jnothman/uspetplot-gh167.ipynb

## uspetplot-gh167.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-09T11:20:14.234777Z",
     "start_time": "2021-09-09T11:20:14.232154Z"
    }
   },
   "outputs": [],
   "source": [
    "from upsetplot import from_contents, plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-09T11:15:58.006192Z",
     "start_time": "2021-09-09T11:15:57.995175Z"
    }
   },
   "outputs": [],
   "source": [
    "data = from_contents({\"c1\": \"ABDE\", \"c2\": \"AD\", \"c3\": \"BCE\", \"c4\": \"ABD\", \"c5\": \"F\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-09T11:15:58.107232Z",
     "start_time": "2021-09-09T11:15:58.099741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">True</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>False</th>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">False</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>False</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              id\n",
       "c1    c2    c3    c4    c5      \n",
       "True  True  False True  False  A\n",
       "      False True  True  False  B\n",
       "      True  False True  False  D\n",
       "      False True  False False  E\n",
       "False False True  False False  C\n",
       "            False False True   F"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-09T11:16:15.499591Z",
     "start_time": "2021-09-09T11:16:15.279873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'matrix': <matplotlib.axes._subplots.AxesSubplot at 0x7f8be880fa58>,\n",
       " 'shading': <matplotlib.axes._subplots.AxesSubplot at 0x7f8be06aea90>,\n",
       " 'totals': <matplotlib.axes._subplots.AxesSubplot at 0x7f8c182a4550>,\n",
       " 'intersections': <matplotlib.axes._subplots.AxesSubplot at 0x7f8bb8832358>}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x352 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z”"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {
	"ExecuteTime": {
	"end_time": "2021-09-09T11:20:14.234777Z",
	"start_time": "2021-09-09T11:20:14.232154Z"
	}
	},
	"outputs": [],
	"source": [
	"from upsetplot import from_contents, plot"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"ExecuteTime": {
	"end_time": "2021-09-09T11:15:58.006192Z",
	"start_time": "2021-09-09T11:15:57.995175Z"
	}
	},
	"outputs": [],
	"source": [
	"data = from_contents({\"c1\": \"ABDE\", \"c2\": \"AD\", \"c3\": \"BCE\", \"c4\": \"ABD\", \"c5\": \"F\"})"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"ExecuteTime": {
	"end_time": "2021-09-09T11:15:58.107232Z",
	"start_time": "2021-09-09T11:15:58.099741Z"
	}
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<style scoped>\n",
