMOY9b9JgWzevJn2OYViI+jIogYUaYylPAFAnXwXyrzMCttSR2vtMdXKnjZtGgYPHswqgMpGHvv27aPzLCgUG0JHFjVg99WnyFUYi+/4CtTIsGIf6mitGyobeVAoFNtBlUU1UWn12HYujl2eNrgLon+qej/qaK07TCMPCoVSd1AzVDXZfj4eaQXGOgvtvMRYOn00dbRSKJRmD1UW1SCzUIWvIx6zy2892xZ8Pr/KEE/qaKVQKE2dBjdDEUKwadMmZGRkgM/nIz09HZs2bYKjo+VCNteuXcM333yD9u3b4+rVq3jjjTcQFhZWL3KuOHQHSo0xfUTfYFcM7+AFoMTRunTp0nIJ7TZv3kwdrRQKpcnT4Mpiw4YNOHXqFE6fPg0AWLlyJaZPn44jR46Ua/vw4UOMGDECV69eRXBwMGJjY9G3b19cu3YNQUFBdSrnr5cSceKe0ZUt4jH4eFwXs+3U0UqhUJozDaoscnNzsW7dOmzdupVdN3v2bAQFBSEqKqqcnX/VqlXo1q0bgoODAQCtW7dG586dsW7dOmzfvr3O5Dx+Nx1rjt5jl5cMbYMgd3G5dtTRSqFQmisN6rP4+++/oVQq0bNnT3ZdYGAgJBIJ9uzZY9ZWp9PhyJEjZm0BoFevXti/fz8MBkOdyLj9fDxe2xkNrd446WtAqBteGxpaJ+eiUCiUxkqDKouYmBgARtt+afz8/HDz5k2zdU+ePEFRUZHFtgUFBYiPj7eZXF+cjsXUb89h165d+O/fd6EzGBVFD39nfD+zt83O05JRq9XYtWsX1Gp1Q4vSLKH9W7e0xP5tcDMUADg4OJitF4vFyMzMtLotAGRmZiIkJKTC82i1WnZZKBRCKBRWKNejtHw8SMlB/JEjCFoyHhwuHwNCJPhiSnfwGAO02roZxbQkFAoFjhw5AoVCUem9oNQM2r91S1PtX7VababgZDKZ1fs2qLIQCIy1qi2FnJq2WdO29HZLlHV+T5kyBdOmTauw/XBHYHg/IdBvV/EaPYBMnDp1vMJ9KNVn165duHTpUkOL0Wyh/Vu3NMX+3bVrF37//fca7dugysLDwwOAUUuXDpVVKBTw8fGpsG1pTMtl25cmPj7e7PhVjSwKijQoyMtD5/ZtEB8fDzc3NyuviGItubm5CAoKov1bR9D+rVuaav8+88wz+Prrr9llmUxmdSRpgyqLbt26ATCm8m7Xrh27PiUlBUOGDDFr6+vrC6lUipSUFLP1KSkpkEql8PT0LHd8UyZSHo8HPr+k3KnBYEBRUVGFcgkAcIkORUVF7B/FtpTuW9q/tof2b93SlPu39LuQxzOqAGsKjDWoshg2bBjs7e0RHR3NKoukpCRkZmZi4sSJZm05HA5Gjx6N6Ohos/XXrl3D+PHjweGU99Wb7HH+/v41lrGu52+0dGj/1i20f+uW5tK/MpkMzs7OlbZhSG1qVtqADz74ANeuXcPx40Z/wNq1a3HixAmcO3cOP//8MzZu3IiIiAh4eHjgzp07CAsLw507dxAQEIC4uDh06dIFly9fRseOHcsd22AwIDU1FY6OjtWqrkahUCgtAUIIZDIZfHx8LH5wl6bBZ3CvXbsWK1euxOuvvw5XV1fEx8fj8OHDYBgG+fn5yMzMZL33nTp1wsGDB7F8+XL06NED169fx5EjRywqCsA4GikbakuhUCiUEqoaUZho8JEFpeWRmZkJoVBo9UNKoTRWDAYDEhIS2KwSzRmadbYMhBB8/vnnWL58OVasWIG5c+dWKxaZUp6dO3eCYRj2LywsDE5OTgCA48ePY968ediwYQMmTZqEhw8fNrC0TYPz58+XCwIBjD682bNn49NPP8XkyZNx8eJFs+3p6emYN28ePv30U7zyyit1mianKVNR/yoUCri6urLPMpfLRVJSEru9WfcvoZjx0UcfkaFDh7LLH374IRk9enQDStT0GTt2LNmxYwf7d/XqVUIIIWfPniUeHh4kPz+fEELI6dOnib+/P8nLy2tAaRs3MpmM/Pbbb6Rt27ak7M/3wYMHxM3NjTx58oQQQsijR4+Im5sbiYuLI4QQUlRURLp27Up+/vlnQggharWahIaGkn379tXvRTRiKutfQgj55ptvyIoVK9hneffu3ey25t6/VFmUIicnh9jb27M3mxBC4uPjCQBy7ty5BpSs6XLp0iWyYsUKi9v69OlDZs+ebbYuICCArFmzpj5Ea9KsWLGi3MtsypQpZh86hBAyaNAgto+//fZbYm9vT1QqFbt91apVJCQkhBgMhroXuglhqX8JIWTkyJFEr9db3Ke59y81Q5WiOokNKdbxxRdfYP369ejevTs2btwIlcpYZTA1NRWXL1+2mBiS9nXVlE19b02izQMHDqBjx45mE1J79eqFJ0+elAtJb+lYKi1w8uRJ/PXXX/Dx8cHcuXPx4MEDs+3NvX+psihFdRIbUqyjX79+WLBgAbKzs7F8+XIMGDAAcrm80r6+f/8+NBpNQ4jbZLEm0WZMTIzF7QDo820FdnZ2eP/999G6dWts374dPXr0YOvwAGj2/UuVRSmqk9iQYh2vvfYavvnmG8TFxWHlypW4fv06Pvzww0r7Wq/XIycnpyHEbbJYk2gzNze30u2UyhkwYADWr1+PqKgoREZGwtnZGTNmzGBD+5t7/1JlUYrqJDakVA8+n4///Oc/mDdvHvbt21erxJCU8ljTnwKBgPa3jRg0aBAOHDiA9PR0nD9/HgCaff9SZVGKypIVVpaokGI9s2bNQmZmZqV9LRQKIZFIGkK8Jos1iTY9PDxqlIiTYpmwsDC0bt2aHTU09/6lyqIUpRMbliYlJQWdOnVqAImaHy4uLnB3d0fHjh3B4/Es9nVFM/IpFWNNos1u3bpZ3A6APt81xPQ8A2j2/UuVRSlKJzY0UVFiQ0rNiI6OxsiRI+Hm5obw8HCLiSFpX1cfaxJtjh07Fnfu3DErBHbt2jW0bdsWnTt3rm+RmzxKpRLp6eno168fADT//m3o2N3Gxvvvv0+effZZdnnNmjVkwIABzSJOur45c+YMmThxIrl+/TohhJCsrCzyzDPPkJSUFEIIIX///Tdxd3cnBQUFhBBCzp07R9zc3Eh6enqDydxUePfddwkAs5j/mJgYIhaLSUJCAiGEkCdPnhAHBwdy584dQgghSqWShISEkN9++40QQohGoyFt27Yl33//ff1fQCOnbP+qVCoyceJE8tVXX7HvgjfffNNsTlZz71+aG6oMBoMBK1euRGFhIZvYcPPmzU2qwElj4fr165g5cyYSExMxffp0+Pj4YP78+fDy8mLb7N69G8eOHUOnTp1w7do1fPDBB6w5kFIerVaLPXv24IMPPkBiYiI++OADTJ06lTVznDp1Cj/88AObaHP+/PkYOnQou398fDzeffdddO3aFU+ePEH37t2xePHihrqcRkdF/du+fXtMnToVf/75J/r06YPevXtjxIgR5VKCNOf+pcqCQqFQKFVCfRYUCoVCqRKqLCgUCoVSJVRZUCgUCqVKqLKgUCgUSpVQZUGhUCiUKqHKgkKhUChVQpUFhUKhUKqEKgsKhUKhVAlVFhQKhUKpEqosKC2Cl19+GVwuF927d8ewYcPg6uoKhmEwbNgw9OnTB/b29myakaVLl6Jr164NIqfBYMCOHTtgMBhsetx//vkH8fHxNj0mpWVBlQWlRcAwDM6cOYMbN27g1KlTrDI4deoULl++jMePH8PFxQUA0LZtW/Tt27feZdRoNJg9ezZ69+4NDse2P81+/fph586duHTpkk2PS2k5UGVBaRG0a9cOgwYNqnC7j48PRo0aBQBYtGgRvvvuu/oSjWXBggV47rnnzOp5BAcHY8aMGeXaDhkypNz1PH36FHPnzkVwcDBEIhG8vLzw/PPP4/HjxwCA9957Dx9//DFbgpVCqQ5UWVBaBG+++WaVbZYtW8b+X6/X16E05Tl9+jSuXLmCqVOnsuvkcjkSEhIsmsRu376NLl26sMsZGRno1asXLly4gFdffRVff/01FixYgJSUFPB4PAAAl8vF9OnTsWTJkrq/IEqzgyoLSouAz+db1ebWrVtYvHgxevfuDQA4c+YMpk+fjlmzZmHXrl3o0qULHB0dsWHDBqSnp+OVV16BVCrF0KFDIZPJ2GMlJCRg2bJlePnll9GxY0esXLkSlSV4/uyzz/DCCy+Yrbtz5w4IIeWURXJyMnJzc82Uxa+//gqZTIbLly/jvffew5w5c7B69WrcunULgYGBbLvnnnsOv//+O/VfUKoNVRYUSin8/Pwgl8uRk5MDAAgPD0dmZibOnj0LqVSKmzdv4rPPPsOHH36IrVu34rvvvsODBw9w/fp1/PbbbwCMo5JFixbho48+wo8//oitW7di7dq17PayqFQqnD59Gh06dDBbf+fOHQAopyxu3boFAGbKIj8/H1qttlylvLI4OTnBx8cHBw4cqEavUCgAr6EFoFAaExKJxOxLnM/nw9fXF0VFRRg+fDgAYPDgwdDpdBg8eDAEAgGkUik6dOiAe/fuAQD27t2LxMRErFmzBgBACEH//v2RlZVl8ZyxsbHQarXw8PAwWx8TEwNPT094enqarb99+zY4HI5ZXeeZM2di+/btGDJkCHr06IGpU6dixowZ8Pb2Lnc+T09PXL9+vfqdQ2nRUGVBoVQToVBocV1hYSEA4MaNG+jatSs2bNhg1fEKCgoAAAKBwGz9nTt3LPorbt68ieDgYDg4OLDrWrdujUePHuHAgQP4448/8OGHH2LNmjX4+++/0b9/f7P97ezsKlRcFEpFUDMUhWIjTD4JpVJp0SegVqst7md66cvlcrP1MTExaNOmjdk6g8GAM2fOmJmgTIjFYsycORN79+7FrVu3oFarsWfPnnLt9Ho9RCKRdRdFoRRDlQWFYmNat26NK1eusL4FwKhINm/eXGF7DoeD/Px8dl1mZiaysrKQlpZm1vaLL75AdnY2OnfuzK7Lzs4ud0w7Ozvo9Xr4+PiU25aXl4fg4OBqXhWlpUPNUJQWiSlySalUwt7e3mybVquFRqNhl8uG0ZpmV+t0OrM2ppHFjBkz8OGHH2Ls2LH49NNPIZVKsW3bNixatMiiLGKxGGFhYbh79y67LiYmBgBw4sQJLFq0CO3atcOlS5dw/PhxAMD169dx+fJl9OnTB0uWLMGdO3cwZswYBAUFIS0tDdu2bYOfnx9effXVcrInJiZi6NCh1ncWhQIAhEJpQSQlJZENGzYQHo9HAJD58+eTy5cvs9sjIiJI165dCcMwZMuWLSQyMpIEBgYSqVRKjh07RrKzs8nKlSsJADJ9+nSSkJBAjh07RlxcXEjr1q1JREQEIYSQyMhI0qVLFyIUCkmXLl3IH3/8UalcP//8MwkLC2OXN23aRLhcLjl27BgJCQkhIpGIDB8+nMTExJCQkBDi5+dHrl+/TgghZPv27WTo0KHE3d2dCIVCEhoaSl5//XWSmZlZ7jxXr14l/v7+RKPR2KA3KS0JhpBKgr8pFEq9YDAYMGzYMHz++efo1q0b5s6di3PnzuHRo0c2Pc+sWbMwduxYjB8/3qbHpTR/qM+CQmkEcDgc/PLLL/jggw9QVFSEmJiYcvMuasvBgwcRHBxMFQWlRlBlQaE0Enx9ffH999/js88+w71792yqLP755x8UFhZi1apVNjsmpWVBzVAUSiPjyZMnCA0NxS+//GIxiWBNIISAYRibHIvSMqHKgkKhUChVQs1QFAqFQqkSqiwoFAqFUiVUWVAoFAqlSqiyoFAoFEqVUGVBoVAolCqhyoJCoVAoVUKVBYVCoVCqhCoLCoVCoVQJVRYUCoVCqRKqLCgUCoVSJVRZUCgUCqVK/h80Bvy1xCpBXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 500\n",
    "    for n in range(0, N):\n",
    "        # res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * (n + 0.5) + η**4 * ((1/8) * (1 + 2 * n * (1 + n)))) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-15/\"\n",
    "# number = 8591\n",
    "# file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-15/\"\n",
    "number = 8591\n",
    "file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "times = np.linspace(0, 170, 200) # Time array for simulation\n",
    "\n",
    "# /Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/2024-03-15/23\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:-10]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:-10]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [1, 0.04, 0.1, 20]  # Example initial guesses for a, b, c, and nbar\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, 1, b_opt, 0, nbar_opt - nbar_err)\n",
    "fit_upper_bound = fit_function(times, 1, b_opt, 0, nbar_opt + nbar_err)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "plt.plot(times, fit_function(times, 1, b_opt, 0, nbar_opt), lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / a_opt, yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "number_drop = 2\n",
    "plt.errorbar(rabi_t[number_drop:], (pmt_counts_avg_thresholded[number_drop:] - c_opt) * (1/a_opt), yerr=errorbar[number_drop:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation without delay time', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.02, 1.05)\n",
    "pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat_no_shuttling_no_delay.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5a507b",
   "metadata": {},
   "source": [
    "### with 300 us wait time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34dd2cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 1.1697156916466431, b = 0.023471568881785948, c = -0.04333616722569373, nbar = 43.00784288745962\n",
      "Optimal parameters with their standard errors:\n",
      "a = 1.1697156916466431 ± 0.07608563026516037\n",
      "b = 0.023471568881785948 ± 0.0002104972315615569\n",
      "c = -0.04333616722569373 ± 0.03865783706535982\n",
      "nbar = 43.00784288745962 ± 4.6608933463224655\n"
     ]
    },
    {
     "data": {
      "image/png": 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8+LDC3JY6St83Vfe2tCut/DkqeR8lEgnzzGlqV6VRqdPntZgnT56QZcuWkUGDBpE7d+6UWTc8PJz4+voSFotFtm7dSnJycgghhBQUFJDZs2eTgQMHkmXLlpFPP/2U8aqJjIwkrq6uxMrKipw+fZokJycTf39/AoAsXLiQPHnyhLDZbBIUFER+/vln8tlnn5GMjAyV1z906BBp0KABcXJyIqGhoUQikZCCggLSo0cPYmRkRAYNGkQeP35MGjduTNq1a0fi4+MJITJvj27dupHx48eToKAgMnv2bJKenl5mm0pTVhtjY2PLbMOdO3dI27ZtiZGREenYsSPjGaPq2lFRUaRr164EAFm3bp3SdmZmpsY27927l5iZmZGhQ4eSzMxMldcpqz3a3De5h4oqduzYoeD9c/v2bXLw4EGtnqXSsp8+fZpYWFgQJycncvHiRRIfH08CAgIIABIcHKzg9UYIITExMcTd3Z00aNCAzJgxg6xYsUKhXYTIPP6WLl1KgoODSUhICPm///s/kpmZWe46cjZv3kw4HA45d+4c8fT0JEZGRqRfv34kOjqaeHp6EhcXF8Ybbt++fcTPz4/Y2dkRHo9HmjRpQubOnUvS0tLU9ufz589J7969iampKenYsSPZvn078fPzI7/88gtzH65du0ZatmxJeDweadmyJTlz5gwhhOj0/MhJSUkh9vb2Sl5hqih5306cOKF0b69du0bc3NyIra0tOXfuHMnIyCAhISEEAAkMDCRxcXHk3LlzxNLSknh5eTGeYuraVZmwCKls59zah0gkwuXLl9G+fXvY2tpWtzgUSq1k+vTpuHHjBl68eFHdouiVZ8+eYfv27dixY0d1i1KlUG8oFRgaGpZ7cpRCoSgSHR2NZs2aVbcYemfv3r2YOnVqdYtR5VBlQaFQ9A4hBE+fPkWfPn2qWxS9cO3aNXz33Xfw9vbGq1evlNZ01AeosqBQKHonNjYWBQUFdWZkwefzERkZCYFAoHVIlboGnbOgUCgUikao6yyFQqFQNEKVBYVCoVA0UqfnLKRSKd69ewdzc3Ol4GcUCoVS3yGEID8/H05OTmCzyx471Gll8e7duzIDjVEoFApFFraldJ6S0tRpZSGPjJmQkAALC4tyHZuVlQV3d3fExsYyiY8o+oP2b+VC+7dyqSv9m5eXB1dXV4UowuqoMcri5s2bWL58OcLDw8us9/LlS3z33Xdo1qwZ7t+/j/Hjx2Po0KEq68pNTxYWFuVWFvI4O+bm5uU+lqIZ2r+VC+3fyqWu9a82ZvpqVxYFBQU4c+YMvvnmG/z3339l1k1PT4efnx+OHDmC7t27IysrC97e3jh79qxSfmEKhUKh6I9q94YyMzPD+PHjtUoi8sMPP4DL5aJ79+4AZBEdhwwZgiVLllS2mBQKhVKvqXZlIUebPBFhYWFo27atQlm7du1w7do1vacU5PF4GDt2rNaJgCjlg/Zv5UL7t3Kpj/1b7WYobSkuLsarV6/wySefKJS7uLhAKpXi8ePHauPQZGVlKcT95/F4Gm8ym83G+PHjwWazFY6l6Afav5UL7d/Kpbb2r0AgUMiol5+fr/WxtUZZZGdngxCikLMXkJmxAJSZpN7d3V1he+zYsRg/frxW17106VI5JaWUB9q/lQvt38qltvVvaGgo/vjjD52OrTXKgsvlAlCetZdvy/erIjY2VsE1TJuRhUgkwqVLl9CvX79qyY9c16H9W7nQ/q1camv/9unTRyEPR35+vtLHtDpqjbKwtraGgYEBCgsLFcrl22WlHLW2ttbZvc3Q0LBWPQy1Ddq/lQvt38qltvWvoaEhY42Rb2tLjZng1gSLxULLli2RlJSkUJ6UlAQDAwN4e3tXk2QUCoVS96k1ygIAhg8fjvv37yuU3b17F35+frV6FSWFQqHUdGqMspDP0EulUqbs8uXL8PLywpMnTwAAM2fOREZGBiIjIwEAubm5OHXqFBYuXFj1AlMoFEo9otrnLEQiEY4ePYrQ0FAAQEhICMaNG4fmzZsjLy8P6enpzLyEg4MDrly5gu+++w43btxAdHQ0fvrpJ/Tr1686m0ChUCh1nmpXFoaGhggMDERgYKDSPn9/f/j7+yuUtW7dGkePHq0q8SgUCoWCGmSGolAoFErNhSoLCoVCoWiEKgsKhUKhaIQqCwqFQqFopNonuCmVh0QiQUREBJKTk+Ho6Iju3btrFd2XQqFQSkOVRS1HnUIICwvDvHnzkJiYyNR1cXHB1q1blTzMKBQKRRNUWdRiVCmEho5OGD12HLZv3QxCiEL9pKQkBAQE4Pjx41RhUCiUckHnLGopYWFhCAgIUFAUAJCS/A4/btmkpCgAMGXz58+HRCKpEjkpFErdgCqLWohEIsG8efNUKgRNEEKQkJCAiIiISpCMQqHUVaiyqIVEREQojSjKS3Jysp6koVAo9QGqLGoh+njROzo66kESCoVSX6AT3LUQW3sHnY9lsVhwcXFB9+7d9SgRhUKp69CRRS1EZNsUHHPbch8nT0G7ZcsWut6CQqGUC6osahlFAhE2Xn4F6z4zVO5nsVhgsViY+FkQLGwVRyDW9g2p2yyFQtEJaoaqZRz8Jx5v0gth4t0FjccsQ+7VvcjJSGH2u7i4YMuWLfD390fqhnUI+uEIrj18CY6ZFRp93BqDh/StRukpFEpthSqLWoRQKMEvEbHM9rjRAbCcPA5WeW9gQQqUQno4NDDF3q8mY9C2CLzLKUZaoQTbr7/Gwv7eNBQIhUIpF1RZ1CIO/xuPtAIhAMDVigcLYwO0dbNBn48+Bput2qLYwISLVcN9MP3gPQDAgZtv4Jz9CP9b+CUNBUKhULSGzlnUEggh2HvjDbPdzs0GjpbG6ORho1ZRyOn7cUN0drcGAKRF38SEceOU1mnIQ4GEhYXpX3gKhVLrocqilnDpaSqScgUAAOcGPDS04KFtY2uYGxlqdfziwR+BSCXIurIbAA0FQqFQygdVFrWEg5FxzP99nC3gYWeGZg0ttD6+lasVPMVvIcnPUFuHhgKhUCjqoMqiFpCcXYArV8NR+PQ6pEnRaGRpjBbOljAwKN/t69WIq931aCgQCoVSCjrBXcMJCwvDtJlzkJuRypRtvrgN3tu34aPRAeU6V3dfL63q0VAgFAqlNHRkUYORhyEvqSgAIDs9BWPHjin3ZHSPHj1gba9eEbBYLLi6utJQIBQKRQmqLGooZYUh13UymsPh4MdtW1Xuo6FAKBRKWVBlUUPRFIZc18noCWNHY+SiTUqxpVxcXGgoEAqFohY6Z1FD0XaSWZfJ6KWfT8E9eEKQ+ATOXAF+mNKLruCmUChlQpVFDUXbSWZdJqPbNLKEu60Z4tgtkQnAs2UHqijKAQ2VQqmPUDNUDaV79+6wsFGft6Iik9EsFguj2zditk/er1jWvfpEWFgY3Nzc0Lt3b0yYMAG9e/eGm5sbXflOqfNQZVFD4XA4cBo4S+U+fUxGD23pxPz/5MMknc5R35B7p9FQKZT6CFUWNZTX6QUocmkPuxFLwLXQ/2R0IxsTtHBuAACIzeDjbSa/QvLWdSrDO41CqU3QOYsayrnHsolrE+8u6N1/IAbZ54InzNerjXxwC0dEJ+UCAE4+SMK8vtot2quPlMc7rVevXlUnGIVSRVBlUUO5+vzDQjzfRjYY2bsjGphoF65DW4a0cMT6888BAGEPEqiyKIPK9E6jUGoD1AxVA+ELxXicIPviN+dx0MLZQu+KApCZopo7y4IRxmcWUVNUGVSmdxqFUhugyqIGEvkqA9L3/3exNoa7nXmlXWtwiw8vt2P3EirtOrWd7t27w8XFhXEuKA0NlUKp61BlUQO5/OyDCcrL3hxODYwr7VpDSiiLM4/eVdp1ajscDgdbt8pCpZRWGDRUCqU+oHdlsWnTJn2fst4R8epDzon2btawNtO/CUpOYxtTNHM0AwDEZVKvqLLw9/fH8ePH4ezsrFBOQ6VQ6gM6KYvw8HC0adMGxsbG4HA4Cn+LFi3St4z1iqScIiRmFwMAHMx5+NhR+wRHuvKJ74eXXxhdoFcm/v7+iIuLQ3h4OI4cOYLw8HDExsZSRUGp8+jkDTVp0iS0a9cOs2fPBpf74atXIBDg8OHDehOuPnLjvw8mKDdbEzg0MKr0a/Zv5oDvz/8HALjwNAXz+zWt9GvWVKRSKdILhCgWScAGC5amhkqpazkcDnWPpdQ7dHadPX78uEr7bN++fSskUH3n8tM05v8+Tg3gaF75ysLTzgyNrIzwNrsYz5LzkcsXoYGJdrm96wpSqRTPU/IRnZiD1DwBBGICFgsw43HgaW+Gli6WcLCo/HtBodRUdFIWX331FW7cuIHevXsr7Xv37h08PDwqLFh9RCIl+CcuCwBgyAG6edqWO3WqLrBYLPTzccS+m7EAgFMP3qKRKKHeBMorKBbj+ss0PErIwZv0AmQWiJBfJALPkAVzIy6ep+TjZWo+ujaxRQvnBmCzqV8Ipf6hk7J4+/Ytdu/ejbZt2yqUE0Jw8+ZNvHz5Ui/C1Teik3JRKJCFi3CxMoGTVeV5QZWmz8f22HczFvz/bmP27mngZ38Y4bi4uGDr1q110i6fyxfiQkwKTjxIxIP4bAilqus5WPDwOr0Qw32d0KWKlHhthUblrZvopCzevHkDQghiY2MVysViMdLT0/UiWH2k5HyFl705GlbBfIWc9m7WkLyORPqfa5X2yQPl1TWPn/wiAdb9EoajN6JRaGAOnosPWGzVL7XUPAFO3E9CXGYhJISgZ1M7OsJQQVhYGObNm6cQGqUuf2zUJ3RSFtOnT0e/fv1U/lguXLhQYaHqK7deZzH/b+1qCctKWLWtDjYIsq/uUbmPEAIWi4X58+dj+PDhdeIr8fjx45j5eTCy0lKYMo65LTqM/wL+/qNgb8ZDnkCE2PRC3HqdiZQ8mYfavfgcrDn7FGajWqK9u011iV8jkUflLR1ssa5+bNQ3dPo0GjBgANhsNl69eoVjx47h1KlTSElJYfZRyo9IIsWjhGwAgAmXg9aNLKv0+hERESjMSlO7X9c0rjWRsLAwjB4zRkFRAIAkPxORu5fhwY0LSMsXQCyVwtnaGEN9HdHR3Zqp9yqDjyUnY/AyNb+qRa9SJBIJrl27htDQUFy7dq3MiLr6ispbnmtSqhadlIVIJMLEiRPh7e2NsWPHYuTIkWjUqBG++OILfctXb3jyLg/FYtmPyrmBUZV73tSXQHkSiQRBwXMBFS81QFZ2ef96jGjVEBM6NMbY9q4Y6uuE0W1dMKR5Q8jXbr9MK8DSsMfILxZVmexVSXmTPOkjZzxNLFWz0UlZLFu2DJGRkdi2bRtu3ryJ58+f48KFC8jPz8eaNWv0LWO94E5sJvN/D3uzKllfUZL6Eijv+vXrSH5XVrIngrTkd0h8/gD2FkZwtjRBSxdL+LdxRnAfL0zo4MoojDvxOfjur2eQStXMitdStEnyVHoEkJSkXQItdR8bNLFUzUenOYs7d+7g0aNHMDMzY8qaNm2K3r17Y+HChXoTrj5x8+WHEB9tG1nChFu10ePlgfISE5Mg/8IuCYvFgouLS60PlHfy1hOt6pV+qbHZbHzkaIFFAz+ClACh/8qCLobeSUAXT1t84uuk6jS1Dk3mJBaLhRkzZihNYtva2irVV4Wqjw1trlmX5stqKzqNLLp166agKEpS176yqgKplOBubDqK3z6G4L/rIMlPq9xWKw+Upyqoal0JlJdVWIy/XhdpVdfe3l6l7dzShIvlQ5uh90eylyMBEHIqBmnvJ8BrO9qYkzIzM5XqZGRkqDlChjwqb5cuXZT6VR8mLErlo5OySE9Ph1gsVioPDQ3Fv//+W2Gh6hvb9x/Gi21TkRq6BCl/bsCs8cOrxVbr7++PY8eOVUoa15rAqjPPILb/CBxz9V/BLBYLNjY2mDJlilrbuQnXAJvHtIaLlcxUmMUX4asTj6qkDZWNXuak1ETlHTduHDw9PZX69dSpU1UnG0VndLJ1jBgxAh9//DEGDRoEU1NTJCQkIDIyEomJifjrr7/0LWOdJiwsDPOmT0Jp0091uRuOGjUKX8ITv5z4G5KCbEzt2xrffT6mVo8oAOBhQjb+fPgOLDYH1n1mIP3P7wAWFCa6WSwW8+VcmtL3w9KEi52B7TDip5sQS4Gr/2Xg8rNU9P3YoQpbpX/0MSfFNjKHtCiP2XZwdELg+PHYuHGjSrfaLVu2VJlsFN3RaWQxcOBA7NixAw8ePMDWrVtx6tQpeHh44MqVK+jTp4++ZayzyG21quYIyuNuqG8GtnCGUaOWMG3WE0nGbrVeUQDAN2c+zFX07D8EC9fvhLOTYqhxZ2dn2NioXjuh6n40d2mAz3s3YeosPxkNkaR2m2E1JXnSBqs+n8Fh/FrYDl0Eh/FrYT31Z+w+8GuZbrUcDocmlqrh6LwEtX///oiIiACfz0d+fj4uXbqEbt26qTRPUVRTU221HT2swTOQ/XAfvM2BUFi7fd0vP0vBg7cf0tR29bLF/BkTER+vGGr8wIEDKkcVclTdj2A/LzSxMwEAJOcJsP7v55XbmEqmrCRP2mJr7wiT9x8bRo1aIvNNDPIzU8s8RiKRMJPZJakr82V1Ab3HK6AhyrWnpq5t4Blw0MFD9oXNF0kR/rL2hnAhhOC7vz68wNu5WaOzhzWcLU2YUOPjx49Hr169kJamflFiSUreDwMOG1vGtWG2f7kZizdptXuxnrokTw6OTuAYl5XilwVLO0d8Pv4TBPk1waAWDeFsaQRJQbZW150/fz5NLFWD0WrOYuTIkWjdujVCQkIglUoxYsQIFBQUKNUTCoV4/PgxJk+erHdB6yI1eW3DwGYOiHgh83C5GJOCAT4Nq1wGfXA+OgWv0wsBADamXPT/2AG+LpYq6+p6P5o7N8CUTo1x4J94SAEsORmD32d2rojY1Y6/vz+GDx/OBATMkppgawwL0udRKuOHyUcA27ZuwYhuHhCIJcgtEiM5h49donj8dkbzNYcPH46NGzfW+SCEEokEjxJz8TgxF0m5RRCKpTDjGcLRwght3SzhaWcObg0MVKmVsiCEMHZaNpsNGxsbFBYWolGjRgr1RCIREhIS9C9lHaV79+4ws7ZHgZowG9W5tqHPxw2x9E+ZnT/yTQakUmmtC5xHCMGGix9GFZ08rNHe3RpGatawyO31SUlJKu3rZd2PRYM+wtmYZGQUCPFPbBauv0hDz6b2+mtMNSAfed1+nYGQfVEQEcDEuws8x4Ug7dIuBdOSi4sLtmzZwowAzGEIWzNZrpTWCwNx8aflSEtRneO9ZL/W5cRSd2Iz8cvNONx+nY68YvWm3YbmPPRoaoOpXT3xsVPlZ8rUFq2UxZ9//qmwPX36dDRt2hR2dnZKdc+ePasXweoDHA4HrkPm4NmvK5X2VbettmEDI3jYmuBNBh/vcgV4npKPZk4NqlyOinDpSQreZMhyituacjG4hSM87EzV1pfb6wMCAhjPKDma7ocpzwBfD/oYXx6TudCuOPUEV760A4et+0SxPpBIJLh165bOX+oxSbmYsi8Kovfz9s6WRhjlNwHd1gQjN/YxMtJSNZ7X1JiHn3f8+D7IIKDKoaMuz0lExWZizbmneJyYp7kygJR8AY7ee4ej996hi6c1lg32QTPn6lcaOn0qZmdnq1QU9+/fB4/Hq7BQ9YUcvhB8p3awG7EEhjVwbYPfRx++jM89rn0+7tuvvWL+37WJDdo0stI4OlJnr9fmfoxs7YxmjrLFqnGZfPzx79sKSF9xIiMj0aRJE51jLSVk8TFx7z9Mjg8XSyNM7eqOUW1d0MHDFv36+DHzPZpe9PJ+dXFR7FeOuS0GzfseI0eO1KmNNZnsQiGCDt/D2F3/KCgKllQCs6znsEv9Fx9J4uHnbY3uTWzR3MkCNqaKGSpvv87CkB8jsCTsMYqF1es8pNM6ixs3buCTTz5RKvf09MSECRPw/Hnt9gipKh68zQEgG9p37zsQ072EyMlMqzG22kEtnLD3ZhwA4MaLdCwa+FG1ylMeHibkMD9QC2MDjPB1hqOldsmkStvrtb0fbDYLIUObY9zufwAA68//h+GtnGHKq9rQLQBw8uRJrF+/Xqlc2/U7RUIJpu3/FzlFsheUgzkXn3X3xBDfhrA10y1uWcl+PRL+EKdfFoHn4oMnbA7WnHuGZZ800+m8NZF78VmYeegeMgqFTJmFkQGs0x7g0bGtiMv4EPHY0ckZK9dugN/YT5AvEOPpu1z8FZ2CO3FZEEkICIAjdxJw/b80bB7bCh08tAutom+0fooFAgE+//xzXL16FdnZ2Th+/LjCfkIIUlNT4eRUN2LkVAUP3n7wEvnIqQEG9W9ZjdIo08rVEuY8A+QLxHiekof0/CLYmVdd9r6KsOPqh1FFF08b+DayKtfxutrOO3nYYICPAy48SUVukQg7wl/ifwM/Lvd5KoJEIsGCBQtU7isdawmAklJks9lYfPwhXqbLnFgsjDiY1dMTw1s7VTjHirxfe/XqhR7/xGHZ+3mxvTdj4WZriomdGlfo/NUNIQS7rr/C+r+eoSjxCSQF2eBaWKF3j56wy4rG1p1LlObDUpLfYdbUQEaBt25khVFtXPA0OQ87wl/jynPZnGZSrgBjdkch2K8JFvRrWqG1MLqgtbLg8XjYu3cvvvrqK0RERKB///4K+1ksFqytrevkcLKyuBP3IdlRZ8/q+VooCw6bhU4eNrj0LBUiKXDpSRom1IIf89tMPi49k02+GhmwMaatC6zNqi6R1FeDPsblp6mQEGBPRCwmd3Gv0pDzERERZUaBla8XWbNmDfbs2aOU1W747CU4mydzXuGwgGld3TGyjYvek3FN7OSGHL4QGy/K0jAv+zMGrlZG6OldO1fBC8VSLPjjAY4eD0PWld2Q5H+Il3X5iiPEIqHWwRKNuAZo09gaeyZZ4sqzNKw4FYN3eQIAwI9XXyEmKRfbJ7Sp0lFrua+0bt06PHnyBD4+Pkr7cnJyVLrUViZSqRRxcXHw8PCo0utWFImU4HFCDgDA2JANb3vVgRmrmwHNGzIv3kvPUmuFsth948Ooor2bFVq5WpdRW/+425ri/zq74cDtOIgkBN///Rw/jG1VZdfXdl3OihUrlMoSk5KwY+kc2I1YAhPvLhjVxhkTOjautKyNn/f2wrscAY7ckc3vzPztPk593g3eDctaz1HzKBCIMfPQv7j011mVrsWpKWXfk5ILPkuOaNlsNvr5NESPprZYefoJQv+VKfbw/9IxZFsEDk3riEY2Jnptizp0muD28fGBQCBAYmIi3r59y/w9ffoUo0aNKte5CCHYtGkTFi9ejGXLlmH69OnIz1e/qKmwsBBWVlZgsVhgsVjgcDi10l33VVoB+O9dTFwsjWFXxcmOtKVfiVhHD9/mgF/Nk2yayC4U4uhd2fPAYQGBHRtV6ahCTrBfE5gayn5eJx4k4UUVZtWr0Lqc91++WVd2o30jCwT7NYV9JT6bLBYLq0c0h19TmcNMsUiKSfuikFkgqLRr6pvMAgHG7bqNmy/TkXVld4XOpU7R8wwN8N0oX2wb1wq891NncZl8DNsegUfvPzorG52UxcmTJ2FnZ4fGjRvD3d2d+evevTvS08u32nfdunU4d+4c1q9fj2+//RZOTk4IDAxUW//QoUMICgrC/v37sX//fvz+++/o2bOnLs2oVu6XmK/wtDeFhVHVT4JqQwMTQzRzlLntZReJcCc2S8MR1cvRuwmQRyfxdbFEO/eqHVXIsTHjYY6fF7P9zemYKrt29+7dlby5yoskPwMDbXPgWgVfrRw2CzsmtsXHjrLRRGq+AJ/9ehdCcc2Ps5VZKMT4Pf8g5l0+BIlPFExPuqBJ0Q9r5Yy/5vWAi6VMgecUiTFm521ceVZ2OBV9oJOy2Lt3Lw4ePIi//voLixYtwtWrV3Hp0iXMmTMHZ85osVTzPVlZWfj2228xZcoUpmzatGk4c+aM2nhIZ86cwTfffIMpU6ZgypQpGDt2rC5NqHbuxX2IQdSmsWaXzuqkl/cHN+kLMSll1KxeJFKCQ5GxzPbo9i46e+7og0+7ucPBXDaqufU6C7eqKGwKh8PBpk2bACjHdyrPpKiJpOpMysZcDn6Z0h7W711H78fnYGnYY5U2/prE9AN38SJV1k+cohydz1OeYIme9uY4N7cHWrlaAgAEEoJPD97F4ah4na+vDTq9oYYNG4aRI0diwIABsLGxQY8ePeDn54fvv/8eixcv1vo858+fB5/PR9u2bZkyNzc32NjY4OjRo0r1L126hL///htOTk6YPn16rXbRvfvebRaQxSuqyQxt+eFr5583mTX2i+/Gi3Qk5cjMF42tjeH3UfVOlBoZcrBwwAd342/OPIFUWjUvv5EjR2Lx4sVK3okuLi745ptvtDpHVYeZcWxgjF+mdIA80sWx+0n45VZclcqgLdl8mUvsy3SZedGMy0HQkPY6nUuXBbgNTAzxx8xOGFQiDM++iFgIxJUX9FMnZRETE4Pff/8db9++Rf/+/fHZZ5/h2bNn+P333xEeHq71eaKjowHIHuCSuLi44OHDh0r1jY2NsWTJEnh5eWHfvn1o06YNrly5oksTqpVcvghx71cW25tx4WxZNRNUuvKRowWsjGVmsrhMPl7X0EB5B29/GFUMau5YpR5I6vBv44Im71eNv0grRNgD7XJV64POnTvj1atXCpF1Y2NjsXTp0jLDkFdnSPBWrpb4YUwrZnv12ae49p92AR6ritwiEWYcusdsm3I52DyuFf43daTGfrWxsVH5vtNlAS7PgIMdgW3wWTc32JhysXdyO/AMKm9tlk6G8jFjxmDkyJGYP38+li1bBgcHB/j4+IDFYpXrAcvKktm/TU0VQzCYmZmpjADarVs3dOvWDQBw/fp1jBs3DhMnTkRcXFyZK8ezsrIgEomYbR6Pp3Glubx+yeMqgkQiwc2bN5GcnIxMqQm4LAOw2Bw0sTOChSFLb9epLHp4WeP8E5ld9EJ0EhOWW1f02b8SiQR//n0FF05fg4GZNew8W2B0G6ca06fLBntj5m+yl8vmC08xqJktuJX4owY+9KtUKkXXrl2Zcnna4x9++AHjxo2TZbUraep5/6LbuHEjpFJptaRJHuxjj5c9GmPP+1HFF0fu4veZneFhV/0eg4VCMWYcuos3ablAI8CKx8Ia/xbo4WUDqVTK9Ku6cDE//fQThg0bxrwLHB0d0a1bN3A4HJ2f1/8NaIqZPdzQwJir8RwCgQACwQfngbKciUrDInoyCkZGRiI5ORkDBw6EiYl2L5Lg4GBs374dEolEwWbfvXt35OTkMCOPsq7ZpUsXXL58WWXSpby8PDRooBzPaOzYsRg/frxWMuqDyMhI7N27VyFXgo2NDaZPn47OnWt3dNKqQCKR4OnTp8jOzoaVlRWaNWvGDNc19W1Zx9Z3VPWdra0tPv300xr5XNa0e6lOnprcr6Ghofjjjz+UynNzc2FhUXb8KZ2UxaFDhzBp0iSl8gcPHuDp06dlejOVZPXq1QgJCUFeXh7MzT/4Vbdp0wZ2dna4cOGCxnM0bdoU33zzjcqXv1xZxMbGKpxf25HFpUuX0K9fPxgaGpZZtyxOnjyJcePGqZ2oW/rDbqwInqLz+auKYqEEHdZehhSAiQEbJz/vCmcr3UcX2vbvyZMnsWDBAoVFZs7OzswErsq+ZbHAAvDFF1/gjz/+UHlsZS4eLTmKlH85vkgrxOhdkQAAIw4Llxf0gqVp5bn0aupfQghmH76PiBepKEp4CifDIqwa1w09e/aoMcqULxRj3K4ovMksQP7z28i5uhdFOR+cBKriXsoRiiUICn2A269lCkDy6jZyruxWUAgl5VH1DNSEflU1snB3d9dKWYDowKJFi1SWP3v2jDRq1Ejr85w+fZoAIM+ePVMot7e3JwsWLNDqHO3btyeXLl1SuS83N5cAILm5uVrLJEcoFJI///yTCIXCch8rRywWExcXFwJZmE2Vf47OLkQsFut8japk5PYI0njxWdJ48Vny+534Cp1Lm/49ceIEYbFYSn0mL7OxsSmzb1X9sVgswmKxyIkTJyokf1kyl77nLi4u5MSJE2TWoX+Z/lt09GGlXF+Opv797Z84Rhaf5X+R+Mz8SpVHVxKz+aTRmGXVci/lCMUS8umBD/fO0X8JgZrnsirk0SfleUdqPcEtEAgwbdo0cDgc/PDDD+BwOEp/Pj4+KqPRqqNv374wMTHB/fv3mbKEhASkpaUhICBA4/F8Ph8pKSno0qWL1tesSjSlTQWA5KTEKk+bqis9S3gXXYhJqVR7tjw/OSkjb3NZKVDVQVTk0tYXYWFhCAgIULrn8uB9bchLxqPk+L1EvErVLmS1vonPLMSac8+Y7YUDP0Ij6+qfD1BFQ3Muim/8onJfZd5LORIpwRd/PMTl9+sYOEQCwc39ivM8VShPdaK1suDxePjll1+wY8cODBw4EL/88ovC3/79+3Hq1CncuHFD64sbGxtj/vz5OHjwIFN24MABdOvWDZ06dcLBgwfh4+ODtLQ0CAQCjB49Gjt27GBuyvLly7FmzRqt50iqmpqaNlVXhvl+cMOMeZeH5LziSruWNopWV0gl5DbXRrmtXrYY49rJFstJIct5UdVIpARfHn0E/vuVi508rDGli3uVy6EtERERapMmAZWbp14skeLLow9x9n14fjaAAJdCZKWp/71WpjzVTbm9oWbNmoVhw4apjS4rFpcvHIR83mLu3LmwsrJCbGwsTp06BRaLhZycHEZRGBjIRP3f//6HEydOoEOHDvjkk0/Qu3fv8jahyqjJaVN1wd3WFM4NeEjKFSAtX4DohJxKc/utCgWqz2toUm7yl0gX8wycNGShSERw600W/o5+h0Etqi5S887rr3E3XhY9wNrEED8HttVwRPVSXR9cArEEwUce4ELMO9nK7IJsBPZqiZbW2i1qrC0fgOVBJ9fZssKQHz58uFw5uNlsNr799luV++bNm4d58+Yx28eOHdNeyBpARdJ01lR6fWSPw1Gy2EtnHiejv0/DSll9XhUKVJ/X0PblkJ+Vjjm92uGHSy8AACtPP0GPpvZVEj30cWIONr+/LgAs/6QZrCpxkl0fVMcHF18oxoxD93Dx3GmF6LFbz8i8mqpanpqCVk/oyJEj0bp1a4SEhEAqlWLEiBEqo8sKhUI8fvy4XMqiLlNWmk45tS2d5Mg2zvgtMg6CxCe4GHcbxy3SMWpwP723QRtFa2LeAIV5OeU+d2Uo6fK81Mb0aILj9xIQn1WE1HwhVp2JwfqAVnqTRRV8oRjzf38I8fsV5H0/ssPINi4ajqp+ND0HAGDj4KjzvZRIJAr5PNybt8XnoQ9x5+p5ldFjMzLKjv1UGz8AtUWrT0JCCDNhw2azYWNjAxaLhcaNGyv8ubm5wcqqfElm6jrq0nQaW9nj6NGj1Zo2VRfi715D8q5PkRq6BK/+WIuxwwaVK1WntsgVLaA+vlHj4fNhN2IJOOaKX3uurq5YtGgRE5lY1bH6VtLyl5o2q6I5bBa+G9WC2XfsbhL+eV2xAHSaWHPuGd5kFAIAnBoYVWnI9IpQ1nMgh915KjZeeFHuSeWwsDC4ubkppJ318vRA1JW/dIoeW1nPVo1BF3ermzdvkrS0NJX7zpw5o8spK4Xqdp0tiVgsJkEbfyW2QxcRh/Fryco/H+nlvFVJWa6s5XUZ1LZ/Vbmiurq6ktU//sK4Mvb+/hIJDw8nR44cIeHh4YwrsrpjK9NtVt4X2vTPnN/uMm3w2xhOcvkCvclSsn//fJDIXMfzq7PkytMUvV2nqlB1L40t7YndiCVM28buvEUSswq0Pp+qZ7k8fxYWFlX2bFUW5XlH6m0Fd0pKCho2bKi5YhUiX5Sn1YKTUohEIvz1118YPHhwhRbllWTK/ju49p9sUdHuiW3Rv3nN6q+ykEgkcHNzUzuJKx9+x8bGavVVVZ7+LW0q6N69O2b+9oBxZ1wyyBszejbR+tjK/OoLCwvDvHnzFPrJ1dUVW7Zsgb+/v4I8xg1ssPwfEfLfp2ke184V345oDgODis8ByfvXs213+P8cyYRtH9/BBd/5+1b4/NVB6Xvp27YTFhx/jPD/PizUMzfi4PNeTTC5c2MY81Q/V5qeZW2ZP38+hgwZgvT09Cp5tiqD8rwjdZpVu3DhAkaPHo39+/czyY4SEhKwc+dOhISE1Ohw29WFVEpw730aVSMDNj52ql2ZwLT19imd6UsflM6HnZjNx9XnMkVhYcTBpE5uWh9b2fj7+2P48OEqFZQqRWJt3xCcLtNg4t0FR+8moL2bFUa1c9WbPPNC7zOKopWLBb4qEQW3tqHqXv44vg02XHiGw/+8hZgA+cUSrDv/Hw7ejsfwVk4Y3toJnnbm4JZQwOevhOvFLdvGxgY9e/bU28dkTUcnZbFmzRpMnz5d4ca1b98ed+/exZIlS7Bu3Tp9yVdniM0sRL5A9qt1sTSGnVnZ4UZqGjVpzciRqLeQR/ru5W0PoyrMQ6wNql5q8gV7pQfy2empIH+uZdKYrj73FG62pmhbwbD1YolswWRiTjEAFmzNDLFqRAs0MK1dz50mzIwM8PWgj+HtYI6frr1+314gOa8YO2+8wc4bb2BtaghrYy5YHCC3UIjYO5crdE0WiwVnZ2c0a9ZMH02oNeg0BPDz88OmTZtgY2OjUO7s7IwDBw7oQ646x/34D5nxmjqawYhbs15wmqgpa0YEYgn++FfmussCsLB/00q9nj7QtGCPxWIh5+oeSMVCpPx3H5OWbcahE+d0XgVMCMHqsx8W/PE4LCzq743mTuUzxdYWjLgGGNOuEbaMb42RrZyYBEpysgpFeJVRiJephUgrEIFjpr0TjjoHCXkUi/qETsqisLBQZfnvv/9e7kV59YUHJfLktm9cs5MdqUKTtw8g8/bp0qULrl27htDQUFy7dk3vYQ/+jk5BZqHMyO/raolGNjUzTEVJtDHhifLSkfTzFKSGLsHTw99icsAncHFtVG4vM0IIVp19ghMPZKueWQAmdmqMIS2d67R52MCAjXaNrbFwoDeWD2mGgDbOaOFsAXtzHthEguK3j1H49DqK3z6G60e+MLJUH5ZI7rl27NgxJS9Gee6JqgheWNPQ6fN2wIABGDNmDObMmQMnJyfEx8dj586dOHnyJBYuXKhvGesEd2M/xDGqrrzQFUHzmhGCYSMD4OnpqfBidHFxwdatW/XmInwoMo75/6fdam6YipJoa5qT8hVjRaUkv8OogAAcO3pUq1hpYokUX598jGN3k8B7/9E7wMcBU7q5w6yG5njXN86WJhjeyggtXS3xIiUff/55En/8shq5GR/SAQtsG8JvyEj8dWQPWIDCs1zS/dXf3x8jR45UOf9UU3KlVCU6PUF9+/aFQCDA3LlzERMjS0Rva2uLtWvX4n//+59eBawLFAjEeJEmG43ZmXHhWoHQ3tWJfM1I6UlajrktbFv2xo4ftygFWJMH0dMlE1hpYpJycf99OtqG5lx80rJ2rJKtkGmOEMwKmovu/QbBoYGpyioSiQSnzl/G2uORSBTwwHPxATiyUcTUru619nnTFTabDU87MzyKuIg9K4KUPmxyM1Px95E9WLRwIUJDQ5U+buSKAqh6B4majM6fG0OGDMGQIUOQl5cHkUjEzF+IxeJaOdyVSCQKXwsikQgGBgYoLi6usCklOj4bzuayTz0fZ1NwIUZxcc3MY62JwYMHY8CAAbh37x4Sk1Kw524Gii1ckXY0BI0bNVJ5DIvFwoYNGzBgwADGzqtL//4e+Zrpx9HtHBXi8tdk2rdvj44dOyI1NVXtKmRNrNwdhnGDe8PZyhhWplzwOBxIpFKc+us8Nqxfj/xsmfuoEQCOqRXajpgKAwNPeFhxUVxceQEfawoSiQT37t1DWloa7O3t0apVK3z//fdoVMYzGRERgSdPnuDhw4fMcW3btgWHw9HYZ/p8P1QUDodTJR5ZOq2zkHesKkJCQrBq1aoKC6YPtPEhJoQgJSUFubm5Cj9kQgiKiopgbGxchp1eO/KLRcgtks3lmBsZoIFx3XG1yy4UoqCoCNLCHI11HRwcYGQky4td3v4VS6VIzRWAAGCzgIYWRmCzK3ZfqhI+n4/09HTNFdXANraAAdcIBhw22CwWAAKhoBjCghy1x1hYWMDS0rLCz29Nh8/nIysrS+GlzWaztQqhX/KZLA/6fD/oAx6PB1tb23KvJ6v0dRZr167Fli1bFMqysrLw5Zdf4tChQzVGWWhDbm4ucnJyYGdnB1NTU+bGS6VSFBQUwMzMrMIjpaRsPngCmbJwsjSGuVHdURb2AhHeJmdCYqz5UWrYsCETDqa8/ZuaVwypqWxi25xnAKdaaFrJzc1FSkqKwgiWw+Fo9WVq0MAeLEPFl5ooMwkwUh/YjsPhwM3NrVaO9LUlNzcXhYWFOocZKvlMlgd9vh8qAiEEIpEIubm5TEbI8ioMbdFJWVy/fh3Tpk3D+vXrYWdnhz179mDJkiVgs9k1NhGRKgghSEtLg4WFhVI0SalUCqFQCCMjowo9DIQQFBMBWAayL8IGZibgGtQdlztDLg+pOXzwleNKKmFmZsZ8xZWnf8USKfJExWAZcMEC4GRrDiPD2teHRkZGsLe3R0FBAYRCIbhcLkxNTRETEwOhUKj2OLaBIXimDSAs5oNIJGBxOLKpIVK2kpFIJBCLxZX28qhuCCFITU2t0DlKPpPlQV/vB31gbGwMc3NzJCYmIiMjo2Ypi0uXLiEvLw+jR49GYWEhHj58iBkzZmDNmjU622SrA4lEAolEUqk/JqFYCsn7FWSGHDYMOHXrK4/DZsHa0gL8TANAqt5tmsvlwsxMNzfXzEIhM29uyjOolYpCDovFUsgHD8jCgbx+/VrtMWYWDVCYFgeJ+MOIhMXWrg/qsteOXOnqSkWeyZoGi8VCgwYNkJSUBJFIVClzGDopi61btyIpKQkRERHo27cvBg8ejOnTp8PS0lLP4lUu8jUh8sRKlUGh8MPXn7Gh3N5ct7A05iKtgR2E2epdRF1dXXWy7UqlBBkFHyay7czr1gpkALCysoKnpycSEhIUXn5cLhfW1tZISUlROoZItZtUrcuhKCqiKADdn8maivxeSySSmqMs1qxZA1dXVxw9ehSjRo2CUCjEmjVrcODAATRr1gwzZ87Ut5yVSmU+MHzhh69tkxoWlkJfcA3YsGhgiRwCiPPSFUYYXC4Xrq6uOtuUMwsFzMjM2JBTJUmCqgMrKytYWlqqNFHpioGBQZ35clYFl6td4iYDAwOFxcIVfSZrKpWt+HT65Q0ZMgS///47TE1lft9cLhfffPMNHj9+jLFjx9Y6ZVGZ8AUfvgBNa1mID21hs1mwNjVEfrEp2EamIMIimHFZsDIzhlUDC50fYolUirT8D6MKWzNenRyZySltosrPz0dOTg74fL5CPW3t7NpmdautmJmZgcvlljnC4HK5aN68OQoLCxklbGZmVqdGFFWFTgb0rVu3MopCzsqVK3Hy5Ens3l3+pCF1FbFEimKxTFkYctjgGtTdB9TIkANjrsyOzuIag3DNwOYaV+icGflChVGFeT1ZhSxHKBTit99+w6BBgxT+Ll26pFCvtBmVy+XCw8NDL6OKNWvWgMVioXnz5kr7njx5gtGjR8PDwwMmJiawtbVFjx49cObMGa3PLxAIsHjxYjg5OcHY2BgdO3ZUal9BQQFWrFiBgQMHwtraGiwWCwcOHGDCcpSFq6sr2Gw2zM3NYWNjA3Nz8wopCnl/tGzZslzH3b9/H8OGDYO1tTVMTEzQvHlzbNu2rcJ1qxKtlAWHw0HPnj0ZRWBra4u8vDzmD5ApCx6PR1c7loBfYr6CZ8AGp4q9JkQiERYvXoy2bduiR48ejGvw+vXrmTrz5s2Dr2/F8xsYctgK60eKhGLwhRIUiyS4efMmgoOD4ebmpr3sYinSS8xVbPzma7Rt07rCcuobTf136tQpjB49GosXLy73ublcLgYPHozt27cr/HXu3FmhnoeHB7y9veHu7g5vb2+0aNFCL/OHiYmJWLt2rdKHoZz4+Hjk5+dj8uTJ2Lp1K5YvXw4AGDZsmNYfjVOmTMGmTZsQGBiIrVu3gsPhYPDgwbh58yZTJyMjA6tWrcKzZ8+U+lo+31PaJMXlcuHp6alXU5Om/lDHxYsX0blzZ6SlpWH58uXYunUrPvnkE5XxwspTt6rR6lOtUaNGuH79OrP9/fff47vvvsPw4cPxySefYNKkSTAwMMBXX32F/fv3V5qwtY2SysKkGkxQGzduxIkTJ/Do0SOYmpoiLS0Nw4YNU5gw9fb21ssKXxaLBbP3nkrFIgkIgCKhBFwDNjp26oQrV64gPj5e6/O9yy1CUVEReEZGMOFy0LzZx4Ck5nn2qOq/4uJixkw0aNAgLFu2DA4ODuU+t5mZGTw8PODioj5XNpfLVfm1rA+vxIULF6JTp06QSCQqc08PHjwYgwcPVigLCgpC27ZtsWnTJsyYMaPM89+5cwe///47NmzYwMSUmzRpEpo3b47//e9/uH37NgBZuJTk5GQ0bNgQd+/eRfv27RXOo2q+R52pqVevXnBzc9MpOram/lBFXl4eJk2ahCFDhuD48eNlutmWp251oJU0Pj4+CtvffvstvLy8cOLECUybNk1hGKxquFpfKTm5bcarenfPkydPomXLlsyXkL29PY4fP46cnBymzpw5c7Br1y69XI9rwIFFidEFXyhGsUgCvoioDbugioJiER48jsGR/bvAYrFgY8pDcNDnepNTn5Tuv2fPnimYDLhcrlIof23o3bs3evbsiaysLMydOxc9e/bE4MGDERoaqlCvsjx6bty4gePHjystvtUEh8OBq6urwjOmjuPHj4PD4SgoFSMjI3z66aeIjIxEQoIsFD2Px9OYhVM+36MPU5MqdO2PI0eOIDU1FWvWrAGbzUZhYaHaleXlqVsdaKUsTEyUV8u6u7urvCHlHaLVVaSEMCMLDotVLWsDxGIxLl26hJcvXzJlLi4uSl+D+optw2GzYMblwPh9WwlkE/xFQjGE4rIfekII8vPzkZGRiQfPXuLLWVNQXFQEMx4Hpu8VbXXH4FGHXK7MzEwEBAQoTUjrQnR0NHJychAYGIguXbpgwYIFsLW1xebNm/Hq1SuVZhaRSISMjAzmLzMzU2G75F9ZLyGJRILg4GBMnz4dLVq00ChrYWEhMjIy8Pr1a2zevBl///03+vTpo/G4Bw8eoGnTpkrrnDp06AAAePjwocZzlEXp/sjIyIBIJIJAIKjU/ijJ5cuXYWFhgaSkJHh7e8PMzAwWFhaYPXu20oi0PHWrA53HOeo0d20J7lbZFIskkL43BXAN2OBUQxyjwMBAFBQUoEuXLjhx4gRTPnr0aADAo0ePEBQUxPw4r169isDAQEyePBmhoaFo2bIlzM3NsW7dOqSkpGDq1KmwtbWFn58f8vPzAcgywBkaGmLlypUAgMcP7iFweH8M6iybACwWSyAUS1FcSllERERg0aJF2LlzJ/r164fNmzfjv//+Q1xcLC7/+QdSkxIRee0yNq1ejshbNxXklJOfn4/58+djxYoV+Oyzz9CjRw/GdBEZGYnJkycjMDAQx44dg5eXF+zs7HD48GG1/fXnn38yUQiSk5MREREBOzs7NGzYEDdu3GD6zM7ODrdu3VLqvzNnziAxMREXL17EwoUL8e7dO4Xzb9iwAa6urnBwcMCRI0fUypGcnIzMzEykpKQgKioKGzduxKpVq3Do0CEQQpCbm4sWLVoo2eNv3boFOzs72NnZwcHBAU2aNIGDgwNTVvLv7du3aq+/c+dOxMfHY/Xq1WrrlOTLL7+EnZ0dmjRpgoULF2LkyJHYvn27xuPkYb9LIy8r3X/lpWR/yP9u376N33//vVL7oyQvX76EWCzG8OHDMWDAAMYas3PnTkydOlXnutWBVob0p0+f4tdff1WwgyYlJSmVCQQC5kdV2xm24xbS8orfu2rqsJiMEMaTh81iVUhZ2JnzcCa4W7mPW7BgAVJTU/HDDz8gICAAQ4cOxd69e5kgkC4uLigoKEBmpizXRvfu3fHdd9/h5cuXmDhxIh4+fIg9e/YgKCgIxcXF2LVrFzZs2ABPT08cOXIEM2fOhL+/v8IPvkvnTvDr0we//forU5ZXLGYUZ6FADGNDNvz9/fHdd98hICAABQUFWL58OS5cuAAjIyMMHToUe/bsQYd2bRASsgxsAPv372fkBGQjkSFDhiAwMJBx1d6+fTv69OmDqKgodOjQAWvWrEFMTAxGjBiBp0+fYsGCBQgODsb48eNV2oNHjBiBPn36gMPhwNHREY6OjggKCsLu3bvRvXt3AECzZs3Qr18/dO3aFZmZmQr9N2XKFKxcuRL9+/dnlKecS5cu4ccff8Tbt28xd+5czJ07F+PHj1f50RUdHQ0AWLFiBTNfwWKxmEnrBg0aqDzO19eX8SSSSqXg8/kwMTFR2VZ1Zp3MzEyEhIRg+fLlsLNTnyCoJPPnz0dAQADevXuHo0ePQiKRaLVgrqioCDye8iJL+XxPUVGRVtdXR8n+kPPll1+iYcOGWLRokUK5PvujJAUFBeDz+Zg1axZjnvT394dQKMSuXbuwatUqeHl5lbtudaC1spgyZYpCGSFEZVld8V/OyBcgLb9iK0SrGxaLhe+//x6jR4/GrFmzcObMGbRq1QrXrl1D06ZNYWNjo+ChZGhoCGdnZxQVFaFfv34AZBOCYrEYvXr1ApfLha2tLZo1a4anT58yx5V+GRlyOGCzwEx2SwlB0XuTXF6xCAIxB3OCgtGxY0ckJCTA2NgYfD4fOTk5Sj/a5KQktGjRQsmT6sqVK4iIiFDIJDdz5kysWbMG69atw5EjR2Braws3NzdmJDV06FBs374dqampanNMTJs2DZMmTUJqaiocHBzQqVMnrFy5EhEREejRowfOnTuHUaNGAYBS/5VF586d0b9/fwDAJ598gu3btyMtLU3lxLdcWYwYMUKh/Pnz5wBkk+qqsLKyQt++fQHIlEVeXh4sLCzKNVG6bNkyWFtbIzg4WOtjPvroI3z00UcAZBPU/fv3x9ChQxEVFVXm+8DY2FilJUJucjE2rpjrdcn+KFnm6OioVK4OXfqjJPI2jB8/XqF8woQJ2LVrFyIjIxkFUJ661YFWyqJZs2aYPXu2Rr/twsJC7N27Vy+CVTe25jxICdF5ZCGSSmVGewAGHBZYOpxDTkVDXLRv3x5RUVH44osvsH37dkyYMAF3797V6lhVX348Ho9xmVYFhy1rrymPA7FECrH0wyiLw2JBKJZizhf/Q/Sdmzh79ixyc3MBQKXdWCgUoqBAOUrhvXv3ACjOkRkaGqJ58+Z48OCBSrnk7pVlmUpHjhwJMzMzHD58GAsWLMDRo0fRpUsX/Pbbb+jRowdOnz5d4Yl2eZ+q+3J+/PgxGjZsqJTS89GjRzAwMECzZs1UHicUCpGVlQVA1pf5+fng8/kqlYWdnZ1SDumXL19i9+7d2LJli4IJqLi4GCKRCHFxcbCwsIC1ddmZHgMCAjBz5ky8ePFCrWIDZOYmeaTUksgzCzo5OZV5ncpGU3+8ffsWTk5OZS5+dHJywpMnT5Q+CuSj++zsbJ3qVgdaKYvly5dj7NixWp1QFxfBmsjpz7vq9GUGAEKxBM9TZDZ9rgEb3g76987QxJ07d2BtbY0mTZoAkC3c+vHHH/HkyROEh4cjKytL44++LDS5ZrJYAI/DgaUJC9n8DyO01HwBzHgcbP5uFVIT47FgwQI8fPgQhw4dUnsuVSYN+YsuISEBTZs2ZcptbW0rFInUyMgI48aNw6FDhzBmzBhYWFggODgYs2fPxtdffw07O7tKj7cUHR2tcu3G48eP0bRpU5UKHABu376N3r17a3WN2NhYpVFRUlISpFIpYyYrjbu7O+bNm6fRI0iuBOUfAepo1aoVwsPDmd+ZnKioKGZ/daKpP3x9fTF37lxs3bpV7Tnatm2LS5cuMZPWcuTKp6Rpqzx1qwOtlIW2igIAM0SvzxQKSgYP5FSLaY7L5WL16tU4ePCgQnnbtm0RGRmJBg0a6O1aLBZLYVQg9w4y5XEgLpK+D9Mh20cIwc1bt/Hjpg24cuUKOByOyhFFyTzfqmIAderUCQBw8+ZNBWXx7t27CofJnzp1Knbu3InZs2djy5YtcHZ2xsyZMzFhwgSNI2fV+cm1RyKR4NmzZ4wZsCSPHj1C69bqFyZWdM6iefPmOHnypFL5smXLkJ+fj61bt8LT05MpV5UETSQS4dChQzA2NmZGQHw+H2/fvoWtra3CV3hAQAA2btyI3bt3M+ssBAIB9u/fj44dO2pcna0L165d07qupv5Yu3atwlIBVe0cM2YM1q1bh3379sHPz4+pu3fvXhgYGCgsYi5P3eqgfsVPqCIKBCXXV1RPF3t4eODQoUPw9fXFF198ARaLBT6fj7/++gszZsxQSG9a8su9tHuq/EVeMhCbRCJReCG6u7vj77//xvz58/HmzRtcv34dOTk5EPALYMw1AV8kgakhizk2PVW2KDAmJgZt27ZHeHg4ANnLR+4uaGFhgbi4OLBYLMTGxirJ2a1bNwwYMACbN29GYGAgeDweYmNj8eTJE2bBlVgsVpBTfrwm3/UOHTqgWbNmjHsqIPshR0dHK605Ki2XtbU1nj9/DpFIhOfPn6NFixYQi8VKKXtL96mcly9fori4WGlkUVRUhFevXmHy5Mlq5a7onIWtra3SPAkAZiRRet/MmTORl5eHHj16wNnZGSkpKTh8+DCeP3+OH374gTFb37lzB71798aKFSsUJv47duyI0aNH4+uvv0ZaWhqaNGmCgwcPIi4uDvv27VO41vbt25GTk8N8Zcs9zwAgODhY5cdPamqq0gS3OkaOHKnk9q+pP4YMGaIwIlLVztatW2PatGn45ZdfIBaL0bNnT1y7dg3Hjh3D119/rWBqK0/daoHUYXJzcwkAkpubq3J/UVERefr0KSkqKlLaJ5FISHZ2NpFIJOW+7rPkXPIoIZs8TsgmxUJxuY/XF9bW1gQA8fb2Jv7+/qR79+4kJCSECAQCQggh4eHhxNfXl7BYLLJ161Zy7do14ubmRmxtbcm5c+dIRkYGCQkJIQBIYGAgiYuLI+fOnSOWlpbEy8uLhIeHE0IIiYqKIl5eXsTCwoIsWrSI7Ny5k4wePZqcOHGCiCUScjE8grTv1JkAICHffEvuxLwkHTt3JUZGRqRnbz8SGhpKHB0dyccff0zOnDlD/v33X7Js2TJiYmJCBg4cSE6cOKEgZ05ODiGEkIKCAjJ79mwycOBAsmzZMvLpp5+SR48eEUIIiYyMJK6ursTKyoqcPn2aJCcnE39/fwKALFy4kBQWFpbZdzt27CD//PMPs3379m1y8OBBhTql+y8nJ4fs3buXmJmZkaFDh5LMzExy+vRpYmFhQZycnMjFixdJfHw8CQgIIABIcHAw4fP5Cuc8evQoAUBiYmIUyu/cuUMAkLNnz2p17yvy/JamZ8+exMfHR6k8NDSU9O3blzg4OBADAwNiZWVF+vbtS06dOqVQLzw8nAAgK1asUDpHUVERWbhwIWnYsCHh8Xikffv25Pz580r1GjduTCCbBVT6i42NVSm3/Lra/Kk7R1n9Ubp/1bVTKBSSlStXksaNGxNDQ0PSpEkTsnnzZpXnLk/d0pT1PlOHpndkSXTKwV1b0JRftri4GLGxsXB3d1eK4qmrN4lQLMXzFNnkL5fDhnfDqp+vqGkIxVLkFYsgkkjBM2C/n+wnYImKIDU0Rm5ODnLSUxSS+9TVMNJVha7PL0U7amL/lvU+U0el5+CmqKewhAnKmFs98xU1Da6BLMhgfrEIArEULLDAZgFcAEKRBOYNLOFobwNRcRENI02h1FCostAjhBBk5eZCWiQAi8OBmWXF/MTrEoYcNixNuCgWyVZ0iyWyeQMzI0MYczngsNkwMjTXcBYKhVJdUGWhJ7Kzs5XSYibmp4HdqBE1pbyHzWLBhGsAE658GF8MEy6nxgzjKRSKeuivVA9kZ2fj9evXSusBRCIRXr9+Xe2LaWoK5H2wQHmYjDo8XUah1DnoyKKCEEKYUMrqSEhIgKWlZb22wasaeRkYGKBRo0YVWhxIoVCqBjqyqCDyhCtloS5kRX1B3chLLBbjzZs3dORFodQCqLKoINpE1yxPvbqGtiMvapKiUGo2VFlUEFWhKCpSr65BR14USt2AKosKYmZmplERyNcN1EfoyItCqRtQZVFBWCyWyoBnGRkZ2L17NzIyMiotT3JtgI68KJS6AVUWesDS0hJcK0eA/cG5LCMjA3v27IGRkVG9XmdBR14USt2Aus7qAb5QAvBMwbU3haFUAHtTAyamv6Z4K3Ud+cjr9evXauvU55EXhVJboCMLPZBfIh6UZQML2NjYKIU7rmrOnTuHXr16gcViwd7eHgEBARg+fDg6duyIqVOn4vbt21Umi5WVFTw9PZVGGAYGBvDw8KjXIy8KpbZARxZ6IL/oQ7TUBsaGkEgkTNrSu3fvwtfXVymFZWUzZMgQsFgsXL9+HTNnzsTq1asByCJTrl+/Ht26dcPMmTOxY8cOrcJtFBcXax3JUhVWVlawtLRkvKMMDWX9pM8kTBQKpfKgI4sKIhRLUSSSJQwy5LDx95lTcHNzw8yZMwHIEsS4ubkhLCysymUzMTEBAAVFZWRkhBUrVmD16tXYuXMnQkJCtDrX7NmzKywPi8WCubk5bGxsaFRZCqWWQZVFBckv/jCquHHxLEaPHs1k8JKTlJSEgICAalEY6li8eDHc3d2xceNGpKenl1l3586dTPY5CoVSP6HKooLkFcvmKyQSCVYt/Z/Klcjysvnz5yulLa0uDAwMMGzYMAgEAly9ehVPnjzBrFmzsGvXLgQEBOCnn34CIFN0p0+fBgAsXLiQURoREREICgrCzp07MXjw4BqlCCkUiv6hyqICSKSEybf98N9IvEtKUltXHvYiIiKiqsTTSJMmTQAA8fHxmDhxIlxdXTFz5kwsWbIEwcHBiI+Ph7OzM8aMGQMA2LhxI6ZMmQJCCEaMGIHOnTtj1qxZmD59OiZPnsx4gFEolLoHVRYVIK9YxIwacjPLNuXISU5OrkyRyoWBgQHz77Rp0zB48GAAgKmpKaRSKeLj41Uex2KxMG/ePHTt2pWpX1BQoNGcRaFQai/UG6oC5PI/zFd4NHLR6hhHR8fKEqfcvHv3DgDQuHFjjBo1Cq9fv8aGDRsglcqy2Mn/VUVISAgePnyI77//HpmZmRrrUyiU2g0dWeiIWCJl1ldwWCwM7NsLLi4uaj185IvTunfvXpVilsnVq1fB4/HQt29f/Pzzz5g/fz6CgoIwduxYjccuXboU27Ztw5dffolBgwZVgbQUCqU6ocpCR/KKxYwJypjLAdfQEFu3bgUAJYUh396yZUuVr7dQx/nz53Hr1i189dVXyM/PR1BQEGbOnAljY2OlEYJcfnl7IyMjsXbtWixYsAAcDoeOKCiUegBVFjqSzf8QJdXaVLYy2d/fH8ePH4ezs7NCXRcXFxw/fhz+/v5VKiOfzwcABQ8sQghCQ0MxevRozJkzByEhIUhNTYVUKsWdO3cgEAhw9OhRADIzVWZmJpPJ7vnz57h//z5jvoqKigKfz2c8oRITE2kiIwqlrkLqMLm5uQQAyc3NVbm/qKiIPH36lBQVFSntk0gkJDs7m0gkEqV9xUIxeZSQTR4lZJMnSTlEIpEq7BeLxWTXrl0EANm1axcRi8X6aVA5+Pvvv0nv3r0JAOLo6EjGjRtHRo4cSbp27UoCAwPJ9evXmboSiYSMGjWKGBkZkS5dupAHDx6Q1q1bk6ZNm5IHDx6QgoIC0rp1a+Li4kIOHTpECgoKSI8ePYiRkREZNGgQefz4MWncuDFp164diY+P10q+svqXUnFo/1YuNbF/y3qfqUPTO7IkdIJbB7JKjCosjAzBZiuanTgcDtq1awcAaNeuXbWYngYOHIiBAwdqVZfNZuP48eMKZffv3y9z+/r16wrbcXFx5ReSQqHUGqgZqpxICUF24QcvKDtzXjVKQ6FQKFUDVRblJIcvhPj9hK4xlwOeoepRg6OjI1asWFGjXGUpFApFV6gZqhwQQpCe/8EEZWemflTh6OiIlStXVoFUFAqFUvnQkUU5yC0SQSCWeRbxDNhoYGxYzRJRKBRK1UCVhZZIpQQpecXMtq0Zj4bYplAo9YZqN0MRQrB582akpqbC0NAQKSkp2Lx5M8zNzVXWv3v3Ln766Sd8/PHH+Pfff/HFF1+gc+fOlS5neoEAQrFsroJnwGbWVlAoFEp9oNqVxbp163D58mVcuXIFgCzmUGBgIBMWuyT//fcfBgwYgH///RceHh54+fIlOnXqhLt378Ld3V1nGYiKsOIl4QvFSMsXMNtOlsZ0VEGhUGoUmt5jFaVazVBZWVn49ttvMWXKFKZs2rRpOHPmjMpQ3itWrECrVq3g4eEBAPDy8kKLFi3w7bff6nR9Q0PZnIN8pbMqRBKCuEw+cyPMjQxgbkTnKigUSs2isLAQLBaLea/pm2odWZw/fx58Ph9t27Zlytzc3GBjY4OjR48qBN0Ti8U4ffo0goKCFM7Rrl077N27F3v27NEql3RJOBwOLC0tkZaWBkCWhpTFYoEQAqFIAqFQiMS8LEikMkXB5bBhb8JDcXFxWaelaIFUKoVQKERxcXG57xtFM7R/K5ea0r+EEIjFYuTl5SEvLw+WlpaVtgi4WpVFdHQ0AFnspJK4uLjg4cOHCmWvX79GUVGRyrq5ubmIjY2Fp6enyutkZWVBJPqwkI7H44HHk7m92tjYQCKRIDU1ldmfWSCEUCKBJRfIEQIELHBYgI0ZD/H51PykDwghKC4uhpGRETXpVQK0fyuXmta/HA4HdnZ2sLCwUHjXlUYgEEAg+GBSz8/P1/oa1aossrKyAMiS55TEzMyM+drXpi4ApKWlqVUWpeczxo4di/HjxyuUsVgsJY2cUuo8/Ew1DaFQKJRqQiKRaD1fERoaij/++EOn61SrsuByZR5FqkJ6y/dpU7fkflXExsYqeFeVHFmo4vKzVPx5Lw6DrTNh7tEG3bwdasTXQ11CJBLh0qVL6NevX6XZWOsztH8rl9rav3369MGOHTuY7fz8fK2dg6pVWdjb2wOQTcyUfJkXFhbCyclJbd2SyLdL1y+JtbU1LCwstJZrUEsX9P3YAX/99Re6f9SwVj0MtQ1DQ0Pav5UI7d/Kpbb1r6GhIWONkW9rS7XOfLVq1QoAkJSUpFCelJSE5s2bK5Q5OzvD1tZWZV1bW1s4ODjoVTaBQIDQ0FAF+x5Ff9D+rVxo/1Yu9bF/q1VZ9O3bFyYmJgrhrxMSEpCWloaAgACFumw2G0OHDlUKlX337l34+/vr3SNBIBDgjz/+qFcPQ1VC+7dyof1budTH/q1WZWFsbIz58+fj4MGDTNmBAwfQrVs3dOrUCQcPHoSPjw8z2b1gwQJERkYiPj4eAPDmzRtERUVh7ty51SI/hUKh1BeqfQX36tWrERISgrlz58LKygqxsbE4deoUWCwWcnJykJaWxmjv5s2b4+TJk1i8eDHatGmDe/fu4fTp0/Dx8anmVlAoFErdhkUqe414NZKbmwtLS0skJCSUa4IbkLnquru7IzY2lslBTdEftH8rF9q/lUtd6d+8vDy4uroiJycHDRo0KLNunVYWiYmJcHV1rW4xKBQKpUaTkJCgtOC5NHVaWUilUrx79w7m5uZ0nQSFQqGUghCC/Px8ODk5aXQSqtPKgkKhUCj6gUYYo1Q5aWlpyM3NrW4xKJQKI5VK8ebNm+oWo0qgyqIUhBBs2rQJixcvxrJlyzB9+vRyBduiKHP48GGwWCzmr3PnzozDwYULFzBjxgysW7cOo0ePxn///VfN0tYObt68id69eyuV3717F9OmTcOGDRswZswYREZGKuxPSUnBjBkzsGHDBkydOhX79u2rKpFrFer6t7CwEFZWVsyzzOFwkJCQwOyv0/1LKAqsXbuW+Pn5MdvLly8nQ4cOrUaJaj/Dhw8n+/fvZ/7+/fdfQggh169fJ/b29iQnJ4cQQsiVK1eIq6sryc7OrkZpazb5+fnkyJEjxNvbm5T++T5//pxYW1uT169fE0IIefHiBbG2tiZv3rwhhBBSVFREfH19yaFDhwghhAgEAtKkSRNy/Pjxqm1EDaas/iWEkJ9++oksW7aMeZZ///13Zl9d71+qLEqQmZlJTExMmJtNCCGxsbEEALlx40Y1SlZ7+eeff8iyZctU7uvYsSOZNm2aQlnjxo3JqlWrqkK0Ws2yZcuUXmZjx45V+NAhhJCePXsyffzzzz8TExMTUlxczOxfsWIF8fT0JFKptPKFrkWo6l9CCBk0aBCRSCQqj6nr/UvNUCXQlIyJUn62bduGNWvWoHXr1vj++++ZxFHv3r1DVFSUQl8DsmRWtK81Uzqcvjw5mKr+PHHiBKRSKcLCwuDj46MQcbldu3Z4/fq1Uhid+o6qBEKXLl3C33//DScnJ0yfPh3Pnz9X2F/X+5cqixKUJxkTRTu6dOmCWbNmISMjA4sXL0a3bt1QUFBQZl8/e/YMQqGwOsSttWiTHCw6OlrlfgD0+dYCY2NjLFmyBF5eXti3bx/atGmDK1euMPvrev9SZVGC8iRjomjH559/jp9++glv3rxBSEgI7t27h+XLl5fZ1xKJBJmZNNNUedAmOVhWVlaZ+yll061bN6xZswYRERG4du0aGjRogIkTJzLhiOp6/1JlUYLyJGOilA9DQ0N88803mDFjBo4fP16hZFYUZbTpTy6XS/tbT/Ts2RNhYWFISUnBzZs3AaDO9y9VFiUoK8FSWcmVKNozefJkpKWlldnXPB4PNjY21SFerUWb5GD29vY6JQ+jqKZz587w8vJiRg11vX+psihBeZIxUXTD0tISdnZ28PHxgYGBgcq+plGEy482ycFatWqlcj8A+nzriPx5BlDn+5cqixKUJxkTRTfu37+PQYMGwdraGt27d1eZzIr2dfnRJjnY8OHDERMTA5FIpLDf29sbLVq0qGqRaz18Ph8pKSno0qULANT9/q1u392axpIlS0j//v2Z7VWrVpFu3brVCT/pqubq1askICCA3Lt3jxBCSHp6OunTpw9JSkoihBBy/vx5YmdnR3JzcwkhhNy4cYNYW1uTlJSUapO5tvDVV18RAAo+/9HR0cTMzIzExcURQgh5/fo1MTU1JTExMYQQQvh8PvH09CRHjhwhhBAiFAqJt7c32bNnT9U3oIZTun+Li4tJQEAA2b59O/MuWLBggcKarLrevzSQYCmkUilCQkKQl5fHJGPasmVLrY5ZX13cu3cPkyZNQnx8PAIDA+Hk5ISZM2eiYcOGTJ3ff/8d586dQ/PmzXH37l0sXbqUMQdSlBGJRDh69CiWLl2K+Ph4LF26FOPGjWPMHJcvX8bevXuZ5GAzZ86En58fc3xsbCy++uor+Pr64vXr12jdujWCgoKqqzk1DnX9+/HHH2PcuHH466+/0LFjR3To0AEDBgxQCglSl/uXKgsKhUKhaITOWVAoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVAoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVAoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVDqBVOmTAGHw0Hr1q3Rt29fWFlZgcVioW/fvujYsSNMTEyYMCPz5s2Dr69vtcgplUqxf/9+SKVSvZ739u3biI2N1es5KfULqiwo9QIWi4WrV6/iwYMHuHz5MqMMLl++jKioKLx69QqWlpYAAG9vb3Tq1KnKZRQKhZg2bRo6dOgANlu/P80uXbrg8OHD+Oeff/R6Xkr9gSoLSr3go48+Qs+ePdXud3JywpAhQwAAc+bMwa5du6pKNIZZs2Zh4MCBCvk8PDw8MHHiRKW6vXv3VmrP27dvMX36dHh4eMDIyAgNGzbE4MGD8erVKwDA119/je+++45JwUqhlAeqLCj1ggULFmisM3/+fOb/EomkEqVR5sqVK7hz5w7GjRvHlBUUFCAuLk6lSezx48do2bIls52amop27drh1q1b+Oyzz7Bjxw7MmjULSUlJMDAwAABwOBwEBgYiODi48htEqXNQZUGpFxgaGmpV59GjRwgKCkKHDh0AAFevXkVgYCAmT56M0NBQtGzZEubm5li3bh1SUlIwdepU2Nraws/PD/n5+cy54uLiMH/+fEyZMgU+Pj4ICQlBWQGeN27ciE8++UShLCYmBoQQJWWRmJiIrKwsBWXx22+/IT8/H1FRUfj666/x6aefYuXKlXj06BHc3NyYegMHDsQff/xB5y8o5YYqCwqlBC4uLigoKEBmZiYAoHv37khLS8P169dha2uLhw8fYuPGjVi+fDl27tyJXbt24fnz57h37x6OHDkCQDYqmTNnDtauXYsDBw5g586dWL16NbO/NMXFxbhy5QqaNWumUB4TEwMASsri0aNHAKCgLHJyciASiZQy5ZXGwsICTk5OCAsLK0evUCiAQXULQKHUJGxsbBS+xA0NDeHs7IyioiL069cPANCrVy+IxWL06tULXC4Xtra2aNasGZ4+fQoAOHbsGOLj47Fq1SoAACEEXbt2RXp6usprvnz5EiKRCPb29grl0dHRcHBwgIODg0L548ePwWazFfI6T5o0Cfv27UPv3r3Rpk0bjBs3DhMnToSjo6PS9RwcHHDv3r3ydw6lXkOVBYVSTng8nsqyvLw8AMCDBw/g6+uLdevWaXW+3NxcAACXy1Uoj4mJUTlf8fDhQ3h4eMDU1JQp8/LywosXLxAWFoYzZ85g+fLlWLVqFc6fP4+uXbsqHG9sbKxWcVEo6qBmKApFT8jnJPh8vso5AYFAoPI4+Uu/oKBAoTw6OhpNmzZVKJNKpbh69aqCCUqOmZkZJk2ahGPHjuHRo0cQCAQ4evSoUj2JRAIjIyPtGkWhvIcqCwpFz3h5eeHOnTvM3AIgUyRbtmxRW5/NZiMnJ4cpS0tLQ3p6OpKTkxXqbtu2DRkZGWjRogVTlpGRoXROY2NjSCQSODk5Ke3Lzs6Gh4dHOVtFqe9QMxSlXiL3XOLz+TAxMVHYJxKJIBQKme3SbrTy1dVisVihjnxkMXHiRCxfvhzDhw/Hhg0bYGtri927d2POnDkqZTEzM0Pnzp3x5MkTpiw6OhoAcPHiRcyZMwcfffQR/vnnH1y4cAEAcO/ePURFRaFjx44IDg5GTEwMhg0bBnd3dyQnJ2P37t1wcXHBZ599piR7fHw8/Pz8tO8sCgUACIVSj0hISCDr1q0jBgYGBACZOXMmiYqKYvaHh4cTX19fwmKxyNatW8m1a9eIm5sbsbW1JefOnSMZGRkkJCSEACCBgYEkLi6OnDt3jlhaWhIvLy8SHh5OCCHk2rVrpGXLloTH45GWLVuSM2fOlCnXoUOHSOfOnZntzZs3Ew6HQ86dO0c8PT2JkZER6devH4mOjiaenp7ExcWF3Lt3jxBCyL59+4ifnx+xs7MjPB6PNGnShMydO5ekpaUpXefff/8lrq6uRCgU6qE3KfUJFiFlOH9TKJQqQSqVom/fvti0aRNatWqF6dOn48aNG3jx4oVerzN58mQMHz4c/v7+ej0vpe5D5ywolBoAm83Gr7/+iqVLl6KoqAjR0dFK6y4qysmTJ+Hh4UEVBUUnqLKgUGoIzs7O2LNnDzZu3IinT5/qVVncvn0beXl5WLFihd7OSalfUDMUhVLDeP36NZo0aYJff/1VZRBBXSCEgMVi6eVclPoJVRYUCoVC0Qg1Q1EoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVAoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVAoFApFI1RZUCgUCkUjVFlQKBQKRSNUWVAoFApFI1RZUCgUCkUj/w8SJG8/AmhCwwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 500\n",
    "    for n in range(0, N):\n",
    "        # res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * (n + 0.5) + η**4 * ((1/8) * (1 + 2 * n * (1 + n)))) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-15/\"\n",
    "# number = 8591\n",
    "# file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8652\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "times = np.linspace(0, 170, 200) # Time array for simulation\n",
    "\n",
    "# /Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/2024-03-15/23\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:-10]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:-10]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [1, 0.03, 0.1, 20]  # Example initial guesses for a, b, c, and nbar\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, 1, b_opt, 0, nbar_opt - nbar_err)\n",
    "fit_upper_bound = fit_function(times, 1, b_opt, 0, nbar_opt + nbar_err)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "plt.plot(times, fit_function(times, 1, b_opt, 0, nbar_opt), lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / a_opt, yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "number_drop = 2\n",
    "plt.errorbar(rabi_t[number_drop:], (pmt_counts_avg_thresholded[number_drop:] - c_opt) * (1/a_opt), yerr=errorbar[number_drop:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation with 300 $\\mu$$s$ delay time', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.05, 1.05)\n",
    "pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat_no_shuttling_300us_delay_v2.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d170430",
   "metadata": {},
   "source": [
    "### with 500 us wait time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ada771bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 0.9148293193394383, b = 0.0222136998497034, c = -0.015607656609398186, nbar = 51.222917772566994\n",
      "Optimal parameters with their standard errors:\n",
      "a = 0.9148293193394383 ± 0.07544576830151813\n",
      "b = 0.0222136998497034 ± 0.0002766591830801581\n",
      "c = -0.015607656609398186 ± 0.037338484236563024\n",
      "nbar = 51.222917772566994 ± 6.831408173740202\n"
     ]
    },
    {
     "data": {
      "image/png": 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u6p5tRVNa+XtU/jlKJBLFO6etXkbDqMvn9ZjHjx+TFStWkCFDhpDbt29rzBseHk7atWtHGAwG2bJlC8nLyyOEEFJUVETmzp1LBg8eTFasWEFmzpypsKqJjIwkHh4exM7Ojpw6dYqkpqaS0aNHEwDks88+I48fPyZMJpMEBweTn3/+mXz00UckKytLbfn79u0jNjY2xM3NjRw6dIhIJBJSVFREevfuTUxNTcmQIUPIw4cPiaenJ+ncuTNJSEgghMisPfz8/MjEiRNJcHAwmTt3LsnMzNRYp4poqmNcXJzGOty+fZt06tSJmJqakq5duyosY9SVHRUVRXr27EkAkA0bNqgcZ2dna63zrl27iKWlJRk+fDjJzs5WW46m+ujy3OQWKurYvn27kvXPzZs3yd69e3V6lyrKfurUKWJtbU3c3NzI+fPnSUJCAgkMDCQASEhIiJLVGyGE3L17l7Rq1YpYWFiQ2bNnk9WrV5PU1FSlPBKJhCxfvpyEhISQ0NBQ8uGHH5Ls7Owq55GzadMmwmKxyNmzZ0nTpk2JqakpGTBgAImNjSVNmzYlfD5fYQ23e/duEhAQQBwdHQmXyyXNmjUj8+fPJxkZGZW257Nnz4i/vz+xsLAgXbt2Jdu2bSMBAQHkf//7n+I5XLlyhfj6+hIul0t8fX3J6dOnCSFEr/dHTlpaGnFyclKxClNH+ed2/PhxlWd75coV4uXlRXg8Hjl79izJyspSWKoFBQWR+Ph4cvbsWWJra0uaN2+usBSrrF7GhEGIsY1z6x8ikQgXL17Ee++9Bx6PV9viUCj1klmzZuHatWt4/vx5bYtiUJ4+fYpt27Zh+/bttS1KjUKtodTA4XCqvDhKoVCUiY2NRatWrWpbDIOza9cuTJ8+vbbFqHGosqBQKAaHEIInT56gX79+tS2KQbhy5QrWr18PHx8fvHz5UmVPx7sAVRYUCsXgxMXFoaioqMGMLAQCASIjI1FWVqbRpUpDhq5ZUCgUCkUr1HSWQqFQKFqhyoJCoVAoWmnQaxZSqRQpKSmwsrJScX5GoVAo7zqEEBQWFsLNzQ1MpuaxQ4NWFikpKRodjVEoFApF5ralYpySijRoZSH3jJmYmKgISq8rOTk5aNy4MeLi4hSBjyiGg7avcaHta1waSvsWFBTAw8NDyYtwZdQZZXH9+nWsXLkS4eHhGvO9ePEC69evR6tWrXD//n1MnDgRw4cPV5tXPvVkbW1dZWUh97NjZWVV5Wsp2qHta1xo+xqXhta+ukzT17qyKCoqwunTp/HVV1/h33//1Zg3MzMTAQEBOHjwIHr16oWcnBz4+PjgzJkz6Nq1aw1JTKFQKO8etW4NZWlpiYkTJ+oUROSHH36AiYkJevXqBUDm0XHYsGFYtmyZscWkUCiUd5paVxZydIkTERYWhk6dOimlde7cGVeuXDF4SEEul4vx48frHAiIUjVo+xoX2r7G5V1s31qfhtKV0tJSvHz5Eu+//75SOp/Ph1QqxcOHDyv1Q5OTk6Pk95/L5Wp9yEwmExMnTgSTyVS6lmIYaPsaF9q+xqW+tm9ZWZlSRL3CwkKdr603yiI3NxeEEKWYvYBsGguAxiD1jRs3VjoeP348Jk6cqFO5Fy5cqKKklKpA29e40PY1LvWtfQ8dOoTDhw/rdW29URYmJiYAVFft5cfy8+qIi4tTMg3TZWQhEolw4cIFDBgwoFbiIzd0aPsaF9q+xqW+tm+/fv2U4nAUFhaqfExXRr1RFvb29mCz2SguLlZKlx9rCjlqb2+vt3kbh8OpVy9DfYO2r3Gh7Wtc6lv7cjgcxWyM/FhX6swCtzYYDAZ8fX2RnJyslJ6cnAw2mw0fH59akoxCoVAaPvVGWQDAiBEjFAHn5dy9excBAQH1ehclhUKh1HXqjLKQr9BLpVJF2sWLF9G8eXM8fvwYADBnzhxkZWUhMjISAJCfn4+TJ0/is88+q3mBKRQK5R2i1tcsRCIRjhw5gkOHDgEAQkNDMWHCBLRp0wYFBQXIzMxUrEs4Ozvj0qVLWL9+Pa5du4bY2Fj89NNPGDBgQG1WgUKhUBo8ta4sOBwOgoKCEBQUpHJu9OjRGD16tFJahw4dcOTIkZoSj0KhUCioQ9NQFAqFQqm7UGVBoVAoFK1QZUGhUCgUrVBlQaFQKBStUGVBoVAoFK1QZUGhUCgUrVBlQaFQKBStUGVBoVAoFK1QZUGhUCgUrdT6Dm5K7SCRSBAREYHU1FS4urqiV69eOoW2pVAo7yZUWTQwSoViPE0rxJucYuQUC8Gz5KIxzxItnK3AZssGkmFhYViwYAGSkpIU1/H5fGzZskXFvQqFQqEAVFk0KNILSnH5aTriswXgcpjgslh4XlSEpykFeOZoiYAWzrhy/gwCAwNBCFG6Njk5GYGBgTh27BhVGBQKRQW6ZtFASM0rwV+xKXiTW4ImPEvwLLkAg8CCy4a7vRleZBTidHQigkPmqygKAIq0hQsXQiKR1LT4FAqljkNHFvUciUSCcxcu46+opxCb2sKzVQfsuv4aLzKKFHmsTdno5GmLFzFRSE1JrvRehBAkJiYiIiICffv2rQHpKRRKfYEqi3qMurUHlhUP9v1mw9ynhyKtoFSM8H+zgJf/6nTf1NRUg8tKoVDqN3Qaqp4SFhaGwMBAJUUBAJLCLGT+uQ7MhNvo6mWPlq5WYDFk50pNbHS6t6urq6HFpVAo9Rw6sqiHSCQSLFiwQO3agwwGCsJ3YezimWCxWMgsLMWB2wmIl7YGy4oHSWGW+qsYDPD5fPTq1ct4wlMolHoJHVnUQyIiIlRGFMoQZKen4OmDKIgkUthZmGBe32bwcbGBfb/Zaq9gMGTDj82bN9P9FhQKRQU6sqiH6Lqm8PhVAswa+YIAkEilGOrrgvxSfwBAzqVflEYYfD4fmzdvpmazFApFLVRZ1EN0XVPw7+CDwe/xIZUSZBaV4VFSPvp6OyK3qAfMmndFWdJjDPTiYsbAjnQHN4VC0QhVFvWQDu91hw3PBflZaeozMBhwd3fHrLFDFQqgsaMlWrpa49qLTKTll+LqiyyYNvJFtAkbXm3eo4qCQqFohK5Z1EMSckvgNvhj9ScZDDAAbN2yRUUBWJlyMKilCz7s7gkXK1MAMrPa7eEvjCwxhUKp71BlUc+QSqW4+CQNAvfOcBy5DBxrntJ5vru7RpcdbDYTvb2d8FHvxoq0k9HJiM8qNqrcFAqlfkOnoeoZybml+OtROgDA3KcHgsaPhjj5CYSF2RjerTWGDAjQOqVkwmZi3HseOPMwFQ8S8yCUABv/eYrtQZ1rogoUCqUeQpVFPSPiZSZe/ufKw5LLQlt3O2TbdcGwti5o5abbpjtANiX15bAWmLDjFqQA/nmcjtR8AVxtzI0kOYVCqc/Qaah6RFGpGMfuvt1f0bu5IzIKy9DS2RItXKyqfL8uXg7o18IRACCWAv936aXBZKVQKA0LqizqEc/TC/AwOQ8AwGYBbdysYc5loZ2nHZhM/R7lkiEtFP8Pu5+EghKhIUSlUCgNDKos6hFH7iZBLJX9v6OHHQpKxWjpag0PO/2njpo5W6N/CycAQKmY4LcbcYYQlUKhNDCosqgn5BQJEf4sQ3Hsy7eFJZcFX75tte+9aKC34v9/3H6jwecUhUJ5V6HKop5w/VUm0gvLAAB8W1OwmQw0d7aCs7Vpte/d2s0Gbd2tAQCpBUJEPM+s9j0pFErDgiqLesKf994GLfLl28DUhIlW7rpbP2ljlt/bfRe/XqdTURQKRRmqLOoB2UVliIrPBiB7YK625vB0sKjWWkVFhrR1g605BwAQ8SIL6QWlBrs3hUKp/1BlUQ84F5uGYqFsZdvH2RJcFgM+epjKasKEzcTkLh6KY7rQTaFQykOVRT3gbGyK4v/eLlZwtOaiiYOFwcsJ6u6F/4Lq4Y/biRBJpAYvg0Kh1E+osqjj5AtEeJCYBwDgsBjgWXLR0sUapiaG33zvamOG/i1lm/TySkS49DTd4GVQKJT6icGVxY8//mjoW77TXHiShpIyEUrfPIT5m1vIeH4fjezNjFbeh93fLnTvv5VgtHIoFEr9Qi9lER4ejo4dO8LMzAwsFkvp7/PPPze0jO80O/YeQvKOmUg/tAwP93+NbZ9NQVffFggLCzNKeT2b8eBoKVvovvkyG9lFZUYph0Kh1C/0msuYMmUKOnfujLlz58LExESRXlZWhgMHDhhMuHedw0eO4dK2JSrpycnJCAwM1OiKXF9YTAZGd/LAzquvIQXw54MkzOzV1KBlUCiU+ofeE9/Hjh1T6wq7f//+1RKIIkMikSB4/ny15wghYDAYWLhwIUaMGGHwKHdjOvKx8+prAMCh24lUWVAoFP2moZYuXYpr166pPZeSkqI2nVI1IiIikJWeWul5QggSExMRERFh8LK9na3Q2lVmmvsysxjP0wsNXgaFQqlf6DWyePPmDX755Rd06tRJKZ0QguvXr+PFCxqms7qkplauKPTJV1XGdvbA49NPAACHbr/BquGtjVJOfUQikSAiIgKpqalwdXVFr169aAxzSoNHr5HF69evQQhBXFyc0t/Lly+RmUn9ChkCAUu3TXeurq5GKX94OzfFy3HiQTIkUupcsLBUhC27f4cr3wP+/v6YNGkS/P394dHI02gGBxRKXUGvkcWsWbMwYMAAtTEU/vnnn2oLRQGSuI3AsuJBUpil9jyDwQCfz0evXr2MUr6DJRf+LRxx6Vkm8gQiRDzPQN8WzkYpq64jEIrxOLkAu/f/gZ2hwQCUFWdqSgrGBAZi976DmDF5Qu0I2UCgo7a6i14ji0GDBoHJZOLly5c4evQoTp48ibS0NMU5SvW5+ToH9v1m/3fEUDrHYMiON2/ebNQfUmCnt+4//riTpCFnwyWjoBR/PUzF+UfJOLhlDSoqChkEIMBnixfjfnwWpFK6810fwsLC4OXlpTRq8/LyoqO2OoJeykIkEmHy5Mnw8fHB+PHjMWrUKDRq1AiLFi0ytHzvJIUlQjxPL4a5Tw94jlsBW0flL3o+n28Us9mK+LdwggVH9opcfpqGUpHEqOXVNRJzBTjzMAXX/s3AwVPnUZitaUc7QW5mKrYdOo0rzzMgFFOFoQ6JRIIrV67g0KFDuHLlCiQS2TsVFhaGwMBAJCUpf5TIzcSpwqh99JqGWrFiBSIjI7F161Z06NABPB4PycnJOHDgANauXYvly5cbWs53ivOP0xXfr617DsDspR/BJv91jQ/NTTksDGrjgrAHKRBKgX8epWFEB3ejl1sXSMwV4ExMCk5Gp+BZWiGK03VzffLP7adg89tAJCbwb+EMEzb1qCMnLCwMCxYsUFIIfD4fP/74IxYvXqw26JaxzcQpuqOXsrh9+zZiYmJgaWmpSPP29oa/vz8+++wzgwn3rnL537cR8dztzNDUyQZtOvStFVlGdeQj7IHMHPrwncR3QlnkFAlx6kESdl+PR45ABABgWdrpdK3EzA7nH2cgLbcELCYDfb2dwKYKQzFyqKgQkpOTMW7cOI3XljcT79u3rxGlpGhCr7fYz89PSVGUh87XVp/br7MV//dxtoabjfF8QWmjexMHRZyLW6+zUVAqqjVZaoJSoRjH7ydix5XXCkVhwmLgwxGDYO/kiorrR29hwNTWCVy+zMT4YUoh1v71FJGv1Bso1Hcqm06qLO+CBQsqHTnoirHMxCm6oZeyyMzMhFgsVkk/dOgQ7ty5U22h3mVeZRYis1jWSbnbmqKRvRnsLU20XGU82Cwm3veVmedKARy9+6bWZKkJLj9Lx9bLL1EolHV+9hYczO/XDHx7Swz7aKksE6OCwvjveOL8Fejl7ahIjssSYOWpx3iUnF8jstcUVV2IjoiIUFmL0AdjmYlTdEOvaaiRI0eiZcuWGDJkCCwsLJCYmIjIyEgkJSXhr7/+MrSM7xTnHr79enK3NUNjnvoRXE0yuiMf+2/JlMTJ6BTM9GuY7j8ep+Rj9aknKCyVfQg5WZrgw25eKCgVg29nhqXzpuL9dm74dPEipc7Pg8/H5s2b0TVgMB4m5sPewgR/PUyDmADx2QJ8diQae2d0gXMtjhANhabpJLm/shEjRiiZvyYnJ1dyN90wtpk4RTf0UhaDBw/G9u3b8fXXX+PevXtgsVjo1q0b9u7dCz8/P0PL+E5x9cXbaYsmjhZwtjGtcRkq2rr7+fnB2ZqL9IIyPEwqQEq+AG42hgvpWhcoLhNh+YlHyCgSAgBsTNkY29kDpRIp3vOyQ/cmPFiastFqbCDGjB5V6V4AV2tTNHWyhL0FF/tuJkAK4Fl6EZadeISfJ3eq1wve2qaTGAwGZs+erbKIzePxdC6DwWAo3b+mzMQp2tHbkeDAgQMxcOBAlXSxWAw22/CBed4FhGIpot/kAgDMOQx4O1vB1apmlUVlFiu9pnyGdDQDABy6lYhPB/nUqFzGZtP5fxFdLsjUyI5u4LAZ8GvmiI6NbJU2oLJYrEoXWplMJrydrbCwvze4bCZ2XpOFp730LANbLj7D54NbGbsqRkPbdBIhBNnZ2SrpWVna120sHZwxdu4ynNm1AZlpb0fX/P9GbdrMxOlmPuNj8F79wIEDmDp1qqFv+04Q9Sobov/sA9ztzNGYZ1mjljSaphj+WL8IvBFfwtynB848TG5QyiLqdTb23Hwb6GlQK2fYmZugf0sXtHG30euetuYmWNTfG7kCIY7clU3D/HwlDu087DGwtYtB5K5pjLnAbNZrJi6LmsJx5q/wLnwNbysx+nVugQnDByo6/coUQmUfOFu2bDH6XiR9qK+KTSdlMWrUKHTo0AGhoaGQSqUYOXIkioqKVPIJhUI8fPiQKgs9ufAsTfH/RvbmcLeruTluXaYYCsJ/hVnzrojLLsGz1AK0cLWuMfmMhUgswZLjMRD/V+32HjZo7GgJfx8nvRWFHFMTNtaOaIuU3BJcf5UDKYBPjzzAP4v71MtpPEMsMDPNrCEtKVAcs6x4sO83G+Y+PQAAxWKg2KwJksTAq+dclN6MR2AnD1z6+4xahTBx4kR8//33GtdQ6pLCqG+KrTw6KQtCiMI0jslkwsHBAcXFxWjUqJFSPpFIhMTERMNL+Y5w63WO4v8tXKzgWoPrFbpMMZTlZ6Is6TFMG/ni4O03WDOiTY3JZyx+uPAc8dklAAA7czY6N7JD9yb2aOeh274KbXA4LPzy4Xv4YHsEXmYKUFgmRfD++zg+r6diPr6+0KtXL/D5fCQnJ1fJ5LU8dv0+gr2jE+xQAgcnZ/BbtoeYMFFYKkZafikSc0sUeRNySvD12WfY9fthRO1aoVJmUlISNm7cqLacuriZTxfjgLqsMHRSFn/++afS8axZs+Dt7Q1HR0eVvGfOnDGIYO8axWViPE+XjdZszdho5mwFK1NOjZWv6xSDpCgXRCrB0VN/o0XxQ7i5udWbYXRFXmcWYVfEa8Vxr+aO6ORljy5eDgYtx9yUjT3Tu2LgpqsQiKS4n5iP7eEvEBzgrZSvrk9PsFgsbNmyBYGBgSoL0boyoHML+L7XExYmLJj992fCZEIolaKkTILs4jLEJOYhNqUARWUSEKkEd/7YpFdZdWkzny4jd10VW3KuAHficvE8vQCpeaUoEUtAAAxp44yRHTw0Xlsd9FqzyM3NVaso7t+/Dy6XW22h3kUiX75dBHSzNUUje4saLV/XKQYbURbid8yEpDALQf+TpdWXYXRFloY9hNyFUytXK3RtYg+/5jyjrBPx7c3x/VhfzDsYDQD48fwL9GvhjJZusqmu+jI9MXr0aBw7dkytrCUlJcjJyamkY2fA1tEZgcMGwMfVFk5WXNiamyhZhwnFUuQKhEgvKMW/aQU4E52KC5cvV+p5WVfqwmY+XUbumhRbcq4Ae2/G4/KzdLzOFEDd1mdTNtOoykKvX0VlUfKaNm2KkJCQagn0rhL+/G0ckEYOlnCyrlmlK59iqGxqhMFgwMHBAa/+3qPy462Pzt6uPMvA7bj/LM9MmOjf0hm9vZ2MOpob6uuOCZ35AGQbHOfsv4dSkaTeOdEbPXo04uPjER4ejoMHDyI8PBzx8fFY/+PWShUFGMD3P/yIMZ090cbdBk7WpipmxCZsJpytTeHLt8XYzo2wIdAXY1pW/6OpLmzm0zeY2cv0Iny09w78vg3HLxFxeFmJogAAsZ5Tg7qi88iirKwMn3zyCS5fvozc3FwcO3ZM6TwhBOnp6XBzczO4kO8Ct+Pemhy2crWEcw2bzGqaYlA+rv/O3iRSglWnHoFIJShLeoxGVmKYZ4vgZt3M6GWvHtEGt+Nz8TqrGG9ySrD29CPsMdD0RE1S0Xz4RXohfo7nwXHkMuRc+kXpg8LV3R3btlZ9hORqa4YxvXyxTU8Z69JmPl0VljxfSl4Jvj79BOcep6nkseay4GJrBgdzE1iYssFhMWHOYWF4O+MqRZ2VBZfLxa5du7B06VJERESo7LFgMBiwt7fHqFGjDC5kQ6ewVIQXGcUAZIusLV1tamXzlqYphlmzZmHVqlWVXluX5oe1sf9WAp5GXlJ0aukAIn4B1n5hmGkfTWsPphwWdnzYCUO3XINYCvx67C+kV2N6oi7wMqMIgT/fRH6pGOY+PeDctic6cdPRxFyI7r7NEdC3j96KTtdF9bq+mU9bPeSKrUdPP+yKeIXvzj2DsNwQwoQFtHSxRit3azR3tIKrrRkcrbgw57LAZbPAZjLgbmQPAVVes9iwYQMeP36M1q1VYzLn5eWpNak1JlKpFPHx8WjSpEmNlmtIIl+XW6+wMYO7be25hRg9erSKu4ZevXrhyJEjOl1fF+aHNVFYKsKqLbuR+ec6lXOaXFboutisy9qDt7MVPh/og/V//wtJUa5OctfVdn2TLcD4X2SKAgAcLDiY2sMTw3x7o6lj9V3V6LKoPnVOCC6dPaHS5rps5qsptI3cAWDJV+sx+qdIPEp9a1pswgLauNsiwMcJ7RvZwNPeEs42qlN4NYFeC9ytW7dGWVkZMjMzlbzMJiUlYdGiRYiKitL5XoQQbNq0Cenp6eBwOEhLS8OmTZtgZaU+BnVxcTH4fD7y8vIUaVeuXKnXyuLqv2/XK5o61vx6RUXU7VCu6jC6rrL5wjMknduh9pwmlxW6LDZXxTTyo95Ncf5pOm680c1Ety62a1p+Kcb/ehPZRTLHlw7mHIQENMfQtq5wsjbcNGplI175Ho0oRz8cvbISpYmP66wlGaB55D4+ZCW2vrRDmeStomjhYonAjnx0aeKAZk6WMDepXc8YepV+4sQJTJ06FcXFxSrnPD09q3SvDRs24OLFi7h06RIAIDQ0FEFBQTh16pTa/Pv27UNwcDCaNpU5szMzM0OfPn2qWIO6xa1yLsnbulvD3rz2vMxWhq7D6LowP1wZGYWl2Hn4jEbrmspcVmizha+qaSSTycDm8R0wIDEHWbUYa11f8ktECPo1Eql5ZQBkvrQWD/TGB+3djWIkUHHEa2XviO1PWHiaLkCJSIqp/7uD3dO6YGIdnaqTU7Eedjwn/J1ti6PRaZCvB1qbsjGygxvGdOTDx9kKprWsJOToJcWuXbuwd+9emJubIzw8HEOGDIFEIsGJEyfw8ccf63yfnJwcfPPNN9ix4+2X3owZM9C4cWNERESo/YGcPn0aZ86cUfLVU5/JLxHhVaYAAGBnzkFLN+s6WTddpgPqyvxwZWy//AJlBbpN+1RE22KzPqaRHvbmWDOqPYKfzlY7LVbX5t3lCMVSzN57B6+yZO+tpSkbS4a0wMgO7kb9+q044u3nL8HU/0XhdnwuyiQE036Lwi9TOiOghXPlN6kDyOuRlCvAjN9u43nG20XsNm5WCAlojh7NeDW6z0oX9OqVPvjgA4waNQqDBg2Cg4MDevfujYCAAHz33XdYsmSJzvf5+++/IRAI0KlTJ0Wal5cXHBwc1M6RX7hwAefOnYObmxtmzZqFZ8+e6SN+neJWueA4fDszuFjXXTfW8mG0u7tytDyWFQ8//7a/zswPqyMtvxQHot7oHPFOHeU7/Iroaxo5tjMfQz8YAceRy8CyUvbOWlOx1qsCIQRLwx4iKl6mdE3ZTHwx0Bujjawo1GFmwsLvs7qi738xRMRS4KO9d/H3I93Xd6oSxMmQ3I3PwfCtEXj+n2ELmwGM68zH9qBOGNTGtc4pCkDPkcWjR4/wxx9/oEePHhg4cCA++ugjfPrpp7h16xbCw8N1vk9sbCwA2Y+iPHw+H9HR0Sr5zczMsGzZMly7dg27d+/GwYMHcfr0afTr10+fatQJyq9XNKsll+RVofwwesWBq3hZbAIuvzWKXb21X1yL/N+l5xBLAS6/NcxsHVGan6W3ywp1ikHfNR0Gg4GNge1wPyEXZs27oizpMca3ssQov7Z1ct59y6UXCLsvc4zIBBDSrxnGdfaotakSLpuFX6d2xvyDD3DucRokBJi7/z42T2iPEe01hwDWxRjBGLvqj9xNxJfHH0Ly3+tnY8rGkqE+GNmeX+vrEprQS7Jx48Zh1KhRWLhwIVasWAFnZ2e0bt0aDAajSnOrOTkyX0gWFsobbywtLZGRkaGS38/PTxEv4+rVq5gwYQImT56M+Ph4jTvHc3JyIBK9DQfK5XK17jSX5y9/nTG4l5AFLkv21nT0sIEpy/hlGoKePXtivpknVp56DAC49CQNs3t6gqvjy15T7QvIbNZP3H8DLgtgc5iYsXotvlo0R2+XFY6Ojipyd+vWDe7u7khJSal0Tcfd3R3dunVTudbWlIn1I1th8dEYmDZui6siJha37gipVKp3mGJDtK9EIsH169cVHWW+dVP8HP4c3P/6ymndPDG1Cx8sBqn1d3bTuLYw/xM481/wsC+OPkBZmQijOqpXGCdOnMCECRMqNUb4448/AACLFy9WCt7k7u6OH3/8Ee+//z6AqrWvRErw4/l/sfdWAthMWefraW+GdaPa/ueLzPjtWFZWhrKyMsVxYWGhztcyiL6fVxWIjIxEamoqBg8eDHNz3TxqhoSEYNu2bZBIJErz9L169UJeXp5i5KGpzB49euDixYtqRxcFBQWwsVH1HDp+/HhMnDhRJxkpDZPIyEjs2rVLaTHbwcEBQqFQ4w+Ix+Nh586dar8uIyMj8e2331Z67ZIlS9C9e/fqCV5DVNY+s2bNqjd1qAyJRILZs2erNWSQY2VlpfE9qE/PsjyHDh3C4cOHVdLz8/Nhba3