diff --git a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb b/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb
deleted file mode 100644
index 0e9ce57..0000000
--- a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Imports and units\n",
-    "\n",
-    "import numpy as np\n",
-    "import mpmath as mp\n",
-    "from scipy.optimize import curve_fit\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "mm = 1e-3\n",
-    "um = 1e-6\n",
-    "nm = 1e-9\n",
-    "kb = 1.380649e-23\n",
-    "au = 1.66054e-27\n",
-    "m = 133 * au\n",
-    "uk = 1e-6\n",
-    "ms = 1e-3\n",
-    "us = 1e-6\n",
-    "\n",
-    "raw_pixel = 3.75 * um\n",
-    "mottolensdistance = 15+7\n",
-    "lenstocameradistance = 6\n",
-    "magnification = mottolensdistance/lenstocameradistance\n",
-    "pixel = magnification * raw_pixel\n",
-    "binpixel = pixel * 4 \n",
-    "\n",
-    "wavelength = 852 * nm\n",
-    "sigma_0 = 3*wavelength**2/(2*np.pi)\n",
-    "\n",
-    "formationrun =  '02182021_H11M46S31MS785_Formation_Time'\n",
-    "expansionrun = '02182021_H11M44S44MS263_Expansion_Time'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import formation run file \n",
-    "filename = formationrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y'  #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n",
-    "\n",
-    "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n",
-    "#path = r'//?/S:/flir_images/binaries/'\n",
-    "file = np.load(path+filename+'.npz')\n",
-    "\n",
-    "index = file['index']\n",
-    "variable = file['variable']\n",
-    "transmission = file['transmission']\n",
-    "amp = file['amp']\n",
-    "amperror = file['amperror']\n",
-    "sigmax = file['sigmax']\n",
-    "sigmaxerror = file['sigmaxerror']\n",
-    "sigmay = file['sigmay']\n",
-    "sigmayerror = file['sigmayerror']\n",
-    "\n",
-    "# Fix units\n",
-    "variable = variable * ms\n",
-    "sigmax = sigmax * binpixel\n",
-    "sigmaxerror = sigmaxerror * binpixel\n",
-    "sigmay = sigmay * binpixel\n",
-    "sigmayerror = sigmayerror * binpixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Given Amp, Sigma_x, Sigma_y, returns the exact atom number\n",
-    "def atomnumber(A, sigma_x, sigma_y): \n",
-    "    N = 2*np.pi*mp.polylog(2,A) / (sigma_0 / np.sqrt(sigma_x**2 * sigma_y**2))\n",
-    "    return N\n",
-    "\n",
-    "# Given Amp, Sigma_x, Sigma_y, returns the approximate atom number\n",
-    "def atomnumber_taylor(A, sigma_x, sigma_y):  \n",
-    "    N_taylor = (2 * A * np.pi * sigma_x * sigma_y) / sigma_0\n",
-    "    return N_taylor\n",
-    "\n",
-    "# Given N (obtained form A, sigma_x, sigma_y fit data) vs time from a formation time scan, calculates atom number. R, tau are fit parameters\n",
-    "def atomnumbervstime(t, R, tau):       \n",
-    "    N = R*tau * (1 - np.exp(-t/tau))\n",
-    "    return N\n",
-    "\n",
-    "# Given mot size (sigma) vs time from an expansion time scan, calculates temperature\n",
-    "def tempfind(time,temp):\n",
-    "    sigma_t = np.sqrt(sigmax[0]**2+(kb*temp/m)*time**2)  \n",
-    "    return sigma_t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading rate is : 48132834580.92892 \n",
-      "Error in loading rate fit is : 3768782596544480.0\n",
-      "Time constant is : 375.74641624027197 \n",
-      "Error in time constant fit is : 29861310.716279566\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Given N (obtained form A, sigma_x, sigma_y fit data) from an expansion run, calculates atom number. R, tau are fit parameters\n",
-    "# This fit requires a formation time scan!\n",
-    "\n",
-    "N_fit_data = atomnumber_taylor(amp,sigmax,sigmay)\n",
-    "t_fit_data = variable\n",
-    "\n",
-    "for i in range(len(N_fit_data)): #This changes any nan values to the mean of the surrounding values\n",
-    "    if np.isnan(N_fit_data[i]) == True:\n",
-    "        print(\"Data point \",i,\" failed. Averaging nearest neighbors.\")\n",
-    "        N_fit_data[i] = (N_fit_data[i-1]+N_fit_data[i+1])*0.5\n",
-    "    else:\n",
-    "        pass\n",
-    "\n",
-    "plt.xlabel('Time (s)')\n",
-    "plt.ylabel('Atom number')\n",
-    "plt.title('Atom Number vs Time')\n",
-    "plt.plot(t_fit_data, N_fit_data, label='Raw') # y axis should be 1k-10million, x axis should be 1-100's ms\n",
-    "\n",
-    "params, covariance = curve_fit(atomnumbervstime, t_fit_data, N_fit_data, p0=[1e5,1])\n",
-    "R_fit = params[0]\n",
-    "tau_fit = params[1]\n",
-    "\n",
-    "print('Loading rate is :', R_fit,'\\nError in loading rate fit is :', np.sqrt(np.diagonal(covariance)[0]))\n",
-    "print('Time constant is :', tau_fit,'\\nError in time constant fit is :', np.sqrt(np.diagonal(covariance)[1]))\n",
-    "plt.plot(t_fit_data,atomnumbervstime(t_fit_data,params[0],params[1]), label='Fitted')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import expansion run file \n",
-    "filename = expansionrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y'  #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n",
-    "\n",
-    "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n",
-    "#path = r'//?/S:/flir_images/binaries/'\n",
-    "file = np.load(path+filename+'.npz')\n",
-    "\n",
-    "index = file['index']\n",
-    "variable = file['variable']\n",
-    "transmission = file['transmission']\n",
-    "amp = file['amp']\n",
-    "amperror = file['amperror']\n",
-    "sigmax = file['sigmax']\n",
-    "sigmaxerror = file['sigmaxerror']\n",
-    "sigmay = file['sigmay']\n",
-    "sigmayerror = file['sigmayerror']\n",
-    "\n",
-    "# Fix units\n",
-    "variable = variable * us\n",
