custompackets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n",
       "       10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n",
       "       10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,\n",
       "       10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = 10\n",
    "def create_packet_radii(no_packets): # function for creating the array of radii of packets if each packet starts at the same radius (used in BlackBody)\n",
    "    return np.ones(no_packets) * r\n",
    "\n",
    "n_packets = 50\n",
    "create_packet_radii(n_packets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.25135724, 31.66273651, 40.0906871 , 24.53837026, 24.55640939,\n",
       "       16.98095437, 24.00653674, 19.14465708, 16.17599823, 29.87414606,\n",
       "       40.7722341 , 12.73567005, 18.49377973, 41.19016043, 25.01323276,\n",
       "       28.50201278, 45.3014239 , 17.84828587, 27.75415446, 20.63308389,\n",
       "       34.62005322, 14.26382056, 40.48805616, 42.80167606, 21.93734977,\n",
       "       42.54527304, 20.97162325, 45.46610373, 27.28924703, 27.90817379,\n",
       "       26.51654244, 16.61282776, 39.41315099, 23.36931814, 43.32116997,\n",
       "       38.52596118, 21.06235131, 42.63117337, 42.05004993, 10.77863702,\n",
       "       33.24587443, 22.68721083, 23.30578455, 31.64799079, 42.29857555,\n",
       "       48.62262724, 45.39017215, 25.9717403 , 33.69924599, 36.67382657])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_inner = 10\n",
    "r_outer = 50\n",
    "def create_packet_radii_rand(no_packets, r_inner, r_outer): # function for creating the radii of packets where each packet is assigned a random radius between two boundaries\n",
    "    return np.random.uniform(r_inner, r_outer, no_packets)\n",
    "\n",
    "create_packet_radii_rand(n_packets, r_inner, r_outer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3958786 , 0.89845847, 0.83931281, 0.66422029, 0.66695787,\n",
       "       0.98483059, 0.99598414, 0.95752185, 0.63738821, 0.94691118,\n",
       "       0.36160372, 0.84366806, 0.8782852 , 0.81035753, 0.67925805,\n",
       "       0.42689886, 0.64861162, 0.80462674, 0.9408303 , 0.59150819,\n",
       "       0.65866408, 0.26221923, 0.95309492, 0.65238845, 0.22613789,\n",
       "       0.31711871, 0.40001538, 0.28912749, 0.37533393, 0.65673337,\n",
       "       0.55624666, 0.91690987, 0.73666757, 0.58936662, 0.37236138,\n",
       "       0.85386625, 0.88392788, 0.19985003, 0.77894103, 0.74470761,\n",
       "       0.73750153, 0.95315562, 0.84651614, 0.40445064, 0.95809017,\n",