	" .dataframe tbody tr th:only-of-type {\n",
	" vertical-align: middle;\n",
	" }\n",
	"\n",
	" .dataframe tbody tr th {\n",
	" vertical-align: top;\n",
	" }\n",
	"\n",
	" .dataframe thead th {\n",
	" text-align: right;\n",
	" }\n",
	"</style>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th>id</th>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>c1</th>\n",
	" <th>c2</th>\n",
	" <th>c3</th>\n",
	" <th>c4</th>\n",
	" <th>c5</th>\n",
	" <th></th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th rowspan=\"4\" valign=\"top\">True</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <td>A</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>False</th>\n",
	" <th>True</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <td>B</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <td>D</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>False</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <th>False</th>\n",
	" <td>E</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th rowspan=\"2\" valign=\"top\">False</th>\n",
	" <th rowspan=\"2\" valign=\"top\">False</th>\n",
	" <th>True</th>\n",
	" <th>False</th>\n",
	" <th>False</th>\n",
	" <td>C</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>False</th>\n",
	" <th>False</th>\n",
	" <th>True</th>\n",
	" <td>F</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" id\n",
	"c1 c2 c3 c4 c5 \n",
	"True True False True False A\n",
	" False True True False B\n",
	" True False True False D\n",
	" False True False False E\n",
	"False False True False False C\n",
	" False False True F"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {
	"ExecuteTime": {
	"end_time": "2021-09-09T11:16:15.499591Z",
	"start_time": "2021-09-09T11:16:15.279873Z"
	}
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{'matrix': <matplotlib.axes._subplots.AxesSubplot at 0x7f8be880fa58>,\n",
	" 'shading': <matplotlib.axes._subplots.AxesSubplot at 0x7f8be06aea90>,\n",
	" 'totals': <matplotlib.axes._subplots.AxesSubplot at 0x7f8c182a4550>,\n",
	" 'intersections': <matplotlib.axes._subplots.AxesSubplot at 0x7f8bb8832358>}"
	]
	},
	"execution_count": 9,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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uTYhb9DHFmoTYg3i+3S0F9n0S+HRCGVD8GFIfyaylmKjWFdxvONvxZ8l5DGZllCae01bDPcAFeJd4O3A78Agu1sYkgrMS4q7HB0I6DXA8jk8iSOES4M3445x2926b8cG1nyWWsRp/ltlpQOohyt1fN7Md+AOdHyttAR5OjA9ev8n4YFYrDE++ltTFD9FWx+XZ1szHehD3Qfykm8VQYW3DW9huJg08AZyIT6CYh5+Yjd7aAN61vQafXNHNrKWV+COXmQw9R7cAj9HljCL84jaAD641P8Lajl8wH6W7+g8C9+G9p70YOhbwZBa/6K3MCOok2vE8LbEsj2fbZPyE2U53s6CaWQP8KXAMcA7wnCz+zcAXSOvWt+KxbJuCXxieIr11bcWT