sample_truncated_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf64a3fe-4a1b-4359-9879-278c78e56f02",
   "metadata": {},
   "source": [
    "# About\n",
    "\n",
    "- 切断指数分布からサンプルを取得する\n",
    "- 逆関数法を利用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38fa323d-f8be-4185-8b3a-fbaefde5d78b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "250b76e8-210b-487c-9d3a-e251217ead89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "c_list = sns.color_palette().as_hex()\n",
    "color_num = len(c_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3fc6e1-89b1-4c47-981c-15e7a6a8cf06",
   "metadata": {},
   "source": [
    "# 指数分布に従う乱数の生成\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d38be2-a163-4639-9c36-5c229a57c0f8",
   "metadata": {},
   "source": [
    "## `scipy`の利用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bd5e73b-69d7-4547-a420-192841e46bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 0.5\n",
    "samples_exp = stats.expon(scale=1./mu).rvs(size=10000)\n",
    "\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.subplots(1,1)\n",
    "\n",
    "bins = np.linspace(0, samples_exp.max(), 50)\n",
    "_ = ax.hist(samples_exp, bins=bins, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3529614-d344-4278-ad3d-e055e3a2c953",
   "metadata": {},
   "source": [
    "## 逆関数法を使って指数分布に従う乱数の生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fff5c6f2-9582-446d-a509-6a204292a597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ran = stats.uniform.rvs(size=10000)\n",
    "sample_exp_f = -1./mu * np.log( 1.0 - ran )\n",
    "\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.subplots(1,1)\n",
    "_ = ax.hist(sample_exp_f, bins=bins, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766c5834-aca5-49ca-a2b2-239bddaf87d9",
   "metadata": {},
   "source": [
    "## 切断指数分布に従う乱数の生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb939d08-6f26-42ee-bf3a-503e65512816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 3\n",
    "ran = stats.uniform.rvs(size=10000)\n",