       "       0.95218832, 0.53992438, 0.91629289, 0.22224106, 0.3544264 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_packet_mus(no_packets):   # comes from energy packet initialization mu is the cos of the angle from radial direction\n",
    "    return np.sqrt(np.random.rand(no_packets))  # The way to randomly assign these values is to take the sqrt of a rand number from 0 to 1\n",
    "\n",
    "create_packet_mus(n_packets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,\n",
       "       0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,\n",
       "       0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,\n",
       "       0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,\n",
       "       0.02, 0.02, 0.02, 0.02, 0.02, 0.02])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_packet_energies(no_packets): # function used in BlackBody. each packet is assigned the same energy and they all add up to 1\n",
    "    return np.ones(no_packets)/no_packets\n",
    "\n",
    "create_packet_energies(n_packets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
       "       5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
       "       5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_packet_energies2(no_packets, energy): # creates array of energies given a specific energy\n",
    "    return np.ones(no_packets) * energy\n",
    "\n",
    "energy = 5\n",
    "create_packet_energies2(n_packets, energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_packet_nus_uniform(no_packets, nu_min, nu_max): # creates frequencies given a min and a max using a uniform distribution\n",
    "    return np.random.uniform(nu_min, nu_max, no_packets)\n",
    "\n",
    "nu_min = 400e12\n",
    "nu_max = 790e12\n",
    "n_packets = 1000\n",
    "uniform_nus = create_packet_nus_uniform(n_packets, nu_min, nu_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 93.,  98., 102., 103.,  86.,  94.,  95., 106., 107., 116.]),\n",
       " array([4.00018110e+14, 4.39006825e+14, 4.77995541e+14, 5.16984257e+14,\n",
       "        5.55972973e+14, 5.94961689e+14, 6.33950405e+14, 6.72939120e+14,\n",
       "        7.11927836e+14, 7.50916552e+14, 7.89905268e+14]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(uniform_nus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_packet_nus_gaussian(no_packets, mean, standev):\n",
    "    return np.random.normal(mean, standev, no_packets)\n",
    "\n",
    "mean = (nu_max + nu_min)/2\n",
    "standev = 5e13\n",
