Monte-Carlo problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as ss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7820906249999999, 0.005625407272639265)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = 400000\n",
    "m2 = 300000\n",
    "errm1 = 500\n",
    "errm2 = 1000\n",
    "g = 6.67384 * 10**-11\n",
    "r = 3.2\n",
    "errr = 0.01\n",
    "f = g * m1 * m2 / r**2\n",
    "errf = f * np.sqrt((errm1/m1)**2 + (errm2/m2)**2 + 4 * (errr/r)**2)\n",
    "f, errf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# это мы посчитали силу и ее погрешность по старинке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "distm1 = np.random.standard_normal(1000000)*errm1 + m1\n",
    "distm2 = np.random.standard_normal(1000000)*errm2 + m2\n",
    "distr = np.random.standard_normal(1000000)*errr + r\n",
    "distf = distm1 * distm2 * g / distr**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Это мы посчитали распределение для F "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pavel/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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nbF4X4wkrdBMXFbXN5OdkMKNopNdRYsoRZZFTbVvoxhMRF7qI+ERku4j8Kvx4uohsEZEaEfmJiGTGLqZJdlVHWlg0rQBJhStaDGCRU02NTqa1o9vrKCbNDGYL/XPA3osefxX4uqqWAi3A/dEMZlJH49kuDjWeS/nx816Lwhe82H601eMkJt1EVOgiMhm4A/hO+LEAK4Gnw095Arg7FgFN8kuX8fNe851D+AhSeaTZ6ygmzUS6hf4N4K+B3gmsC4FWVQ2EHx8HJkU5m0kRVUeayfQ7XDUpPeYKz5Eu5krthV9kxsTLgIUuIncC9apadfHiPp7a56kUIvKAiFSKSGVDQ8MQY5pkVlHbwoLJY8jy+7yOEjflzn52Hm+lO5A6F/EwiS+SLfQbgLtEpBZ4itBQyzeAMSLiDz9nMlDX14tV9XFVLVfV8qKioihENsnkfHeQXSfaWJQmwy29yp1qOntcdtel7lmxJvEMWOiq+gVVnayqxcCHgZdV9aPABuBD4aetAZ6NWUqTtHYcayXgatrsEO1VHp6vxoZdTDwN5zj0vwH+XEQOEBpT/250IplUUhXeMbhoamqf8n+pcdLKtMIc2zFq4so/8FN+R1VfAV4J3z8ELIl+JJNKKmpbKBs/itE56XfRh0XT8tm4vwFVTYvj74337ExREzNBV9l2pCXtxs97LS4uoOlcN7VNHV5HMWnCCt3EzP5TZzjTFUi78fNe5dNC611Ra8MuJj6s0E3M9I6fp/qUuf2ZUTSSMTkZVFqhmzixQjcxU1HbwoS8bCbnj/A6iiccR1g0NZ/KI3aki4kPK3