    "samples_t_exp = -1.0/mu * np.log( 1.0 - ran*(1.-np.exp(-1*mu*(L))) )\n",
    "\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.subplots(1,1)\n",
    "\n",
    "_ = ax.hist(samples_t_exp, bins=bins, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4ce94a-9b5b-48cf-a5ff-12da4886e1cc",
   "metadata": {},
   "source": [
    "## 比較"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "104dc9ab-058b-4016-9ced-4f9940a0e78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xffff7abb77c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.subplots(1,1)\n",
    "\n",
    "_ = ax.hist(samples_exp, bins=bins, alpha=0.5, density=True, label=\"using scipy\")\n",
    "_ = ax.hist(sample_exp_f, bins=bins, alpha=0.5, density=True, label=\"inversion_method Exp(x)\")\n",
    "_ = ax.hist(samples_t_exp, bins=bins, alpha=0.5, density=True, label=\"truncated Exp\")\n",
    "_, _, ymin, ymax = ax.axis()\n",
    "_ = ax.vlines(x=L, ymin=ymin, ymax=ymax, colors=\"red\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31d5edf-6d9c-4ce0-8274-b1ee80b9392b",
   "metadata": {},
   "source": [
    "# 切断正規分布ならScipyに実装されている\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b48220d-d80c-4b1e-a1a0-e271be03f5b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xffff78992df0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X7/6PcW9fja/qGKgmTD2GofUYjiltAEp86nm98QR7HO5vXXyGYeA75UB37MV7aBvu7Wtwf7kSNbkHlmGTMfW9GkVtfWenLXKu683mwhO8vm4f/5k7ImQ+XYj212SCT05OJiMjg4KCAnJycigoKCAjI+Oc8kx6ejpbtmxp/Pcf//hHamtreeKJJwITtQg7W3aVcOB4Nd+fMhCrxS8fHFvE0D14Ctfj3r4a40wVanIPoq77HuY+V6JEtbxUpCgKWmI6WmI6lowb8LlO4z2wFU/hOlwb/oby6VtYhk/BPHAcitq+7Y21mZk2tjeL1+1j+4EKhvfr3K7HF6GjWe+8OXPmMHPmTF588UXi4+Mbh0DOmDGDRx55hKFDhwY0SBHe6jw6b7x3gB6psYwdmtb0Dn5kGAbeQ9uo2/waRnUpWtoALON+gNZtiF97tqo1DsvgiZgHjUc/9CV1XxRQt2kRnp3vEjX6Tkzd2/caGX95VzZsO8Zr6/czuHcSJk0eeemIFCOE5hqVGnxktuXtD4tYurGIJ/JGMqBH+42c8VWX4tr4Cvqxr1AT04kak4ep25AWv05rzolhGOiHvsC1eXH9H5YeI7Beew9qbHKLj99aX+4v5/dvbOfOiZdx45X19wYi8f0VCYJWgxeiLapO17Fy8yGuGGBvt+RuGAaeXRuo2/w6KCpRY+7CPGh8u5ZKFEXB1GskMd2H4N6xFvfnSznzxi+xjrkb02Vj2qUuPqxvMoN7J7FsUxGjh3SRJ4Y7IPncJgLqrQ8O4PMZ3D6+X7scz1d7Eueq56nbtAgttR8xtz+NZciN7V4Hb6BoZqJGTCHmO0+jJXXH9d4CXO/8AZ8r8D1PRVHIndAPp9vL25uKAn48EXokwYuA2X/0JB/tOMGkUd1JSbQF/Hhexx5q35yN7thL1LX3Ypvys3YtiVyKGp+CLWsmUdfcgffIDmrfmoNeejDgx22YDmLDtmOyvF8HJAleBIRhGLz09k5ibGayRvcK+LHc21fhLJgHFivRt/wKy6AJITc8UFFVLMMmE53zCwBq334Wd+H6gC+5l3NdH8wmlSUbDgT0OCL0SIIXAbFtXzk7D1Rwy3W9ibYGrjxi6B5cG/5G3ebXMfUcScwtc9CSgvuwUVM0e29ibp2L1nUQdZsWUbfpFQxf4FZkSoixkDWmfrbJL/fK8n4diSR44XeGYbDkvQN0T41l3IjAzRZpuGpwrvxfvPs/xjLqVqw3/gTFEvhSkD8o1lhskx/FMnwKnl3v4Vz9Wwy3M2DHu3FUN5Ljrby84quAHUOEHknwwu90n0FJZS3Xj+yGpgbmLearLuXMsqfRSw5gnfAAUZdPC7mSTFMURSXq6u8Sdd330I8VUvv2M/hqzp/Ezx/MJo0rM1IodkTGsELRPJLghd+5PfXzkUdZAvOovl55lNq3n8VwncaW9d+Y+10TkOO0F0vGDdhu/k98p8vrk/ypksAcx6Ti1X2tetZEhCdJ8MLvPN76erLF7P8Er5cepHb5rwGIzn4SU5f+fj9GMJi6DSE6ayZ46qh9+1n0yiN+P0bD+fB4ZUGQjkISvPC7hgRiMfk3wXsde6hd8RyKJZroaU+iJXX16+sHm2bvhW3ak6Cq1C7/H78PozSb6i93j6z41GFIghd+525I8Gb/vb28jj04Vz2PGtOJ6GlPosan+O21Q4nWKZ3oaU+iWKKpXfkbvyb5hgTv9gRuxI4ILZLghd819uD9VKKpT+4voMYmY8t6AjUmtFeCais1zk509kyUqNj6JF/mn6dQLQ09eCnRdBiS4IXfuf1Yg9dP7KtP7jGd6pN7dGKbXzMcqLHJRGc9gRIVQ+2K36CXF7f5NRtKZm5J8B2GJHjhdw09xKg2Jni9/BC1q15AiUnElj2zwyT3Bmpc5/okb7HhXPk8etXxNr2eWXrwHY4keOF3DT3EhoTSGvrJ4zhX/i9KVDTRU/+7wyX3Bmqcneipj4Oi4Fz5G3ynW/8k6tcJXmrwHYUkeOF3be3B+06X41zxv6AoRE95PGQmDAsWNaELtimPY3jqqF3xG3xn149tKSnRdDzNSvBFRUXk5uaSmZlJbm4uxcXF523z5ptvkp2dTU5ODtnZ2SxatMjfsYow0TBKozU1eJ/rNLUr/xfD46qfDTKxi7/DC0tacneib/5PjNpTOFc9j+GubfFrfD2KRhJ8R9GsBD979mzy8vJYs2YNeXl5zJo167xtMjMzefvtt1m2bBmLFy9m4cKF7N692+8Bi9DXMM66pcMkDU9d/ZwsNRXYJj+KltwjEOGFLS21H7abHsZXeRznmj9g6J4W7d9wPjy6lGg6iiavwIqKCgoLC8nKygIgKyuLwsJCKivPnTMjNja2cS4Ql8uFx+MJu7lBhH94PC0fJmn4vDjfnY+vrAjrxAci5glVfzN1G4L1hh+iO3bj2vA3DKP5vXHz2XVZPdKD7zCaTPAOh4PU1FQ0rf5i1TSNlJQUHA7HeduuW7eOqVOnMn78eO677z4GDBjg/4hFyGvpMEnDMKjbuAj9yHaixt6DudcVgQwv7JkvG0PU1bl4D35C3ceLm7+fWWrwHY1fJ+qeOHEiEydO5Pjx4zz00EOMGzeOPn36NHv/Sy0e2xS7Pa7V+4aacG+L2VK/9qfFpDarLVWb3qBmzwckjr2NpOtzAh1eq4TaOTEm3k6Fr4bqT1YQn96dhCunNrlPTFx9SSfKag659rRWpLQDAtOWJhN8WloaJSUl6LqOpmnouk5paSlpaWkX3Sc9PZ2hQ4fy3nvvtSjBV1TUtGqmO1ldPbScqnZiNqkoitJkWzz7PsL1/mJM/UbjHZQVkm0P1XNiDL8NU5mDincWcoZYzL0uv+T23rP3RqpO1oZke1oqVM9La7S2LaqqXLJj3GSJJjk5mYyMDAoKCgAoKCggIyODpKSkc7Y7cODr5cAqKyvZsmUL/ftLHbUjcnt9jfXeS/E69uB6/x9oaQOxXv8DuWfTQoqqYp3wI1R7L1zr/tLkvDWaqqAqUqLpSJo1zGHOnDnk5+eTmZlJfn4+c+fOBWDGjBns2LEDgNdff52pU6eSk5PD9773Pe6++26uvfbawEUuQpbHq2NuYgSN79QJnO/8ATXeju2mh1E0cztFF1kUUxS2zEdRouNxrvk9vpqKi2+rKFjMmjzJ2oE0qwbft29flixZct7PFyxY0Pj9k08+6b+oRFhze32NE1tdiOGqoXb1b1EUFdvkx1CiYtoxusijRidgm/wYtUufxrn6d2dno7zw0oWS4DsWeZJV+J3H67voXPCG7sW59k8Ypyuw3vRIxE772960Tl2x3fgQvqpjONf/BcN34SRuMWuNo5xE5JMEL/zO4/VhukAP3jAM6ja9gu7YjfX6H2DqclkQootcpm5DiBp7N/rhL6nb/NoFt7GYVOnBdyB+HSYpBNRPVXChEo1nx2o8ezZiGZmN+bIxQYgs8lkGTcB30oFn5zuondKxZNxw7u/NmkxV0IFID174necCNXjv4S+o2/wvTL1HYRl1S5Ai6xiirrkDrdsQ6jb9E+/xXef+zqzJkn0diCR44Xcerw/zN2rweuUxnOv+gprcA+sNM1AUedsFkqJq2CY9iJqQgnPtn/BVlzb+zmLW8MiSfR2GXGnC79xeX+PMhT7XaZxrfnd2ON9PUcxRQY6uY1As0dgyHwXAufp3jbNPms2qjIPvQCTBC7/zeOtr8IbuwbX2Txi1VdgyH0GNTWp6Z+E3akIqtht/gu9UCc519SNromSYZIciCV74XUMPvnz139Ede7Be/0O0lL7BDqtDMqVn1I+sObKduq3/wmKSBN+RyCga4Xcer4/+tZ9z+vC7WEZkYe43OtghdWiWQePxVR7Fs301fVLMfOlNDXZIop1ID174XW+OMKh8LdH9r8Ry5a3BDkcAUWPuROs6iGFlK0nznT/Vt4hMkuCFX3mqjnNv9AecibKTMu2nMmImRCiqCdvEB3GZE7jLsu6Sc9aIyCFXn/Abo+4MrjW/x4fC7j53oUZdeD4UERyKNZY9l92Dhl4/ssbjCnZIIsAkwQu/MHw6znV/xqgp56WaG/BFJwc7JHEBvrguvHJmHL6qo7g2LGjRkn8i/EiCF35Rt/k19KM70S+/k4Pe1MZx8CK0mE0auz1dUa64HW/xZ7g//XewQxIBJFehaDP3rvfw7FyLeWgmrp71I2YuNV2wCJ6os/P0e/pNwDxgHO5ty/Hs3xzkqESgyFUo2sR7fBd1m/6J1n0YUVfnNo6xNl9kumARXA0LoXt8BlHX3ouWNgDX+39HLz3QxJ4iHEmCF63mO1WCc+2f6p+YnPgAivr1VLSWJlZ0EsHRmOA9OopmwnrjT1CiO+Fc8wcZWROBmnUVFhUVkZubS2ZmJrm5uRQXF5+3zfz585k6dSrZ2dnceuutbNy40d+xihBi1J2pX5UJBdvkR1Es0cDX631KiSY0RZ1N8A3nSbXGYct8FMPrxrlGRtZEmmZdhbNnzyYvL481a9aQl5fHrFmzzttm2LBhvPHGGyxfvpxnn32Wxx57DJdL3iyRyPA1rMpUhvWmh89ZlclzdrWgCy34IYKv4eb3N6cr0JK6Ypv0Y3yVR3Gt/+tFV4MS4afJq7CiooLCwkKysrIAyMrKorCwkMrKynO2u+6667DZ6sc9DxgwAMMwOHnypP8jFkFVvypTPvrxXVjH/QBT2oBzft+wmMTFluwTwdVYovnWfDSm7sOIGn0X3kPbqNv6r2CEJgKgybloHA4HqampaFr9G0PTNFJSUnA4HCQlXXh2wKVLl9KjRw+6dOnSomCSk2NbtP032e1xrd431IRyW05ufpua3e+