    "gaussian_nus = create_packet_nus_gaussian(n_packets, mean, standev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  21.,  81., 192., 259., 244., 130.,  52.,  11.,   3.]),\n",
       " array([4.34786701e+14, 4.68665333e+14, 5.02543965e+14, 5.36422598e+14,\n",
       "        5.70301230e+14, 6.04179862e+14, 6.38058495e+14, 6.71937127e+14,\n",
       "        7.05815759e+14, 7.39694391e+14, 7.73573024e+14]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGvCAYAAABxUC54AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhE0lEQVR4nO3df2zU9eHH8detV07o2hst9K43D+hM2ZztlIAB6g/AlmKlEIUIDqewIdEAdV0hCPqHdeHbohs/shAxEFIERExU0AUmlCBlyMigSgR0WAUEtJcOrHctY1esn+8fhHNHf8CVtvfu9flIPon3ufcd73vnHfvMp3dXm2VZlgAAAAzyo2hPAAAA4GoECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADj2KM9gfb4/vvv9fXXXysxMVE2my3a0wEAANfBsizV19fL4/HoRz9q+xpJtwyUr7/+Wl6vN9rTAAAA7XDmzBndfPPNbY7ploGSmJgo6fILTEpKivJsAADA9QgEAvJ6vaGf423ploFy5dc6SUlJBAoAAN3M9bw9gzfJAgAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOPZoTwBA5xq0cFu0pxCxU0vGR3sKAKKMKygAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjBNRoJSVlenOO+9UYmKiUlNT9eCDD+r48eNhY2bMmCGbzRZ2jBgxImxMMBhUYWGh+vXrp4SEBE2cOFFnz5698VcDAABiQkSBUllZqTlz5ujAgQOqqKjQd999p7y8PF24cCFs3P3336+amprQsX379rD7i4qKtGXLFm3evFn79u1TQ0ODCgoK1NTUdOOvCAAAdHsRfZPse++9F3a7vLxcqampqqqq0r333hs673A45Ha7W3wOv9+vtWvXasOGDcrNzZUkbdy4UV6vV7t27dK4ceMifQ0AACDG3NB7UPx+vyQpOTk57PyePXuUmpqqwYMHa9asWaqtrQ3dV1VVpUuXLikvLy90zuPxKDMzU/v372/x3wkGgwoEAmEHAACIXe0OFMuyVFxcrLvvvluZmZmh8/n5+Xrttde0e/duLV26VAcPHtR9992nYDAoSfL5fOrVq5f69u0b9nwul0s+n6/Ff6usrExOpzN0eL3e9k4bAAB0A+3+Y4Fz587Vxx9/rH379oWdnzp1aui/MzMzNWzYMA0cOFDbtm3TpEmTWn0+y7Jks9lavG/RokUqLi4O3Q4EAkQKAAAxrF1XUAoLC/Xuu+/q/fff180339zm2LS0NA0cOFDV1dWSJLfbrcbGRtXV1YWNq62tlcvlavE5HA6HkpKSwg4AABC7IgoUy7I0d+5cvf3229q9e7fS09Ov+Zjz58/rzJkzSktLkyQNHTpU8fHxqqioCI2pqanR0aNHlZ2dHeH0AQBALIroVzxz5szRpk2b9M477ygxMTH0nhGn06nevXuroaFBJSUlmjx5stLS0nTq1Ck9++yz6tevnx566KHQ2JkzZ2revHlKSUlRcnKy5s+fr6ysrNCnegAAQM8WUaCsWrVKkjR69Oiw8+Xl5ZoxY4bi4uJ05MgRrV+/Xt9++63S0tI0ZswYvfHGG0pMTAyNX758uex2u6ZMmaKLFy8qJydH69atU1xc3I2/IgAA0O3ZLMuyoj2JSAUCATmdTvn9ft6PAlzDoIXboj2FiJ1aMj7aUwDQCSL5+c3f4gEAAMZp98eMAaCzcNUHAFdQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgnokApKyvTnXfeqcTERKWmpurBBx/U8ePHw8ZYlqWSkhJ5PB717t1bo0eP1rFjx8LGBINBFRYWql+/fkpISNDEiRN19uzZG381AAAgJkQUKJWVlZozZ44OHDigiooKfffdd8rLy9OFCxdCY1566SUtW7ZMK1eu1MGDB+V2uzV27FjV19eHxhQVFWnLli3avHmz9u3bp4aGBhUUFKipqanjXhkAAOi2bJZlWe198L///W+lpqaqsrJS9957ryzLksfjUVFRkZ555hlJl6+WuFwuvfjii3ryySfl9/vVv39/bdiwQVOnTpUkff311/J6vdq+fbvGjRt3zX83EAjI6XTK7/crKSmpvdMHeoRBC7dFewo9wqkl46M9BcB4kfz8vqH3oPj9fklScnKyJOnkyZPy+XzKy8sLjXE4HBo1apT2798