QTE6pKRW0zi4rz03qHYHlxAYcaztF0tsvrKCYNWKGbmDjRep6TbZ0sKU7P4ZZevfPXVNlWuokDK3QTE707AheneaHPmzSaTJ9jwy4mLqzQTUxsPdzCqGw/ZRNGeR3FU9kZPq6ePNqOdDFxYYVuYqKitplF0/LxOek7ft5rUXE+u0600dkT9DqKSXFW6Cbqms91c6D+bNoPt/RaPK2AnqCy85hd8MLElhW6ibre466XTLdCh9AUAICNo5uYs0I3UVdR20ymz2FemlzQYiD5uZnMHDfSTjAyMWeFbqKuoraF+VNGk52RPhe0GEj5tHyqjrTgun1eB8aYqBjUbIvGXPBQ31vfHZrFrq7/5gHfr+Gh2+McKnGVFxfwVMUxaurPpv2RPyZ2bAvdRNUOdwYB/Cx29nsdJaH0TlBmhy+aWLJCN1FVoWUILgvDV+wxIVMLchg7MsvOGDUxZYVuoqrCnc1sOcposTnALyYiLC7OZ+th20I3sWOFbqImoA7b3FIW29Z5nxYXF3Ci9TzHW+yXnYkNK3QTNXt0Gh1ks9jZ53WUhLS0pBCALYdsK93EhhW6iZqt7mwAllih92n2hFGMHpHBlsNNXkcxKcoK3UTNVnc2U+U048VOce+L4whLphfwpm2hmxgZsNBFJFtEtorIThHZLSL/L7x8uohsEZEaEfmJiGTGPq5JVK4KW9wrWers8TpKQltaUsjR5g7qWs97HcWkoEi20LuAlao6H1gA3CYiS4GvAl9X1VKgBbg/djFNotunU2hjJEudvV5HSWjXhue3sWEXEwsDFrqGnA0/zAh/KbASeDq8/Ang7pgkNElhszsHwLbQB3DlxDzysv22Y9TERERj6CLiE5EdQD2wDjgItKpqIPyU48Ckfl77gIhUikhlQ0NDNDKbBPSmO4dpcoorxIrqcnwXxtFtC91EX0SFrqpBVV0ATAaWAFf29bR+Xvu4qparanlRUdHQk5qEFbwwfm7DLZFYWlJIbVMHp9o6vY5iUsygJudS1VYReQVYCowREX94K30yUBeDfCYJ7NWptJPLdTbc8k79TGB2rTsd+Ge2fO0DrPa90c9r22KXy6SsSI5yKRKRMeH7I4BbgL3ABuBD4aetAZ6NVUiT2N608fNBmSO1jKKDN92+/tA1Zugi2UKfCDwhIj5CvwB+qqq/EpE9wFMi8k/AduC7McxpEtib7hymy0kmiE08FQmfKEucfWyxQjdRNmChq+pbwDV9LD9EaDzdpLHQ+Pls7vRt8TpKUrnW2cv6wELqdQzj7EQsEyV2pqgZlj1azBlyWers9jpKUukdnrJhFxNNVuhmWHoL6To7wmVQ5sgRRtHBZneu11FMCrFCN8PypjuHEqmzYYNB8ovLUmcPm9yrvI5iUogVuhmyHvWxxZ1tR7cM0TJnF8d0HEfdcV5HMSnCCt0M2U6dwVlyWO687XWUpLQs/N/tNdtKN1FihW6G7LXgPBxcrrct9CEpkZNMpInX3XleRzEpwgrdDNlr7jzmySFGyzmvoyQlEVjme5vX3bkEVbyOY1KAFboZknYdwU6dYcMtw7TM2UUbI9mtxV5HMSnACt0MyWZ3LkF8LPdZoQ/H9c4uIPTXjjHDZYVuhmSTexU5dHKN1HgdJakVSTuz5Qiv245REwVW6GZIXnOvZqmzh0wJeh0l6S1zdlHplnFe7SqOZnis0M2gHWvuoFYnsCw8XGCGZ5nzNt1kUOGWeR3FJDkrdDNomw40AtgO0ShZ4uwnkx422Ti6GSYrdDNom2oamUATM+WE11FSQo50Ue7s5xV3vtdRTJKzQjeD0hN0ebWmgeW+txE7dDpqVjo7qNYpHNexXkcxScwK3QxK1ZEWznQGWOls9zpKSrnJ2QHAhuACj5OYZGaFbgZlw756MnxiO0SjbIbUMVVOs8G1QjdDZ4VuBuXlffUsLi5glJz3OkpKEYEVzg7ecOfSqRlexzFJygrdROxYcwc19WdZOdume42FFc52Oslic/ii28YMlhW6idgr++sBWGGFHhNLnb1k08UrNuxihmjAQheRKSKyQUT2ishuEflceHmBiKwTkZrwbX7s4xovvbyvnmmFOZSMzfU6SkrKlh6ud3azwV2AqnodxyShSLbQA8BfqOqVwFLg0yIyB3gQWK+qpcD68GOTos53B3njYBMrysYhdrxizKxwdnBUx3OwwaYkNoM3YKGr6klV3Ra+fwbYC0wCVgNPhJ/2BHB3rEIa720+1EhXwLXx8xhb4Qsdvrh+72mPk5hkNKgxdBEpBq4BtgDjVfUkhEof6PNfuog8ICKVIlLZ0NAwvLTGMy/tPs3ILD/XlhR4HSWlTZZG5sphXtx9yusoJglFXOgiMhL4OfB5VW2P9HWq+riqlqtqeVFR0VAyGo8FXWXdntOsmD2OLL/P6zgp71ZfJduOtlLf3ul1FJNkIip0EckgVOb/q6rPhBefFpGJ4e9PBOpjE9F4rbK2maZz3dw2d4LXUdLCrU4FAC/usWEXMziRHOUiwHeBvar67xd96zlgTfj+GuDZ6McziWDt7lNk+h1uKrO/sOJhlhxn+thcXrJhFzNIkWyh3wD8IbBSRHaEv94HPAysEpEaYFX4sUkxqspLu09zY+lYcrP8XsdJCyLw3rnj2XywibaOHq/jmCQSyVEum1RVVPVqVV0Q/vqNqjap6s2qWhq+bY5HYBNfu060c6L1PLfacEtc3Tp3AgFXWb/Phl1M5OxMUXNZa3efxOcIt1w53usoaWXB5DGMz8uyo13MoFihm36pKmt3neLa6QXk59r1LuPJcYT3zpnAxuoGOroDXscxScIK3fRr78kzHGw4x/vmTfQ6Slq64+qJdPa4rLOjXUyErNBNv57deQK/I1boHllSXMCEvGye31nndRSTJKzQTZ9cV/nVzpMsLx1LgQ23eMJxhPfPn8jG6gZaO7q9jmOSgBW66VPV0RZOtJ5n9YJJXkdJa6sXTKInqLywy3aOmoHZgcXp7qHRfS5+rufjZPMeVv3yGni2K86hTK+5V+RRUpTLsztOcO+SqV7HMQnOttDNu/Soj18Hl3KLs41csTL3kohw1/wr2HK4mVNtNreLuTwrdPMum9x5NJPHXb43vI5igLvmX4EqtnPUDMgK3bzLT4I3UUgbNzk7vI5igJKikSyYMoafVh6zKxmZy7JCN+/QpKP4rbuQD/peI1OCXscxYX+weAo19WfZfqzV6ygmgVmhm3f4RXA5Afzc49vodRRzkffPv4KcTB8/rTjmdRSTwKzQzQWqoeGWa6SGUueE13HMRUZm+blj3kSe31nHuS6bCsD0zQ5bNBds15nU6GQe9j/udRTTx+Gkf+DO4mfdD/Hrf/wg9/j7+QvqobYYBzOJzLbQzQVPBVcwgk7u8G3xOorpwyKppkTqeCq4wusoJkFZoRsAmnUUzwZv4AO+TYyS817HMX0QgY/41rNNZ7HLLfY6jklAVugGgKeCN9FFJmt8L3kdxVzG7/s2kkMn3w/e6nUUk4Cs0A0BdfhRYBXXObspc457Hcdcxmjp4Pd8r/Jc8HoaNc/rOCbBWKEbfusuoo6xrPG96HUUE4E1vpfoJoMngyu9jmISjBW64XuB25hEA7c427yOYiIw06njRmcnPwysolt9XscxCWTAQheR74lIvYjsumhZgYisE5Ga8G1+bGOaWKlyS9mqV3Kf/0X84nodx0ToPt9a6snnOfd6r6OYBBLJFvr3gdsuWfYgsF5VS4H14ccmCT0WWE0+Z7jXt97rKGYQbnJ2MkdqeSywmqCK13FMghiw0FX1VaD5ksWrgSfC958A7o5yLhMHe0+2s95dyH3+tTZNbpIRgU/7n+WQXsEL7hKv45gEMdQx9PGqehIgfDsuepFMvHzrlYPkct4OVUxStzlbKZE6Hg3cjU3CaCAOO0VF5AERqRSRyoaGhlh/nIlQ9ekz/OqtOj7mW8doOed1HDMEPlE+7X+WfTqNde4ir+OYBDDUQj8tIhMBwrf1/T1RVR9X1XJVLS8qKhrix5lo+9qL+8nN9PNJ/6+8jmKG4S7nDabLSb4WuMfG0s2QC/05YE34/hrg2ejEMfGw7WgLL+05zQM3lpAvZ72OY4YhQ4L8lf8nVOsUfh680es4xmORHLb4JLAZKBOR4yJyP/AwsEpEaoBV4ccmCagq/7J2H2NHZvJ/lk33Oo6JgtudrcyXA3w98CE6e+yiJOkskqNc7lXViaqaoaqTVfW7qtqkqjeramn49tKjYEyCemnPad481MxnVpaSm2WzJ6cCEfhCxo85SSHfe/2w13GMh+xM0TRyvjvIl5/fQ9n4UXz02qlexzFRtNTZxyqnkv9Yf4C6VpstM11ZoaeRx145wInW83x59Vz8PvvRp5ov+X+Aonz5+T1eRzEesX/VaeJgw1m+vfEQdy+4gmtLCr2OY2JgitPIZ1aWsnb3KTbs6/fAM5PCrNDTQNBV/vJnOxmR6eNv77jS6zgmhv5keQkzx43k7365i/bOHq/jmDizQk8Dj796iO1HW/ny6rmMG5XtdRwTQ5l+h3/90NWcau/koed2ex3HxJkVeorbU9fO19dVc/tVE7hr/hVexzFxcM3UfD69YibPbDvBC2+f9DqOiSMr9BTW3tnDn/5vFWNyMvjHu69CxM4kTBefWTmTqyeP5sFn3uZYc4fXcUyc2IHIqeCh0e9apAp/3fN5jrmLeCrznxj7tf0eBDNeyfA5/Me91/D+/9jEAz+s4plPXc+ITLsYRqqzLfQU9Wjwbta6S3jQ/ySLHSvzdDStMJdH7r2Gfafa+cIzb6E2JWPKs0JPQU8Hl/NvgXv4oPMqf+z7jddxjIdWlI3jL1bN4pc76vjaS/aLPdXZkEuK2RCcz4M9f8INzi4ezvhvbNg8zfQx/PZphRO++/nPDVDw2j9wv39tP69ti3E4E2tW6Cnk5eACPtnzfymTY3wr4+tkik3UZEJzvfyT/3u06Cj+MfBHZBDkj/zrvI5lYsCGXFLE2uBiPtHz55TJMX6c+RXyxObzML/jE+WRjEdZ5VTypcB9PBZ4v9eRTAxYoSc5VeW/AnfyqZ7PcZUc5keZX7ErEJk+ZUmAxzIeYbXzOv8SuJe/67mPbrUjX1KJDbkksbNdAb70y108E/gIdzqb+VrGf5Etdrq36V+GBPl6xmNMCDTz7eD7qXYn85+Zj1Ak7V5HM1FgW+hJauexVu785mv8cscJPu9/mm9mPGplbiLiiPKFjCd5JONRduoMbuv6KmuD5V7HMlFghZ5k2jt7eOi53XzgsdfpCrg8+SdL+bz/GRyxY4zN4Kz2vcHzmV9kojRKbWrXAAAHs0lEQVTzyZ4/57NPbudkm+17SWY25JIkOnuCPLX1KI9uOEjTuS4+eu1U/uq9sxmdk+F1NJPEZjkn+EXml/jP4Goe230PL+05xSdunMH9y6eTl23/byUbiefZY+Xl5VpZWRm3z0sFzee6+XnVcb6z6RCn27tYMr2Av79jDvMmX3S8cR/HHhszWMc+e5KH1+7j12+dZFSWn48sncp9109nwmibodNrIlKlqgOOi1mhJ5JwMXdqBq+7V/Fc8HpecJfQTQZLnd18zvcM1/n2ehzSpKzwiUW7TrTxXxsP8pvwTI3LSov40KLJ3Dx7nF2H1iORFvqwfjoichvwCOADvqOqDw/n/dKV6yo19WfZGriFze5cNrpXc44RjOIc9/pe5iO+9ZQ5x72OaVJdeIPiKuBR4GjGOH4WfA8/r17OZ6sbyKSHJc4+bnJ2cK2zj9lylAwJ2hmmCWTIW+gi4gOqgVXAcaACuFdV+72gYbpvoQddpeFMFydaO6g+fZbq02eoPn2G3XXttHaEjlAZTzM3+7Zxq1PJdc5uO9vTeM5VYYs7mw3uNbzsLuCATgYgi26ulkPMWXorM8aNZEbRSEqKchk3KhufY3NORFM8ttCXAAdU9VD4A58CVgMJc4VaVUUVtPf+heWghL536eO+notCUJXugBv6Cgbp7HHpDoYed4WXd3QHaD/fQ3tngLbzPbR19NDe2UPDmS5OtnVyur2TgPu7X6AjMnzMGj+SW+dMYPH0ApY8u4IpUm/zr5iE4ohynW8v1/n28rf8mBNayHZ3JtvdUra7M/n5thOc7Qpc9HwoGpXFhLxsxuVlU5ibSd6IDEZl+ckbkUHeCD8jszLI8jtk+R0y/Q5Zfl/4NvTl9zk4AiKCI+BzBEcEEXBE8IXv2xz/7zScQp8EHLvo8XHg2uHF6dsnf1jFxuqGd5Qul5YyvyviRJglNItuRnOOPDlHIe0skSYmSjMT/U1MkkZK5QSTpBGnUaER2IUdRGqSwiRpYpKviTt9W4DQv7eGrDEccK/gkE7ktBZw6lw+p8/mc/REPjt0FGfIoZOsqGcR4XflzkXlfknPX1r78o6nSp/fKy7M5TefWx69sHEwnELv61fju6pURB4AHgg/PCsiyTiH51hCtZvsUmU9wNYlwbQDR1NgPS4Yuxca5fNex7hgWiRPGk6hHwemXPR4MlB36ZNU9XHg8WF8judEpDKS8atElyrrAbYuiShV1gOSd12G80d+BVAqItNFJBP4MPBcdGIZY4wZrCFvoatqQET+DHiR0GGL31PV3VFLZowxZlCGdRy6qv4GSIdrnCX1kNFFUmU9wNYlEaXKekCSrktczxQ1xhgTO3agnDHGpIi0LnQRuU1E9ovIARF5sI/vf11EdoS/qkWk9aLvTRWRl0Rkr4jsEZHieGa/1FDXRURWXLR8h4h0isjd8V+Dd2Qdzs/lX0Rkd/jn8k3x8MyTYa7HV0VkV/jrD+Kb/N0iWJepIrJBRLaLyFsi8r6LvveF8Ov2i8it8U3+bkNdFxEpDC8/KyKPxj95BEJnU6bfF6EduQeBEiAT2AnMuczzP0Nox2/v41eAVeH7I4GcZF2Xi5YXAM3Jui7A9cDr4ffwAZuBm5JwPe4A1hHax5ULVAJ5ifwzITTm/Knw/TlA7UX3dwJZwPTw+/iSdF1ygWXAJ4FHvVqHy32l8xb6hakLVLUb6J26oD/3Ak8CiMgcwK+q6wBU9ayqdsQ68GUMeV0u8SHghSReFwWyCf1DzQIygNMxzHo5w1mPOcBGVQ2o6jlCpXNbTNNeXiTrokBe+P5ofndOymrgKVXtUtXDwIHw+3llyOuiqudUdRPQGa+wg5XOhd7X1AWT+nqiiEwjtHXxcnjRLKBVRJ4J/1n2r+HJyrwynHW52Ifpu+jjacjroqqbgQ3AyfDXi6rq1XzDw/mZ7ARuF5EcERkLrOCdJ/HFWyTr8hDwMRE5TujIt88M4rXxNJx1SXjpXOgRTV0Q9mHgaVXtnfrQDywH/hJYTOjPt49HO+AgDGddQm8gMhGYR+i8Ai8NeV1EZCZwJaGzlicBK0XkxpikHNiQ10NVXyJUJG8Q+gW7GQj089p4iGRd7gW+r6qTgfcBPxQRJ8LXxtNw1iXhJUXIGIlo6oKwS7dcjwPbw3+2BYBfAgtjkjIyw1mXXvcAv1BVr680PZx1+QDwZngI7CzwArA0JikHNqyfiar+s6ouUNVVhEqoJiYpIxPJutwP/BQu/KWUTWiOmsH8d4iH4axLwkvnQo9o6gIRKQPyCW0lXfzafBEpCj9eibfTBg9nXXr1N64eb8NZl6PAe0TELyIZwHsAr4ZchrweIuITkcLw/auBq4GX4pK6b5Gsy1HgZgARuZJQCTaEn/dhEckSkelAKbA1bsnfbTjrkvi83ivr5RehP6eqCe31/mJ42ZeBuy56zkPAw328dhXwFvA28H0gM4nXpRg4AThe/0yGsy6EjmD4NqES3wP8e5KuR3Y4/x7gTWBBov9MCO3IfZ3Q+P8O4L0XvfaL4dftB25P8nWpJXQk2FlCW/v9HrnkxZedKWqMMSkinYdcjDEmpVihG2NMirBCN8aYFGGFbowxKcIK3RhjUoQVujHGpAgrdGOMSRFW6MYYkyL+P/NjTP/BjNz+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(f - 5*errf, f + 5*errf, 0.00001)\n",