Zi7ReymLfvn2YMmWKSvqDBw/w5MkTBAUF6XSfr7/+GqGhoSgoKFAyle3YsSMcHR3xzz//aL2Ht7c3vvrqK7Wdv1xZxMXFKd1f15HFhQsXMGDAAHA4xpk/vPg0HQsPRwMAfN2s8W1gO3jY1y/X1Z8cuKeI7jeuEx+hw1X336ijJtoXABJzBBi6NQIEAJsJ7JneBe3/8yhb8cvZz88Pp06dwoQJEwBArS38H3/8oXHj6YkTJ1S+Rvl8Pn744QeMGjVKbZlyxVNYJsLQTdeQ+9+ehUX9vTHTr7Fe9a5O+1b21S2n55xvcPaHRTA3qXvz6oQQrP3rGf648zZW/OL+zTCjXCjgq1evYsCAAXqXIR8lbtmyBYMHD9bavnFZxZh/6AHist9aj47rzMeywS1rNF4NoH5k0bhxY52Uhd5rFuqQrynoqizat28PQDb0a9GihSI9OTkZ/v7+Ot3D1tZWrVPD8tjb22ttiMrgcDhG68wiXuaiTCLrhBo5WOL5w7uIysqos3bi6ni/vQfOP5N9oV15kY2QEkmVbOyN2b4AsP1qPEr/a+N+LVzQyYunGMVyOBz0799fKf+4cePAZrPVzmXrsslr3LhxGDNmjF5Beuw5HKwb0x4z9t0DAHz3zwv0aeGMlq76x9WoavtKJBJ8+umnGqfn4s/ugOX2pXX2/Vw9oi1YLBb+dyMeALD+n1coFjKwaKA3GAwGMjMzNd9AC4QQJCUl4cmTJxg+fLjG9r38LB0LDkWjsEwMgAFTNgMr32+NoG5V22JgKDgcDiwtLZWOdaVKvqHmzp2LvXv3AgB++OEHtfk6dOigc+H9+/eHubk57t+/r1AWiYmJyMjIQGBgoNbrBQIB0tLS0KNHD53LrEtEvpJ1soJ/b2LPb7/hxwzlcJJ1zeOoOvx9nGBrxkZeiRiJuaW4l5CDIW3rhn+wV5lFOH5f1jFz2QwsHNBcJ7Pkynax69o5qtvUqOuGvYBWLhjZ3h1/RidDCmDe/vs4v7gPOCz9vkAlEglu3Lihcz20mQADQHJSUp12P8JgMBA6vDU4LIYirO3W8JfIKS7DVyPbGmyDY25u5WbYYrEYn/7fYRy8EgOWhR24/NZwtTHDlokd0bWJYV3g1xRV8g31v//9D126dMHp06cxbtw4pfNy31ABAQE6F25mZoaFCxdi7969mDRpEgBgz5498PPzQ7du3bB371589913CA8Ph42NDSZPnoy+ffti3rx5YDAYWLlyJdauXavzGkldIqdYiLjsYgj+vanV9URdVhhWZhz0aOqAvx6lAwBOPEiBv49TjVrHVGaxsuXiC0WeYW1d4e2i++hSU5xtfeSryoa9daNa4+bLDGQUiRCXLcCqk4+xbnTbKpcbGRmJTz75RGVKTNNHiL4mwHWRL4e2ApvFxPbwVwCA/bcT8TyjCDs/7FbtIE4AYGen3gz790NHMDc4BMU5b410TG0dEbp1S71VFAAAogfJycmVnhOJRFW6l0QiIcuXLychISEkNDSUfPjhhyQ7O5sQQsjmzZsJj8cjb968IWKxmAQGBhJzc3Pi7+9PlixZQi5fvqzx3vn5+QQAyc/Pr5JMhBAiFArJn3/+SYRCYZWv1YVzsSmk0ecnCcuKRyDbuqnyx2AwiIeHBxGLxUaRwVCcf5RCPJecIZ5LzpDOX/9DHqfkab3GUO17/PhxwufzldqNz+eTrbv2K2RqseIvkpRTXK1yqkN4eHilz7j8X3h4uOKaK8/SFfJ7LjlD/nmUUqUyDx8+XOk7xWAwyPHjxw0ma11ny8V/ldqy6zfnyZZdvyvaomL7ACAODg4q58rn4fP55Pjx4yrv74af9ujV7rVFVfpIvZSFJvbs2WPoW+pNXVYWy088JM4T1zWIH2ZhiYi89815xY9x68V/iUQi0XiNIdr3+PHjan/Q8jTHkcuI55Iz5MvjD/UuwxAcPHhQp+d88OBBpeu+OBqjaNM2q86Rf1N1e4/FYjFxd3fX6yNELBYTB2fXev8BU5Gjd94Qn+VnFe3ZZOkZMm3lVpUPDQ8PD3L8+HHFu6VOmTAYDHL48GGl91dQJibrzzyqdx9/VekjdZorGDVqFDp06IDQ0FBIpVKMHDlSrXdZoVCIhw8fYurUqbrc9p0m8lVOvfc4KsfSlI1+LZ1x6LYs/vo/j9MwsiMfHnbGmx7UNrUDADmXfoFj6+74dGDtbhjUd8Ne6PBWuPU6Cwk5JSgslWDG3rsIm9sdTlp2+UdERChNPVWElNuJ3qtXL6UpPLGTD1jdpwP1yP2INiQSCXhFrzDRIREHYwtQyvOBhMlCuLAJWi7cg68aCWAmLlJZ06nM6d/mzZsxfPhw/PXXXyCE4MzDFKw98wRxj+5o9TtW193Na0InZUEIUWyDZzKZcHBwQHFxMRo1aqSUTyQSITEx0fBSNjDyBSK8yizS2fVEXfQ4WpFxnfg4ejcRYinwLK0Q0Ql5RlUWuizESgqz0Nk0Ew6WtevFV18njBZcNrYHdcKEXyJRWCLEy5goDJt/DV9N9MOg/v6Vdti6flycPHkSH374oVI7sq14sOs3G44jl6Hoyq8oyXtrOVTX3H7rgjoLNDNbR1j2/QjmPj3wPLMUX2cy0b9FUwQ3ba7UppoMHcrKhACAoF9v4U6ibD9GQ/n4qwydlMWff/6pdDxr1ix4e3urNVk9c+aMQQRryNyOl+1c5/Jbw9TWEWWVuJ6oqx5H1eHjao3WbtaIfpOLojePsWlnNKQfvIf3B/Yzyleorj+4vh617z5Bl1gGlX2tt3G3wRDLBGzatEIRqGnYXsDN3R3/t3Wr2o5b14+LzZs3q6SJC7OQ+ec6+E5bg5PPXyL+8f067fZbE5VZoJXmZ6Hkz/XgBy4Hq2k3EAAXnmXiwrNMtHa1woBWLujj44hWrtbgct4aOpQIJYiKy8WFJ+m4+CQFn7UEHqYUAJA9QycXZ+gSLbw+fPypQ69fUs+ePVXS0tLS4OLiotgGT6mcW69kX2sMJgsDZyzB6U2f1/lIX9owN2HDPf8Rzu4IVXRq4TuMZwKs6w+usafxAthXBU2xDDR9rYeFheGHJXNVOrwUDdZyvXr1gru7u8apKBaLpdFpXvo/O+Gxaxm86uF0CaCbBVrp9f+hj/8APEgsVPhpepxaiMephdh86QU4TJm1H5vFQKlQgoLSt+0ld9EDADZmbPRqxsPg1u0w569NSNXg8qW+fPypQy/j7X/++QfW1tY4fvy4Ii0xMRGrV6/W25fNu8SNVzmK/384aZxaT6510eOoJsLCwrBj5Scqc7ZyE+CwsDCDlief2pErVXXUtR/m6NGjER8fj/DwcBw8eBDh4eGIi4ur9Blr6vAAWacXPH++SqfPYrHw448/AoBK+8iPtXlXTU9NVutdt76gi8v4rLQUjHApwqcDvdGpkS0suMofZSIpkFMsQkaBUElRlKeftyMW9/dGcEBzDPV1w/9t3Qqg8navLx9/6tBLWaxduxazZs1SWqR577334OzsjGXLlhlKtgZJYakIz9Jkc5x2Zmx4O1tVuROpa+iy2Lxw4UKDun+WT+0Aqj9MGQxs2bKlzv0w5fs3Jk6ciL59+1Z7g1xqcjK2HzyFUqFYKX3UqFFYsmQJ3NyUN0jy+XwsXLhQJ1nr69w6oLvs7LJ8TOneGF8M9sHCAG980M4V73naoTHPHHbmbJiymWCzAAsTJtxtuGjrbo2AFjxMfE+2Xju1pxfGvueBFq6yODSVufGvbx9/6tBrGiogIACrV69WSXd3d8dXX32FDRs2VFeuBsvdhLeLYB4O5nD8b/HVkJvAahp9Av8Ygsqmdjg2jvj9l58wevRoo7iYril07fA2nYqC2LkVhrZ1RTNHS4W/oe7du2P16tW4deuWUv13/nEGwGat962vc+tA1SzQLE3Z6NqEh1ZuNuiaZY+XGYXIKChDsVACkUQ2U8IAwGEzYWHChpuNKbwcTPH6fhw6etqDw1HuRqvrAaCuopeyUBdOFZA5WROLxWrPUWREvX47BdXcyVKhLOoztbnrd/To0Xh/+AfouXA74hOTwbK0w6pZYzA+wFuneAV1GV07vBKODbZefomLT9MxuI0LujVxAM9c9tMWSgg6deuJEqEEd+JzMGlXFG69YoBVD0O5VgV9LNCsTDnw5dvCl2+LwlIR8kpEKBFKIJUSsJkMmHFZsDM3gbkJGyKRCK81lF+fP/4qQy9lMWjQIIwbNw7z5s2Dm5sbEhISsGPHDpw4cQKfffaZoWVsUNx49dYUsUcTXo17nTQG+u4jMBQnolOQadUMFq2awd3WFNN6NtbZF1NdRluHBzDAseaBy5d5+n2SWognqYXgWSbA05aLKR7A4sPRyBJI8CZHgByBLFYCg8mCfT95KFcG5LGfgYYxtw5UzwINkCmOuhitrjbRq6fq378/pk6divnz56NFixYYNGgQIiIisG7dOjoFpQGBUIzHSQUAZNGxfFz184Rb19BlsdnJ1c0oX6oFpSJ8e+6p4nhOn6YwYTNrfA3FGGhal2EwGAADGBu8HL29ncAt99GRVSTEo1TZexbxKhvRSfkKRQEAZhwWPhg5AhO+3AQHZxel+zaEuXU5DXn9oDbQ2wh92LBhGDZsGAoKCiASieDgIHOQJRaLdfLsWdeQSCRKUapEIhHYbDZKS0sN1qncj8+Bq5XsS8bbyRy2XKC0tNQg965ttm/fjgULFgCA2k46MHgF0nKLFBvkDNW+/7vyEmYsAncrFhrzzDGslQNu3LgBFosFT09PjdfeuHEDXbp00bvsmmDo0KH4888/sW7dOqSlpSnSXV1dsXTpUri06oLohBw4Fb9GcloGMsVclFh7gstmgs1mwM2SCaFUpmjcbE3RwsUaPCsuTNlMtOk6Fj8vnYmH0Q+QkZEBJycndOrUCSwWq8G8l0OHDsWgQYNw7949g9bRGP2DPnA4nBobAeoV/Eje6OoIDQ3FmjVrqi2YIZAHP9IU2IMQgrS0NOTn5yt1coQQlJSUwMzMTOMXc5XkKRGh4L/ANuYmLNiZcwx277qAQCBATk6O8o+HwQTT1Apm5mawMGGBIRVDIpGAxWJBKpVWq31FEikyCspAIJtMsbfgwMyEjeLiYmRlad8exePxVEL61lUIISgrK1O0HZfLBYPBUNvmTCYLZlbWMDdhQwg2mAyGYiqGAGAzGTBhM/V2e0552z8wmUyVZ1KTMBgM2NjYwMXFRa+ydekj5eg1sli3bp3K7s+cnBx8+umn2LdvX51RFrqQn5+PvLw8ODo6wsLCQtHgUqkURUVFsLS0NMhIiRCCuNRsmAjFYLCYcHGwha25SbXvW9cghEAgEKC0TAiBmEAg/e8VE5ZAUpyr6NTkPzB7e3vY2trqVVZiTjEcLWX3MzdhwdXGDGwWs1IDjIo0bty43igLdeTn56O4uLhSV9kODg5gWdiCAGAyGGAxGDDlMMFls8BkNpyPlNogLy8PqampSu+zSCSCi4uL2lDOxoAQguLiYmRmZsLMzEzv35Gu6KUsrl69ihkzZuDbb7+Fo6Mjfv31VyxbtgxMJrNeBSIihCAjIwPW1tbg8XhK56RSKYRCIUxNTautLHJzc/HmTSJEIqEiLV2QC26jRpX+0OszZmZmIIQgr0SE9PxSlBYXQlygGp1MIpEgKSkJXC63yu2QLxCiRMoCg80Ck8GAs505LM1kC5JcLhfJyckQCoWVXm9iYgJ7e/t6O7IjhCA9PV1jnry8PDRzcQOLyQSLyQCHxay39a1L5ObmqjUVl/vGMzExqbHftZmZGcrKypCRkQEbGxujPl+9lMWFCxdQUFCAsWPHori4GNHR0Zg9ezbWrl1brWAiNY1EIoFEItE75Kou5Obm4tWrVyrpIpEIr169QtOmTRukwmAwGDBls2DBZaEoVXMYy8TERNja2ur8ooslUiTllSiOrc04MDd5O2/LYDDg4eGhtt3leHh41OuOs6ioSKMyBGTrh1JhKSyN+H6/a8j3DGmiqu9zdbG2tkZBQQEkEgnYbOP5QtPrk3nLli345ptvEBERAXt7ewwdOhSzZs2Cra1tver45HtCjNXAur5Y9UnBVgUuhwmmuAyQat57IxQK1bq8VwchBEm5JZBIZW1mwmbC3oIDdoX5dzs7OzRt2hQmJspTfSYmJg1CQWtTFHLKG21Qqo8uSroq77MhkPdfxt7jplcvuXbtWnh4eODIkSMYM2YMhEIh1q5diz179qBVq1aYM2eOoeU0Ksb6AqjKi2VlZWUUGWoTJoMBJnSzFNG188ssKkNB6X/7BRiAnbkJzDjqrUHs7Oxga2ureA4mJiawtLSs1yMKORWVYGVwOHSvgCHR9T3VNZ8hqKn3WS9lMWzYMPzxxx+KxUETExN89dVXePjwIcaPH1/vlIWxqIsvVk1jbqrbDnVdOr/8/9ZA5FhxObAyZYOlYU2JwWDUa0VcVFSk8pVqY2MDS0tLmJiYaHx32Gw2LC0tjS3iO4WuSlrXfPUJvaehKlqRrF69GidOnMAvv/xiEMEaAu/yiyXH2soKbC1ftxwOR6NVEiEEBSVCJOUIFHuNzTgsWJmyYcquv7uMdeH777+Hq6ur0t/hw4cV6zKaqGi0oQtXrlwB4z9T24p/t27dUspbVFSEVatWYfDgwQpjgT179uhUzp07dxAcHIzWrVvDwsICjRo1wrhx4/D8+XO98hmT+/fv44MPPoC9vT2cnZ0xYcIE/PHHH5Xml49gAeDFixeYMGEC+Hw+zM3N0aJFC6xZswYCgUCR//Hjxxg7diyaNGkCc3Nz8Hg89O7dG6dPnzZ63aqCTiMLFosFPz8/BAUFYfbs2eDxeCgoKFCct7a2xurVq7Fhwwb07du3zu+MrSl0+for/2IZGpFIhBUrVuDixYuwsLDAvXv3IBAIsGHDBixZsgQAsGDBAly5cgUxMTFGkYHBYCAlORl79+5FREQETp06pZLHmueCglIxzExYMClnsSOVEgglUhSWirBwfjDuRN7E0b8uwcTMHJambFhw2bVuAqqt/U6ePIn9+/ejSZMm+Pbbb6t8/ylTpsDPz08prXVrmXsP+bpMYmKi0jtmYmICPp9fLSu++fPn47333lNKa9asmdJxVlYW1qxZg0aNGqFdu3a4cuWKzvf/9ttvcePGDYwdOxa+vr5IS0vDtm3b0LFjR9y6dQtt2rSpUj5jcf78eQwfPhwdOnTAypUrYWlpicePHyMjI6PSa+TGE4mJiejSpQtsbGwQHBwMe3t7REZGYtWqVbh37x5OnjwJAEhISEBhYSGmTp0KNzc3CAQCHD9+HB988AF27tyJ2bNnG7WOOqNLUG8vLy+l4+XLlxMmk0lGjRpFdu/eTUQikeKct7e3LresEbQFIy8pKSFPnjwhJSUlKuckEgnJzc0lEomkWjLk5OSQO3fuVPqXk5NTrftrYt26daRp06akqKiIEEJIeno66dq1K1m4cKEiz/bt28ns2bONJgMhsrb87PMvCACluj948IA8jU8hmYWlJCVPQFLzSkhmYSnJKSoj2UVl5E1GHnnxJpXcvf+AfPHFF2TUqFHkzp075N6DaJKUlkGkUqlR5dYFde1X/n0qKysjbdq0IZ988onRZJBKpaSgoIBkZWWRgoICIpVK9X5/w8PDCQBy9OhRrXlLS0tJamoqIYSQO3fuEADkt99+06mcGzdukLKyMqW058+fEy6XS4KCgqqcTxf69OlDpk6dqnP+/Px84uzsTEaNGqXSjtnZ2eTBgwdK73NMTIzS73nt2rUEAHn06JHStVOmTCEANP72xWIxadeuHfHx8dEqp6Z+TBva+sjy6DSykH/JyPnmm29w7NgxHD9+XGVxxdiavr5hY2sLjp0rRPmZSlZBJiYm8PDwMKpVzokTJ+Dr66uY4nFycsKxY8ewcuVKRZ558+YZrXw5TCYTrVq2AADw3L0gFosgEAEedqZIFjCQXVQGW3MTsJkMSKQEEhD8++wpTv8ZhvGjRwAAxo4dq7ifVCxCamICzE3YtW7VVLH9nj59itOnT+OLL74AIHvOclc4VcHf3x8SiQSbN2/G8uXLcf36dVhbW+OLL75QuFWRo25dhhjAwq6wsBBmZmaVWgtyuVy4uLioPacNdfuxmjdvjtatW+Pp06dVzmcMDh48iPT0dKxduxZMpmyzp5mZGZhMpsI0lsViQSQSqTWekM++ODs7K93X1dUVTCZT4/Qzi8WCh4cH7ty5Y5zK6YFO41Rzc3OVtMaNG6tdha/PO2KNQYlQAgbXAiZOXrBw8oCnV2P4+Pigbdu2Ru/oxGIxLly4gBcvXijS+Hw+hg4dqpSvJqYN5e+KuYUlbGzt4GD3dgNRmViK9IJSZBaVobBUjITkNMz4cBLys5X3Z1SUs66YHcvlys7ORmBgoNJ8tL7ExsYiLy8Pw4cPR6dOnRRrF4sWLUJsbKzaa0QiEbKyshR/2dnZSsfl/7RFtJw+fTqsra1hamoKf39/3L17t9p10gb5b6OhtrUWXfJVbIusrCyIRCKUlZXp3BYXL16EtbU1kpOT4ePjA0tLS1hbW2Pu3LkoLS0Fg8GApaUlHBwcYGVlpdIfyl2Uz5w5E9HR0UhMTMThw4fx888/Y/78+Sp9pdxNzatXr7Bp0yacO3cO/fr106Hlaghdhipjx45VSRsyZIjavOPGjdPlljVCXZiGSssvITGJuSQmMZckZhdX615V5fvvvycACI/HI8eOHVM5Hx0dTT755BPSsWNHQgghly5dIpMmTSJTpkwhBw8eJG3btiWWlpZk/fr1JDU1lUybNo04ODgQf39/UlBQQAgh5Pjx44TNZpNVq1YRQgiJiooiPXr0IJ6enkpl/fbbbwQAKSwRkpQ8ATn990Uya9YsErr+R+Ln35/88Ms+Ev0qhTx4nki++nYTsbS0Im3btiVBQUFkx44dZOzYsaRFixZKw/4rV66QuXPnktDQUDJr1izSq1cvcuPGDUIIITdv3iRTpkwhkyZNIkeOHCHNmjUjPB6P7N+/v9L2OnHiBGEwGKR79+4kJSWFXLt2jfB4POLs7EyuXr2qaDMej0euX7+u0n6//fYbsba2Jt27dyeffvopSU5OJoTIpj8++eQT8t133xE+n0+cnJzIgQMHKpUjJSWFACCOjo4kMTFRkf7kyRMCgOzdu1ftdfIpJF3+4uLi1N7jxo0bZMyYMWT37t3k5MmTZP369cTBwYGYmpqS+/fvVypzVaeh1PH7778TAGT37t3VzmeItvD19SXm5ubE3NychISEkOPHj5OQkBACgIwfP16n/uHrr78mZmZmSuUtX75cbd45c+Yo8jCZTBIYGKjTNHWdmoZ68uQJfv/9d6WvuOTkZJW0srIyXLt2rYrqqm7ywfYbyCgoBZPBgMxNnX6IpVLIm4jFZPx3v6rhaMXF6RA/7RkrsHjxYqSnp+OHH35AYGAghg8fjl27dimcQPL5fBQVFSE7OxuAzNX4+vXr8eLFC0yePBnR0dH49ddfERwcjNLSUuzcuRMbN25E06ZNcfDgQcyZMwejR49WilPRpUsX9O/fH3v37lUrkzmXDaFEiikTx2HDhvUInDQVjvbWWLLwY3T55x+YmppiaIAfdmy2RLdu3TB79mzk5eXh9OnTyM/PV9yHEIJFixYhKCgIn376KQBg27Zt6NevH6KiotClSxesXbsWjx49wsiRI/HkyRMsXrwYISEhmDhxotrF35EjR6Jfv35gsVgKy6Pg4GD88ssvCvfqrVq1woABA9CzZ09kZ2crtd+0adOwevVqDBw4UCWS5IULF/B///d/ePPmDebPn4/58+dj4sSJakfn8pHDqlWrwOfzFenyPROVTV+0a9cOFy5cACBzVyMQCGBubq62rpVNH/Xo0UNp6ueDDz5AYGAgfH198eWXX+Lvv/9We111efbsGT755BN0794dU6dOrXa+8m0h59NPP4WLiws+//xzpfTK2qKoqAgCgQAff/wxtv4XW3v06NEQCoXYuXMnPv/8c3To0EFjvby8vNC7d2+MGTMGDg4OOHv2LNatWwcXFxcEBwcr5V24cCECAwORkpKCI0eOQCKR1Cmzep2VxbRp05TSCCFq0xrChicAyCosQ0Zh3XlQ+sBgMPDdd99h7Nix+Pjjj3H69Gm0b98eV65cgbe3NxwcHODl5aXIz+Fw4O7ujpKSEgwYMACAbCgtFovRt29fmJiYgMfjoVWrVnjy5IniuoqdkaZ3gMlgwNqUg9nzPkG3bt1QXJgPUiaAQCBAXl6e2h+ura0t3NzccP/+fUXa7du38eDBA4SFhSnS5syZg7Vr12LDhg04ePAgeDwevLy8FOsdw4cPx7Zt25Cenl5pIKYZM2ZgypQpSE9Ph7OzM7p164bVq1cjIiICvXv3xtmzZzFmzBgAUGk/TXTv3h0DBw4EALz//vvYtm0bMjIyVOazgbfKYuTIkUrpz549AwD4+PioLcPOzg79+/cHIFMWBQUFsLa2rrZvs2bNmmHEiBEICwtTOIA0JGlpaRg2bBhsbGxw7NixSu+vaz5AuS3Kp7m6uqqkV4aZmRkAYOLEiUrpkyZNws6dO3Hnzh2NyuKPP/7A7Nmz8fz5c4XSHz16NKRSKZYsWYKJEycqrWe1aNECLVrI1vamTJmCgQMHYvjw4YiKiqoT/apOyqJVq1aYO3euVhPP4uJi7Nq1yyCC1TY8Ky6khFRrZCElROGWgsEA2Hr+aB2tqhd69b333kNUVBQWLVqEbdu2YdKkSTrPQXO5qmVzuVwl0+mqwmYxsWb1Kty6eQO7//c/5OXK4pJrm0cvj7zjLO8qn8PhoE2bNnjw4IHaa+Rf5GVlZZXed9SoUbC0tMSBAwewePFiHDlyBD169MD+/fvRu3dvnDp1Cjt37tRZTnXI27SkpETt+YcPH8LFxUUlaE9MTAzYbDZatWql9jqhUIicHFnYXqlUisLCQggEArXKwtHRsUqdvoeHB4RCIYqLiw3qSy0/Px9DhgxBXl4eIiIi4ObmVq18hsTNzQ2PHz9WUejydy4vL0/j9T/99BM6dOigNDoEZKO1PXv24MGDBxoVV2BgIObMmYPnz59X+oFQk+ikLFauXInx48frdEN1X0r1kVOf9Kz2l1lqfgkyC2UdE8+SCzdbM0OKqJHbt2/D3t5eYRvPZrPxf//3f3j8+DHCw8ORk5MDe3t7ve9PqrmwvDp0JV6/fo1FixYhOjoa+/btq9L18meSlJQEb29vRTqPx9PqjVUTpqammDBhAvbt24dx48bB2toaISEhmDt3Lr788ks4Ojoa3YVGbGws2rVrp5L+8OFDeHt7q1XgAHDz5k34+/vrVEZcXJzOoyIAeP36NUxNTQ26J6i0tBTDhw/H8+fPcfHixUqVoK75DE2nTp1w4cIFxQK3nJSUFADQauWWnp6u1ohF7q9Lmy8n+cdE+enX2kQnZaGrogCgGKJTgMLSty+DlanxvEGqw8TEBF9//bXK2kGnTp0QGRlpUJ/7DAZDaVSgzboqMjIS69evx4ULFxRBkNTdU66QKppumpiYYPDgwdi6dSuuX7+upCxSUlKq7SZ/+vTp2LFjB+bOnYvNmzfD3d0dc+bMwaRJk7SOnCvGe64qEokET58+VUwDlicmJkbjtIch1iwyMzPh6OioUu6pU6cwZMgQvT6cBAIB3rx5Ax6Pp7BgkkgkGD9+PCIjI3Hy5El0795d7bW65tOFqmwaBIBx48Zhw4YN2L17NwICAhTpu3btApvNVtosqa6O3t7eOH/+PJ4/f670jh46dAhMJhO+vr4A1AeTE4lE2LdvH8zMzGpMOWqjZnuwdwixRIpSkazTZDMZSjGSa4ImTZpg3759aNeuHRYtWqSIqvbXX39h9uzZiikIkUiktIhWsaOXd+Tlv4IkEolSh9i4cWOcO3cOCxcuxOvXr3H16lXk5eUhPz9foZTk10skEsWXWUxMDLp27Yrw8HAAsh+N3DzR2toa8fHxEIvFEIlEsLGxgVQqVZgw+vr6YtCgQdi0aROCgoLA5XIRFxeHx48fK1xOiMViJTnl9dQ23dWlSxe0atVK4aEWkHUcsbGxKnuOKrafvb09nj17BpFIhGfPnqFt27aKOpS/pmKbynnx4gVKS0tVRhYlJSV4+fKlxkVdQ6xZjB8/HmZmZujRowecnJzw5MkT/PLLLzA3N8eGDRtU8m/btg15eXmKZ3r69GlFrIeQkBDY2Njg9u3b8Pf3x6pVqxQL/59++ilOnTqF4cOHIycnB/v371e67+TJk6uUryLp6ekqC9yVMWrUKLUm/x06dMCMGTPwv//9D2KxGH369MGVK1dw9OhRLF26VGndS10dP//8c5w7dw69evVCcHAwHBwccObMGZw7dw6zZs1STKXNmTMHBQUF6N27N9zd3ZGWloYDBw7g2bNn+OGHH+qOf68q21rVI2rTdDa3uExhMvsqo7BWdhvb29sTAMTHx4eMHj2a9OrVi4SGhip2xIaHh5N27doRBoNBtmzZQq5cuUK8vLwIj8cjZ8+eJVlZWSQ0NJQAIEFBQSQ+Pp6cPXuW2NrakubNm5Pw8HBCiMxctnnz5sTa2pp8/vnnClPX48ePK8737NmTACAbNmwgSUlJpHfv3sTU1JT07NmTHDp0iLi6upKWLVuS06dPkzt37pAVK1YQc3Nz0qdPH3Ls2DElOfPy8gghhBQVFZG5c+eSwYMHkxUrVpCZM2eSmJgYQgghkZGRxMPDg9jZ2ZFTp06R1NRUMnr0aAKAfPbZZ6S4WLMZ8/bt28mtW7cUxzdv3lQxWa3Yfnl5eWTXrl3E0tKSDB8+nGRnZ5NTp04Ra2tr4ubmRs6fP08SEhJIYGAgAUBCQkKIQCBQuueRI0fU7vq9ffs2AUDOnDmj07PX9/3dsmUL6dKlC7G3tydsNpu4urqSyZMnkxcvXqjN7+npqdUkVW7GKjevJkRmTlzZdeW7JV3zVcQQprOEECIUCsnq1auJp6cn4XA4pFmzZmTTpk0q7auujoTI3v0hQ4YQFxcXwuFwiLe3N1m7dq2S14tDhw6R/v37E2dnZ8Jms4mdnR3p378/OXnyZKVylaemTGf1isFdX9AWX7a0tBRxcXFo3LgxTE1Nlc5V15okKVeAnGLZF6eztSmcrU21XPFuIW9fqVSK169fV5qvIcSeqA0MaQ1FUaUuta+mfkwbVYnBTd8iA0IIQWFhIbKzs5Gf/9ZayIpLZ/sqw9bWtkEHKaJQGgq0FzMQubm5Kt4/wWTD1NYJJmwa1lITDTlIEYXSUKDKwgBUFmcbUjFKc1JQaGdGv5C1UN+DFFEoDR06DVVNyDseZ5tCobwbUGVRTepiAHcKhUIxNFRZVBMaZ5tCobwLUGVRTWicbQqF8i5AlUU1kcfZ1oQx42xTKBRKTUCVRTVhMBjw8PDQmEcewJ1CoVDqK1RZGAA7Ozs0bdoULPZbb6RZWVnYvXs3LCwsqNkshUKp91BlYSDs7Oxg7d4UHHt3sG1cwOCYYseOHZXGLKBQKJT6BN2UZyAkUikEZWIwTMzAZjJgZ03XKCgUSsOBjiwMREGpGPJtd2YcFtis2m3as2fPom/fvmAwGHByckJgYCBGjBiBrl27Yvr06bh582atykehUOoXVFkYiHzB23gFliZMRdjSu3fvag0GZAyGDRuGL774AoDMX/6xY8dw8uRJXL16FV5eXvDz88PcuXN1DmVaWlpqTHEpFEodhyoLAyCREhSWypTF5XOn0bGND+bMmQNA1lF7eXkhLCysxuUyNzcHAKVYy6ampli1ahW+/vpr7NixA6GhoTrda+7cuUaRkUKh1A+osjAAhaUiEAAXz53G4jlTkfxfpDA5ycnJCAwMrBWFURlLlixB48aN8f333yMzM1Nj3h07diiiz1EolHcTqiwMQH6JCBKJBN+tWqrWYaA8beHChbUyJaUONpuNDz74AGVlZbh8+TIeP36Mjz/+GDt37kRgYCB++uknADJFd+rUKQDAZ599plAaERERCA4Oxo4dOzB06NA6pQgpFIrhocqimoilUhSUiHD/diTSU1MqzSf3ThsREVGD0mmmWbNmAICEhARMnjwZHh4emDNnDpYtW4aQkBAkJCTA3d0d48aNAwB8//33mDZtGgghGDlyJLp3746PP/4Ys2bNwtSpU6mZMIXSgKHKopoUlMimoLLS03TKn5qaalyBqgCbzVb8O2PGDAwdOhQAYGFhAalUioSEBLXXMRgMLFiwAD179lTkLyoq0jqdRaFQ6i90n0U1yf3PCorn7KJTfldXV2OKUyVSUmQjIU9PT4wZMwavXr3Cxo0bFRZSmiylQkNDER0dje+++w7Z2dla81MolPoNHVlUA6FYguIyMQCgS7ce4PP5lfqAkvuQ6tWrV02KqJHLly+Dy+Wif//++Pnnn7Fw4UIEBwdj/PjxWq9dvnw5tm7dik8//RRDhgypAWkpFEptQpVFNcgpfhujwsaciy1btgCAisKQH2/evFnJjLU2+fvvv3Hjxg0sXboUhYWFCA4Oxpw5c2BmZqYyQpDLL1+oj4yMxLp167B48WKwWCw6oqBQ3gGostATKSHIKX67EY9nycXo0aNx7NgxuLu7K+Xl8/k4duwYRo8eXaMyCgQCAFCywCKE4NChQxg7dizmzZuH0NBQpKenQyqV4vbt2ygrK8ORI0cAyKapsrOzYW9vDwB49uwZ7t+/r5i+ioqKgkAgUFhCJSUlITc3tyarSKFQagrSgMnPzycASH5+vtrzJSUl5MmTJ6SkpETlnEQiIbm5uUQikai9Nq+4jMQk5pKYxFzyPK2ASKVSxTmxWEx27txJAJCdO3cSsVhsmApVgXPnzhF/f38CgLi6upIJEyaQUaNGkZ49e5KgoCBy9epVRV6JRELGjBlDTE1NSY8ePciDBw9Ihw4diLe3N3nw4AEpKioiHTp0IHw+n+zbt48UFRWR3r17E1NTUzJkyBDy8OFD4unpSTp37kwSEhJ0kk9b+1KqB21f41KX2ldTP6YNbX1keegCtx4QQpBZ9HYKyt7CRGnqicVioXPnzgCAzp0718rU0+DBgzF48GCd8jKZTBw7dkwp7f79+xqPr169qnQcHx9fdSEpFEq9gU5D6YFAKIFAKFvYZjOZsDXnaLmCQqFQ6jdUWehBRmGZ4v/2FhywmKrN6OrqilWrVtUpU1kKhULRFzoNVUWKy8QKp4EsJgM8S67afK6urli9enUNSkahUCjGg44sqgAhBKn5b11125qZ1HrcCgqFQqkJaE9XBfJLROXWKhhwsVE/qqBQKJSGRq1PQxFCsGnTJqSnp4PD4SAtLQ2bNm2ClZWV2vx3797FTz/9hJYtW+LOnTtYtGgRunfvbnQ5hWIpkvPeOspztOKqXaugUCiUhkitK4sNGzbg4sWLuHTpEgCZz6GgoCCFW+zy/Pvvvxg0aBDu3LmDJk2a4MWLF+jWrRvu3r2Lxo0b6y0DUeNWvDxSQpCYK4BEKstnxmFVulZBoVAoNYm2/stQ1OqncU5ODr755htMmzZNkTZjxgycPn1arSvvVatWoX379mjSpAkAoHnz5mjbti2++eYbvcrncGQmr/KdzuogAN5kCxQ+oFhMBjwdzCv1AUWhUCg1ibz/kvdnxqJWRxZ///03BAIBOnXqpEjz8vKCg4MDjhw5ouR0TywW49SpUwgODla6R+fOnbFr1y78+uuvYFZxWojFYsHW1hYZGRkAZGFIGQwGCCEQiiQQCoV4npwDoUTm+4gBgGdtCqlYhFKxSMOdKdqQSqUQCoUoLS2t8nOjaIe2r3GpC+1LCIFAIEBGRgZsbW2Nvvm3VpVFbGwsAJnvpPLw+XxER0crpb169QolJSVq8+bn5yMuLg5NmzZVW05OTg5EoredO5fLBZcrm0ZycHCARCJBenq64nx2URmEEilsTYA8IUDAAAOAjRkH2QIWsvWtMEUBIQSlpaUwNTWlozQjQNvXuNSl9rW2toaDg4NSH1cZZWVlKCt7u0+ssLBQ53JqVVnk5OQAkAXPKY+lpaXia1+XvACQkZFRqbKouJ4xfvx4TJw4USmNwWCoaOaK4YxKQaFQKHUHiURSpTWLQ4cO4fDhw3qVVavKwsTEBIB6l97yc7rkLX9eHXFxcUrWVeVHFur4+3Ea/olJxADbTMC9LQa35YPJpF9nhkQkEuHChQsYMGCA0eda30Vo+xqX+tq+/fr1w/bt2xXHhYWFOhsH1aqycHJyAgAUFxcrdebFxcVwc3OrNG955McV85fH3t4e1tbWOss1vL0HBrd2wV9//YWh7Tzq1ctQ3+BwOLR9jQhtX+NS39qXw+EoZmPkx7pSqytf7du3BwAkJycrpScnJ6NNmzZKae7u7uDxeGrz8ng8ODs7G1S2srIyHDp0SGl+j2I4aPsaF9q+xuVdbN9aVRb9+/eHubm5kvvrxMREZGRkIDAwUCkvk8nE8OHDVVxl3717F6NHjza4RUJZWRkOHz78Tr0MNQltX+NC29e4vIvtW6vKwszMDAsXLsTevXsVaXv27IGfnx+6deuGvXv3onXr1orF7sWLFyMyMhIJCQkAgNevXyMqKgrz58+vFfkpFArlXaHWd3B//fXXCA0Nxfz582FnZ4e4uDicPHkSDAYDeXl5yMjIUGjvNm3a4MSJE1iyZAk6duyIe/fu4dSpU2jdunUt14JCoVAaNgxSU3vFa4H8/HzY2toiMTGxSgvcgMxUt3HjxoiLi1PEoKYYDtq+xoW2r3FpKO1bUFAADw8P5OXlwcbGRmPeBq0skpKS4OHhUdtiUCgUSp0mMTFRZcNzRRq0spBKpUhJSYGVlVWt77KkUCiUugYhBIWFhXBzc9NqJNSglQWFQqFQDAP1MEapcTIyMpCfn1/bYlAo1UYqleL169e1LUaNQJVFBQgh+PHHH7FkyRKsWLECs2bNqpKzLYoqBw4cAIPBUPx1795dYXDwzz//YPbs2diwYQPGjh2Lf//9t5alrR9cv34d/v7+Kul3797FjBkzsHHjRowbNw6RkZFK59PS0jB79mxs3LgR06dPx+7du2tK5HpFZe1bXFwMOzs7xbvMYrGQmJioON+g25dQlFi3bh0JCAhQHK9cuZIMHz68FiWq/4wYMYL89ttvir87d+4QQgi5evUqcXJyInl5eYQQQi5dukQ8PDxIbm5uLUpbtyksLCQHDx4kPj4+pOLP99mzZ8Te3p68evWKEELI8+fPib29PXn9+jUhhJCSkhLSrl07sm/fPkIIIWVlZaRZs2bk2LFjNVuJOoym9iWEkJ9++omsWLFC8S7/8ccfinMNvX2psihHdnY2MTc3VzxsQgiJi4sjAMi1a9dqUbL6y61bt8iKFSvUnuvatSuZMWOGUpqnpydZs2ZNTYhWr1mxYoVKZzZ+/HilDx1CCOnTp4+ijX/++Wdibm5OSktLFedXrVpFmjZtSqRSqfGFrkeoa19CCBkyZAiRSCRqr2no7UunocqhLRgTpeps3boVa9euRYcOHfDdd9+htFTm6D0lJQVRUVFKbQ3IglnRttZORXf68uBg6trz+PHjkEqlCAsLQ+vWrZU8Lnfu3BmvXr1ScaPzrqMukNCFCxdw7tw5uLm5YdasWXj27JnS+YbevlRZlKMqwZgoutGjRw98/PHHyMrKwpIlS+Dn54eioiKNbf306VMIhcLaELfeoktwsNjYWLXnAdD3WwfMzMywbNkyNG/eHLt370bHjh1x6dIlxfmG3r5UWZSjKsGYKLrxySef4KeffsLr168RGhqKe/fuYeXKlRrbWiKRIDubxiOsCroEB8vJydF4nqIZPz8/rF27FhEREbhy5QpsbGwwefJkhTuiht6+VFmUoyrBmChVg8Ph4KuvvsLs2bNx7NixagWzoqiiS3uamJjQ9jYQffr0QVhYGNLS0nD9+nUAaPDtS5VFOTQFWNIUXImiO1OnTkVGRobGtuZyuXBwcKgN8