vSaqqqtKlS5fCxng8HmVmZobGXC0YDCoQCIQdAAAgdrU7UCzLUnFxse6++25lZmZKknw+nyTJ5XKFjXW5XKH7fD6fevXqpb59+7Y65mplZWVyOp2hw+v1tnfaAACgG2h3oMydO1cff/yxXn/99Wb32Wy2sNuWZTU7d7W2xixatEh+vz90nDlzpr3TBgAA3UC7AqWwsFDvvvuu3n//fd18882h8263W5KaXQmpra0NXVVxu91qbGxUXV1dq2Ou5nA4lJSUFHYAAIDYFVGgWJaluXPn6u2339bu3buVnp4edn96errcbrcqKipC5xobG1VZWans7GxJ0tChQxUfHx82pqamRkePHg2NAQAAPZs9ksFz5szRpk2b9M477ygxMTF0pcTpdKp3796y2WwqKipSaWmpMjIylJGRodLSUvXp00fTpk0LjZ05c6bmzZunlJQUJScna/78+crKylJubm7Hv0IAANDtRBQoq1atkiSNHj067Hx5eblmzJghSVqwYIEuXryo2bNnq66uTsOHD9fOnTuVmJgYGr98+XLZ7XZNmTJFFy9eVE5OjtatW6e4uLgbezUAACAm3ND3oEQL34MCXD++B6Vr8D0owLV12fegAAAAdAYCBQAAGIdAAQAAxiFQAACAcSL6FA/Q0/GGUwDoGlxBAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYJ+JA2bt3ryZMmCCPxyObzaatW7eG3T9jxgzZbLawY8SIEWFjgsGgCgsL1a9fPyUkJGjixIk6e/bsDb0QAAAQOyIOlAsXLuj222/XypUrWx1z//33q6amJnRs37497P6ioiJt2bJFmzdv1r59+9TQ0KCCggI1NTVF/goAAEDMsUf6gPz8fOXn57c5xuFwyO12t3if3+/X2rVrtWHDBuXm5kqSNm7cKK/Xq127dmncuHGRTgkAAMSYTnkPyp49e5SamqrBgwdr1qxZqq2tDd1XVVWlS5cuKS8vL3TO4/EoMzNT+/fvb/H5gsGgAoFA2AEAAGJXhwdKfn6+XnvtNe3evVtLly7VwYMHdd999ykYDEqSfD6fevXqpb59+4Y9zuVyyefztficZWVlcjqdocPr9Xb0tAEAgEEi/hXPtUydOjX035mZmRo2bJgGDhyobdu2adKkSa0+zrIs2Wy2Fu9btGiRiouLQ7cDgQCRAgBADOv0jxmnpaVp4MCBqq6uliS53W41Njaqrq4ubFxtba1cLleLz+FwOJSUlBR2AACA2NXpgXL+/HmdOXNGaWlpkqShQ4cqPj5eFRUVoTE1NTU6evSosrOzO3s6AACgG4j4VzwNDQ36/PPPQ7dPnjypw4cPKzk5WcnJySopKdHkyZOVlpamU6dO6dlnn1W/fv300EMPSZKcTqdmzpypefPmKSUlRcnJyZo/f76ysrJCn+oBAAA9W8SBcujQIY0ZMyZ0+8p7Q6ZPn65Vq1bpyJEjWr9+vb799lulpaVpzJgxeuONN5SYmBh6zPLly2W32zVlyhRdvHhROTk5WrduneLi4jrgJQEAgO7OZlmWFe1JRCoQCMjpdMrv9/N+FHSpQQu3RXsKMNSpJeOjPQXAeJH8/OZv8QAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMY4/2BAAgFgxauC3aU2iXU0vGR3sKQIu4ggIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOxIGyd+9eTZgwQR6PRzabTVu3bg2737IslZSUyOPxqHfv3ho9erSOHTsWNiYYDKqwsFD9+vVTQkKCJk6cqLNnz97QCwEAALEj4kC5cOGCbr/9dq1cubLF+1966SUtW7ZMK1eu1MGDB+V2uzV27FjV19eHxhQVFWnLli3avHmz9u3bp4aGBhUUFKipqan9rwQAAMQMe6QPyM/PV35+fov3WZalFStW6LnnntOkSZMkSa+++qpcLpc2bdqkJ598Un6/X2vXrtWGDRuUm5srSdq4caO8Xq927dqlcePG3cDLAQAAsaBD34Ny8uRJ+Xw+5eXlhc45HA6NGjVK+/fvlyRVVVXp0qVLYWM8Ho8yMzNDY64WDAYVCATCDgAAELs6NFB8Pp8kyeVyhZ13uVyh+3w+n3r16qW+ffu2OuZqZWVlcjqdocPr9XbktAEAgGE65VM8Npst7LZlWc3OXa2tMYsWLZLf7w8dZ86c6bC5AgAA83RooLjdbklqdiWktrY2dFXF7XarsbFRdXV1rY65msPhUFJSUtgBAABiV4cGSnp6utxutyoqKkLnGhsbVVlZqezsbEnS0KFDFR8fHzampqZGR48eDY0BAAA9W8Sf4mloaNDnn38eun3y5EkdPnxYycnJGjBggIqKilRaWqqMjAxlZGSotLRUffr00bRp0yRJTqdTM2fO1Lx585SSkqLk5GTNnz9fWVlZoU/1AACAni3iQDl06JDGjBkTul1cXCxJmj59utatW6cFCxbo4sWLmj17turq6jR8+HDt3LlTiYmJoccsX75cdrtdU6ZM0cWLF5WTk6N169YpLi6uA