-    "sigmax = sigmax * binpixel\n",
-    "sigmaxerror = sigmaxerror * binpixel\n",
-    "sigmay = sigmay * binpixel\n",
-    "sigmayerror = sigmayerror * binpixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 126.71946357  259.12053485  381.35724204           nan    0.\n",
-      "    0.                   nan    0.            0.         2942.84597866\n",
-      "    0.        ]\n",
-      "[ 0.  5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(variable,sigmay)\n",
-    "print(sigmax/um)\n",
-    "print(variable/ms)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data point  3  failed. Averaging nearest neighbors.\n",
-      "Data point  6  failed. Averaging nearest neighbors.\n",
-      "Temperature (uK) is : 3.849164489145072 \n",
-      "Error in Temperature fit is : 4.670158067060396\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\dpean\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in sqrt\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Given mot size (sigma) vs time from an expansion time scan, calculates temperature\n",
-    "# Requires an expansion time scan!\n",
-    "\n",
-    "sigma_magn_fit_data = np.sqrt(sigmax**2 + sigmay**2)\n",
-    "t_fit_data = variable\n",
-    "\n",
-    "for i in range(len(sigma_magn_fit_data)): #This changes any nan values to the mean of the surrounding values\n",
-    "    if np.isnan(sigma_magn_fit_data[i]) == True:\n",
-    "        print(\"Data point \",i,\" failed. Averaging nearest neighbors.\")\n",
-    "        sigma_magn_fit_data[i] = (sigma_magn_fit_data[i-1]+sigma_magn_fit_data[i+1])*0.5\n",
-    "    else:\n",
-    "        pass\n",
-    "\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('MOT size (um)')\n",
-    "plt.title('MOT size vs Time')\n",
-    "plt.plot(t_fit_data, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n",
-    "\n",
-    "params, covariance = curve_fit(tempfind, t_fit_data, sigma_magn_fit_data, p0=[200 * uk]);\n",
-    "T_fit = params[0]\n",
-    "\n",
-    "print('Temperature (uK) is :', T_fit/uk,'\\nError in Temperature fit is :', np.sqrt(np.diagonal(covariance)[0])/uk)\n",
-    "plt.scatter(t_fit_data,tempfind(t_fit_data,params[0])/um, label='Fitted')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/PostProcessing.ipynb b/PostProcessing.ipynb
deleted file mode 100644
index 0e9ce57..0000000
--- a/PostProcessing.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Imports and units\n",
-    "\n",
-    "import numpy as np\n",
-    "import mpmath as mp\n",
-    "from scipy.optimize import curve_fit\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "mm = 1e-3\n",
-    "um = 1e-6\n",
-    "nm = 1e-9\n",
-    "kb = 1.380649e-23\n",
-    "au = 1.66054e-27\n",
-    "m = 133 * au\n",
-    "uk = 1e-6\n",
-    "ms = 1e-3\n",
-    "us = 1e-6\n",
-    "\n",
-    "raw_pixel = 3.75 * um\n",
-    "mottolensdistance = 15+7\n",
-    "lenstocameradistance = 6\n",
-    "magnification = mottolensdistance/lenstocameradistance\n",
-    "pixel = magnification * raw_pixel\n",
-    "binpixel = pixel * 4 \n",
-    "\n",
-    "wavelength = 852 * nm\n",
-    "sigma_0 = 3*wavelength**2/(2*np.pi)\n",
-    "\n",
-    "formationrun =  '02182021_H11M46S31MS785_Formation_Time'\n",
-    "expansionrun = '02182021_H11M44S44MS263_Expansion_Time'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import formation run file \n",
-    "filename = formationrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y'  #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n",
-    "\n",
-    "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n",
-    "#path = r'//?/S:/flir_images/binaries/'\n",
-    "file = np.load(path+filename+'.npz')\n",
-    "\n",
-    "index = file['index']\n",
-    "variable = file['variable']\n",
-    "transmission = file['transmission']\n",
-    "amp = file['amp']\n",
-    "amperror = file['amperror']\n",
-    "sigmax = file['sigmax']\n",
-    "sigmaxerror = file['sigmaxerror']\n",
-    "sigmay = file['sigmay']\n",
-    "sigmayerror = file['sigmayerror']\n",
-    "\n",
-    "# Fix units\n",
-    "variable = variable * ms\n",
-    "sigmax = sigmax * binpixel\n",
-    "sigmaxerror = sigmaxerror * binpixel\n",
-    "sigmay = sigmay * binpixel\n",
-    "sigmayerror = sigmayerror * binpixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Given Amp, Sigma_x, Sigma_y, returns the exact atom number\n",
-    "def atomnumber(A, sigma_x, sigma_y): \n",
-    "    N = 2*np.pi*mp.polylog(2,A) / (sigma_0 / np.sqrt(sigma_x**2 * sigma_y**2))\n",
-    "    return N\n",
-    "\n",
-    "# Given Amp, Sigma_x, Sigma_y, returns the approximate atom number\n",
-    "def atomnumber_taylor(A, sigma_x, sigma_y):  \n",
-    "    N_taylor = (2 * A * np.pi * sigma_x * sigma_y) / sigma_0\n",
-    "    return N_taylor\n",
-    "\n",
-    "# Given N (obtained form A, sigma_x, sigma_y fit data) vs time from a formation time scan, calculates atom number. R, tau are fit parameters\n",
-    "def atomnumbervstime(t, R, tau):       \n",
-    "    N = R*tau * (1 - np.exp(-t/tau))\n",
-    "    return N\n",
-    "\n",
-    "# Given mot size (sigma) vs time from an expansion time scan, calculates temperature\n",
-    "def tempfind(time,temp):\n",
-    "    sigma_t = np.sqrt(sigmax[0]**2+(kb*temp/m)*time**2)  \n",
-    "    return sigma_t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading rate is : 48132834580.92892 \n",
-      "Error in loading rate fit is : 3768782596544480.0\n",
-      "Time constant is : 375.74641624027197 \n",