eotugQHc3Jy0it4GEU93bt3R/PmzRWjhobevlRZlKMqwZgo+mFrawtHR0e0bt0abDZbbVtTL8JVR5fgYO3bt1d7HgB9v/VE/j4DaPDtS5VFOaoSjImiH/fv38eQIUNgb2+PXr16qQ1mRdu66ugSHGzEiBF49OgRRCKR0nkfHx+0bdu2pkWu9wgEAqSlpaFHjx4A0PDbt7Ztd+say5YtIwMHDlQcr1mzhvj5+TUIO+ma5vLlyyQwMJDcu3ePEEJIZmYm6devH0lOTiaEEPL3338TR0dHkp+fTwgh5Nq1a8Te3p6kpaXVmsz1haVLlxIASjb/sbGxxNLSksTHxxNCCHn16hWxsLAgjx49IoQQIhAISNOmTcnBgwcJIYQIhULi4+NDfv3115qvQB2nYvuWlpaSwMBAsm3bNkVfsHjxYqU9WQ29fakjwQpIpVKEhoaioKBAEYxp8+bN9dpnfW1x7949TJkyBQkJCQgKCoKbmxvmzJkDFxcXRZ4//vgDZ8+eRZs2bXD37l0sX75cMR1IUUUkEuHIkSNYvnw5EhISsHz5ckyYMEExzXHx4kXs2rVLERxszpw5CAgIUFwfFxeHpUuXol27dnj16hU6dOiA4ODg2qpOnaOy9m3ZsiUmTJiAv/76C127dkWXLl0waNAgFZcgDbl9qbKgUCgUilbomgWFQqFQtEKVBYVCoVC0QpUFhUKhULRClQWFQqFQtEKVBYVCoVC0QpUFhUKhULRClQWFQqFQtEKVBYVCoVC0QpUFhUKhULRClQXlnWDatGlgsVjo0KED+vfvDzs7OzAYDPTv3x9du3aFubm5ws3IggUL0K5du1qRUyqV4rfffoNUKjXofW/evIm4uDiD3pPybkGVBeWdgMFg4PLly3jw4AEuXryoUAYXL15EVFQUXr58CVtbWwCAj48PunXrVuMyCoVCzJgxA126dAGTadifZo8ePXDgwAHcunXLoPelvDtQZUF5J2jRogX69OlT6Xk3NzcMGzYMADBv3jzs3LmzpkRT8PHHH2Pw4MFK8TyaNGmCyZMnq+T19/dXqc+bN28wa9YsNGnSBKampnBxccHQoUPx8uVLAMCXX36J9evXK0KwUihVgSoLyjvB4sWLteZZuHCh4v8SicSI0qhy6dIl3L59GxMmTFCkFRUVIT4+Xu2U2MOHD+Hr66s4Tk9PR+fOnXHjxg189NFH2L59Oz7++GMkJyeDzWYDAFgsFoKCghASEmL8ClEaHFRZUN4JOByOTnliYmIQHByMLl26AAAuX76MoKAgTJ06FYcOHYKvry+srKywYcMGpKWlYfr06eDxeAgICEBhYaHiXvHx8Vi4cCGmTZuG1q1bIzQ0FJocPH///fd4//33ldIePXoEQoiKskhKSkJOTo6Ssti/fz8KCwsRFRWFL7/8EjNnzsTq1asRExMDLy8vRb7Bgwfj8OHDdP2CUmWosqBQysHn81FUVITs7GwAQK9evZCRkYGrV6+Cx+MhOjoa33//PVauXIkdO3Zg586dePbsGe7du4eDBw8CkI1K5s2bh3Xr1mHPnj3YsWMHvv76a8X5ipSWluLSpUto1aqVUvqjR48AQEVZxMTEAICSssjLy4NIJFKJlFcRa2truLm5ISwsrAqtQqEA7NoWgEKpSzg4OCh9iXM4HLi7u6OkpAQDBgwAAPTt2xdisRh9+/aFiYkJeDweWrVqhSdPngAAjh49ioSEBKxZswYAQAhBz549kZmZqbbMFy9eQCQSwcnJSSk9NjYWzs7OcHZ2Vkp/+PAhmEymUlznKVOmYPfu3fD390fHjh0xYcIETJ48Ga6urirlOTs74969e1VvHMo7DVUWFEoV4XK5atMKCgoAAA8ePEC7du2wYcMGne6Xn58PADAxMVFKf/Tokdr1iujoaDRp0gQWFhaKtObNm+P58+cICwvD6dOnsXLlSqxZswZ///03evbsqXS9mZlZpYqLQqkMOg1FoRgI+ZqEQCBQuyZQVlam9jp5p19UVKSUHhsbC29vb6U0qVSKy5cvK01BybG0tMSUKVNw9OhRxMTEoKysDEeOHFHJJ5FIYGpqqlulKJT/oMqCQjEwzZs3x+3btxVrC4BMkWzevLnS/EwmE3l5eYq0jIwMZGZmIjU1VSnv1q1bkZWVhbZt2yrSsrKyVO5pZmYGiUQCNzc3lXO5ublo0qRJFWtFedeh01CUdxK55ZJAIIC5ubnSOZFIBKFQqDiuaEYr310tFouV8shHFpMnT8bKlSsxYsQIbNy4ETweD7/88gvmzZunVhZLS0t0794djx8/VqTFxsYCAM6fP4958+ahRYsWuHXrFv755x8AwL179xAVFYWuXbsiJCQEjx49wgcffIDGjRsjNTUVv/zyC/h8Pj766CMV2RMSEhAQEKB7Y1EoAEAolHeIxMREsmHDBsJmswkAMmfOHBIVFaU4Hx4eTtq1a0cYDAbZsmULuXLlCvHy8iI8Ho+cPXuWZGVlkdDQUAKABAUFkfj4eHL27Flia2tLmjdvTsLDwwkhhFy5coX4+voSLpdLfH19yenTpzXKtW/fPtK9e3fF8aZNmwiLxSJnz54lTZs2JaampmTAgAEkNjaWNG3alPD5fHLv3j1CCCG7d+8mAQEBxNHRkXC5XNKsWTMyf/58kpGRoVLOnTt3iIeHBxEKhQZoTcq7BIMQDcbfFAqlRpBKpejfvz9+/PFHtG/fHrNmzcK1a9fw/Plzg5YzdepUjBgxAqNHjzbofSkNH7pmQaHUAZhMJn7//XcsX74cJSUliI2NVdl3UV1OnDiBJk2aUEVB0QuqLCiUOoK7uzt+/fVXfP/993jy5IlBlcXNmzdRUFCAVatWGeyelHcLOg1FodQxXr16hWbNmuH3339X60RQHwghYDAYBrkX5d2EKgsKhUKhaIVOQ1EoFApFK1RZUCgUCkUrVFlQKBQKRStUWVAoFApFK1RZUCgUCkUrVFlQKBQKRStUWVAoFApFK1RZUCgUCkUrVFlQKBQKRStUWVAoFApFK1RZUCgUCkUr/w/I4WQdk46iygAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 500\n",
    "    for n in range(0, N):\n",
    "        # res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * (n + 0.5) + η**4 * ((1/8) * (1 + 2 * n * (1 + n)))) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-15/\"\n",
    "# number = 8591\n",
    "# file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8662\n",
    "file_path = data_path + date + f\"01/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "times = np.linspace(0, 170, 200) # Time array for simulation\n",
    "\n",
    "# /Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/2024-03-15/23\n",
    "\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:-10]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:-10]\n",
    "\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# Define your fitting function parameters\n",
    "initial_guess = [1, 0.03, 0.1, 20]  # Example initial guesses for a, b, c, and nbar\n",
    "# Perform the curve fitting\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "# Extract the optimal values of a, b, c, and nbar\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "# Calculate the standard error (square root of the diagonal of the covariance matrix)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "# Extract the standard errors\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "# Print the optimal parameters and their standard errors\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, 1, b_opt, 0, nbar_opt - nbar_err)\n",
    "fit_upper_bound = fit_function(times, 1, b_opt, 0, nbar_opt + nbar_err)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "# plt.plot(times, fit_function(times, *popt) / np.max(fit_function(times, *popt)), label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / np.max(fit_function(times, *popt)), yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(times, fit_function(times, *popt) / a_opt, lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f}$', zorder=1)\n",
    "plt.plot(times, fit_function(times, 1, b_opt, 0, nbar_opt), lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "# plt.errorbar(rabi_t, pmt_counts_avg_thresholded / a_opt, yerr=errorbar, fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "number_drop = 2\n",
    "plt.errorbar(rabi_t[number_drop:], (pmt_counts_avg_thresholded[number_drop:] - c_opt) * (1/a_opt), yerr=errorbar[number_drop:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation with 500 $\\mu$$s$ delay time', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.05, 1.05)\n",
    "pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat_no_shuttling_500us_delay.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d629bd11",
   "metadata": {},
   "source": [
    "### with 1 ms wait time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11730a63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0766831679958584\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "Optimal parameters: a = 0.8248808283686755, b = 0.020634984102409507, c = 0.14793724478599574, nbar = 52.64979628493957\n",
      "Optimal parameters with their standard errors:\n",
      "a = 0.8248808283686755 ± 0.12151650556375053\n",
      "b = 0.020634984102409507 ± 0.0004930209620433412\n",
      "c = 0.14793724478599574 ± 0.059475617903702815\n",
      "nbar = 52.64979628493957 ± 12.553180692564956\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries for quantum physics calculations, optimization, numerical analysis, plotting, and file handling.\n",
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "n_ion = 9\n",
    "s = \"scan_data\"\n",
    "kb = 1.38064852e-23\n",
    "γ397 = 1 / 7e-9\n",
    "ħ = 6.62607004e-34 / (2 * np.pi)\n",
    "ω = 2 * np.pi * 0.177e6\n",
    "η = 2 * np.pi * np.sqrt( ħ / (2 * ω * 6.6551079e-26)) / 729e-9 / np.sqrt(n_ion)\n",
    "print(η)\n",
    "\n",
    "def thermal_carrier(time, Ω, n̄, t0=0):\n",
    "    A = 1\n",
    "    res = [A/2 for t in time]\n",
    "    N = 500\n",
    "    for n in range(0, N):\n",
    "        # res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * n) * (time - t0)) / (2 * (n̄ + 1))\n",
    "        res -= A * (n̄ / (n̄ + 1))**n * np.cos(2 * np.pi * Ω * (1 - η**2 * (n + 0.5) + η**4 * ((1/8) * (1 + 2 * n * (1 + n)))) * (time - t0)) / (2 * (n̄ + 1))\n",
    "    return res\n",
    "\n",
    "def fit_function(times, a, b, c, nbar):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    temp = 0\n",
    "    for i in range(len(intensity_distribution)):\n",
    "        temp += a * thermal_carrier(times, intensity_distribution[i] * b, nbar) + c\n",
    "    return temp / len(intensity_distribution)\n",
    "\n",
    "# File loading\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):    \n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "\n",
    "n_samples = 50\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "index = \"01\"\n",
    "number = 8666\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = rabi_t[:-9]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[:-9]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded)))\n",
    "\n",
    "# Data fitting\n",
    "initial_guess = [1, 0.01, 0.1, 0]  # Example initial guesses for a, b, c, and nbar\n",
    "popt, pcov = curve_fit(fit_function, rabi_t, pmt_counts_avg_thresholded, p0=initial_guess)\n",
    "a_opt, b_opt, c_opt, nbar_opt = popt\n",
    "print(f\"Optimal parameters: a = {a_opt}, b = {b_opt}, c = {c_opt}, nbar = {nbar_opt}\")\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "a_err, b_err, c_err, nbar_err = perr\n",
    "print(f\"Optimal parameters with their standard errors:\")\n",
    "print(f\"a = {a_opt} ± {a_err}\")\n",
    "print(f\"b = {b_opt} ± {b_err}\")\n",
    "print(f\"c = {c_opt} ± {c_err}\")\n",
    "print(f\"nbar = {nbar_opt} ± {nbar_err}\")\n",
    "fit_lower_bound = fit_function(times, 1, b_opt, 0, nbar_opt - nbar_err)\n",
    "fit_upper_bound = fit_function(times, 1, b_opt, 0, nbar_opt + nbar_err)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "half_column_width_inches = 4.25\n",
    "aspect_ratio = 2  # You can adjust this for your desired aspect ratio\n",
    "plt.figure(figsize=(half_column_width_inches, half_column_width_inches / aspect_ratio))\n",
    "plt.plot(times, fit_function(times, 1, b_opt, 0, nbar_opt), lw=2, label=f'Simulation with $\\\\bar{{n}} = {nbar_opt:.2f} \\\\pm {nbar_err:.2f}$', zorder=1)\n",
    "plt.fill_between(times, fit_lower_bound, fit_upper_bound, color='C0', alpha=0.2)    #, label='Uncertainty in $\\\\bar{n}$'\n",
    "plt.fill_between(times, fit_upper_bound, fit_lower_bound, color='C0', alpha=0.2)\n",
    "number_drop = 2\n",
    "plt.errorbar(rabi_t[number_drop:], (pmt_counts_avg_thresholded[number_drop:] - c_opt) * (1/a_opt), yerr=errorbar[number_drop:], fmt='o', c='black', lw=1, zorder=2, label='Data')\n",
    "plt.title(r'9 ions rabi oscillation with 1 $m$$s$ delay time', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "legend_fontsize = 12\n",
    "plt.legend(fontsize=legend_fontsize)\n",
    "plt.grid(True)\n",
    "plt.ylim(-0.05, 0.99)\n",
    "pdf_filename = 'Rabi_9_ions_without_filter_and_with_AWG_repeat_no_shuttling_1000us_delay.pdf'\n",
    "plt.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45eed7b6",
   "metadata": {},
   "source": [
    "### Heating rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07cf0921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Provided data\n",
    "delay_time = np.array([0, 300, 500, 1000])  # Delay times\n",
    "nbar_list = np.array([28.65, 41.11, 51.22, 52.65])  # Corresponding nbar values\n",
    "nbar_errorbar = np.array([5.87, 4.65, 6.83, 12.55])  # Error bars for nbar\n",
    "# Perform linear fit with covariance matrix to get the errors of the fit parameters\n",
    "fit_coefficients, cov_matrix = np.polyfit(delay_time, nbar_list, 1, cov=True)  # '1' for linear fit\n",
    "fit_function = np.poly1d(fit_coefficients)\n",
    "# Standard errors of the fit parameters\n",
    "std_errors = np.sqrt(np.diag(cov_matrix))\n",
    "slope_error = std_errors[0]\n",
    "# Create a range of delay times for plotting the fit\n",
    "fit_delay_time = np.linspace(min(delay_time), max(delay_time), 500)\n",
    "fit_nbar = fit_function(fit_delay_time)\n",
    "# Calculate the upper and lower bounds of the fit considering the error in the slope\n",
    "fit_nbar_upper = np.poly1d([fit_coefficients[0] + slope_error, fit_coefficients[1]])(fit_delay_time)\n",
    "fit_nbar_lower = np.poly1d([fit_coefficients[0] - slope_error, fit_coefficients[1]])(fit_delay_time)\n",
    "\n",
    "# Provided data\n",
    "delay_time_new = np.array([0, 300, 500])  # Delay times\n",
    "nbar_list_new = np.array([28.65, 41.11, 51.22])  # Corresponding nbar values\n",
    "nbar_errorbar_new = np.array([5.87, 4.65, 6.83])  # Error bars for nbar\n",
    "# Perform linear fit with covariance matrix to get the errors of the fit parameters\n",
    "fit_coefficients_new, cov_matrix_new = np.polyfit(delay_time_new, nbar_list_new, 1, cov=True)  # '1' for linear fit\n",
    "fit_function_new = np.poly1d(fit_coefficients_new)\n",
    "# Standard errors of the fit parameters\n",
    "std_errors_new = np.sqrt(np.diag(cov_matrix_new))\n",
    "slope_error_new = std_errors_new[0]\n",
    "# Create a range of delay times for plotting the fit\n",
    "fit_delay_time_new = np.linspace(min(delay_time_new), max(delay_time_new), 500)\n",
    "fit_nbar_new = fit_function_new(fit_delay_time_new)\n",
    "# Calculate the upper and lower bounds of the fit considering the error in the slope\n",
    "fit_nbar_upper_new = np.poly1d([fit_coefficients[0] + slope_error, fit_coefficients[1]])(fit_delay_time_new)\n",
    "fit_nbar_lower_new = np.poly1d([fit_coefficients[0] - slope_error, fit_coefficients[1]])(fit_delay_time_new)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "plt.figure(figsize=(4.25, 4.25 / 2))\n",
    "plt.errorbar(delay_time, nbar_list, yerr=nbar_errorbar, fmt='o', c='black', label='Heating measurement')\n",
    "plt.plot(fit_delay_time, fit_nbar, '-', label=f'{fit_coefficients[0] * 1000:.2f} ± {slope_error * 1000:.2f} quanta / ms')\n",
    "# plt.plot(fit_delay_time_new, fit_nbar_new, '-', label=f'{fit_coefficients_new[0] * 1000:.2f} ± {slope_error_new * 1000:.2f} quanta / ms')\n",
    "# plt.fill_between(fit_delay_time, fit_nbar_lower, fit_nbar_upper, color='grey', alpha=0.2, label='Fit uncertainty')\n",
    "plt.title(r'9 ions COM mode heating rate measurement', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Delay time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(f\"$\\\\bar{{n}}$\", fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlim(-20, 1020)\n",
    "plt.legend(fontsize=12, loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_heating_rate_measurement_linear_fit_with_uncertainty_v3.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fecd3215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Provided data\n",
    "delay_time = np.array([0, 300, 1000])  # Delay times\n",
    "nbar_list = np.array([5, 41.11, 52.65])  # Corresponding nbar values\n",
    "nbar_errorbar = np.array([1, 4.65, 12.55])  # Error bars for nbar\n",
    "# Perform linear fit with covariance matrix to get the errors of the fit parameters\n",
    "fit_coefficients, cov_matrix = np.polyfit(delay_time, nbar_list, 1, cov=True)  # '1' for linear fit\n",
    "fit_function = np.poly1d(fit_coefficients)\n",
    "# Standard errors of the fit parameters\n",
    "std_errors = np.sqrt(np.diag(cov_matrix))\n",
    "slope_error = std_errors[0]\n",
    "# Create a range of delay times for plotting the fit\n",
    "fit_delay_time = np.linspace(min(delay_time), max(delay_time), 500)\n",
    "fit_nbar = fit_function(fit_delay_time)\n",
    "# Calculate the upper and lower bounds of the fit considering the error in the slope\n",
    "fit_nbar_upper = np.poly1d([fit_coefficients[0] + slope_error, fit_coefficients[1]])(fit_delay_time)\n",
    "fit_nbar_lower = np.poly1d([fit_coefficients[0] - slope_error, fit_coefficients[1]])(fit_delay_time)\n",
    "\n",
    "# Provided data\n",
    "delay_time_new = np.array([0, 300, 500])  # Delay times\n",
    "nbar_list_new = np.array([28.65, 41.11, 51.22])  # Corresponding nbar values\n",
    "nbar_errorbar_new = np.array([5.87, 4.65, 6.83])  # Error bars for nbar\n",
    "# Perform linear fit with covariance matrix to get the errors of the fit parameters\n",
    "fit_coefficients_new, cov_matrix_new = np.polyfit(delay_time_new, nbar_list_new, 1, cov=True)  # '1' for linear fit\n",
    "fit_function_new = np.poly1d(fit_coefficients_new)\n",
    "# Standard errors of the fit parameters\n",
    "std_errors_new = np.sqrt(np.diag(cov_matrix_new))\n",
    "slope_error_new = std_errors_new[0]\n",
    "# Create a range of delay times for plotting the fit\n",
    "fit_delay_time_new = np.linspace(min(delay_time_new), max(delay_time_new), 500)\n",
    "fit_nbar_new = fit_function_new(fit_delay_time_new)\n",
    "# Calculate the upper and lower bounds of the fit considering the error in the slope\n",
    "fit_nbar_upper_new = np.poly1d([fit_coefficients[0] + slope_error, fit_coefficients[1]])(fit_delay_time_new)\n",
    "fit_nbar_lower_new = np.poly1d([fit_coefficients[0] - slope_error, fit_coefficients[1]])(fit_delay_time_new)\n",
    "\n",
    "# Plotting the data and the fit\n",
    "plt.figure(figsize=(4.25, 4.25 / 2))\n",
    "plt.errorbar(delay_time, nbar_list, yerr=nbar_errorbar, fmt='o', c='black', label='Heating measurement')\n",
    "plt.plot(fit_delay_time, fit_nbar, '-', label=f'{fit_coefficients[0] * 1000:.2f} ± {slope_error * 1000:.2f} quanta / ms')\n",
    "# plt.plot(fit_delay_time_new, fit_nbar_new, '-', label=f'{fit_coefficients_new[0] * 1000:.2f} ± {slope_error_new * 1000:.2f} quanta / ms')\n",
    "# plt.fill_between(fit_delay_time, fit_nbar_lower, fit_nbar_upper, color='grey', alpha=0.2, label='Fit uncertainty')\n",
    "plt.title(r'9 ions COM mode heating rate measurement', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Delay time ($\\mu$$s$)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(f\"$\\\\bar{{n}}$\", fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlim(-20, 1020)\n",
    "plt.legend(fontsize=12, loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_heating_rate_measurement_linear_fit_with_uncertainty_v3.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94a007d",
   "metadata": {},
   "source": [
    "### Coherent heating simulation - Half speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "40a74734",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install pynverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ef7ead8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:571: RuntimeWarning: overflow encountered in multiply\n",