14SAADo7myWZVnRnkSkAoGAnE6n/H4/70dBlxq0cFu0pwB0qFNLxkd7CuhBIvn5zd/iAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAce7QngJ5r0MJt0Z4CAMBQXEEBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADG6fBAKSkpkc1mCzvcbnfofsuyVFJSIo/Ho969e2v06NE6duxYR08DAAB0Y/bOeNLbbrtNu3btCt2Oi4sL/fdLL72kZcuWad26dRo8eLAWL16ssWPH6vjx40pMTOyM6QAAWjFo4bZoTyFip5aMj/YU0AU65Vc8drtdbrc7dPTv31/S5asnK1as0HPPPadJkyYpMzNTr776qv7zn/9o06ZNnTEVAADQDXVKoFRXV8vj8Sg9PV2PPPKITpw4IUk6efKkfD6f8vLyQmMdDodGjRql/fv3t/p8wWBQgUAg7AAAALGrwwNl+PDhWr9+vXbs2KE1a9bI5/MpOztb58+fl8/nkyS5XK6wx7hcrtB9LSkrK5PT6QwdXq+3o6cNAAAM0uGBkp+fr8mTJysrK0u5ubnatu3y7zdfffXV0BibzRb2GMuymp37X4sWLZLf7w8dZ86c6ehpAwAAg3T6x4wTEhKUlZWl6urq0Kd5rr5aUltb2+yqyv9yOBxKSkoKOwAAQOzq9EAJBoP69NNPlZaWpvT0dLndblVUVITub2xsVGVlpbKzszt7KgAAoJvo8I8Zz58/XxMmTNCAAQNUW1urxYsXKxAIaPr06bLZbCoqKlJpaakyMjKUkZGh0tJS9enTR9OmTevoqQAAgG6qwwPl7Nmz+vWvf61z586pf//+GjFihA4cOKCBAwdKkhYsWKCLFy9q9uzZqqur0/Dhw7Vz506+AwUAAITYLMuyoj2JSAUCATmdTvn9ft6P0o11xy+IAhB9fFFb9xXJz2/+Fg8AADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwjj3aE0DHGLRwW7SnAABAh+EKCgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDj8sUAAQLfSHf846qkl46M9hW6HKygAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDh8iqcF3fEd4gAAxBKuoAAAAOMQKAAAwDgECgAAMA6BAgAAjBPVN8m+/PLL+tOf/qSamhrddtttWrFihe65555oTgkAgA7XHT98Ee2v54/aFZQ33nhDRUVFeu655/TRRx/pnnvuUX5+vk6fPh2tKQEAAENELVCWLVummTNn6oknntCtt96qFStWyOv1atWqVdGaEgAAMERUfsXT2NioqqoqLVy4MOx8Xl6e9u/f32x8MBhUMBgM3fb7/ZKkQCDQKfP7PvifTnleAAC6i874GXvlOS3LuubYqATKuXPn1NTUJJfLFXbe5XLJ5/M1G19WVqYXXnih2Xmv19tpcwQAoCdzrui8566vr5fT6WxzTFTfJGuz2cJuW5bV7JwkLVq0SMXFxaHb33//vb755hulpKS0ON4kgUBAXq9XZ86cUVJSUrSnE1WsxWWsww9Yi8tYhx+wFpfF6jpYlqX6+np5PJ5rjo1KoPTr109xcXHNrpbU1tY2u6oiSQ6HQw6HI+zcT37yk86cYodLSkqKqU12I1iLy1iHH7AWl7EOP2AtLovFdbjWlZMrovIm2V69emno0KGqqKgIO19RUaHs7OxoTAkAABgkar/iKS4u1mOPPaZhw4Zp5MiRWr16tU6fPq2nnnoqWlMCAACGiFqgTJ06VefPn9cf//hH1dTUKDMzU9u3b9fAgQOjNaVO4XA49Pzzzzf7FVVPxFpcxjr8gLW4jHX4AWtxGesg2azr+awPAABAF+Jv8QAAAOMQKAAAwDgECgAAMA6BAgAAjEOgdKCysjLZbDYVFRW1OmbPnj2y2WzNjn/9619dN9FOUFJS0uw1ud3uNh9TWVmpoUOH6qabbtLPfvYzvfLKK100284T6TrE6n644quvvtJvfvMbpaSkqE+fPrrjjjtUVVXV5mNicV9Eug6xui8GDRrU4uuaM2dOq4+Jxf0Q6TrE6n64lqh+1X0sOXjwoFavXq1f/epX1zX++PHjYd8O2L9//86aWpe57bbbtGvXrtDtuLi4VseePHlSDzzwgGbNmqWNGzfqgw8+0OzZs9W/f39Nnjy5K6bbaSJZhyticT/U1dXprrvu0pgxY/S3v/1Nqamp+uKLL9r8FuhY3BftWYcrYm1fHDx4UE1NTaHbR48e1dixY/Xwww+3OD4W94MU+TpcEWv74VoIlA7Q0NCgRx99VGvWrNHixYuv6zGpqand7uv6r8Vut1/zqskVr7zyigYMGKAVK1ZIkm699VYdOnRIf/7zn7v1/3ikyNbhiljcDy+++KK8Xq/Ky8tD5wYNGtTmY2JxX7RnHa6ItX1x9Q/UJUuW6JZbbtGoUaNaHB+L+0GKfB2uiLX9cC38iqcDzJkzR+PHj1dubu51P2bIkCFKS