ROOZWksZOPu/3tsMnAeiSUt+GUG5LS0RKO065TgJgi7ac36bxt1DurqD601UkdO1J/OU3tX+ALRQp5wUC0xa/Tza2detWfv/73/OPf/yjxftWVNTg8xkt3s9uj6Os7HSL9wtFodwWz8FPcK1bhKnPlXgHZ10wzoqq+nnHT1c7wR4bsm1piVA+Jy3VcPO7vPLMBdtkjPgOWskxylcv4AyxmLoPbe8Qmy2Szktr26KqyiU7xk2WaNLS0igpKUHX62uruq5TWlpKWlraedtu27aNxx9/nPnz59OnT58WBytCl156ANeGv6Gm9r3kqkzus6sFyYNOoeliJZoGiqphm/hj1KSuON+dj15xpD3DE37W5FWYnJxMRkYGBQUFABQUFJCRkXFeeWb79u089thj/OEPf2Dw4MGBiVYEha+6DOea36NEJ2K76REUk+Wi2zYOk5QafEiyfGsUzYUoFhu2zMdQzFacq3+L70xVe4Un/KxZ3aw5c+aQn59PZmYm+fn5zJ07F4AZM2awY8cOAObOnYvL5WLWrFnk5OSQk5PDnj17Ahe5aBeGq4baVc9j+HRsNz+Gaou/5PaNDzrJOPiQ9M1x8JeixiZhm/wYhrsW56oXMNzO9ghP+FmzavB9+/ZlyZIl5/18wYIFjd+/+eab/otKhATD66Z2ze8wasqxTf1vtMT0Jvdxe32YNAVVUdohQtFSDVMVNGddVq1zT2yTHsK5+nc41/4J2+THUDRZIyicSDdLXJDh8+Fa/1d8JQewjv8Rpi79m7Wf26tL/T2EmTQVhYvX4M/bvvtQrOO+h37sK1wf/APDaPkgCBE88udYnMcwDOo+ysdb/BlRY+7C3OfKZu/r8fpkHpoQpigKZpPaonVZzQOuw3emCvenb+GOTiTq6u8GMELhT5LgxXncny3FU7gey/ApWIbc2KJ9PV6fTFMQ4swmFbf30jX4b7OMzMaoPYn7y5Uotjgsw24OUHTCnyTBi3O4v3oX9+fLMPW/DstVt7d8f69PSjQhzmLWmlWD/yZFUYgaczeG6zR1m19HscZj7j82QBEKf5EELxp59m+m7sNXMfUciXXc91BacaPU45EafKgzayreFiZ4AEVVsY6/H2fdGVzvv4RiicbUa2QAIhT+IleiAMB7aBuuDQvQ0vpjnfhjFLV1dXS31ydj4EOc2ay2uAffQNHM2G58GLVzT5zr5uM9Vujn6IQ/SYIXeI9+hfPd+aide2DLfPSSDzI1xaNLiSbUWVpRg/8mxWIj+ub/Qo3vgnPN79BP7PNjdMKf5Ers4Lwn9uF85/eoCV2Ivvm/UCy2Nr2exyM3WUOd2aTh8bRtSmDFGott6s9QYjpRu/oF9PJi/wQn/EquxA5MP7EP56rnUWKSsE15HMXa+tk8G8g4+NBnNql49LbP+a5GJxI99b9RLNHUrvgNevkhP0Qn/EmuxA5KL9lP7arnUaITiM56AjU6wS+vK+PgQ5/FpDbO299Wamwy0VkzUUxR1K54Dr3isF9eV/iHJPgOSC89QO3K51Fs8URnzUSN6eS31/Z4fbIea4irf9Cp9TX4b1Pj7URn1yd5Z4Ek+VAiV2IH43XsoXbFb1CssfU9dz8md5Bx8OHAYmr5OPimqPEpRGc9ASYztQXz0MuK/Pr6onXkSuxAvEe/wrny+fra6bQnUWOT/X4Mj9TgQ15LpypoLjUhlejsn6NYbNQWPIdXRtcEnVyJHYT30Daca36LmpCCLfvnfu+5A/h8Bl7dkHHwIS5QCR7O9uSzf44SnYBz5W9knHyQSYLvADx7NuJ854+oSd3ra+5+uqF63nH0hsU+5G0Vyizm+nHwgZoZUo1NJjp7JmpcCs5VL+A5uDUgxxFNkysxghmGQd0XK3G9/xJaekb9kDY/DIW8mMbFPiTBhzSzScMwQG/F+sfNVV8G/DmavTeud/+M+6t1ATuWuLhmXYlFRUXk5uaSmZlJbm4uxcXF522zadMmbr31VoYMGcK8efP8HadoIcPno+7jxbi3/gtT36vrF2to40NMTZH1WMODWas/P4Eq0zRQomKwTf0ZWo/h1H34T+o+eVPmk29nzboSZ8+eTV5eHmvWrCEvL49Zs2adt0337t155pln+OEPf+j3IEXLGJ46XGv/iGfnO5iH3Ih1wo/aZSUeWY81PFhasKpTWymmKGw3PYx5wDjc25bjWv9XDK874McV9ZpM8BUVFRQWFpKVlQVAVlYWhYWFVFZWnrNdz549ycjIwGSSCSqDyXemitrlz+I9/AVRY+7COuYuFKV9etRuKdGEhYbz09S6rP6iqBpR476P5arv4D2wGeeK3+BzVrfLsTu6Jq9Eh8NBamoqmlbfK9M0jZSUFBwOR8CDEy2jlx6kdun/w3fyBLbMn7Z4sY62auzBy4NOIa3hE1Z79OAbKIpC1IgsrJMeRC8vpnbpU/JAVDsIqe52cnLrbwDa7XF+jCS4WtOW01+up2zVXzHFJpF657NEpfbyf2BNOFFdB0Dn5NjGNkTKeYmUdgAkJ8UAEBtnbf922Sfi6taDkjeew/n2M9izHiJ2UOsXDomk8xKItjSZ4NPS0igpKUHXdTRNQ9d1SktLSUtL83swFRU1+FpxZ99uj6Os7LTf4wmGlrbF0D3UfbwYT+F6tK6DsE58kGo1FoLw/1FWXgNA7Zk6yspOR8x5iZR2QH1bXLX1f4hLy2qIjwrC/RJLF6zTZ+FaO5/Sf79A1YFdRF11G4rasv5mpJ2X1rRFVZVLdoyb/CydnJxMRkYGBQUFABQUFJCRkUFSUlKLgxH+5asupXbZM3gK12MeNhnbzf8V0GGQTWmYwErGwYe2hhp8W+aEbys1OhFb1hOYM8bj2b4K5/J5+GoqghZPpGrWlThnzhzy8/PJzMwkPz+fuXPnAjBjxgx27NgBwKeffsq4ceNYuHAhr732GuPGjWPjxo2Bi7yD8xz8hDNvzsZXXYr1poexXnNHq1dh8ltMugyTDAcWc/37JNDDJJuiaCas1/0H1gkPoFceofbN2XgPfxnUmCJNsz4T9e3blyVLlpz38wULFjR+P2rUKD744AP/RSYuyHA7cX30f3j3bkS198E26ceocfZghwXQuIiEJPjQ1l7j4JvL3O8atM69cK6bj3P1bzEPmkDUNbkopqhghxb2Quomq7g0r2MPrvcWYNRUYBmZjeXynHYZ395cbhkHHxbM5uCXaL5NTexCdM6vqPvkTTw71uA9Voht/P1oKX2CHVpYC53sIC7KcDup2/oGnsL1KPF2oqf9Ai21X7DDOo9MVRAegjFMsjkUkwXr6Dsx9RyBa8MCapc9hXloJlGjbpHefCtJgg9x3sNf4Nq4CONMFeYhk4i68jYUszXYYV1QwyISMg4+tDU+6BRiCb6BKT2DmO88Rd2Wf+HZvhpv0WdYx30fU9dBwQ4t7EiCD1G+6lLqPl6M99A21E7p2CaFZq/9m9xeH6qioKmS4ENZqCd4qJ/Hxjru+5j6XYNr48s4VzyHqc9VRF2TG5B1DCKVJPgQ43M7qfvkTdzbV4GiYbnqO1iGTg6pWvvFeLy+xvquCF2NwyTbaaqCtjClZxBz21O4v1yJ+4sVeA9/gWVEFpZhk4MdWlgI/azRQRg+L57dH3Bk29voZ05i6ntNfW8lAAtzBIrb65Mx8GFAVRRMWuAW/fA3xWQh6orpmPuPpe7j13B/+haeXRuovuFOjLRRKPKJ8aIkwQeZ4dPxHthC3efLME6VYO2eQdSkn4R8OeZCPF5dEnyYsARwVadAUePs2G56GO/xXdRtWUL5ihdRO6VjuXw6pt6S6C9EEnyQGLoX7/6PqdtWgFFdgprUHWvmo3S54lrKzz7yH248Xh8mGSIZFswmNeRG0TSXKT0DbfqviK78ivJ1r+Ja9yJqYjqWy7Mx9bkq6A/8hRJJ8O3MqDuDe9d7eL56F+NMFWpyT6JuehhTz5EoioqiKMEOsdXcHinRhIv6dVlDvwZ/MYqiEDtwNLWdBuEt+gT352/jWv9XlK1vYBlyE+aB4wK+wE04kATfTvTyQ3h2bcCz72Pw1qF1HYTluu+hdR8W1kn9m6REEz4sZi1se/DfpKgq5r5XY+pzJd5D2/