      "  - (n + alpha) * _ufuncs.eval_genlaguerre(n - 1, alpha, x)) / x\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  dy /= np.exp((log_dy.max() + log_dy.min()) / 2.)\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:193: RuntimeWarning: divide by zero encountered in divide\n",
      "  w = 1.0 / (fm * dy)\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading function\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "# # For 1 ms data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"01\"\n",
    "# number = 8666\n",
    "\n",
    "# For shuttled data\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "index = \"01\"\n",
    "number = 8663\n",
    "\n",
    "# # For shuttled data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"00\"\n",
    "# number = 8658\n",
    "\n",
    "# File loading\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = np.array(rabi_t)  # Convert list to numpy array if not already\n",
    "pmt_counts_avg_thresholded = np.array(pmt_counts_avg_thresholded)\n",
    "indices_to_remove = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -18, -22]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 1.05 - 0.15    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "rabi = 0.055\n",
    "n_ions = 7\n",
    "N = 500\n",
    "nbar = 6\n",
    "nalpha = 60\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "# You already have these lines in your code\n",
    "time = np.linspace(np.min(times), np.max(times), 200)\n",
    "\n",
    "# Running simulation for different nalpha values\n",
    "nalpha_values = [40, 50, 60]\n",
    "results = []\n",
    "\n",
    "for nalpha in nalpha_values:\n",
    "    D_population, _ = pe_model_optimized(time, nbar_fit=nbar, ndisplace_fit=complex(0, np.sqrt(nalpha)))\n",
    "    results.append(D_population)\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_half_speed.npy\", rabi_t)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed.npy\", pmt_counts_avg_thresholded)\n",
    "np.save(\"./data/save_rabi_t_shuttling_half_speed_simulation.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed_simulation.npy\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9582655f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/73/s1xpd2ts0712_vwy8r1jbjyc0000gn/T/ipykernel_14693/1008437697.py:8: RuntimeWarning: invalid value encountered in sqrt\n",
      "  errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 283x283 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rabi_t = np.load(\"./data/save_rabi_t_shuttling_half_speed.npy\")\n",
    "pmt_counts_avg_thresholded = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed.npy\")\n",
    "time = np.load(\"./data/save_rabi_t_shuttling_half_speed_simulation.npy\")\n",
    "results = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed_simulation.npy\")\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(2.83, 2.83))\n",
    "errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n",
    "plt.errorbar(rabi_t[1:], pmt_counts_avg_thresholded[1:], yerr=errorbar[1:], fmt='o', c='blue', lw=2, zorder=2, label='Data')\n",
    "# plt.scatter(rabi_t[:], pmt_counts_avg_thresholded[:], c='blue', s=30, zorder=2, label='Data')\n",
    "plt.plot(time, results[1], c='blue', lw=3, zorder=3, label=f'$\\\\bar{{n}}_{{α}} = {np.average(nalpha_values):.1f} \\\\pm {(np.average(nalpha_values) - nalpha_values[0]):.1f}$')\n",
    "plt.plot(time, results[0], '--', c='blue', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.plot(time, results[2], '--', c='blue', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.fill_between(time, results[0], results[2], color='blue', alpha=0.3)\n",
    "# plt.title('9-ion rabi oscillation after half speed shuttling', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time (µs)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-10, 170)\n",
    "# plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_half_speed_shuttling.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0d6098",
   "metadata": {},
   "source": [
    "### Coherent heating simulation - Static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f366af84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:571: RuntimeWarning: overflow encountered in multiply\n",
      "  - (n + alpha) * _ufuncs.eval_genlaguerre(n - 1, alpha, x)) / x\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  dy /= np.exp((log_dy.max() + log_dy.min()) / 2.)\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:193: RuntimeWarning: divide by zero encountered in divide\n",
      "  w = 1.0 / (fm * dy)\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading function\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "# # For 1 ms data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"01\"\n",
    "# number = 8666\n",
    "\n",
    "# # For shuttled data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"01\"\n",
    "# number = 8663\n",
    "\n",
    "# For shuttled data\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-15/\"\n",
    "index = \"23\"\n",
    "number = 8591\n",
    "\n",
    "# File loading\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = np.array(rabi_t)  # Convert list to numpy array if not already\n",
    "pmt_counts_avg_thresholded = np.array(pmt_counts_avg_thresholded)\n",
    "indices_to_remove = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -18, -22, 0]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices] - 5\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 0.95 - 0.08    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "rabi = 0.060\n",
    "n_ions = 9\n",
    "N = 500\n",
    "nbar = 6\n",
    "nalpha = 0\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "# You already have these lines in your code\n",
    "time = np.linspace(np.min(times), np.max(times), 200)\n",
    "\n",
    "# Running simulation for different nalpha values\n",
    "nbars = [nbar - 3, nbar, nbar + 3]\n",
    "results = []\n",
    "\n",
    "for nbar in nbars:\n",
    "    D_population, _ = pe_model_optimized(time, nbar_fit=nbar, ndisplace_fit=complex(0, np.sqrt(nalpha)))\n",
    "    results.append(D_population)\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_static.npy\", rabi_t)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_static.npy\", pmt_counts_avg_thresholded)\n",
    "np.save(\"./data/save_rabi_t_shuttling_static_simulation.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_static_simulation.npy\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21d67fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/73/s1xpd2ts0712_vwy8r1jbjyc0000gn/T/ipykernel_7488/1489434910.py:8: RuntimeWarning: invalid value encountered in sqrt\n",
      "  errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 283x283 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rabi_t = np.load(\"./data/save_rabi_t_shuttling_static.npy\")\n",
    "pmt_counts_avg_thresholded = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_static.npy\")\n",
    "time = np.load(\"./data/save_rabi_t_shuttling_static_simulation.npy\")\n",
    "results = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_static_simulation.npy\")\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(2.83, 2.83))\n",
    "errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n",
    "plt.errorbar(rabi_t[:], pmt_counts_avg_thresholded[:], yerr=errorbar[:], fmt='o', c='red', lw=2, zorder=2, label='Data')\n",
    "# plt.scatter(rabi_t[:], pmt_counts_avg_thresholded[:], c='red', s=30, zorder=2, label='Data')\n",
    "# plt.errorbar(rabi_t[:], pmt_counts_avg_thresholded[:], yerr=errorbar[:], fmt='o', c='C0', lw=1, zorder=2, label='Data')\n",
    "plt.plot(time, results[1], c='red', lw=3, zorder=3, label=f'$\\\\bar{{n}}_{{th}} = {np.average(nbars):.1f} \\\\pm {(np.average(nbars) - nbars[0]):.1f}$')\n",
    "plt.plot(time, results[0], '--', c='red', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.plot(time, results[2], '--', c='red', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.fill_between(time, results[0], results[2], color='red', alpha=0.3)\n",
    "# plt.title('9-ion rabi oscillation after half speed shuttling', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time (µs)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-10, 170)\n",
    "# plt.legend(loc='lower right')\n",
    "# plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_no_shuttling.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6333d946",
   "metadata": {},
   "source": [
    "### Coherent heating simulation - Full speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88d668e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "diag requires an array of at least two dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 59\u001b[0m\n\u001b[1;32m     54\u001b[0m laguerre(\u001b[38;5;241m0\u001b[39m)(eta_m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m*\u001b[39mlaguerre(\u001b[38;5;241m0\u001b[39m)(eta_n[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# plt.plot(np.abs(np.diagonal(inputstate)))\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# plt.plot(np.diagonal(coherent_dm(N, np.sqrt(60))*coherent_dm(N, np.sqrt(60)).dag()))\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m np\u001b[38;5;241m.\u001b[39msum(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiagonal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoherent_dm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcoherent_dm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdag\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     61\u001b[0m \u001b[38;5;66;03m# data = np.load(\"./data/pmt_counts_avg_thresholded_shuttling_half_speed_500us_delay.npy\")\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;66;03m# data = np.load(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_0us_delay.npy\")\u001b[39;00m\n\u001b[1;32m     63\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./data/pmt_counts_avg_thresholded_shuttling_full_speed.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/multiplexing/lib/python3.9/site-packages/numpy/_core/fromnumeric.py:1766\u001b[0m, in \u001b[0;36mdiagonal\u001b[0;34m(a, offset, axis1, axis2)\u001b[0m\n\u001b[1;32m   1764\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m asarray(a)\u001b[38;5;241m.\u001b[39mdiagonal(offset\u001b[38;5;241m=\u001b[39moffset, axis1\u001b[38;5;241m=\u001b[39maxis1, axis2\u001b[38;5;241m=\u001b[39maxis2)\n\u001b[1;32m   1765\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1766\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43masanyarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiagonal\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis2\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mValueError\u001b[0m: diag requires an array of at least two dimensions"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq,root,brentq,newton,curve_fit\n",
    "from scipy.special import laguerre\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "\n",
    "N = 250\n",
    "M = 100\n",
    "nbar_N = 5\n",
    "nbar_M = 0\n",
    "eta_m = 0.23 * np.array([0.00423397, -0.05020945,  0.2195255, -0.49393711, 0.64077417, -0.49393711,  0.2195255, -0.05020945, 0.00423397])\n",
    "eta_n = 0.23 * np.array([0.33333333] * 9)\n",
    "\n",
    "def coherent_heating(nalpha, nbeta):\n",
    "    # Assuming displace and tensor functions return QuTiP Qobj objects\n",
    "    return tensor(displace(N, np.sqrt(nalpha)), displace(M, np.sqrt(nbeta)))\n",
    "\n",
    "# Create thermal states for N and M\n",
    "thermal_state_N = thermal_dm(N, nbar_N)\n",
    "thermal_state_M = thermal_dm(M, nbar_M)\n",
    "\n",
    "# Create the input state by applying coherent heating before and after the thermal states\n",
    "inputstate = coherent_heating(65,50) * tensor(thermal_state_N, thermal_state_M) * coherent_heating(65,50).dag()\n",
    "\n",
    "# Calculate the population\n",
    "population = np.tensordot(np.abs(np.diagonal(ptrace(inputstate, 0).full())), np.abs(np.diagonal(ptrace(inputstate, 1).full())), axes=0)\n",
    "\n",
    "time = np.linspace(0, 13, 100)\n",
    "pe = np.zeros_like(time)\n",
    "\n",
    "for n in range(N):\n",
    "    for m in range(M):\n",
    "        # Calculate Laguerre polynomials for eta_m and eta_n\n",
    "        lag_m = laguerre(m)(eta_m**2)\n",
    "        lag_n = laguerre(n)(eta_n**2)\n",
    "        \n",
    "        # Transpose or reshape the Laguerre polynomial arrays\n",
    "        lag_m = lag_m.T  # Transpose or reshape as needed\n",
    "        lag_n = lag_n.T  # Transpose or reshape as needed\n",
    "        \n",
    "        # Calculate the contribution to pe for the current n and m\n",
    "        contribution = np.abs(population[n, m]) * np.sin(lag_m * lag_n * time[:, np.newaxis])**2\n",
    "        \n",
    "        # Accumulate the contribution\n",
    "        pe += contribution.sum(axis=1)\n",
    "\n",
    "# Divide by 9 outside of the loops\n",
    "pe /= 9\n",
    "\n",
    "laguerre(0)(eta_m[0]**2)*laguerre(0)(eta_n[0]**2)\n",
    "\n",
    "# plt.plot(np.abs(np.diagonal(inputstate)))\n",
    "# plt.plot(np.diagonal(coherent_dm(N, np.sqrt(60))*coherent_dm(N, np.sqrt(60)).dag()))\n",
    "\n",
    "np.sum(np.diagonal(coherent_dm(N, np.sqrt(60))*coherent_dm(N, np.sqrt(60)).dag()))\n",
    "\n",
    "# data = np.load(\"./data/pmt_counts_avg_thresholded_shuttling_half_speed_500us_delay.npy\")\n",
    "# data = np.load(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_0us_delay.npy\")\n",
    "data = np.load(\"./data/pmt_counts_avg_thresholded_shuttling_full_speed.npy\")\n",
    "time_data = np.linspace(0,12,50)\n",
    "\n",
    "# plt.plot(time_data[:40],(1-(data[:40]-350)/50-0.25)/1.2,\"bo\")\n",
    "# plt.plot(time[:80],pe[:80],\"r\")\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_1.npy\", time_data)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_1.npy\", data)\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_simulation_1.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_1.npy\", pe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d354da9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq,root,brentq,newton,curve_fit\n",
    "from scipy.special import laguerre\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "\n",
    "N = 250\n",
    "M = 100\n",
    "nbar_N = 5\n",
    "nbar_M = 0\n",
    "eta_m = 0.23 * np.array([0.00423397, -0.05020945,  0.2195255, -0.49393711, 0.64077417, -0.49393711,  0.2195255, -0.05020945, 0.00423397])\n",
    "eta_n = 0.23 * np.array([0.33333333] * 9)\n",
    "\n",
    "def coherent_heating(nalpha, nbeta):\n",
    "    # Assuming displace and tensor functions return QuTiP Qobj objects\n",
    "    return tensor(displace(N, np.sqrt(nalpha)), displace(M, np.sqrt(nbeta)))\n",
    "\n",
    "# Create thermal states for N and M\n",
    "thermal_state_N = thermal_dm(N, nbar_N)\n",
    "thermal_state_M = thermal_dm(M, nbar_M)\n",
    "\n",
    "# Create the input state by applying coherent heating before and after the thermal states\n",
    "inputstate = coherent_heating(115,0) * tensor(thermal_state_N, thermal_state_M) * coherent_heating(115,0).dag()\n",
    "\n",
    "# Calculate the population\n",
    "population = np.tensordot(np.abs(np.diagonal(ptrace(inputstate, 0).full())), np.abs(np.diagonal(ptrace(inputstate, 1).full())), axes=0)\n",
    "\n",
    "time = np.linspace(0, 13, 100)\n",
    "pe_old = np.zeros_like(time)\n",
    "\n",
    "for n in range(N):\n",
    "    for m in range(M):\n",
    "        # Calculate Laguerre polynomials for eta_m and eta_n\n",
    "        lag_m = laguerre(m)(eta_m**2)\n",
    "        lag_n = laguerre(n)(eta_n**2)\n",
    "        \n",
    "        # Transpose or reshape the Laguerre polynomial arrays\n",
    "        lag_m = lag_m.T  # Transpose or reshape as needed\n",
    "        lag_n = lag_n.T  # Transpose or reshape as needed\n",
    "        \n",
    "        # Calculate the contribution to pe for the current n and m\n",
    "        contribution = np.abs(population[n, m]) * np.sin(lag_m * lag_n * time[:, np.newaxis])**2\n",
    "        \n",
    "        # Accumulate the contribution\n",
    "        pe_old += contribution.sum(axis=1)\n",
    "\n",
    "# Divide by 9 outside of the loops\n",
    "pe_old /= 9\n",
    "\n",
    "laguerre(0)(eta_m[0]**2)*laguerre(0)(eta_n[0]**2)\n",
    "\n",
    "# plt.plot(np.abs(np.diagonal(inputstate)))\n",
    "# plt.plot(np.diagonal(coherent_dm(N, np.sqrt(60))*coherent_dm(N, np.sqrt(60)).dag()))\n",
    "\n",
    "np.sum(np.diagonal(coherent_dm(N, np.sqrt(60))*coherent_dm(N, np.sqrt(60)).dag()))\n",
    "\n",
    "# data = np.load(\"./data/pmt_counts_avg_thresholded_shuttling_half_speed_500us_delay.npy\")\n",
    "# data = np.load(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_0us_delay.npy\")\n",
    "data = np.load(\"./data/pmt_counts_avg_thresholded_shuttling_full_speed.npy\")\n",
    "time_data = np.linspace(0,12,50)\n",
    "\n",
    "# plt.plot(time_data[:40],(1-(data[:40]-350)/50-0.25)/1.2,\"bo\")\n",
    "# plt.plot(time[:80],pe[:80],\"r\")\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_2.npy\", time_data)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_2.npy\", data)\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_simulation_2.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_2.npy\", pe_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "611662f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d_/n_1tswj10tx48jnbz62dz3_00000gn/T/ipykernel_3855/2797961526.py:18: RuntimeWarning: invalid value encountered in sqrt\n",
      "  errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 283x283 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_data = np.load(\"./data/save_rabi_t_shuttling_full_speed_2.npy\")\n",
    "data = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_2.npy\")\n",