-      "Error in time constant fit is : 29861310.716279566\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deXxcdbn48c+TpEmaSbpmQqH7DFQoO5SCoLJeKMiiokgBWxbpRUFQrwveq+jlcu9VVFx+gohSoAiUVUFkkassCkKbshcodKaUhhYy6T7TJmmS5/fHOSedTrNM2zlzZnner1demXPmzJnnZJnnfJfzHFFVjDHGlK+KoAMwxhgTLEsExhhT5iwRGGNMmbNEYIwxZc4SgTHGlDlLBMYYU+YsERizi0TkKRH5YtBx7CwRmSAiSRGpDDoWEwxLBCZn3A/EtSJSk7H+VhG5Jo9xqIi8JiIVaeuuEZFb8xVDIRGRG90P+qSIdIrIlrTlR1X1PVWtV9XuoGM1wbBEYHJCRCYBHwcUOD3QYBx7AGcHHcSOEEfO/ydV9RL3g74e+B/gbm9ZVU/O9fuZ4mOJwOTKLOB54FZgtrdSROYA5wLfcs9A/+Su38dtQawTkcUicnraa24VkRtE5FH3Nc+KyBgR+bnb4nhLRA4eJJ5rgf8UkarMJ0TkGBFpyVj3roic4D7+gYjcKyK/F5GNbutiioh8R0RaRWSFiJyYsduoiCwQkfUi8qCIjErb9xEi8px7rK+IyDFpzz0lIv8tIs8Cm4BIRlxXish9Get+ISK/dB+fLyJxN85lInLuID+X7YjIJLcVVZUW0zVuzEkR+ZOIjBaRO0Rkg4gsdBO/9/q9ReQJEVkjIktE5KwdjcEEyxKByZVZwB3u10kishuAqt7krrvWPQM9TUSGAH8C/gI0AV8B7hCRj6Tt7yzgu0Aj0AH8E3jRXb4PuG6QeB4ANgDn7+TxnAbcDowEXgIex/l/GQtcDfwmY/tZwIU4LZEuwPugHgv8GbgGGAV8A7hfRMJpr/0CMAdoAJZn7Pcu4BQRGeburxLnZ3OniITc9zlZVRuAI4GXd/J4M53txjUWiOL8/G9xj+FN4PtuPCHgCeBOnN/lTOAGEdk3R3GYPCjKRCAic90zs9ez2PYTIvKiiHSJyGf7eH6YiLwvIr/yJ9rSJyIfAyYC96jqIiAGnDPAS44A6oEfqmqnqv4NeBjnQ8TzB1VdpKrtwB+AdlWd5/Zj3w0M1iJQ4HvAVZljFln6u6o+rqpdwL1A2I13CzAfmCQiI9K2v11VX1fVlPu+Z7kf2ucBj6jqI6rao6pPAM3AKWmvvVVVF6tql7v/rQehuhwnAX7KXXUcsElVn3eXe4D9RGSoqq5S1cU7cax9uUVVY6q6HngUiKnq/6X9PLyf/6nAu6p6ixv/i8D9wHb/a6ZwFWUiwOl+mJHltu/hnBXe2c/z/wU8veshlbXZwF9Utc1dvpO07qE+7AGsUNWetHXLcc4+PR+mPd7cx3L9YEGp6iM4v/85g23bh8z3a0sbTN3sfk+PYUXa4+XAEJzWy0Tgc2630DoRWQd8DNi9n9f25U62Jslz3GXcpPN54BJglYj8WUT2zubgspDtz38icHjG8Z0LjMlRHCYPtus/LQaq+kx6HyWAiESB63HO3DYBF6vqW6r6rvt8T8ZuEJFDgd2Ax4Bp/kZdmkRkKE5XRaWIfOCurgFGiMiBqvoKztl5upXAeBGpSEsGE4C3fQjxuzhn8OknAimgLu0YKnH+bnbF+LTHE4AtQBvOh/ztqnrxAK8drATwvcBPRWQc8Gngo70vVH0ceNz9PVwD/BZn0D5fVgBPq+q/5PE9TY4Va4ugLzcBX1HVQ3H6YW8YaGN3dsZPgW/mIbZS9imgG5gKHOR+7QP8HaffHJyzyfRB0BdwPoy/JSJD3MHT03A+sHNKVZ8CXmPbFsrbQK2IfNIdr/guTvLaFeeJyFQRqcMZQ7jPbUH8HjhNRE4SkUoRqXUHq8ftwDEkgKdw+uiXqeqbACKym4ic7vbTdwBJnN9FPj0MTBGRL7i/yyEicpiI7JPnOMwuKIlEICL1OANl94rIyzgDebsP/Cq+jNN3O1iz3AxsNk5/8nuq+oH3BfwKONediXIzMNXtOvijqnbiTDE9Gees+QZglqq+5VOM38UZ5ATA7ff+MvA74H2cpNTS90uzdjtOl+UHQC1wufteK4AzgH8HEjhn0N9kx//37gROYNuWTQXwbzgtrDXA0TjHlTequhE4EWdweSXO8f+IXU+sJo+kWG9M43YNPayq+7kzKpaoar8f/uJcTPSwqt7nLt+B04TuwenvrAZuUNUrfQ7dGGMKSkm0CFR1A7BMRD4HvRfmHDjIa85V1QmqOgmnK2meJQFjTDkqykQgInfhzGv+iIi0iMhFODMVLhKRV4DFOM1x3P7KFuBzwG9EJFfT64wxpiQUbdeQMcaY3CjKFoExxpjc8e06AhGZi3PVYauq7tfH8wL8AucKy03A+e5ViQNqbGzUSZMm5ThaY4wpbYsWLWpT1T6vl/HzgrJbcaYQzuvn+ZOBvdyvw4Ffu98HNGnSJJqbm3MUojHGlAcRyaxj1cu3riFVfQZnbnN/zsCZqaNu3ZQRIjLY3H9jjDE5FuQYwVi2rbHSwra1ZnqJyBwRaRaR5kQikZfgjDGmXASZCKSPdX1OYVLVm1R1mqpOC4d3tSSMMcaYdEEmgha2LdQ1DucSdWOMMXkUZCJ4CJjlXgV8BLBeVVcFGI8xxpQlP6eP3gUcAzS6V/Z+H6dGO6p6I/AIztTRpTjTRy/wKxZjjDH98y0RqOrMQZ5X4FK/3t8YY0x27MpiY4zJUsvaTTzxxoeDb1hkLBEYY0yWfvtMnEt+v4j2Lfm+/4+/LBEYY0yWliaSdPcoy1dvCjqUnLJEYIwxWYonUu73ZMCR5JYlAmOMyUKqo4tV69sBiFkiMMaY8rOsLdX72GsZlApLBMYYkwWvFdDUUGMtAmOMKUexRIoKgeP2biKeSFFKd3e0RGCMMVmIJ5KMH1XH3mMa2NjRRSLZEXRIOWOJwBhjshBLpIg0hoiE653l1tIZJ7BEYIwxg+jpUZa1JYmG64k2OYkg3lY64wSWCIwxZhAr12+mfUsPkXA9uw+rpXZIhbUIjDGmnHjTRaPhEBUVQqSx3loExhhTTrwrib3xgUg4VFLXElgiMMaYQcQSKRpqq2isrwYgGq5nxdpNJVN8zhKBMcYMIu4OFIs4t1qPhEOoUjLF5ywRGGPMIGKtKSLhUO9y1JtCWiJXGFsiMMaYASQ7uvhgQ3vvhz/A5EYnKZRKFVJLBMYYM4BlaTOGPKGaKnYfXkusRAaMLREYY8wAvGmi6S0Cb9laBMYYUwa8YnMTRtdts96bQloKxecsERhjzABibrG5mqrKbdZHGkNO8bmNxV98zhKBMcYMIJ5IbdctBPTWHCqFcQJLBMYY0w+v2FykMbTdc5ESmkJqicAYY/rhFZvzzv7T7T6slqFDKkui1IQlAmOM6Yf3Id9Xi6CiQpjcGCqJ4nOWCIwxph+xjGJzmaJN9dY1ZIwxpSyeSDEsrdhcpkhjiJa1m4u++JwlAmOM6UcskSSSVmwuk1d87t3VxT1OYInAGGP60d/UUY/3XLEPGFsiMMaYPnjF5tKrjmbynou1Fvc4gSUCY4zpw9Zic/23COqqq9hjeC3xNmsR9EtEZojIEhFZKiJX9vH8BBF5UkReEpFXReQUP+MxxphsbS0213+LAJwZRcVefM63RCAilcD1wMnAVGCmiEzN2Oy7wD2qejBwNnCDX/EYY8yOiLUm+yw2lykaDhEr8uJzfrYIpgNLVTWuqp3AfOCMjG0UGOY+Hg6s9DEeY4zJWqwtxYQ+is1lioTrSRZ58Tk/E8FYYEXacou7Lt0PgPNEpAV4BPhKXzsSkTki0iwizYlEwo9YjTFmG7HWZL8XkqXzBoyXFnH3kJ+JoK+Jt5ltp5nArao6DjgFuF1EtotJVW9S1WmqOi0cDvsQqjHGbNXTo7y7OjXo+ACUxhRSPxNBCzA+bXkc23f9XATcA6Cq/wRqgUYfYzLGmEF5xeayaRGMKYHic34mgoXAXiIyWUSqcQaDH8rY5j3geAAR2QcnEVjfjzEmULEBis1lqqgQIuFQUdcc8i0RqGoXcBnwOPAmzuygxSJytYic7m72b8DFIvIKcBdwvhbz0LsxpiR400H7Kj/dl0i4vqirkFb5uXNVfQRnEDh93VVpj98AjvIzBmOM2VGxRJJhtVWMDvVdbC5TpDHEw6+upH1LN7VDBp5lVIjsymJjjMkQT6SINvVfbC5TtKm+qIvPWSIwxpgMsUSSSGN23UKwdSwh1mqJwBhjil6yo4sPN3QQbRp8oNjjXUtQrKUmLBEYY0yaZb0zhrJvERR78TlLBMYYk8abBprNxWTpivm2lZYIjDEmTTyRpLJCBi02lynSGCJepMXnLBEYY0yaWCLF+JFDBy02l8krPtdahMXnLBEYY0yaWCI54M1o+uO9phi7hywRGGOMq6dHWdaWGvD2lP3ZOnOo+AaMLREYY4zr/XWb6ejq2akWwZhhtdRVV1qLwBhjipk3/TObqqOZKiqEye6AcbGxRGCMMa5Yq3M2vzNdQ+CME1iLwBhjili8LcnwoUOyLjaXKRIO8f66zbRv6c5xZP6yRGCMMa5YqzNQnG2xuUyRsFN8blmRXWFsicAYY1zxtp2bOuqJFunMIUsExhjD1mJzOzs+ADC5sTiLz1kiMMYYtn5470ixuUx11VWMHTG06AaMLREYYwxbu3P23IHy032JhENFV4XUEoExxuCUhqisECaM2sVE0Bgi1posquJzlgiMMQanRTBhVB3VVbv2sRhtqifV2V1UxecsERhjDN7tKXetNQBbxxiKaZzAEoExpux5xeaiTTs/UOzxbnEZK6IppJYIjDFlzys2l4sWgVd8rpimkFoiMMaUPa8bZ2eKzWUSESLhkLUIjDGmmHhTR3f0PsX9iTTWW4vAGGOKSSzhFJsbtZPF5jIVW/G5AROBiFSIyFn5CsYYY4IQT6SI7kKxuUzRIis+N2AiUNUe4LI8xWKMMYGItyVzMj7gKbbbVmbTNfSEiHxDRMaLyCjvy/fIjDEmDza2b9nlYnOZiu1agqostrnQ/X5p2joFIrkPxxhj8svrvtmV8tOZhlZXMnbE0KIZMB40Eajq5HwEYowxQfDO2nM1Y8hTTFNIB+0aEpE6EfmuiNzkLu8lIqf6H5oxxvgvnkjlpNhcpmjYmUJaDMXnshkjuAXoBI50l1uAa7LZuYjMEJElIrJURK7sZ5uzROQNEVksIndmFbUxxuRILJHMSbG5TJFwqGiKz2Vz5FFVvRbYAqCqm4FB51iJSCVwPXAyMBWYKSJTM7bZC/gOcJSq7gt8dcfCN8aYXeNNHc01b8wh1lr44wTZJIJOERmKM0CMiESBbFLcdGCpqsZVtROYD5yRsc3FwPWquhZAVVuzjtwYY3ZRt1tsLpdTRz3eLKRYEVxLkE0i+D7wGDBeRO4A/gp8K4vXjQVWpC23uOvSTQGmiMizIvK8iMzoa0ciMkdEmkWkOZFIZPHWxhgzuJU5LDaXySs+VwwtgmxmDT0hIi8CR+B0CV2hqm1Z7Luv7qPMUZMqYC/gGGAc8HcR2U9V12XEcBNwE8C0adMKf+TFGFMUemcM5aD8dCav+Fwx3LYy29GRo4HjgWOBj2f5mhZgfNryOGBlH9s8qKpbVHUZsAQnMRhjjO+86Z1+tAic/dYXRYsgm+mjNwCXAK8BrwP/KiLXZ7HvhcBeIjJZRKqBs4GHMrb5I05yQUQacbqK4tmHb4wxOy+eSDKiLnfF5jJFw/WsXF/4xeeyubL4aGA/dSfDishtOElhQKraJSKXAY8DlcBcVV0sIlcDzar6kPvciSLyBtANfFNVV+/ksRhjzA6JJ1JEGnNXbC5TJBzqLT63z+7DfHmPXMgmESwBJgDL3eXxwKvZ7FxVHwEeyVh3VdpjBb7ufhljTF7FEkk+MSXs2/57p5AmksWZCETkTziDu8OBN0Vkgbt8OPBcfsIzxhh/bGzfQuvGjpzWGMo0ubE4qpAO1CL4Sd6iMMaYPPM+nHNZdTSTV3yu0KuQ9psIVPXp9GURGTbQ9sYYU0zibV6xOf9aBOAkmkJvEWQza2iOiHyIMy7QDCxyvxtjTNGKtXrF5up8fZ9iKD6XzRn+N4F9s7yIzBhjikK8LclEH4rNZYq6xec+3NDBmOG1vr7XzsrmJxADNvkdiDHG5FM8kfJ1fMDj1TEq5JvUZNMi+A7wnIi8QFqxOVW93LeojDHGR909Srwt5evUUU/6FNIj92z0/f12RjaJ4DfA33AuIuvxNxxjjPHfynWb6ezq8aX8dKbdhtUQqq4s6LuVZZMIulTVLvgyxpSMpW43jR/lpzM5xefqC3oKaTZjBE+6M4d2F5FR3pfvkRljjE+86Zx+Tx31FPoU0mxaBOe437+Ttk6BSO7DMcYY/8V8LjaXKdJYz0OvrGRzZzdDqyvz8p47Ipv7EUzORyDGGJMv8UTSt9LTfYk2bS0+N3WPwqs5NGgiEJFZfa1X1Xm5D8cYY/wXT6Q4Og8zhjyRRncKaVuyOBMBcFja41qcG9S8CFgiMMYUHa/YXD4Gij2TG0OIOFczF6Jsuoa+kr4sIsOB232LyBhjfLR1oDh/XUNDqyvZY/jQ3vpGhWZnrq3ehN1O0hhTpGJ5nDqaLhIOFewU0mzGCLz7EoCTOKYC9/gZlDHG+CWeSFFVIUwc7W+xuUzRcD33Nq9AVX27I9rOymaMIP2+BF3AclVt8SkeY4zxVbwtyYRRdQyp9LfYXKZCLj6XzRjB04NtY4wxxSLWmp9ic5nSaw4VWiLI5n4EnxGRd0RkvYhsEJGNIrIhH8EZY0wudfcoy1an8nZFcbpCrkKaTdfQtcBpqvqm38EYY4yf3l/rFJsLokVQyMXnsukk+9CSgDGmFMTydHvKvhRy8blsWgTNInI38Ee2vR/BA75FZYwxPoi1BjN11BMJh2h+d20g7z2QbBLBMJxrB05MW6eAJQJjTFGJt6XyWmwuUzRcz4MvF17xuWxmDV2Qj0CMMcZv8UQykG4hjzc2UWjF5/I7kdYYYwIUS6TyWnU0U/oU0kJiicAYUxY2tG8hsbGDaFNwLQKv+Fyh3aTGEoExpix4H75Btghqh1QydsTQgmsRZFNraAQwC5iUvr2qXu5fWMYYk1vehVxBtgjAmbFUaFVIs5k19AjwPPAa0ONvOMYY4w+v2NyEUfktNpcp0hii+d01BVV8LptEUKuqX/c9EmOM8VEsEUyxuUzRpno2dXbzwYZ2dh8+NNBYPNn8RG4XkYtFZHcRGeV9+R6ZMcbkUDyRCuxCsnRRd4yikAaMs0kEncCPgX8Ci9yv5mx2LiIzRGSJiCwVkSsH2O6zIqIiMi2b/RpjzI7YWmwuuIFijzdGUUgDxtl0DX0d2FNV23ZkxyJSCVwP/AvQAiwUkYdU9Y2M7RqAy4EXdmT/xhiTLa/YXJAXk3maGpzic8XWIliMU2JiR00HlqpqXFU7gfnAGX1s9184FU7bd+I9jDFmUFtvTxl8i6AQi89l0yLoBl4WkSfZtujcYNNHxwIr0pZbgMPTNxCRg4HxqvqwiHyjvx2JyBxgDsCECROyCNkYY7YK6j7F/YmGQywsoOJz2SSCP7pfO6qveVHa+6RIBfAz4PzBdqSqNwE3AUybNk0H2dwYY7YRb0sxMsBic5ki4Xr+WEDF57IpOnebiFQDU9xVS1R1Sxb7bgHGpy2PA1amLTcA+wFPuXNpxwAPicjpqprVYLQxxmQj1posmNYAbK05FG9Lsu8ewwOOJrtbVR4DvIMz8HsD8LaIfCKLfS8E9hKRyW4iORt4yHtSVderaqOqTlLVSTgXrVkSMMbkXLytMGYMebyxikIZMM6ma+inwImqugRARKYAdwGHDvQiVe0SkcuAx4FKYK6qLhaRq4FmVX1ooNcbY0wueMXmCqlF4BWfK5QB42wSwRAvCQCo6tsiMiSbnavqIzglKtLXXdXPtsdks09jjNkR3ll3IUwd9XjF54qpRdAsIjcDt7vL5+JcVGaMMQUvXkBTR9MVUvG5bK4j+BLOtQSXA1cAbwD/6mdQxhiTK7FEsiCKzWWKhkPEEylUg58ImU2L4BJVvQ64zlshIlcAv/AtKmOMyZF4IsWE0cEXm8sUCRdO8blsEsFstv/QP7+PdQbo6VFWbWgnnkgST6RY1pbi3dUpRgwdwuTGeiaHQ0QaQ0xqDFFfk82P3xizK2KJJJHGwhkf8HizmGKtqcJNBCIyEzgHmCwi6TN8hgGr/Q6s0G1s38KythTxRIp4IknMfbysLUn7lq23bQhVVzJxdIh3Pkzy4CsrSW8F7jashsmNISY31hNpDBEJh5jcGGJ8AZTKNaYUdPco77Zt4ti9m4IOZTvp1xJ8bK/GQGMZ6JT0OWAV0IgzhdSzEXjFz6AKRVd3Dy1rNxNvc87uY+6HfrwtRWJjb7UNKgTGj6oj0hjiyOhoIuEQkcZ6IuEQTQ01vTefaN/SzburUyxLpIi3pdxEkuSx11exdtPWa/Qq3f7MyY1O62GymyAijfXsNqymYG5mYUyha1m7ic7uHqIF2CJoaqihvqaqIGYO9ZsIVHU5sBz4aPp6ETkK+CVwqb+h5c+aVGdvV068beuH/fLVKbZ0bz2FH1k3hEi4nmOmhN0unnqi4RATRtdRUzX4ZeK1QyrZe8ww9h4zbLvn1qY6WdabJJK9rY3nYm3btDDqqivdVsTWJBFprGdSY4jhQ7Oa1WtM2ei9T3GBzRgCr/hcqCCuJciqk1pEDsLpJjoLWAY84GdQfujo6ua91Zucs3r3DN/7wF+XdjY+pFKYONr5kD1hn92IhENE3Q/bkT7WKRkZqmZkqJpDJozcZn1Pj/LBhvbebievJfFqy3oeeW0VPWldTY311VuTRLieyY0hDh4/gqZhtb7FbUwh8z5kC+kagnSRxsIoPjfQGMEUnLIQM3HGBO4GRFWPzVNsOfWbp+Nc98TbvctNDTVEwiFO2X93Io0homGnK2fsiKFUFVD/fEWFsMeIoewxYuh2/YgdXd2sWLOptyWzzB2c/ttbCe5pbgGgvqaK+XOOYL+xwdczMSbfYgmn2JyfJ3G7IuoWn9vU2UVddXCTRwZ657eAvwOnqepSABH5Wl6i8sFJ+45h4ui63jPmhtri70apqapkz6YG9mxq2O65De1beOfDjXzlzpc4/5aFPPClI5kwurDmURvjt3giWbCtAdhaFntZWyrQ4nMDnfqeCXwAPCkivxWR4+m7tHRR+MiYBs44aCwHjBtREklgMMNqh3DoxFHMu2g6XT09zJr7Am3JjsFfaEwJiSVSBTk+4Ik2uVNIAx4w7jcRqOofVPXzwN7AU8DXgN1E5NcicmKe4jO7aM+mBm6efRgfbGjnglsWkuzoCjokY/Ji/eYttCU7CrpFMGm0U3wuHvCA8aCd4aqaUtU7VPVUnHsKvAz0eyN6U3gOnTiSG849hDdWbeCS2xfR2dUz+IuMKXLxArsrWV8KpfjcDo2KquoaVf2Nqh7nV0DGH8ftvRv/+5n9+cfSNr5x7yv09ARf38QYPxXy1NF00QK4f7HVOCgjZ00bT1uyg2sfW0JjfQ3fO3UfuzjNlKx4W2EWm8sUCYdYsGwNPT1KRUUw/4+WCMrMl46O0rqhg7nPLqNpWA2XHB0NOiRjfBFrLcxic5mi4Xo2b3GKz+0xIpiaQ5YIyoyIcNWpU2lLdvDDR98iXF/DmYeOCzosY3Iu3lbYU0c96betDCoRFHaqNL6oqBB+etaBHLXnaL51/6s8+VZr0CEZk1NesblCHx8A2DOt+FxQLBGUqZqqSm4871D2HtPAl+94kZfeC/4yd2NypZCLzWUKu8XnYq2WCEwAGmqHcOsF0wk31HDhrQsDn7lgTK703qe4qfBbBF7xuXhbcFNILRGUuXBDDfMunE5lhTDr5gV8uKE96JCM2WXeSU0h3pCmL9FwvbUITLAmNYa49YLprNvUyey5C1i/ecvgLzKmgMUSKUa5FX2LQaQxxMr17WzqDObKf0sEBoD9xg7nN1+YRiyR5OJ5zbRv6Q46JGN2mnN7ysLvFvJEm9wB44CuMLZEYHp9bK9GfnrWQSxYtoavzn+Zbrv62BSpeCJVFFNHPb1TSAMaJ7BEYLZx+oF7cNWpU3ls8Qd878HXUbVkYIqLV2yuGKaOeoIuPmcXlJntXPixybRu7ODGp2M0NdTw1ROmBB2SMVkrhmJzmWqHVDJu5NDAylFbIjB9+vaMj5DY2MHP/+8dwg01nHv4xKBDMiYrvVNHi6hFAM4MJ2sRmIIiIvzwzP1Zk+rge398ndGhGmbsNybosIwZVCzhFJsbX+DF5jJFw/WBFZ+zMQLTryGVFVx/7iEcMG4El89/iRfiq4MOyZhBxRMpJhZBsblMkXCot/hcvhXXT8rkXV11FbecfxjjRw7li/OaeeuDDUGHZMyAYolkUY0PeLxZTkFMIbVEYAY1MlTNvIsOp666ktlzF9CydlPQIRnTp+4eZfnq4ig2l8kb0wii1IslApOVsSOGctuF09nU2c2suQtYk+oMOiRjttNbbK4IWwRe8bkgBox9TQQiMkNElojIUhHZ7j7HIvJ1EXlDRF4Vkb+KiE1NKWB7jxnGzbMPo2XtZi68dWFgl8Mb0x/vbLrYZgyBM0EjGg4FMoXUt0QgIpXA9cDJwFRgpohMzdjsJWCaqh4A3Adc61c8JjemTx7F/5t5MK+2rOPSO15kS3dP0CEZ06v3PsVFUmwuUyQczBRSP1sE04GlqhpX1U5gPnBG+gaq+qSqeh3OzwN2q6wicNK+Y7jmU/vz5JIEV97/ml19bApGLJEsqmJzmaLhYIrP+ZkIxgIr0pZb3HX9uQh4tK8nRGSOiDSLSHMikchhiGZnnXP4BL52whTuf7GFHz22JOhwjAGcqqPF2C3kiQQ0c8jPRNDXFRF9njqKyHnANODHfT2vqjep6jRVnRYOh3MYotkVlx+/J+cePoEbn44x9x/Lgg7HGOKJZNF2C0HaFNI8F5/z88riFkYAFlgAABF5SURBVGB82vI4YGXmRiJyAvAfwNGq2uFjPCbHRISrz9iP1clOrn74DRobajj9wD2CDsuUKafYXGdRTh31TBxdhwh5v0mN+NW/KyJVwNvA8cD7wELgHFVdnLbNwTiDxDNU9Z1s9jtt2jRtbm72IWKfpNrg7vNg7btBR+IbVVi7qZPO7h5G1lVTU2Wzkk3+dXb3sCbVyYi6IdRWVQYdzk5LJDsYUlnBiKFDtn/y+O/DQTN3ar8iskhVp/X1nG8tAlXtEpHLgMeBSmCuqi4WkauBZlV9CKcrqB64V0QA3lPV0/2KKe+6OuGeWfD+i3DA50BK8wNSgPquHv76ViupTV0ct3cTo4p0sM4Ur5a2FC9sWMMnJ+9ObW3xllF74+0Emzu7mbFXH7W9Rozffl0O+PrTUtVHgEcy1l2V9vgEP98/UKrw6Ddh+bPwmd85iaCEVQOHbGjnMzc8xw9j3dx3yZFMKqI7RJnid99jb/HbWJzPnjkDiqzOULqn//QGdy14jxNPPSlvxeeK96dV6Bb+DhbdCh/7WsknAc9uw2qZd9F0unuUWXMX0Lox/8WzTPmKJZJMGFV8xeYyRZvyX3yuuH9ihSr+NDz6bZgyA477XtDR5FU0XM8tF0wnsbGDC25ZyMb2LUGHZMpEPJEqymJzmbxZT/msOWSJINfWxOHe2TB6T/jMb6GieAetdtZB40dww3mHsOSDjVzy+0V0dHUHHZIpcV3dPSxfvakoawxlija59y/O47UElghyqX0D3DXTGR+YeRfUDgs6osAc+5EmfnTmATy7dDX/ds8r9PTY1cfGPy1rN9PZ3VPUU0c94foaGmqq8toiKN6h9ULT0w0PzIG2d+ALD8DoaNARBe7MQ8fRluzgfx99i8b6Gr5/2lTc2WHG5FS8zSs2V/wtAhEhEg7ltUVgiSBX/nYNvP0onPxjiBwTdDQFY84nIrRu7ODmfyyjaVgNXz5mz6BDMiUo1lqc9ynuTzRczz/zeEdA6xrKhdfug39cB4fMhukXBx1NQRER/uOUfTjjoD249rEl3NO8YvAXGbOD4m1JRoeqGVFXGtevRMIhVuWx+Jwlgl31/ovw4KUw4Ug45SdgXR/bqagQfvzZA/n4Xo1854HX+OubHwYdkikxsdZUSYwPePJ920pLBLti4wcw/1wINcHnb4eq0jgb8UN1VQW/Pu9Qpu4+jEvvfJFFy9cGHZIpIfG24i42l8mbBpuvAWNLBDtrS7uTBNrXwcw7IdQYdEQFr76milsuOIwxw2q56LaFLG3dGHRIpgSs3+QUm/OmXZYCr/ictQgKmSr86Qp4vxk+fSOM2T/oiIpGY30N8y48nKqKCmbdvIBV6zcHHZIpcjF3xlAptQhqh1QyfmSdtQgK2j9/Ba/Oh2O+A1PPGHx7s40Jo+u49YLD2NDexey5C1i/ya4+NjvPO2uONpVOIgDyOoXUEsGOeucJeOIqJwF84ltBR1O09hs7nJu+cCjvtm3ii/MW0r7Frj42OyeWSDKkUhg/cmjQoeRUNFzPsrZUXi7GtESwIxJvw30XQtO+8KlfQ4X9+HbFkXs2ct3nD6R5+Vouv+slurp7gg7JFKF4IsnE0SGqirzYXKZI2Ck+tyoPxedK6yfnp81r4a6zobLaGRyuLp2BqSCdesAe/OC0ffnLGx/yvQdfx68bJZnSFUukiJRgyfOtU0j9HyewRJCN7i649wJY9x58/vcwYkLQEZWU2UdO4tJjo9y1YAU/+7+sblRnDOAVmyuNqqOZvOsi8nHbSisxkY0nroL4k3DaL2HiR4OOpiR948SPkNjYwS//+g7hhhq+cMTEoEMyRaBl7Wa2dGvJlJZI5xWfy8eN7C0RDObF2+H56+HwS+DQ2UFHU7JEhP/59P6sTnZy1YOv0xiq5uT9dw86LFPgvOmVpdgiEBEiTfV5mUJqXUMDee95ePhrThG5E/876GhKXlVlBb865xAOHj+CK+a/zPN5LLplilPv1NESbBEARBvzM4XUEkF/1q2Au8+D4ePgs7dApTWe8mFodSVzzz+MCaPruPi2Zt5ctSHokEwBiyVKq9hcpmhTPavWt5Pq8Lf4nCWCvnRugvnnOGUkZs6HulFBR1RWRtRVM+/C6dTXVjF77gJWrNkUdEimQDm3pyzN1gDQOxtqmc/jBJYIMqnCg1+GD16Dz94MTXsHHVFZ2mPEUG67cDrtW7qZPXcBq5MdQYdkClC8LVkSN6Ppj3e1tN/jBJYIMj3zE1j8BzjhBzDlpKCjKWtTdmtg7vmH8f66zVx4W3PearOb4uAVmyvlFsHE0XVUiHOthJ8sEaR780/w5DWw/1lw1BVBR2OAaZNG8atzDuG1lnV86fcvssWuPjauWAndnrI/NVWVjBtZ5/tFZZYIPB8uhgf+FfY4BE7/pd1gpoD8y9Td+J9P78/Tbyf49n2v5qX2iil83oVWpTh1NF00HPK9RWBTYQBSq53yETUNcPadMKS0ileVgrOnTyCxsYOfPvE24YYavnPKPkGHZAIWb0uVZLG5TBH3/sU9PUpFhT8nqJYIujrhnlmw8UO44FEYZhcxFarLjtuTRLKD3zwTJ9xQwxc/Hgk6JBOgWGtpFpvLFA3X076lh1Ub2hk7wp+kZ4ngsW/D8n/Ap2+CcYcGHY0ZgIjw/dP2pS3ZwTV/fpPG+ho+dfDYoMMyAYm3lWaxuUzpNYf8SgSlnUoHs/B30DzXGRg+8PNBR2OyUFkhXHfWQRwRGcU37n2FZ95OBB2SCYBXbK7UbkbTFy8R+DlgXL6JYNkz8Oi3Ya+T4PjvBx2N2QG1Qyq5adY09tqtgUt+v4hXW9YFHZLJsxVusblyaBGE62toqK3ydcC4PBPBmmVwz2wYFYUzfwcVlUFHZHbQsNoh3HbBYYwKVXPBLQt9v/LSFBbv7LgcWgQiQiRcT7zNWgS507HRKR+hPTDzLqgdFnREZic1Datl3oXTUWDW3Bdo3ej/nZxMYfCutI2W0A3rBxL1+f7FviYCEZkhIktEZKmIXNnH8zUicrf7/AsiMsnPeOjpgQfmQGIJfO5WGB319e2M/yLhem45/zBWJzs5f+5CNrZvCTokkwfxRIrRoWqG1w0JOpS8iIb9LT7nWyIQkUrgeuBkYCowU0SmZmx2EbBWVfcEfgb8yK94AHjyv2HJIzDjfyF6rK9vZfLnwPEj+PV5h/L2hxuZM28RHV3dQYdkfBZPpEr6iuJMXpltv7pA/Zw+Oh1YqqpxABGZD5wBvJG2zRnAD9zH9wG/EhFRP25c+/r98PefwCGzYPqcnO/eBOvoKWF+8rkD+erdL3Psj58iVGMzo0vZu6tTnHnIuKDDyBvv6ulYIsl+Y4fnfP9+/reMBVakLbcAh/e3jap2ich6YDTQlr6RiMwB5gBMmLCT9wsOheEjn4RTfmrlI0rUpw4ei6I88caHQYdifDZlTANnTy+fe4dPHF3H8Xs3+XbfBT8TQV+ftpln+tlsg6reBNwEMG3atJ1rLUz+hPNlStqnDx7Hpw8unzNFUx5qqiq5+fzDfNu/n4PFLcD4tOVxwMr+thGRKmA4sMbHmIwxxmTwMxEsBPYSkckiUg2cDTyUsc1DgHdH+M8Cf/NlfMAYY0y/fOsacvv8LwMeByqBuaq6WESuBppV9SHgZuB2EVmK0xI42694jDHG9M3XqRWq+gjwSMa6q9IetwOf8zMGY4wxAyu/K4uNMcZswxKBMcaUOUsExhhT5iwRGGNMmZNim60pIglg+U6+vJGMq5bLgB1zebBjLg+7cswTVTXc1xNFlwh2hYg0q+q0oOPIJzvm8mDHXB78OmbrGjLGmDJnicAYY8pcuSWCm4IOIAB2zOXBjrk8+HLMZTVGYIwxZnvl1iIwxhiTwRKBMcaUubJJBCIyQ0SWiMhSEbky6Hh2lIjMFZFWEXk9bd0oEXlCRN5xv49014uI/NI91ldF5JC018x2t39HRGanrT9URF5zX/NLkWBv4yYi40XkSRF5U0QWi8gV7vpSPuZaEVkgIq+4x/yf7vrJIvKCG//dbll3RKTGXV7qPj8pbV/fcdcvEZGT0tYX5P+BiFSKyEsi8rC7XNLHLCLvun97L4tIs7suuL9tVS35L5wy2DEgAlQDrwBTg45rB4/hE8AhwOtp664FrnQfXwn8yH18CvAozh3gjgBecNePAuLu95Hu45HucwuAj7qveRQ4OeDj3R04xH3cALwNTC3xYxag3n08BHjBPZZ7gLPd9TcCX3Iffxm40X18NnC3+3iq+zdeA0x2//YrC/n/APg6cCfwsLtc0scMvAs0ZqwL7G+7XFoE04GlqhpX1U5gPnBGwDHtEFV9hu3v3nYGcJv7+DbgU2nr56njeWCEiOwOnAQ8oaprVHUt8AQww31umKr+U52/onlp+wqEqq5S1RfdxxuBN3HucV3Kx6yqmnQXh7hfChwH3Oeuzzxm72dxH3C8e+Z3BjBfVTtUdRmwFOd/oCD/D0RkHPBJ4HfuslDix9yPwP62yyURjAVWpC23uOuK3W6qugqcD06gyV3f3/EOtL6lj/UFwW3+H4xzhlzSx+x2kbwMtOL8Y8eAdara5W6SHmfvsbnPrwdGs+M/i6D9HPgW0OMuj6b0j1mBv4jIIhGZ464L7G/b1xvTFJC++sdKed5sf8e7o+sDJyL1wP3AV1V1wwBdnSVxzKraDRwkIiOAPwD79LWZ+31Hj62vE79Aj1lETgVaVXWRiBzjre5j05I5ZtdRqrpSRJqAJ0TkrQG29f1vu1xaBC3A+LTlccDKgGLJpQ/dZiDu91Z3fX/HO9D6cX2sD5SIDMFJAneo6gPu6pI+Zo+qrgOewukTHiEi3klbepy9x+Y+Pxyn+3BHfxZBOgo4XUTexem2OQ6nhVDKx4yqrnS/t+Ik/OkE+bcd9KBJPr5wWj5xnEEkb8Bo36Dj2onjmMS2g8U/ZtvBpWvdx59k28GlBbp1cGkZzsDSSPfxKPe5he623uDSKQEfq+D0bf48Y30pH3MYGOE+Hgr8HTgVuJdtB06/7D6+lG0HTu9xH+/LtgOncZxB04L+PwCOYetgcckeMxACGtIePwfMCPJvO/Bffh5/+KfgzDyJAf8RdDw7Ef9dwCpgC07Gvwinb/SvwDvud++PQIDr3WN9DZiWtp8LcQbSlgIXpK2fBrzuvuZXuFedB3i8H8Npzr4KvOx+nVLix3wA8JJ7zK8DV7nrIzizQJa6H5A17vpad3mp+3wkbV//4R7XEtJmjBTy/wHbJoKSPWb32F5xvxZ7MQX5t20lJowxpsyVyxiBMcaYflgiMMaYMmeJwBhjypwlAmOMKXOWCIwxpsxZIjBlS0RGu9UfXxaRD0Tk/bTl53x6z4NF5HcDPB8Wkcf8eG9j+lMuJSaM2Y6qrgYOAhCRHwBJVf2Jz2/778A1A8SUEJFVInKUqj7rcyzGANYiMKZPIpJ0vx8jIk+LyD0i8raI/FBEzhXnvgGviUjU3S4sIveLyEL366g+9tkAHKCqr7jLR6e1QF5ynwf4I3Bung7VGEsExmThQOAKYH/gC8AUVZ2OUzb5K+42vwB+pqqHAWe6z2Xyrvb0fAO4VFUPAj4ObHbXN7vLxuSFdQ0ZM7iF6pYHFpEY8Bd3/WvAse7jE4CpadVRh4lIgzr3UvDsDiTSlp8FrhORO4AHVNUrHdwK7JH7wzCmb5YIjBlcR9rjnrTlHrb+D1UAH1XVzfRvM06tHABU9Yci8mecWjjPi8gJqvqWu81A+zEmp6xryJjc+AtwmbcgIgf1sc2bwJ5p20RV9TVV/RFOd9De7lNT2LYLyRhfWSIwJjcuB6a5Nxd/A7gkcwP3bH942qDwV0XkdRF5BacF8Ki7/ljgz/kI2hjAqo8ak08i8jVgo6oOdC3BM8AZ6tyH1hjfWYvAmPz6NduOOWxDRMLAdZYETD5Zi8AYY8qctQiMMabMWSIwxpgyZ4nAGGPKnCUCY4wpc5YIjDGmzP1/Vdc+/dN1tu4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Given N (obtained form A, sigma_x, sigma_y fit data) from an expansion run, calculates atom number. R, tau are fit parameters\n",