0tTTk6O3n///U6cXdeprq6Wx+NRenq6HnnkEZ04caLVsf/4xz+Ul5cXdm7cuHE6dOiQLl261NlT7VSRrMMVsbgf3n33XQ0bNkwPP/ywUlNTNWTIEK1Zs6bNx8TivmjPOlwRi/viisbGRm3cuFG/+93vWv2jr7G4H652PetwRSzvh5YQKDdo8+bN+vDDD1VWVnZd49PS0rR69Wq99dZbevvtt/Xzn/9cOTk52rt3byfPtHMNHz5c69ev144dO7RmzRr5fD5lZ2fr/PnzLY73+XzN/jCky+XSd999p3PnznXFlDtFpOsQq/tBkk6cOKFVq1YpIyNDO3bs0FNPPaWnn35a69evb/Uxsbgv2rMOsbwvrti6dau+/fZbzZgxo9UxsbgfrnY969AT9kOLLLTb6dOnrdTUVOvw4cOhc6NGjbJ+//vfR/Q8BQUF1oQJEzp4dtHV0NBguVwua+nSpS3en5GRYZWWload27dvnyXJqqmp6YopdolrrUNLYmU/xMfHWyNHjgw7V1hYaI0YMaLVx8TivmjPOrQkVvbFFXl5eVZBQUGbY2JxP1ztetahJbG2H1rCFZQbUFVVpdraWg0dOlR2u112u12VlZX6y1/+IrvdHvYmqLaMGDFC1dXVnTzbrpWQkKCsrKxWX5fb7ZbP5ws7V1tbK7vdrpSUlK6YYpe41jq0JFb2Q1pamn75y1+Gnbv11lt1+vTpVh8Ti/uiPevQkljZF5L05ZdfateuXXriiSfaHBeL++F/Xe86tCSW9kNrCJQbkJOToyNHjujw4cOhY9iwYXr00Ud1+PDh6/r0hiR99NFHSktL6+TZdq1gMKhPP/201dc1cuRIVVRUhJ3buXOnhg0bpvj4+K6YYpe41jq0JFb2w1133aXjx4+Hnfvss8/a/IOgsbgv2rMOLYmVfSFJ5eXlSk1N1fjx49scF4v74X9d7zq0JJb2Q6uifQkn1lz9K56FCxdajz32WOj28uXLrS1btlifffaZdfToUWvhwoWWJOutt96Kwmw7zrx586w9e/ZYJ06csA4cOGAVFBRYiYmJ1qlTpyzLar4OJ06csPr06WP94Q9/sD755BNr7dq1Vnx8vPXmm29G6yV0iEjXIVb3g2VZ1j//+U/Lbrdb//d//2dVV1dbr732mtWnTx9r48aNoTE9YV+0Zx1ieV80NTVZAwYMsJ555plm9/WE/XBFJOsQy/uhLQRKB7s6UKZPn26NGjUqdPvFF1+0brnlFuumm26y+vbta919993Wtm3bun6iHWzq1KlWWlqaFR8fb3k8HmvSpEnWsWPHQvdfvQ6WZVl79uyxhgwZYvXq1csaNGiQtWrVqi6edceLdB1idT9c8de//tXKzMy0HA6H9Ytf/MJavXp12P09ZV9Eug6xvC927NhhSbKOHz/e7L6esh8sK7J1iOX90BabZVlWNK/gAAAAXI33oAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAEAPsXfvXk2YMEEej0c2m01bt26N6PH//e9/NWPGDGVlZclut+vBBx9sc/wHH3wgu92uO+64I+K5EigAAPQQFy5c0O23366VK1e26/FNTU3q3bu3nn76aeXm5rY51u/36/HHH1dOTk67/i0CBQCAHiI/P1+LFy/WpEmTWry/sbFRCxYs0E9/+lMlJCRo+PDh2rNnT+j+hIQErVq1SrNmzZLb7W7z33ryySc1bdo0jRw5sl1zJVAAAIAk6be//a0++OADbd68WR9//LEefvhh3X///aquro7oecrLy/XFF1/o+eefb/dc7O1+JAAAiBlffPGFXn/9dZ09e1Yej0eSNH/+fL333nsqLy9XaWnpdT1PdXW1Fi5cqL///e+y29ufGQQKAADQhx9+KMuyNHjw4LDzwWBQKSkp1/UcTU1NmjZtml544YVmzxMpAgUAAOj7779XXFycqqqqFBcXF3bfj3/84+t6jvr6eh06dEgfffSR5s6dG3pey7Jkt9u1c+dO3Xfffdf1XAQKAADQkCFD1NTUpNraWt1zzz3teo6kpCQdOXIk7NzLL7+s3bt3680331R6evp1PxeBAgBAD9HQ0KDPP/88dPvkyZM6fPiwkpOTNXjwYD366KN6/PHHtXTpUg0ZMkTnzp3T7t27lZWVpQceeECS9Mknn6ixsVHffPON6uvrdfjwYUnSHXfcoR/96EfKzMwM+zdTU1N10003NTt/LQQKAAA9xKFDhzRmzJjQ7eLiYknS9OnTtW7dOpWXl2vx4sWaN2+evvrqK6WkpGjkyJGhOJGkBx54QF9++WXo9pAhQyRJlmV16FxtVkc/IwAAwA3ie1AAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADG+X8orBBP7PhKJAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gaussian_nus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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