BsX0Pd5sXUfbYUc/8xmDPGoyV1C3aYQSMJPoAMVw2eg5/g2bMRX9lB0CyY+l6FZehNaMk9gh2e33m8PnnIKUyYw7AGfymKomLudQXmXleglx7EvXMtnl3v4/lqHVrqZZgGXIu5z5Uoluhgh9quJMH7meFx4T28He+BLfUTJ/m8qJ26EjXmLsyXjUGJigl2iAHj9vqIi7YEOwzRDJGW4L9JS+mDbcKP8I3Jw7t3E55d71P3wULqPszH1HMkpr5XYeo+tEM8HSsJ3g98tSfRj+zAW/w53qM7Qfeg2OIxD56I+bIxqMk9IqYMcykeGSYZNiwmlbowGAffFqo1DsuwmzEPnYyvrAjPvg/xHtiK9+BWMFkwdR+GqdflaN2GoNrigx1uQEiCbwXDW4d+Yj/68V14j+7EV14MgBLTCfPA6zH1HoXWpX+HG7bl9uryoFOYsJg0amo9wQ6jXSiKgpbSBy2lD8boPHTHHrxFn+It+gxv0aeAgmrvjan7ELT0DLSUviimyPgkKgm+GXzOanwlB9BL9qGX7EcvPQg+LygqWkpfLFfehqn7sA7TU78Y6cGHD7NJxaNHZonmUhRVw9R1EKaugzDG3o2v/FB9SfXIl7i3LYfP3wbNjJbSFy21H1qXfqgpfVGt4bk0oCT4bzB8PozTpeiVx/BVHsVXXoxefgjjTGX9BqqG2rkX5iGTMKUPQutymQzF+gaP14dZkzHI4cBsUhtX4OqoFEVFs/dGs/cm6oocDHdtfe/++G70E3txf7kKvqgvYymxyWide6F27oma1A0tqRtKXGcUJbQ7NM1K8EVFRcycOZOTJ0+SmJjIvHnz6NWr1znb6LrO008/zcaNG1EUhfvvv5/bb789EDG3ieHzYpw5ie90Gb7qUozqMnynTpz9KgG94WOrgpqQipbWv/7EpvRB69wrYj66BYJb5qIJG5YwHwcfCIoluv4mbM+RwNlSbGkRvrIi9PJi9PJivMWffb2DZkFNSEVN7IKa0AU1PgUlzo4ab0eJTgyJB66aleBnz55NXl4eOTk5LFu2jFmzZrFo0aJztlm+fDmHDx/mnXfe4eTJk0yfPp3Ro0fTrVvgx6AaPh2f6zS4ajBcNfhcpzGc1fVftacwak/iq63CqKnEcJ4C4xsLeysqSrwdNaEL5m5D0Dp1RT37pZgj/y67vxiGISWaMGI2RcY4+EBSTFGY0gdC+sDGnxkeF76qY+iVR/FVHcd30oFefqi+ln9OXlFQojuhxCah2hJQYhLrk74tHtUaj2KLQ4mKrV9DOYBDN5tM8BUVFRQWFrJw4UIAsrKyeOqpp6isrDxn4e2VK1dy++23o6oqSUlJTJo0idWrV3PfffcFLHgA77FCiv7+Qn1N/EKiYlCjE1CiE1G7DUGNTa7/T4+zo8bZUWKTQuIvbbjz6rLYRzixmCN3mGQgKWZrfX0+pe85Pzd8XoyaSnzVpfhOl2PUVOA7U1n/s5MOfMd3gbv2wi+qmYi/5ymwpPk93iYTvMPhIDU1Fe1sbVXTNFJSUnA4HOckeIfDQXp6euO/09LSOHHihN8D/rYj7ni+YhROn4qLKGqxUouNWqycwYruNIETOG/B9pNnv0KLpqnoYXjzq6HzYpa5aMKC2aSi+wye+MtHwQ6l1UL3WrEAaWe/vmbCSzQuonESgxMbddiow6L4GF8XQ1wAqr8hdZM1OTm2xfvoag82F01qHBFgO/slSwK0P5OqMvHqntiTv36Yy24Pz9EH3xYp7YD6ttw0ujenXV50n9H0DiIgDKAW8Jo04pOS6Jzo/wEbTSb4tLQ0SkpK0HUdTdPQdZ3S0lLS0tLO2+748eMMGzYMOL9H3xwVFTX4WviG04BHckdSVna6RfuFKrs9Lrzb4vM1xh/2bTkrUtoBX7fFqsJdEy8LdjhtEknnpXOirVVtUVXlkh3jJgumycnJZGRkUFBQAEBBQQEZGRnnlGcAJk+ezJIlS/D5fFRWVvLuu++SmZnZ4oCFEEL4R7PuiM2ZM4f8/HwyMzPJz89n7ty5AMyYMYMdO3YAkJOTQ7du3bjpppv47ne/y0MPPUT37t0DF7kQQohLUgzDCJkiXGtKNBBZH9WkLaEnUtoB0pZQ1dq2tLlEI4QQIjyF1CgaVW39PC5t2TfUSFtCT6S0A6Qtoao1bWlqn5Aq0QghhPAfKdEIIUSEkgQvhBARShK8EEJEKEnwQggRoSTBCyFEhJIEL4QQEUoSvBBCRChJ8EIIEaEkwQshRISKmAT/5z//mezsbKZPn05OTg4rV64MdkitNnfuXCZPnsy0adO44447GmfsDEfLli0jOzubQYMGkZ+fH+xwWqyoqIjc3FwyMzPJzc2luLg42CG1yrx585gwYQIDBgxg7969wQ6n1aqqqpgxYwaZmZlkZ2fzk5/8hMrKymCH1WoPPvgg06ZNY/r06eTl5bFr1y7/HsCIENXV1Y3fnzhxwhg5cqRx8uTJIEbUeuvXrzfcbnfj9xMnTgxyRK23Z88eY9++fcbjjz9u/POf/wx2OC12zz33GEuXLjUMwzCWLl1q3HPPPUGOqHU++eQT4/jx48b48eONPXv2BDucVquqqjI2b97c+O//+Z//MX7+858HMaK2+WbeWrt2rTF9+nS/vn7E9ODj4r5eUq22thZFUfD5QnG9xqaNHz8es9kMwIgRIzhx4kTYtqV///7069cPVQ2/t1rDgvNZWVlA/YLzhYWFYdljHDVq1HmrsIWjxMRErr766sZ/jxgxguPHjwcxorb5Zt6qqalBUfw7eVpIzSbZVosXL+aVV17hxIkTPPvss3Tq1CnYIbXZq6++yg033BCWCTLcNXfBeREcPp+PxYsXM2HChGCH0ia/+MUv+PDDDzEMg7///e9+fe2wSfC33HLLRf9Sf/TRR2iaxp133smdd97Jnj17+NnPfsbo0aNDMsk3py0AK1asYPny5bz66qvtGV6LNLctQvjbU089RXR0NHfffXewQ2mTZ555BoClS5fy3HPPsWDBAr+9dtgk+H//+9/N3nbAgAGkpKSwdevWkFwXtjltWbt2Lb/97W95+eWX6dy5cztE1TotOS/hprkLzov2N2/ePA4dOsRf/vKXiPl0O336dGbNmkVVVZXfOqaR8T8D7N+/v/H7I0eOsGvXLvr16xfEiFpvw4YN/PrXv+all16iW7duwQ6nw2rugvOifb3wwgvs3LmT+fPnY7FYgh1Oq505cwaHw9H47/Xr15OQkEBiYqLfjhExC3789Kc/Zf/+/ZhMJjRN47777mPKlCnBDqtVrrnmGsxm8zmJ5OWXXw7JclNTCgoKeO6556iursZsNmOz2fjHP/4RNn98Dxw4wMyZM6muriY+Pp558+bRp0+fYIfVYk8//TTvvPMO5eXldOrUicTERFasWBHssFps3759ZGVl0atXL6xWKwDdunVj/vz5QY6s5crLy3nwwQdxOp2oqkpCQgJPPPEEgwcP9tsxIibBCyGEOFfElGiEEEKcSxK8EEJEKEnwQggRoSTBCyFEhJIEL4QQEUoSvBBCRChJ8EIIEaEkwQshRIT6/9Uj+zB/zrxiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(-3, 3, 100)\n",
    "pdf_trunc_norm = stats.truncnorm(a=-1, b=1, loc=0, scale=1).pdf(x=xs)\n",
    "pdf_norm = stats.norm(loc=0, scale=1).pdf(x=xs)\n",
    "\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.subplots(1,1)\n",
    "ax.plot(xs, pdf_trunc_norm, label=\"truncated norm\")\n",
    "ax.plot(xs, pdf_norm, label=\"norm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eeb549f-4f9f-436c-a253-6429491d947d",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}