-    "# This fit requires a formation time scan!\n",
-    "\n",
-    "N_fit_data = atomnumber_taylor(amp,sigmax,sigmay)\n",
-    "t_fit_data = variable\n",
-    "\n",
-    "for i in range(len(N_fit_data)): #This changes any nan values to the mean of the surrounding values\n",
-    "    if np.isnan(N_fit_data[i]) == True:\n",
-    "        print(\"Data point \",i,\" failed. Averaging nearest neighbors.\")\n",
-    "        N_fit_data[i] = (N_fit_data[i-1]+N_fit_data[i+1])*0.5\n",
-    "    else:\n",
-    "        pass\n",
-    "\n",
-    "plt.xlabel('Time (s)')\n",
-    "plt.ylabel('Atom number')\n",
-    "plt.title('Atom Number vs Time')\n",
-    "plt.plot(t_fit_data, N_fit_data, label='Raw') # y axis should be 1k-10million, x axis should be 1-100's ms\n",
-    "\n",
-    "params, covariance = curve_fit(atomnumbervstime, t_fit_data, N_fit_data, p0=[1e5,1])\n",
-    "R_fit = params[0]\n",
-    "tau_fit = params[1]\n",
-    "\n",
-    "print('Loading rate is :', R_fit,'\\nError in loading rate fit is :', np.sqrt(np.diagonal(covariance)[0]))\n",
-    "print('Time constant is :', tau_fit,'\\nError in time constant fit is :', np.sqrt(np.diagonal(covariance)[1]))\n",
-    "plt.plot(t_fit_data,atomnumbervstime(t_fit_data,params[0],params[1]), label='Fitted')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import expansion run file \n",
-    "filename = expansionrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y'  #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n",
-    "\n",
-    "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n",
-    "#path = r'//?/S:/flir_images/binaries/'\n",
-    "file = np.load(path+filename+'.npz')\n",
-    "\n",
-    "index = file['index']\n",
-    "variable = file['variable']\n",
-    "transmission = file['transmission']\n",
-    "amp = file['amp']\n",
-    "amperror = file['amperror']\n",
-    "sigmax = file['sigmax']\n",
-    "sigmaxerror = file['sigmaxerror']\n",
-    "sigmay = file['sigmay']\n",
-    "sigmayerror = file['sigmayerror']\n",
-    "\n",
-    "# Fix units\n",
-    "variable = variable * us\n",
-    "sigmax = sigmax * binpixel\n",
-    "sigmaxerror = sigmaxerror * binpixel\n",
-    "sigmay = sigmay * binpixel\n",
-    "sigmayerror = sigmayerror * binpixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 126.71946357  259.12053485  381.35724204           nan    0.\n",
-      "    0.                   nan    0.            0.         2942.84597866\n",
-      "    0.        ]\n",
-      "[ 0.  5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD4CAYAAADo30HgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3df3Rc5X3n8fdXsi3bkrHBGhGwcaQZO0ntkkKimJRN+gM2xWm6cTchGxNInWJCkkJ62rTdQn+wDad0w+7Z0mwCpAScEAIxlPzSnjqhbUibdJP6BwEWDDVoBvwjEDSyjI0k27Kk7/4x9wrpMpLGGs3cO6PP6xyfzNy5987zRFhfP/d57ueauyMiIhJqiLsBIiKSLCoMIiIygQqDiIhMoMIgIiITqDCIiMgE8+JuwGxobW319vb2uJshIlJTHnnkkV53T0W310VhaG9vZ/fu3XE3Q0SkppjZvmLbdSlJREQmUGEQEZEJVBhERGQCFQYREZlAhUFERCZQYRARkQlUGEREZAIVBhGRMjyyr48nDh6JuxmzSoVBRKQMf/KNJ/n0/9kTdzNmVV3c+SwiEoeRUee53gGamxrjbsqs0ohBRGSGDh4eZGhklMODJ+kbGIq7ObNGhUFEZIay+f6x17lxr2udCoOIyAzl8gNFX9c6FQYRkRnK5vtZtng+CxobJoweap0mn0VEZiibH2BNWwtHjp0kqxGDiIjk8gOkW1tIt7aQ662fEYMKg4jIDBw5dpLe/hOkU82kU83sPzTIyZHRuJs1K1QYRERmIFyFlEm1kEm1MDzq7O8bjLlVs0OFQURkBsI5hXDEAJDtqY/LSZp8FhGZgVy+n/mNxjlnLGb50EhhW299TECrMIiIzEAuP8CqMxYzv7GBpYsaaG1pqpub3HQpSURkBrL5ftKplrH3mVRz3SxZVWEQETlFwyOj7Ds0SGZcYUinWjRiEBGZqw4ePsbQyOjYpDMURgz1EqanwiAicorCm9kyEy4lFV7Xw6hBhUFE5BRlewpzCZlxI4axJasqDCIic0+ut5/lzQtYtnjB2LaVpy9mQWNDXaSsllQYzGyDme01s24zu67I501mdn/w+Q4zax/32fXB9r1mdsm47VvNrMfMnoyc6wwz+0czezb439Nn3j0RkdmXzQ9MmF8AaGww2lsX18XKpGkLg5k1ArcC7wbWApeZ2drIbluAw+6+GrgFuDk4di2wCVgHbABuC84H8OVgW9R1wPfcfQ3wveC9iEhi5PL9pFtbXrM93VofK5NKGTGsB7rdPefuQ8A2YGNkn43A3cHrB4GLzcyC7dvc/YS7Pwd0B+fD3X8A9BX5vvHnuhv4zVPoj4hIRR0ZPElv/xCZtubXfJZpa2Z/X+2H6ZVSGFYAB8a9PxhsK7qPuw8DR4DlJR4bdaa7vxic60WgrdhOZna1me02s935fL6EboiIlC8brEiabMQwPOrsO1TbYXqlFAYrss1L3KeUY2fE3e9w905370ylUrNxShGRaYWTy5m21xaGcFutX04qpTAcBM4Z934l8MJk+5jZPGAphctEpRwb9ZKZnRWc6yygp4Q2iohUxVh43umLXvNZOCFd62F6pRSGXcAaM+swswUUJpO7Ivt0AZuD15cCD7u7B9s3BauWOoA1wM5pvm/8uTYD3y6hjSIiVZHN97PqjMXMa3ztr8/TFs4ntaSp5uO3py0MwZzBtcBDwNPAA+6+x8xuNLP3BrvdBSw3s27gUwQridx9D/AA8BTwXeAadx8BMLOvAT8G3mhmB81sS3CuzwDvMrNngXcF70VEEiGXH5hwx3NUurW55kcMJcVuu/t2YHtk2w3jXh8HPjDJsTcBNxXZftkk+x8CLi6lXSIi1TQ8Msrzhwa4+OfOnHSfdKqF7zz5YhVbNft057OISIkOHj7GyRGfEIURlUk183KNh+mpMIiIlCjMQUpPcSkpvMxUy5lJKgwiIiUaW6o65Yih9pesqjCIiJSoWHhe1IrTF7FgXm2H6akwiIiUKNvz2vC8qMYGo2N5sy4liYjMBbne/imXqobSqWaNGERE6l0YnjfdiAEKhWFf3yBDw7UZpqfCICJSgmyRx3lOJpNqYWTU2d9Xm2F6KgwiIiUILw1NtVQ1lK7xlUkqDCIiJchOEZ4X9erzn2tznkGFQUSkBLl8P69f3lw0PC8qDNPTiEFEpI5l8wOkW6efeA6lW2t3yaoKg4jINIZHRtl3aKDow3kmk2lrIZsfoPAEgtqiwiAiMo0DQXjeqY4YjhyrzTA9FQYRkWmEcwWnOmKA2nyamwqDiMg0xsLzWk+hMLTW7pJVFQYRkWlk84XwvKWL55d8TBimV4tLVlUYRESmMd3jPIsJw/Q0YhARqUPZfH9JGUlR6VSzRgwiIvXm5cEhDg0MnfKIAQqZSftrMExPhUFEZArZsYykmY0YajFMT4VBRGQKY0tVZzhigNp7/rMKg4jIFHK9A8xvNFaWEJ4XFY4yau2hPSoMIiJTyPaUHp4XtWThfNqWNGnEICJST3K9A2RmML8QKjzmU4VBRKQuhOF5pTycZzLpVO2F6ZVUGMxsg5ntNbNuM7uuyOdNZnZ/8PkOM2sf99n1wfa9ZnbJdOc0s4vN7Cdm9piZ/auZrS6viyIiMxOG581k4jmUSbXUXJjetIXBzBqBW4F3A2uBy8xsbWS3LcBhd18N3ALcHBy7FtgErAM2ALeZWeM057wduNzdzwPuA/6svC6KiMxMeAloJktVQ2MT0DUUplfKiGE90O3uOXcfArYBGyP7bATuDl4/CFxsZhZs3+buJ9z9OaA7ON9U53TgtOD1UuCFmXVNRKQ84aTxqYTnRa0Ol6z21M48w7wS9lkBHBj3/iBwwWT7uPuwmR0Blgfb/y1y7Irg9WTnvArYbmbHgKPA24s1ysyuBq4GWLVqVQndEBE5Nbn8AK0tpxaeF3X2skKYXr2NGKzItugsymT7nOp2gN8Hft3dVwJfAv66WKPc/Q5373T3zlQqVbThIiLlyOb7SZcxWoBXw/RqacRQSmE4CJwz7v1KXnt5Z2wfM