    "time = np.load(\"./data/save_rabi_t_shuttling_full_speed_simulation_2.npy\")\n",
    "pe = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_1.npy\")\n",
    "pe_old = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_2.npy\")\n",
    "\n",
    "# Plotting\n",
    "rabi_t = time_data\n",
    "pmt_counts_avg_thresholded = (1-(data[:]-350)/50-0.25)/1.2\n",
    "\n",
    "indices_to_remove = [6, 7, -19]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "plt.figure(figsize=(2.83, 2.83))\n",
    "errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n",
    "plt.errorbar(rabi_t[:40] * 21.0, pmt_counts_avg_thresholded[:40], yerr=errorbar[:40], fmt='o', c='green', lw=2, zorder=2, label='Data')\n",
    "# plt.plot(rabi_t[:40] * 21.0, pmt_counts_avg_thresholded[:40])\n",
    "# plt.scatter(rabi_t[:], pmt_counts_avg_thresholded[:], c='red', s=30, zorder=2, label='Data')\n",
    "# plt.errorbar(rabi_t[:], pmt_counts_avg_thresholded[:], yerr=errorbar[:], fmt='o', c='C0', lw=1, zorder=2, label='Data')\n",
    "# plt.plot(time[:80] * 21.0, pe[:80], c='green', lw=3, zorder=3,\n",
    "#          label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {65:.1f}$,\\n$\\\\bar{{n}}_{{\\\\beta}} = {50:.1f}$')\n",
    "# plt.plot(time[:80] * 21.0, pe_old[:80], c='red', lw=3, zorder=3,\n",
    "#          label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {115:.1f}$,\\n$\\\\bar{{n}}_{{\\\\beta}} = {0:.1f}$')\n",
    "plt.plot(time[:80] * 21.0, pe[:80], c='green', lw=3, zorder=3,\n",
    "         label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {65:.1f}$, $\\\\bar{{n}}_{{\\\\beta}} = {50:.1f}$')\n",
    "plt.plot(time[:80] * 21.0, pe_old[:80], \"--\", c='green', lw=3, alpha = 0.7, zorder=3,\n",
    "         label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {115:.1f}$, $\\\\bar{{n}}_{{\\\\beta}} = {0:.1f}$')\n",
    "# plt.plot(time, results[0], '--', c='red', lw=0.5, zorder=1, alpha=0.1)\n",
    "# plt.plot(time, results[2], '--', c='red', lw=0.5, zorder=1, alpha=0.1)\n",
    "# plt.fill_between(time, results[0], results[2], color='red', alpha=0.3)\n",
    "# plt.title('9-ion rabi oscillation after half speed shuttling', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time (µs)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-10, 200)\n",
    "# plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_no_shuttling_full_speed_shuttling.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3dec6afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:571: RuntimeWarning: overflow encountered in multiply\n",
      "  - (n + alpha) * _ufuncs.eval_genlaguerre(n - 1, alpha, x)) / x\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  dy /= np.exp((log_dy.max() + log_dy.min()) / 2.)\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:193: RuntimeWarning: divide by zero encountered in divide\n",
      "  w = 1.0 / (fm * dy)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 425x212.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = rabi_t[:]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[:]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 0.9    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "plt.figure(figsize=(4.25, 4.25 / 2))\n",
    "rabi = 0.06\n",
    "n_ions = 9\n",
    "N = 500\n",
    "nbar = 10\n",
    "nalpha = 50\n",
    "time = np.linspace(np.min(times), np.max(times), 50)\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "D_population_1, state_populations_1 = pe_model_optimized(time)\n",
    "state_populations_1 = state_populations_1[:-int(N/2)]\n",
    "plt.plot(range(len(state_populations_1)), state_populations_1, color='C0', lw = 3, label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {nalpha:.1f}$, $\\\\bar{{n}}_{{th}} = {nbar:.1f}$')\n",
    "\n",
    "rabi = 0.06\n",
    "n_ions = 9\n",
    "N = 500\n",
    "nbar = 0\n",
    "nalpha = 50\n",
    "time = np.linspace(np.min(times), np.max(times), 50)\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "D_population_2, state_populations_2 = pe_model_optimized(time)\n",
    "state_populations_2 = state_populations_2[:-int(N/2)]\n",
    "plt.plot(range(len(state_populations_2)), state_populations_2, color='C1', lw = 3, label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {nalpha:.1f}$, $\\\\bar{{n}}_{{th}} = {nbar:.1f}$')\n",
    "\n",
    "rabi = 0.06\n",
    "n_ions = 9\n",
    "N = 500\n",
    "nbar = 50\n",
    "nalpha = 0\n",
    "time = np.linspace(np.min(times), np.max(times), 50)\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "D_population_3, state_populations_3 = pe_model_optimized(time)\n",
    "state_populations_3 = state_populations_3[:-int(N/2)]\n",
    "plt.plot(range(len(state_populations_3)), state_populations_3, color='C2', lw = 3, label=f'$\\\\bar{{n}}_{{\\\\alpha}} = {nalpha:.1f}$, $\\\\bar{{n}}_{{th}} = {nbar:.1f}$')\n",
    "\n",
    "plt.title('Population distribution on the number states', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel('${n}^{{th}}$ state', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel('Population', fontdict={'family': 'STIXGeneral', 'size':12})\n",
    "plt.grid(axis='y')\n",
    "plt.legend()\n",
    "plt.savefig('Number_state_simulation.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# print(np.sum(state_populations))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49e096d",
   "metadata": {},
   "source": [
    "### Data processing (as npy file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3001ff72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n",
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8655\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_shuttling_full_speed\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_shuttling_full_speed\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8658\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_shuttling_half_speed\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_shuttling_half_speed\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8663\n",
    "file_path = data_path + date + f\"01/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_shuttling_half_speed_500us_delay\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_shuttling_half_speed_500us_delay\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8662\n",
    "file_path = data_path + date + f\"01/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_no_shuttling_half_speed_500us_delay\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_500us_delay\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8666\n",
    "file_path = data_path + date + f\"01/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_no_shuttling_half_speed_1000us_delay\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_1000us_delay\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/bingran.you/SynologyDrive/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "number = 8652\n",
    "file_path = data_path + date + f\"00/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_no_shuttling_half_speed_300us_delay\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_300us_delay\", pmt_counts_avg_thresholded)\n",
    "\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-15/\"\n",
    "number = 8591\n",
    "file_path = data_path + date + f\"23/00000{number}-RabiTimeScannThresholded_withOP.h5\"\n",
    "n_samples = 50\n",
    "# Examine the structure within 'datasets'\n",
    "with h5.File(file_path) as file:\n",
    "    datasets_keys = list(file['datasets'].keys())\n",
    "print(datasets_keys)\n",
    "# Load the 'pmt_counts_avg_thresholded' and 'rabi_t' datasets for plotting\n",
    "with h5.File(file_path, \"r\") as file:\n",
    "    rabi_t = file['datasets']['rabi_t'][:]\n",
    "    pmt_counts_avg_thresholded = file['datasets']['pmt_counts_avg_thresholded'][:]\n",
    "np.save(\"./data/rabi_t_no_shuttling_half_speed_0us_delay\", rabi_t)\n",
    "np.save(\"./data/pmt_counts_avg_thresholded_no_shuttling_half_speed_0us_delay\", pmt_counts_avg_thresholded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e497b02a",
   "metadata": {},
   "source": [
    "### Data fitting with shuttling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c2e5b5f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.36897350e-03 1.68448675e-03 8.42243375e-04 4.21121687e-04\n",
      " 2.10560844e-04 1.05280422e-04 5.26402109e-05 2.63201055e-05\n",
      " 1.31600527e-05 6.58002637e-06]\n",
      "0.006737946999085466\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3474b95d0>]"
      ]
     },
     "execution_count": 26,
     "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": [
    "'''\n",
    "import math\n",
    "\n",
    "def pn_cal(nth, alpha, n_index, N):\n",
    "    m_list = [i for i in range(n_index, N + 1)]\n",
    "    sum_list = [(nth**m / (nth + 1)**(m+1)) * np.exp(-alpha**2) * (alpha**(2 * (- n_index + m))) / math.factorial(- n_index + m) \\\n",
    "                for m in m_list]\n",
    "    return np.sum(sum_list)\n",
    "\n",
    "N = 100\n",
    "nth = 20\n",
    "nalpha = 20\n",
    "alpha = np.sqrt(nalpha)\n",
    "pn_list = np.zeros(N)\n",
    "for i in range(N):\n",
    "    pn_list[i] = pn_cal(nth, alpha, i, N)\n",
    "\n",
    "print(pn_list)\n",
    "print(np.sum(pn_list))\n",
    "'''\n",
    "\n",
    "'''\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy.special import logsumexp, gammaln\n",
    "\n",
    "def pn_cal(nth, alpha, n_index, N):\n",
    "    m_list = np.arange(n_index, N + 1, dtype=np.float64)  # Explicitly use float64\n",
    "    # Calculate the sum using logsumexp for numerical stability\n",
    "    sum_list = logsumexp(m_list * np.log(nth) - (m_list + 1) * np.log(nth + 1) - alpha**2 + 2 * (m_list - n_index) * np.log(alpha) - gammaln(m_list - n_index + 1))\n",
    "    return np.exp(sum_list)\n",
    "\n",
    "N = 10000\n",
    "nth = 20.0  # Use float64 implicitly\n",
    "nalpha = 2.0  # Use float64 implicitly\n",
    "alpha = np.sqrt(nalpha)\n",
    "pn_list = np.zeros(N, dtype=np.float64)  # Explicitly use float64 for the array as well\n",
    "for i in range(N):\n",
    "    pn_list[i] = pn_cal(nth, alpha, i, N)\n",
    "\n",
    "# Ensure you print only a few elements to avoid a massive output\n",
    "print(pn_list[:10])  # Print the first 10 elements as an example\n",
    "print(np.sum(pn_list))\n",
    "\n",
    "'''\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy.special import logsumexp, gammaln\n",
    "\n",
    "def pn_cal(nth, alpha, n_index, N):\n",
    "    m_list = np.arange(n_index, N + 1)\n",
    "    # if(n_index == 1):\n",
    "    #     print(m_list)\n",
    "    # Calculate the sum using logsumexp for numerical stability\n",
    "    sum_list = logsumexp(m_list * np.log(nth) - (m_list + 1) * np.log(nth + 1) - alpha**2 + 2 * (np.abs(m_list - n_index)) * np.log(alpha) - gammaln(m_list - n_index + 1))\n",
    "    # sum_list = logsumexp(m_list * np.log(nth) - (m_list + 1) * np.log(nth + 1) - alpha**2 + 2 * (np.abs(- m_list)) * np.log(alpha) - gammaln(m_list))\n",
    "    return np.exp(sum_list)\n",
    "\n",
    "N = 1000\n",
    "nth = 1\n",
    "nalpha = 10\n",
    "alpha = np.sqrt(nalpha)\n",
    "pn_list = np.zeros(N)\n",
    "for i in range(N):\n",
    "    pn_list[i] = pn_cal(nth, alpha, i, N)\n",
    "\n",
    "# Ensure you print only a few elements to avoid a massive output\n",
    "print(pn_list[:10])  # Print the first 10 elements as an example\n",
    "print(np.sum(pn_list))\n",
    "plt.plot(pn_list[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5dcb94",
   "metadata": {},
   "source": [
    "### Re-simulate for full speed shuttling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96c79a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:571: RuntimeWarning: overflow encountered in multiply\n",
      "  - (n + alpha) * _ufuncs.eval_genlaguerre(n - 1, alpha, x)) / x\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  dy /= np.exp((log_dy.max() + log_dy.min()) / 2.)\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:193: RuntimeWarning: divide by zero encountered in divide\n",
      "  w = 1.0 / (fm * dy)\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading function\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "# # For 1 ms data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"01\"\n",
    "# number = 8666\n",
    "\n",
    "# For shuttled data\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "index = \"00\"\n",
    "number = 8655\n",
    "\n",
    "# # For shuttled data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"00\"\n",
    "# number = 8658\n",
    "\n",
    "# File loading\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = np.array(rabi_t)  # Convert list to numpy array if not already\n",
    "pmt_counts_avg_thresholded = np.array(pmt_counts_avg_thresholded)\n",
    "indices_to_remove = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -18, -22]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 1.05 - 0.15    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "rabi = 0.065\n",
    "n_ions = 6\n",
    "N = 500\n",
    "nbar = 3\n",
    "nalpha = 60\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "# You already have these lines in your code\n",
    "time = np.linspace(np.min(times), np.max(times), 200)\n",
    "\n",
    "# Running simulation for different nalpha values\n",
    "nalpha_values = [40, 50, 60]\n",
    "results = []\n",
    "\n",
    "for nalpha in nalpha_values:\n",
    "    D_population, _ = pe_model_optimized(time, nbar_fit=nbar, ndisplace_fit=complex(0, np.sqrt(nalpha)))\n",
    "    results.append(D_population)\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_v2.npy\", rabi_t)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_v2.npy\", pmt_counts_avg_thresholded)\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_simulation_v2.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_v2.npy\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbaead75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d_/n_1tswj10tx48jnbz62dz3_00000gn/T/ipykernel_3855/4002770408.py:15: RuntimeWarning: invalid value encountered in sqrt\n",
      "  errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 283x283 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rabi_t = np.load(\"./data/save_rabi_t_shuttling_full_speed_v2.npy\")\n",
    "pmt_counts_avg_thresholded = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_v2.npy\")\n",
    "time = np.load(\"./data/save_rabi_t_shuttling_full_speed_simulation_v2.npy\")\n",
    "results = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_v2.npy\")\n",
    "\n",
    "indices_to_remove = [2, 6, -7]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Plotting\n",
    "factor = 1.0\n",
    "plt.figure(figsize=(2.83, 2.83))\n",
    "errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n",
    "plt.errorbar(rabi_t[1:] * factor, pmt_counts_avg_thresholded[1:], yerr=errorbar[1:], fmt='o', c='green', lw=2, zorder=2, label='Data')\n",
    "# plt.scatter(rabi_t[:], pmt_counts_avg_thresholded[:], c='blue', s=30, zorder=2, label='Data')\n",
    "plt.plot(time * 1, results[1], c='green', lw=3, zorder=3, label=f'$\\\\bar{{n}}_{{α}} = {np.average(nalpha_values):.1f} \\\\pm {(np.average(nalpha_values) - nalpha_values[0]):.1f}$')\n",
    "# plt.plot(time * 1, results[0], '--', c='green', lw=0.5, zorder=1, alpha=0.1)\n",
    "# plt.plot(time * 1, results[2], '--', c='green', lw=0.5, zorder=1, alpha=0.1)\n",
    "# plt.fill_between(time * 1, results[0], results[2], color='green', alpha=0.3)\n",
    "# plt.title('9-ion rabi oscillation after half speed shuttling', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time (µs)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-10, 160)\n",
    "# plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_full_speed_shuttling_v2.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4775dc41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:133: RuntimeWarning: invalid value encountered in multiply\n",
      "  np.poly1d.__init__(self, poly.coeffs * float(kn))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pmt_counts', 'pmt_counts_avg_thresholded', 'rabi_t']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:119: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  equiv_weights = [weights[k] / wfunc(roots[k]) for\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:571: RuntimeWarning: overflow encountered in multiply\n",
      "  - (n + alpha) * _ufuncs.eval_genlaguerre(n - 1, alpha, x)) / x\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:192: RuntimeWarning: invalid value encountered in divide\n",
      "  dy /= np.exp((log_dy.max() + log_dy.min()) / 2.)\n",
      "/Users/bingran.you/anaconda3/envs/physics/lib/python3.11/site-packages/scipy/special/_orthogonal.py:193: RuntimeWarning: divide by zero encountered in divide\n",
      "  w = 1.0 / (fm * dy)\n"
     ]
    }
   ],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading function\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "# # For 1 ms data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"01\"\n",
    "# number = 8666\n",
    "\n",
    "# For shuttled data\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "index = \"00\"\n",
    "number = 8655\n",
    "\n",
    "# # For shuttled data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"00\"\n",
    "# number = 8658\n",
    "\n",
    "# File loading\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = np.array(rabi_t)  # Convert list to numpy array if not already\n",
    "pmt_counts_avg_thresholded = np.array(pmt_counts_avg_thresholded)\n",
    "indices_to_remove = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -18, -22]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 1.05 - 0.15    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "rabi = 0.055\n",
    "n_ions = 6\n",