    "plt.plot(x, mb.normpdf(x, f, errf))\n",
    "plt.hist(distf, normed=True, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#попробуем то же самое с другими погрешностями, для этого просто копипастим куски решения с себя же"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7820906249999999, 0.5553593043410237)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = 400000\n",
    "m2 = 300000\n",
    "errm1 = 20000\n",
    "errm2 = 100000\n",
    "g = 6.67384 * 10**-11\n",
    "r = 3.2\n",
    "errr = 1\n",
    "f = g * m1 * m2 / r**2\n",
    "errf = f * np.sqrt((errm1/m1)**2 + (errm2/m2)**2 + 4 * (errr/r)**2)\n",
    "f, errf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#погрешность получается огромной, Монте-Карло не должен дать адекватного результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78625759, 0.98742028, 2.26410742, ..., 0.52748927, 0.77025717,\n",
       "       1.69608938])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distm1 = np.random.standard_normal(1000000)*errm1 + m1\n",
    "distm2 = np.random.standard_normal(1000000)*errm2 + m2\n",
    "distr = np.random.standard_normal(1000000)*errr + r\n",
    "distf = distm1 * distm2 * g / distr**2\n",
    "distf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pavel/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n",
      "  \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": [
    "x = np.arange(f - 5*errf, f + 5*errf, 0.00001)\n",
    "plt.plot(x, mb.normpdf(x, f, errf))\n",
    "plt.hist(distf, normed=True, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ну собственно и не показывает"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}