5tH4RJQ3xTHFt1uZingF9x9R7D9fuDCknoiIjLLcvkBMm0zn18IZdqa627EsAtYY2YdZraAwmRyV2SfLmBz8PpS4GEvrM3qAjYFq5Y6gDXAzinOeRhYamZvCM71LuDpmXdPRGRmwvC8ckcMAOnW2grTm3aOIZgzuBZ4CGgEtrr7HjO7Edjt7l3AXcA9ZtZNYaSwKTh2j5k9ADwFDAPXuPsIQLFzBts/CnzdzEYpFIorZ7XHIiIlCMPzZmvEUAjTG2B125Kyz1dppUw+4+7bge2RbTeMe30c+MAkx94E3FTKOYPt3wS+WUq7REQqZWyp6iyNGKBQbGqhMOjOZxGRIrL5mYfnRYX3MtRKZpIKg4hIEbl8P+0zDM+LCsP0aiVlVYVBRKSImT7OczKFx3xqxCAiUpOGR0bZ3zdYVkZSVCbVQq5GwvRUGEREIsLwvHJSVaPSNRSmp8IgIhIR3qU8m5eSMmMT0MmfZ1BhEBGJyPWWH54XFV6WqoWH9qgwiIhEZHvKD8+LOnvZIprmNdTEBLQKg4hIRK63f1bnFyAI02ttroklqyoMIiIR2Xx5z3meTDpVG2F6KgwiIuO8PDhE38DQrC5VDWVStRGmp8IgIjJOuGpoNlckhdKpV8P0kkyFQURknOwshudFhaOQ7h4VBhGRmpHLD7CgsWFWwvOiOloLo5BwOWxSqTCIiIyTzffz+uWLZyU8LyoM08tqxCAiUjty+f6KTDyHMqkWjRhERGrFySA8rxITz6F0qjnxYXoqDCIigQN9g7MenheVCcL0DiU4TE+FQUQkEN6VXImb20LhaCTJd0CrMIiIBMaWqlZ4xDD+u5JIhUFEJJDLD9Da0sTSRbMXnhe1IgjTS3LKqgqDiEhgth/nWUxDDYTpqTCIiARyvQMVXaoayqRadClJRCTpDg+E4XmVHTFAYQL6wOFjnBgeqfh3zYQKg4gIr8ZUVPpSEhRGDCOjzv5DgxX/rplQYRAR4dVU1WpcSkon/PnPJRUGM9tgZnvNrNvMrivyeZOZ3R98vsPM2sd9dn2wfa+ZXTLdOa3gJjN7xsyeNrPfLa+LIiLTy+b7g/C8xRX/rnTCl6zOm24HM2sEbgXeBRwEdplZl7s/NW63LcBhd19tZpuAm4EPmtlaYBOwDjgb+Ccze0NwzGTn/AhwDvAmdx81s7bZ6KiIyFRy+QHaWxfT2GAV/66WpnmceVpTYlcmlTJiWA90u3vO3YeAbcDGyD4bgbuD1w8CF5uZBdu3ufsJd38O6A7ON9U5PwHc6O6jAO7eM/PuiYiUJpvvr8gzGCaTbk1umF4phWEFcGDc+4PBtqL7uPswcARYPsWxU50zQ2G0sdvMvmNma4o1ysyuDvbZnc/nS+iGiEhxJ0dG2X+osuF5UZm2ZrI9/YkM0yulMBQbV0V7Mtk+p7odoAk47u6dwBeBrcUa5e53uHunu3emUqmiDRcRKcWBvkGGR70qE8+hdGsLR48PJzJMr5TCcJDCNf/QSuCFyfYxs3nAUqBvimOnOudB4OvB628Cby6hjSIiM1bJ5zxPJtMWTED3JO9yUimFYRewxsw6zGwBhcnkrsg+XcDm4PWlwMNeGB91AZuCVUsdwBpg5zTn/BZwUfD6l4FnZtY1EZHS5KoQnheVHnvMZ/ImoKddleTuw2Z2LfAQ0Ahsdfc9ZnYjsNvdu4C7gHvMrJvCSGFTcOweM3sAeAoYBq5x9xGAYucMvvIzwL1m9vtAP3DV7HVXROS1svn+iofnRYVhekkcMUxbGADcfTuwPbLthnGvjwMfmOTYm4CbSjlnsP1l4D2ltEtEZDbk8gNVicIYbyxML4EjBt35LCJzXq53oKqXkUKZVEsi47dVGERkTqtmeF5UJtXM/r7BxIXpqTCIyJwW3mRWzaWqoXSqhVEncWF6KgwiMqdle6q/VDWU1Md8qjCIyJyW7a1eeF5UR0JTVlUYRGROy/ZULzwvKqlheioMIjKn5XqrG54XlcTHfKowiMicFYbnZdqqP78QSqeayeWTFaanwiAic9b+IDwv7hHD0ePD9PYnJ0xPhUFE5qzw2n4YaBeH8Ma6JN3opsIgInNWdiw8L75LSZkErkxSYRCROSuX7ye1pInTFlYvPC/q7KWLWDi/QSMGEZEkyOUHxuKv49LQYLQvT1aYngqDiMxZ2Xx/LOF5UZm2ZC1ZVWEQkTmpb2CIw4MnYwnPi8q0NnMgQWF6KgxzTNfjL7Dzub64myESu/CafhzheVGZtkKY3r6EhOmpMMwhd/3rc/zu1x7ljh/k4m6KSOzGlqomoDCE91EkZQK6pCe4SW1zdz7z3X/nb/8lx4Z1r+NvNp0Xd5NEYpfN97NgXgMrTl8Ud1MSF6anwlDnTo6M8sdf/3984yc/5fILVnHjxp+PJSxMJGmy+QHal8cTnhfV0jSP1522MDET0CoMdWxwaJjfufcn/PPePJ961xv45EWrMYv/L4FIEuR6+3njmUvibsaYQmZSMkYMmmOoU30DQ1z2xR384Jk8//195/K7F69RURAJhOF5cd7xHBWmrCYhTE8jhjp0oG+QzVt38tOXj3H7FW/lknWvi7tJIokShuclYeI5lE4180oQppda0hRrW1QY6szTLx5l89adHD85wlevuoC3tZ8Rd5NEEifbE2YkJacwjH/MZ9yFQZeS6siPs4f4L1/4MY0NxoOfuFBFQWQSYfxEki4lhW1JwjyDRgx1YvsTL/J72x5j1fLFfOXK9Zy9LP4leCJJlYTwvKgkhempMNSBe378PDd07eH8c5ax9SNvY9niBXE3SSTRsgkIz4tqaDA6WpORmVTSpSQz22Bme82s28yuK/J5k5ndH3y+w8zax312fbB9r5ldcgrn/JyZxf//UIK5O//rH/by59/ew8VvauPeq96uoiBSgly+P9aH80wmnUpGyuq0hcHMGoFbgXcDa4HLzGxtZLctwGF3Xw3cAtwcHLsW2ASsAzYAt5lZ43TnNLNOYFmZfatrwyOjXP+NJ/jcw918sPMcvnDFW1m0oDHuZokkXhiel7QRAxQmoJMQplfKiGE90O3uOXcfArYBGyP7bATuDl4/CFxshUXzG4Ft7n7C3Z8DuoPzTXrOoGj8T+C/lte1+nVsaISPf/UnbNt1gE9etJrPvP9c5jVqHYFIKcbC8xI4YsikmhMRplfKb5MVwIFx7w8G24ru4+7DwBFg+RTHTnXOa4Eud3+xtC7MLS8PDnHFXTv43r+/xI0b1/EHv/ZG3bgmcgrCa/iZ1iQWhmSE6ZUy+Vzst0701rzJ9plse7GC5GZ2NvAB4FembZTZ1cDVAKtWrZpu97rwwsvH2Lx1J/sODXLrh97Cr597VtxNEqk5ufxAYsLzojpakxGmV8qI4SBwzrj3K4EXJtvHzOYBS4G+KY6dbPv5wGqg28yeBxabWXexRrn7He7e6e6dqVSqhG7UtmdeeoX33/4jfnbkOHdfuV5FQWSGsvkBOpY3JyI8L6o5IWF6pRSGXcAaM+swswUUJpO7Ivt0AZuD15cCD3sh8KML2BSsWuoA1gA7Jzunu/+9u7/O3dvdvR0YDCa057Tdz/dx6e0/YnjUuf9jv8gvZpbH3SSRmpXL9yfqxraoTFtz8kcMwZzBtcBDwNPAA+6+x8xuNLP3BrvdBSwP/nX/KeC64Ng9wAPAU8B3gWvcfWSyc85u1+rDPz71EpffuYPWlia+8YkLWXv2aXE3SaRmnRwZZX/fYKIykqLSrS3kYg7TK+kGN3ffDmyPbLth3OvjFOYGih17E3BTKecssk9yf3pVsG3nfv7km09w7splbN3cyfKWePNTRGrdvkOF8LxEjxiCML18/wnaliyMpQ1a45hA7s7//t6zXPeNJ3jnmhRf++gFKgoisyBJz3meTHpsZVJ8l5NUGBJmZNS54dt7+Ot/fIb3vWUFd27uZPECJZeIzIbw2n2SRwxJCNPTb5wEOX5yhN+//zG+8+TP+Ngvp7luw5t0j4LILArD85YkKDwvKgzTi3NlkgpDQhw5dpKrv7KbHc/18ee/sZYt7+iIu0kidSfXO0AmwaMFeDVML86b3HQpKQFeOnqcD/7tj/nJ/sN8dtN5KgoiFZLN9yfq4TyTyaTiXbKqwhCzbL6f9932Iw70DbL1I29j43nRtBERmQ19A0O8PHgy0RPPoXSqhYOH4wvTU2GI0aP7D3Pp7T/ixPAI267+Rd65pv7v4BaJS3jNPskTz6G4w/RUGGLy/b09fOiLO1iycD5f/8SFnLtyadxNEqlruQSH50WNPf+5J555Bk0+x+C7T77INfc9ys+dtYQvfWR97A/+FpkLkhyeFxWG6cX10B4Vhir75709fPJrj/ILK5dy95XrE71sTqSeZPP9iQ3Pi2pumsdZSxfGNmLQpaQq+rfcIT52zyO84cwlfOm3VRREqimXHyDTlvz5hVA61Uw2phGDCkOVPHbgZbZ8eRfnnLGYr1y5nqWLVBREqmVoeJR9fYOka2B+IZRJtZDriSdMT4WhCp5+8Sibt+5keUsT916l3CORatvfN8jIqNfWiKG1mVdOFML0qk2FocJy+X4+fNcOFs1v5N6rLuDM0+JJSxSZy8aWqtbSiKEtvjA9FYYKOnh4kCvu3IE7fPWqCzjnjMVxN0lkTsrVQHheVHiHdhyZSSoMFdJz9DiX37mD/hPD3LPlAla31c6/VETqTS7fT1vCw/OizjptIQvnN8QyYtBy1QroGxjiirt2kH/lBF+96gI9dU0kZtmEP86zmIYGI93aohFDPTh6/CSbt+5k36FB7tzcyVtWnR53k0TmNHcnmx+oiYykqHSqWXMMtW5waJgtX97F0y8e5fYr3sKFmda4myQy5/UNDHHk2MmaSFWNygRhesdPVjdMT4VhlpwYHuFj9zzCI/sO89lN53PRm86Mu0kiwquxErV2KQkKbY4jTE+FYRacHBnlk/c9yg+f7eXm97+Z97z5rLibJCKBMDxvdY2OGICqP7RHhaFMo6POH/3d4/zDUy/x6feu4wOd58TdJBEZJxuE5529LPnheVFhmF61J6BVGMrg7vzZt5/kW4+9wB9d8kY2X9ged5NEJCKX7yfdWhvheVFhmF61J6BVGGbI3fmr7U9z3479/M6vZLjmV1fH3SQRKSKbH6jJ+YVQJlX9JasqDDP02e89yxd/+BwfubCdP7rkjXE3R0SKGBoeZX/fYE0uVQ2FS1arGaanwjADd/4wx9/807Nc+taV3PAbazGrvSGqyFywv2+AkVGv+RFDtcP0VBhO0X079vOXf/807zn3LG5+/5tpqMHrliJzRTbMSKqh8LyosKhle6o3z1BSYTCzDWa218y6zey6Ip83mdn9wec7zKx93GfXB9v3mtkl053TzO4Ntj9pZlvNLDHhJt969Kf86bee4KI3tXHLB8+ryckskbmkFsPzosIb83K91ZtnmLYwmFkjcCvwbmAtcJmZrY3stgU47O6rgVuAm4Nj1wKbgHXABuA2M2uc5pz3Am8CzgUWAVeV1cNZ8tCen/EHf/c4b+9Yzm2Xv4UF8zTYEkm6bA2G50WdddpCFs1vTNyIYT3Q7e45dx8CtgEbI/tsBO4OXj8IXGyFC+8bgW3ufsLdnwO6g/NNek533+4BYCewsrwulu+Hz+b55H2Pcu6KpXxxcycL5zfG3SQRKUEu31/TE89QCNPraG1O1ogBWAEcGPf+YLCt6D7uPgwcAZZPcey05wwuIX0Y+G6xRpnZ1Wa228x25/P5EroxM7ue7+OjX9lNpq2Fu397PS1NCqQVqQVheF4tX0YKZdpaqnovQymFodiF9Oi6qcn2OdXt490G/MDdf1isUe5+h7t3untnKpUqtkvZnjh4hCu/tIuzly3ini3rWbq4doejInNNLYfnRaVbmzlQxTC9UgrDQWB8zsNK4IXJ9jGzecBSoG+KY6c8p5n9NyAFfKqUTlTCMy+9wm9t3cHSxfO596oLaNVzmkVqShiel6mTEYNXMUyvlMKwC1hjZh1mtoDCZHJXZJ8uYHPw+lLg4WCOoAvYFKxa6gDWUJg3mPScZnYVcAlwmbuPlte9mXm+d4DL79zBgnkN3HfV2zlrae1lrIjMddmewjX5Wp9jgMKIAaqXmTTtBXN3Hzaza4GHgEZgq7vvMbMbgd3u3gXcBdxjZt0URgqbgmP3mNkDwFPAMHCNu48AFDtn8JVfAPYBPw5uHPuGu984az2exgsvH+PyO3cwMup87WNvZ9VyPadZpBblegdoqtHwvKhwnqRaKaslzaS6+3Zge2TbDeNeHwc+MMmxNwE3lXLOYHtss7v5V05wxZ07OHr8JF/76NtZ3bYkrqaISJmyPf101Gh4XtTiBfM4e+nCsRv2Kk2L8QMvDw7x4bt28LOjx/nyb7+Nn1+xNO4miUgZcr21+TjPyaRTLVUbMagwAP0nhtn8pV3kegf44m918tbXnxF3k0SkDGF4Xj0sVQ1lqhimN+cLw7GhEa788i72/PQIt33oLfyH1XpOs0itq4fwvKh0GKb3SuXD9OZ0YTgxPMLHv/oIu57v468/eB7/ca2e0yxSD8Jr8fV1KSlcmVT5eYY5WxjcnU/d/zj/8kyez7zvXN77C2fH3SQRmSXhss7w0Zj1ICxy1ViyOmfzHcyMX1t3Jp3tp/PBt62KuzkiMoty+QHOPK22w/OiXheE6VUjGmPOFgaAjedFI59EpB5k8/01/QyGYhoarPA0tyqE6c3ZS0kiUp/cnVx+gExb/VxGCqWr9PxnFQYRqSuHwvC8OhsxQGHJ6sHDxyoepqfCICJ1pR6e2jaZdKoQpvf8ocrOM6gwiEhdCe8OrqelqqEwTK/SE9AqDCJSV7L5fprmNbCiDsLzosbuZeip7DyDCoOI1JVcfoCO1mYa6iA8LyoM0wufNVEpKgwiUleydfCc56kUHvOpEYOISEmGhkc5cPhYXU48h9KtzWQrHKanwiAidSMMz6v3EUN/hcP0VBhEpG5099TvUtVQeH9GdwUvJ6kwiEjdCOMi0nU8Ynj1MZ+Vm4BWYRCRupHtKYTntTTVbwzc605byOIFjRWNxlBhEJG6keut7xVJUAjT62ht1ohBRGQ67k62p7+u5xdCmVRLRVNWVRhEpC4cGhji6PHhugzPi0pXOExPhUFE6kJ4aSXTNhcKQ2XD9FQYRKQuhJOx6Tp6nOdkMmOZSSoMIiKTytVxeF5Ux1jKamXmGVQYRKQuZOs4PC9q8YJ5rFi2qGJheiUVBjPbYGZ7zazbzK4r8nmTmd0ffL7DzNrHfXZ9sH2vmV0y3TnNrCM4x7PBOReU10URmQtydR6eF5VONVfsXoZpC4OZNQK3Au8G1gKXmdnayG5bgMPuvhq4Bbg5OHYtsAlYB2wAbjOzxmnOeTNwi7uvAQ4H5xYRmdSJ4REOHD42du19LsikWshVKEyvlNsD1wPd7p4DMLNtwEbgqXH7bAT+Inj9IPB5M7Ng+zZ3PwE8Z2bdwfkodk4zexq4CPhQsM/dwXlvn1HvRIDf+NwPOXFyNJbvbmwwvvt7vxTLd8fhr7Y/zff/vafq3zs86oyMel1HYUSlU830nxim55UTnHnawlk9dymFYQVwYNz7g8AFk+3j7sNmdgRYHmz/t8ixK4LXxc65HHjZ3YeL7D+BmV0NXA2watWqErohc1Um1cLJkbgKw9yaxjvztIWsOTOeX87nr1rGL70hFct3x+HcFUt5z5vPYmh49v/bLqUwFJvJiY5dJttnsu3F/rZMtf9rN7rfAdwB0NnZWblgcql5n910ftxNmDO2vKODLe/oiLsZc8L5q07n1g+dXpFzl/LPmYPAOePerwRemGwfM5sHLAX6pjh2su29wLLgHJN9l4iIVFAphWEXsCZYLbSAwmRyV2SfLmBz8PpS4GEvzIh0AZuCVUsdwBpg52TnDI75fnAOgnN+e+bdExGRUzXtpaRgzuBa4CGgEdjq7nvM7EZgt7t3AXcB9wSTy30UftET7PcAhYnqYeAadx8BKHbO4Cv/GNhmZn8JPBqcW0REqsQq+dzQauns7PTdu3fH3QwRkZpiZo+4e2d0+9xaMiEiItNSYefeDVAAAAPySURBVBARkQlUGEREZAIVBhERmaAuJp/NLA/sm+HhrRTun5hL1Oe5QX2uf+X29/Xu/prbxeuiMJTDzHYXm5WvZ+rz3KA+179K9VeXkkREZAIVBhERmUCFIQjim2PU57lBfa5/FenvnJ9jEBGRiTRiEBGRCVQYRERkgrouDGa2wcz2mlm3mV1X5PMmM7s/+HyHmbWP++z6YPteM7ukmu2eqZn218yWm9n3zazfzD5f7XaXo4w+v8vMHjGzJ4L/vajabZ+pMvq83sweC/48bmb/udptn6ly/i4Hn68K/vv+w2q1uVxl/JzbzezYuJ/1F075y929Lv9QiPPOAmlgAfA4sDayz+8AXwhebwLuD16vDfZvAjqC8zTG3acK9rcZeAfwceDzcfelSn0+Hzg7eP3zwE/j7k8V+rwYmBe8PgvoCd8n+U85fR73+deBvwP+MO7+VOHn3A48Wc731/OIYT3Q7e45dx8CtgEbI/tsBO4OXj8IXGxmFmzf5u4n3P05oDs4X5LNuL/uPuDu/wocr15zZ0U5fX7U3cOnA+4BFppZU1VaXZ5y+jzorz5PfSGTPDY3gcr5u4yZ/SaQo/BzrhVl9blc9VwYVgAHxr0/GGwruk/wF+YIsLzEY5OmnP7Wqtnq8/uBR939RIXaOZvK6rOZXWBme4AngI+PKxRJNuM+m1kzhYd/fboK7ZxN5f633WFmj5rZv5jZO0/1y6d9glsNK1Y5o/9CmmyfUo5NmnL6W6vK7rOZrQNuBn5tFttVSWX12d13AOvM7OeAu83sO+6e9JFiOX3+NHCLu/fP0j+mq6WcPr8IrHL3Q2b2VuBbZrbO3Y+W+uX1PGI4CJwz7v1K4IXJ9jGzecBSCo8mLeXYpCmnv7WqrD6b2Urgm8BvuXu24q2dHbPyc3b3p4EBCvMrSVdOny8A/oeZPQ/8HvAnwWOFk27GfQ4ugR8CcPdHKMxVvOFUvryeC8MuYI2ZdZjZAgqTM12RfbqAzcHrS4GHvTB70wVsCmb9O4A1wM4qtXumyulvrZpxn81sGfD3wPXu/n+r1uLyldPnjuAXCGb2euCNwPPVaXZZZtxnd3+nu7e7ezvwN8BfuXstrLwr5+ecMrNGADNLU/j9lTulb4979r2Sf4BfB56hUDH/NNh2I/De4PVCCisVuin84k+PO/ZPg+P2Au+Ouy9V6O/zFP6F1U/hXyJrq93+avYZ+DMK/2J+bNyftrj7U+E+f5jCBOxjwE+A34y7L5Xuc+Qcf0GNrEoq8+f8/uDn/Hjwc/5Pp/rdisQQEZEJ6vlSkoiIzIAKg4iITKDCICIiE6gwiIjIBCoMIiIygQqDiIhMoMIgIiIT/H8AAk54NzGC5AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(variable,sigmay)\n",
-    "print(sigmax/um)\n",
-    "print(variable/ms)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data point  3  failed. Averaging nearest neighbors.\n",
-      "Data point  6  failed. Averaging nearest neighbors.\n",
-      "Temperature (uK) is : 3.849164489145072 \n",
-      "Error in Temperature fit is : 4.670158067060396\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\dpean\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in sqrt\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Given mot size (sigma) vs time from an expansion time scan, calculates temperature\n",
-    "# Requires an expansion time scan!\n",
-    "\n",
-    "sigma_magn_fit_data = np.sqrt(sigmax**2 + sigmay**2)\n",
-    "t_fit_data = variable\n",
-    "\n",
-    "for i in range(len(sigma_magn_fit_data)): #This changes any nan values to the mean of the surrounding values\n",
-    "    if np.isnan(sigma_magn_fit_data[i]) == True:\n",
-    "        print(\"Data point \",i,\" failed. Averaging nearest neighbors.\")\n",
-    "        sigma_magn_fit_data[i] = (sigma_magn_fit_data[i-1]+sigma_magn_fit_data[i+1])*0.5\n",
-    "    else:\n",
-    "        pass\n",
-    "\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('MOT size (um)')\n",
-    "plt.title('MOT size vs Time')\n",
-    "plt.plot(t_fit_data, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n",
-    "\n",
-    "params, covariance = curve_fit(tempfind, t_fit_data, sigma_magn_fit_data, p0=[200 * uk]);\n",
-    "T_fit = params[0]\n",
-    "\n",
-    "print('Temperature (uK) is :', T_fit/uk,'\\nError in Temperature fit is :', np.sqrt(np.diagonal(covariance)[0])/uk)\n",
-    "plt.scatter(t_fit_data,tempfind(t_fit_data,params[0])/um, label='Fitted')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}