    "N = 500\n",
    "nbar = 3\n",
    "nalpha = 60\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "# You already have these lines in your code\n",
    "time = np.linspace(np.min(times), np.max(times), 200)\n",
    "\n",
    "# Running simulation for different nalpha values\n",
    "nalpha_values = [40, 50, 60]\n",
    "results = []\n",
    "\n",
    "for nalpha in nalpha_values:\n",
    "    D_population, _ = pe_model_optimized(time, nbar_fit=nbar, ndisplace_fit=complex(0, np.sqrt(nalpha)))\n",
    "    results.append(D_population)\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_v3.npy\", rabi_t)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_v3.npy\", pmt_counts_avg_thresholded)\n",
    "np.save(\"./data/save_rabi_t_shuttling_full_speed_simulation_v3.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_full_speed_simulation_v3.npy\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5509a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels:\n",
      " - defaults\n",
      "Platform: osx-arm64\n",
      "Collecting package metadata (repodata.json): failed\n",
      "\n",
      "# >>>>>>>>>>>>>>>>>>>>>> ERROR REPORT <<<<<<<<<<<<<<<<<<<<<<\n",
      "\n",
      "    Traceback (most recent call last):\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/exception_handler.py\", line 18, in __call__\n",
      "        return func(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/cli/main.py\", line 84, in main_subshell\n",
      "        exit_code = do_call(args, parser)\n",
      "                    ^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/cli/conda_argparse.py\", line 200, in do_call\n",
      "        result = getattr(module, func_name)(args, parser)\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/notices/core.py\", line 132, in wrapper\n",
      "        return func(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/cli/main_create.py\", line 137, in execute\n",
      "        return install(args, parser, \"create\")\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/cli/install.py\", line 377, in install\n",
      "        unlink_link_transaction = solver.solve_for_transaction(\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/core/solve.py\", line 153, in solve_for_transaction\n",
      "        unlink_precs, link_precs = self.solve_for_diff(\n",
      "                                   ^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/core/solve.py\", line 222, in solve_for_diff\n",
      "        final_precs = self.solve_final_state(\n",
      "                      ^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda_libmamba_solver/solver.py\", line 207, in solve_final_state\n",
      "        index = IndexHelper(\n",
      "                ^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda_libmamba_solver/index.py\", line 136, in __init__\n",
      "        self._index = self._load_channels()\n",
      "                      ^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda_libmamba_solver/index.py\", line 332, in _load_channels\n",
      "        jsons = {url: str(path) for (url, path) in executor.map(self._fetch_channel, urls)}\n",
      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/concurrent/futures/_base.py\", line 619, in result_iterator\n",
      "        yield _result_or_cancel(fs.pop())\n",
      "              ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/concurrent/futures/_base.py\", line 317, in _result_or_cancel\n",
      "        return fut.result(timeout)\n",
      "               ^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/concurrent/futures/_base.py\", line 456, in result\n",
      "        return self.__get_result()\n",
      "               ^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/concurrent/futures/_base.py\", line 401, in __get_result\n",
      "        raise self._exception\n",
      "      File \"/opt/anaconda3/lib/python3.12/concurrent/futures/thread.py\", line 58, in run\n",
      "        result = self.fn(*self.args, **self.kwargs)\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda_libmamba_solver/index.py\", line 254, in _fetch_channel\n",
      "        json_path, _ = subdir_data.repo_fetch.fetch_latest_path()\n",
      "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/gateways/repodata/__init__.py\", line 749, in fetch_latest_path\n",
      "        _, state = self.fetch_latest()\n",
      "                   ^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/gateways/repodata/__init__.py\", line 789, in fetch_latest\n",
      "        cache.load_state()\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/gateways/repodata/__init__.py\", line 585, in load_state\n",
      "        self.load(state_only=True)\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/gateways/repodata/__init__.py\", line 532, in load\n",
      "        with self.lock(\"r+\") as state_file:\n",
      "      File \"/opt/anaconda3/lib/python3.12/contextlib.py\", line 137, in __enter__\n",
      "        return next(self.gen)\n",
      "               ^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/site-packages/conda/gateways/repodata/__init__.py\", line 651, in lock\n",
      "        with self.cache_path_state.open(mode) as state_file, lock(state_file):\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/opt/anaconda3/lib/python3.12/pathlib.py\", line 1013, in open\n",
      "        return io.open(self, mode, buffering, encoding, errors, newline)\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "    PermissionError: [Errno 13] Permission denied: '/opt/anaconda3/pkgs/cache/9e99ffaf.info.json'\n",
      "\n",
      "`$ /opt/anaconda3/bin/conda create -n multiplexing python=3.12.4 anaconda`\n",
      "\n",
      "  environment variables:\n",
      "                 CIO_TEST=<not set>\n",
      "    CONDA_ALLOW_SOFTLINKS=false\n",
      "        CONDA_DEFAULT_ENV=base\n",
      "                CONDA_EXE=/opt/anaconda3/bin/conda\n",
      "             CONDA_PREFIX=/opt/anaconda3\n",
      "    CONDA_PROMPT_MODIFIER=(base)\n",
      "         CONDA_PYTHON_EXE=/opt/anaconda3/bin/python\n",
      "               CONDA_ROOT=/opt/anaconda3\n",
      "              CONDA_SHLVL=1\n",
      "           CURL_CA_BUNDLE=<not set>\n",
      "               LD_PRELOAD=<not set>\n",
      "                     PATH=/opt/anaconda3/bin:/opt/anaconda3/condabin:/usr/local/bin:/System/Cryp\n",
      "                          texes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/var/run/com.apple.sec\n",
      "                          urity.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple\n",
      "                          .security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.s\n",
      "                          ecurity.cryptexd/codex.system/bootstrap/usr/appleinternal/bin://Applic\n",
      "                          ations/Topaz Gigapixel\n",
      "                          AI.app/Contents/Resources/bin:/Library/TeX/texbin\n",
      "         PYTHONIOENCODING=utf-8\n",
      "         PYTHONUNBUFFERED=1\n",
      "    PYTHON_FROZEN_MODULES=on\n",
      "       REQUESTS_CA_BUNDLE=<not set>\n",
      "            SSL_CERT_FILE=<not set>\n",
      "   VSCODE_CODE_CACHE_PATH=/Users/QUBIT/Library/Application\n",
      "                          Support/Code/CachedData/f1a4fb101478ce6ec82fe9627c43efbf9e98c813\n",
      "\n",
      "     active environment : base\n",
      "    active env location : /opt/anaconda3\n",
      "            shell level : 1\n",
      "       user config file : /Users/QUBIT/.condarc\n",
      " populated config files : \n",
      "          conda version : 24.5.0\n",
      "    conda-build version : 24.5.1\n",
      "         python version : 3.12.4.final.0\n",
      "                 solver : libmamba (default)\n",
      "       virtual packages : __archspec=1=m1\n",
      "                          __conda=24.5.0=0\n",
      "                          __osx=15.1.1=0\n",
      "                          __unix=0=0\n",
      "       base environment : /opt/anaconda3  (read only)\n",
      "      conda av data dir : /opt/anaconda3/etc/conda\n",
      "  conda av metadata url : None\n",
      "           channel URLs : https://repo.anaconda.com/pkgs/main/osx-arm64\n",
      "                          https://repo.anaconda.com/pkgs/main/noarch\n",
      "                          https://repo.anaconda.com/pkgs/r/osx-arm64\n",
      "                          https://repo.anaconda.com/pkgs/r/noarch\n",
      "          package cache : /opt/anaconda3/pkgs\n",
      "                          /Users/QUBIT/.conda/pkgs\n",
      "       envs directories : /Users/QUBIT/.conda/envs\n",
      "                          /opt/anaconda3/envs\n",
      "               platform : osx-arm64\n",
      "             user-agent : conda/24.5.0 requests/2.32.2 CPython/3.12.4 Darwin/24.1.0 OSX/15.1.1 solver/libmamba conda-libmamba-solver/24.1.0 libmambapy/1.5.8 aau/0.4.4 c/. s/. e/.\n",
      "                UID:GID : 501:20\n",
      "             netrc file : None\n",
      "           offline mode : False\n",
      "\n",
      "\n",
      "An unexpected error has occurred. Conda has prepared the above report.\n",
      "If you suspect this error is being caused by a malfunctioning plugin,\n",
      "consider using the --no-plugins option to turn off plugins.\n",
      "\n",
      "Example: conda --no-plugins install <package>\n",
      "\n",
      "Alternatively, you can set the CONDA_NO_PLUGINS environment variable on\n",
      "the command line to run the command without plugins enabled.\n",
      "\n",
      "Example: CONDA_NO_PLUGINS=true conda install <package>\n",
      "\n",
      "If submitted, this report will be used by core maintainers to improve\n",
      "future releases of conda.\n",
      "Would you like conda to send this report to the core maintainers? [y/N]: \n",
      "Timeout reached. No report sent.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda create -n multiplexing python=3.12.4 anaconda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d00b6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae26b998",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "167c4186",
   "metadata": {},
   "source": [
    "# Data processing 20241205"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f99c6a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qutip import *\n",
    "from scipy.optimize import fsolve, leastsq, root, brentq, newton, curve_fit\n",
    "import math\n",
    "import cmath\n",
    "import timeit\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from time import time \n",
    "from pynverse import inversefunc\n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import matplotlib\n",
    "import h5py as h5\n",
    "import scipy as sc\n",
    "import scipy\n",
    "from scipy.integrate import dblquad, quad\n",
    "from scipy.special import laguerre\n",
    "from time import time\n",
    "\n",
    "# Update matplotlib settings for consistent appearance in plots\n",
    "matplotlib.rcParams.update(\n",
    "        {\"font.family\": \"STIXGeneral\",\n",
    "         \"xtick.labelsize\": 12,\n",
    "         \"xtick.direction\": \"in\",\n",
    "         \"xtick.major.pad\": 3,\n",
    "         \"xtick.top\": True,\n",
    "         \"ytick.labelsize\": 12,\n",
    "         \"ytick.direction\": \"in\",\n",
    "         \"ytick.right\": True,\n",
    "         \"axes.labelsize\": 12,\n",
    "         \"axes.labelpad\": 3,\n",
    "         \"axes.grid\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "# File loading function\n",
    "def file_loading(data_path, date, index, number, x='rabi_t', y='pmt_counts_avg_thresholded', exp='RabiTimeScannThresholded_withOP'):\n",
    "    file_path = data_path + date + index + f\"/00000{number}-\" + exp + \".h5\"\n",
    "    with h5.File(file_path) as file:\n",
    "        datasets_keys = list(file['datasets'].keys())\n",
    "    print(datasets_keys)\n",
    "    with h5.File(file_path, \"r\") as file:\n",
    "        x_data = file['datasets'][x][:]\n",
    "        y_data = file['datasets'][y][:]\n",
    "    return x_data, y_data, np.linspace(np.min(x_data), np.max(x_data), 300)\n",
    "n_samples = 50\n",
    "\n",
    "# For shuttled data\n",
    "data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "date = \"2024-03-16/\"\n",
    "index = \"01\"\n",
    "number = 8663\n",
    "\n",
    "# # For shuttled data\n",
    "# data_path = \"/Users/QUBIT/Library/CloudStorage/OneDrive-Personal/UCB STUDY/Research/Multiplexing/artiq_working_dir - 20240315/results/\"\n",
    "# date = \"2024-03-16/\"\n",
    "# index = \"00\"\n",
    "# number = 8658\n",
    "\n",
    "# File loading\n",
    "rabi_t, pmt_counts_avg_thresholded,times = file_loading(data_path, date, index, number)\n",
    "rabi_t = np.array(rabi_t)  # Convert list to numpy array if not already\n",
    "pmt_counts_avg_thresholded = np.array(pmt_counts_avg_thresholded)\n",
    "indices_to_remove = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -18, -22]\n",
    "keep_indices = np.ones(len(rabi_t), dtype=bool)  # Initially set all to keep\n",
    "keep_indices[indices_to_remove] = False  # Set indices to remove to False\n",
    "rabi_t = rabi_t[keep_indices]\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded[keep_indices]\n",
    "\n",
    "# Data re-scaling\n",
    "errorbar = (np.sqrt(pmt_counts_avg_thresholded) / np.sqrt(n_samples)) / np.max(pmt_counts_avg_thresholded)\n",
    "pmt_counts_avg_thresholded = np.max(pmt_counts_avg_thresholded) - pmt_counts_avg_thresholded\n",
    "pmt_counts_avg_thresholded = pmt_counts_avg_thresholded / ((np.max(pmt_counts_avg_thresholded))) / 1.05 - 0.15    # / 0.58 - 0.28\n",
    "\n",
    "# Running simulation\n",
    "rabi = 0.055\n",
    "n_ions = 7\n",
    "N = 500\n",
    "nbar = 6\n",
    "nalpha = 60\n",
    "def pe_model_optimized(time, rabi_0_fit = rabi, nbar_fit = nbar, ndisplace_fit = complex(0, np.sqrt(nalpha))):\n",
    "    intensity_distribution = np.array([0.96912696, 0.9844114, 0.99344862, 0.99840835, 1., 0.99840835, 0.99344862, 0.9844114, 0.96912696])\n",
    "    pe_fit = np.zeros_like(time)\n",
    "    diagonal_elements_all = np.zeros(N)\n",
    "    for k in range(len(intensity_distribution)):\n",
    "        if(k < 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += 2 * diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += 2 * diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "        elif(k == 4):\n",
    "            eta = 0.23/np.sqrt(n_ions)    # LD parameter\n",
    "            eta_squared_laguerre_values = [laguerre(i)(eta**2) for i in range(N)]\n",
    "            inputstate_fit = displace(N, ndisplace_fit) * thermal_dm(N, nbar_fit) * displace(N, ndisplace_fit).dag()\n",
    "            diagonal_elements = np.abs(inputstate_fit.full().diagonal())\n",
    "            diagonal_elements_all += diagonal_elements\n",
    "            for i in range(N):\n",
    "                pe_fit += diagonal_elements[i] * np.sin(intensity_distribution[k] * rabi_0_fit * eta_squared_laguerre_values[i] * time)**2\n",
    "    return pe_fit / 9, diagonal_elements_all / 9\n",
    "# You already have these lines in your code\n",
    "time = np.linspace(np.min(times), np.max(times), 200)\n",
    "\n",
    "# Running simulation for different nalpha values\n",
    "nalpha_values = [40, 50, 60]\n",
    "results = []\n",
    "\n",
    "for nalpha in nalpha_values:\n",
    "    D_population, _ = pe_model_optimized(time, nbar_fit=nbar, ndisplace_fit=complex(0, np.sqrt(nalpha)))\n",
    "    results.append(D_population)\n",
    "\n",
    "np.save(\"./data/save_rabi_t_shuttling_half_speed.npy\", rabi_t)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed.npy\", pmt_counts_avg_thresholded)\n",
    "np.save(\"./data/save_rabi_t_shuttling_half_speed_simulation.npy\", time)\n",
    "np.save(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed_simulation.npy\", results)\n",
    "\n",
    "rabi_t = np.load(\"./data/save_rabi_t_shuttling_half_speed.npy\")\n",
    "pmt_counts_avg_thresholded = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed.npy\")\n",
    "time = np.load(\"./data/save_rabi_t_shuttling_half_speed_simulation.npy\")\n",
    "results = np.load(\"./data/save_pmt_counts_avg_thresholded_shuttling_half_speed_simulation.npy\")\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(2.83, 2.83))\n",
    "errorbar = np.sqrt(pmt_counts_avg_thresholded * (1 - pmt_counts_avg_thresholded)) / np.sqrt(n_samples)\n",
    "plt.errorbar(rabi_t[1:], pmt_counts_avg_thresholded[1:], yerr=errorbar[1:], fmt='o', c='blue', lw=2, zorder=2, label='Data')\n",
    "# plt.scatter(rabi_t[:], pmt_counts_avg_thresholded[:], c='blue', s=30, zorder=2, label='Data')\n",
    "plt.plot(time, results[1], c='blue', lw=3, zorder=3, label=f'$\\\\bar{{n}}_{{α}} = {np.average(nalpha_values):.1f} \\\\pm {(np.average(nalpha_values) - nalpha_values[0]):.1f}$')\n",
    "plt.plot(time, results[0], '--', c='blue', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.plot(time, results[2], '--', c='blue', lw=0.5, zorder=1, alpha=0.1)\n",
    "plt.fill_between(time, results[0], results[2], color='blue', alpha=0.3)\n",
    "# plt.title('9-ion rabi oscillation after half speed shuttling', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.xlabel(r'Time (µs)', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylabel(r'Excitation', fontdict={'family': 'STIXGeneral', 'size': 12})\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.xlim(-10, 170)\n",
    "# plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('Rabi_9_ions_half_speed_shuttling.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f418e8ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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