+GSW/mwzXEi9miI5iLe4D+PHILz+3czkAuol2qA8vRJqK27OttEmuUUqyDZ6N9e4FUbawF9bYiAqCGpGx5a200LcMixevHiwV7GCYLwTLW0Q1IwQbRDUjBBtENSMEG0Q1IwQbRDUjBBtENSMEO0ujKSpkvaUVMRtIiV+fxZ/D0lllu0VjS9J07IyUhakFyljtyz+NEk91YOkZ0u6SNLdkpZLukbS/G7/VpXNiJLUq+liLTGzbjyEao2kvXHLk/6m97YBj5lZygqf4fEnA/vh1i0NTNI64GEz62qqniTh83aHzD2WtBl41MyKrKLJK2OPrIwhc48lrQUeMbPtXcTuA74IvAmfRtr4HQ7BTQ8elXSima1IiR8t7U6CpPdJukvSHZKulZRk/CXpQOCZjFyF0w/sL6msI8bw+FPxk294xgjhq4ueLSnF8aERX8BcfDL/8EZlMnBQdlFKRtJ03NxteA+kD7/YHdxla/hV3KhvMiN/h2m4qdvNkvZLCR6i3Xn4D+BIM3s+8F3gU2UDZCdjnu/QLElJiy8yQT2LzufNJOCAlPgZ+7DDJ7gdz5RUxJxtBJIm4ksLO/XEGj2JlPgvBF5LZwO8Pvx3+ruUMkK0FSHpjKxVvV3SZWZ2nZk1JsffTNqJX9RlMbWl2otivkbTJLVbS9qW7KIwM3dHJ9VRchbF7FdnJLa276P9OtpmJgFvyXoupQjRVoCkw4APAy81sxfgXsLNvA1fIF8mZh/FnQynZQIpS5kkaimt+WSKm52lLtUsegwTSLM4fQXFvbQGcIP3UsTSvGp4KfBdM1sNYGZPL0yX9CbgSNz8rQxlL8AT8HWwo1VGSoNQ5kKSOtA42sdQ5n7eSLBpjZa2GloZuyHpRLwFfpWZlV3wPUBx+5LtiaOjZdadpqxRLTPqnLoGdrSP4dES+07CrW9KEaKthmuB10maBSBpZjaA8SVcsGV+eADMzHCrlCKkWs4U/V5Rc7MhmNk2ijsgFj3W4RQ9hi1NYwxl+BzFF70/YGZ3lS0guscVYGbLJH0SuF7Sdnzk+AB81PQ72e3m783sVSVDP4Y773e6p9pOmhsjZrY1exab5+7/mJml+hI/it9Ldur+NrLPpbARd/TI65Y+khj/UuATBfZ7EviHlAJCtBVhZt8EvtnjmNskrcSfQbYS7gCwMmvRUlmF99DaDQStTukpNDCzJyQ9QPvHMluBFakXBTMzSSvwZ8HthPuH1AkcZrZe0in4QGLDG2o4T+JZJIYb+xUiRLuLYWZPSrqHHflpJ+JiXQes7WamTxZ/EFgpaRr++KTxvPRJYI2Zde1LZWbrJD2JP/7ZC79INFrXddmtQDfxByQtxy88M/FHNIN4K7ymy4saZnaDpOOAC/EZUI2/0UbcSfLjwJdTj6My0Y7naYajTSbM1dk2WmU8QbrLf5H4T+Fd1NRual58w++7u54S2Sb+7cDLspltK7O35wO/7PaiEy1tEIwiZnZ/45G4mf2iFzFj9DgIakaINghqRog2CGpGiDYIakaINghqRsfR42x1f9csWrRoQq9iBUFdKaOBThk5oqUNgpoRz2mD8c4B2bYFuJveptOEoQ4Wz8TzB3dFr0XbMtnvihUr2n42ioz3fLZ74FMAG9MY15OeAb4Vk/ApgM3TGNdSfo1uJ6bjv2MjP+1aepf68pXAB4DD8CmSysq5FLgAX3zRDbOB/wmc3vTe0mz7J+C61MDR0u569OPGYcMXY++Fn5wr6T6x8WxG2sJMw/2dHqa7bPPgrdOBjDw/p+OivZ/ia4db8SHg3exoBZv9pt4GvAZ4GTumH5blj4Cf4hfO5mPYHU/G/W18JdBnU4LHPe3OxwK8V1LahgRf2TOH9u4J/fjqlm6cBvejvY+TcEHnmct1Yjf8otOuQZmCH2MqCxgq2OH04wsh/p20v9NuwNXsWKzRism42cGJCfFDtDsZ04CzgVsSvz+LfI+lRrc2hYkFv/uMxPjgrXXeeTkZ7zmk8FE6OyWSlT8DOCkh/kLaL8lrZgou3NKEaKvjdOAm4Ebgkuy9jwD/QvpgSNEWLrUlnEExb6ZJpI0p9FHceC3lGA4Divo+TwPOTSjjXPItYJvrM6dsAXFPWw2HAucDL8fv/2YAz8dHMX+Md9/K0kfx33MSfsEue19Yxms4xbB8N4obtpW2aMVvDQZK7l+WA0vsuw2/FVhZpoAQbTUcDyxmx4DNOuBbwFldxCy7RjNlTWeZ7+yM8csINmV/KD96XnpQMLrH1TDcjXEP4Ln4AMZvgRcD/5dyg1GDFO9WbybtpC9qWAZpj2a2UPykL1OXBksp3kJvx29dyvJriv9t+4HSxm4h2mpYAryaHYM6jVHf52XbLcDrccO3MhR91JL6SGYdxUS1Bb8wlGW0HSUfBq6nmKi2kvZI5jMUu2BtB36A/01LEaKthnvwB/g/wq/m/9ijuI+TbwGzkYQTJcPIn9EzSIKXbxOPkd9jWEPaRQF89DhPVJtxm9s7E+LfiF90txQo45MJ8UO0FXI5cDRu/HX2sM/mU76VbXA/flIPH2QaxD2jfp8Yt8GGLEYrYW0CVpB/wnZiELgPn8E1vEUcwFvLh7uIfxf+WGYjI8VreLf7euDMLso4DfhVFmv4MWzCj+0U/DhL0+uBqJbD/HPnzr2e8mkugnQexv2DG1MAt+Ni61VO4I3ZNoWh0xh7NW93ELdq7cOPQbhgezUV9ib8ccsb8cG/RlrRa/BHbin3ss1sAv4CP+ffi6eBARfpF4Ar6GJKaYwe77oMkt4NLsomejcXuBXbSTclz2Md8Plsa1wMXtfjMq7Ptkb8w3sRNLrHQVAzOra0nRbilmHx4sWDvYoVBL2myeJ0VM7RXsePljYIakaINghqRog2CGpGiDYIakaINghqRog2CGpGTK7YBZE0ATcuOx13kXgU+Ffg2i4ytLcqR/hKFQOe6jaF47DY/cCpwF/is6LuB75mZr/uYRnPA97e9PptwBVmNioTRiRN6Mnf38x6tuE/3ojtggsuaPn+aG69PK46bcCx+KT+DU1/j0bC5AeAF/WgjEm4Hehh+OL95wPPAfYFJvQg/kJ8fm7zMQzgiyHuAA7qMv4zgF/gs7meaipjIz4d8x09OAZl5RzaFP95uNFBfzexo3u8CyHpaOD/4eZqzbYtwi1QDgCWSDqiizJ2A54N7M1Q47NJuGgPkZRsHCfptcBleOvafAx9wFR83fGtkg5IjD8DX/P6Ytxrqrm3OQ2fT/1pSe9KiZ+VIdz1YjZDHTwm4Msx50nK86lqS4h2J0HSWZLulLRU0i8kPbfk94UvnM87GaYCV6gxTac8c+hsHjcZvziURtJU4Ot0PoY+3J7n8yllAP+A9xI62eFMAT4laXZiGbPp7BPVB8xJ/Q1CtDsPl5vZH5vZ4cCngAtLfv9PcTfGPISbm724ZPxGLpoizg97SspzhWzFGyi2EqkPeIWkfcsEz1q3t1Dcv+qdZeJnZTRa0zwm4jarpQnRVoSkMyTdIel2SZeZWfOys6mUX0a3MPteESbjAzxlKWpbqhL7NvNGijsZDuCDbWU4juK+T7szNDtAUfaguK6SRBujxxUg6TDc8/Y4M1staWb2/jnA+/CW4KUdQrRiJsWdDIu2BsMpc6+acl9bRugTKW632qCxNrcoRS8gzYz23yha2op4KfBdM1sNYGaPZ/9+3swOAd6PeyCX4UGKm6INAA+VjN/4XlFSUo88UjL+mpLxV5fcP8WHarT/RiHaihjuxjicb1O++3oFxZ0jnsrKKEvRBemD+CObsnyV4o4OE3H3yjL8knJuj18uGR+8/kWFm7TAP0RbDdcCr5M0C0DSTEnzmj7/c+B3ZQKa2VLgP8k/KQeApWZ2b5n4WRmbKGZd+riZpWTP+z7FWp8t+MBdKfsZMxvA3RKLmMIJ+GaZ+FkZRrEWfSuJljNxT1sBZrZM0ieB6yVtx03c1ks6kR0pHf8qIfRC4Fb83rDV/dJAFvv1SRV37gcOpn22gQ2kdb0xs6cknYJnnGs3qLYVd+R/b0oZeJrJV+Ce0pPb7LMJeL2ZpfQWMLNHsxld7cYNtgErMoGXJkRbEWb2TRKu5DkxV0h6URb3KLy12I0d7og3An9lZqu6KGNA0nL8WeksXLwNF8M1qSd6U/ybJB2PP689hB3PbDfhPcPvA++0RBcIM9sm6WV4i/tmvCvfuEA8gd9Xv8PMfpZ+FGBmqyStxyehNNiK3yen9kQAUKLYS7F48eJbFyxYcOSoFxQ8jaRD8Pvi6fiJ8j0zW1lppUoi6XB2WMmeA3zHzLpN9twcfy/gtey4dz0OuCm1BexQjs/xNUud0DKEaGl3Uczsv4D/U3U9usHMljb5K31hFOKvB74i6cvZ626tU8eEGIgKgpoRog2CmhGiDYKaEaINgpoRog2CmtFx9DhbitU1ixYtmtCrWMH4ZCzOn9Euo0z8Ts+ho6UNgpoRog3qQtGF62UZy7kKPSkrRLvrshuwD2598gx6f9ILn8o4G9iPNrmJE+nD007e1PTeg7h31It6VMZzgIsZOk/6dtzZot286jJMAd6BJ7Fu8AfgImBey28UJE/5PUniu2LFip7FKkEvT6I60Yd7NA1fwL0PvqpkFSOzxJdlZhaveVHCLHyxw0N0kTAZn8T/fdzhsXnRQD+eqPnlwCeBz3ZRxuvxucf9DNXAXOB/A2cDJ5O2nhb8b/MT/GLW7Hc1GXgTfkH6a+CHKcGjpd15eAlwA74KZ0FijAn4idfOcWGP7PNu5sDOwlvXVquIJgEHdii/CN/EV+C0WuUzARfBR4BXJ8b/E7y1m0LrRmsKvlDhe4nx+/B1vs+itUHdxOz9r5LYawjR7jysAs4C/q2LGDPJN17bHe/WpjABt0nthHBRp/DfgBPI755OBj6RWMbHab8kr0E/8EfAf0+I/0rc7TGvF7s78HcJ8UO0FXI6fs92I3AJ8HtgGd11XYuKsYhrY7v4RVrpftJa27MoPlgzEzimZPyDccPwIkwB3l0yPsB5FDt24auK9itbQKzyqYZDgfPx+7PHSW/5mumj+GBTP/mWN63Ia6Ga2R1fn1qGIyh+Tk7AB5NuLhH/UHwBepGBJuEZFMpyaIl9t+KDUg+XKSBa2mo4HljMjoGOJK+goPYkrdsN0VZDSiuXx3a8FSnCtsTyi3grpezb4DcUdygcBO4uGf8eivdGDPhtyfhQrk67475epQjRVsMSfPSz4SHUi+4xFH9EUdZ6tMFaiol9G8UM4IZzMcXdEtdQrmsMcB9wZ8F9N+GjzGW5iGK3BYY/LShjGwuEaKviHuAC4Ef4QNQ/4vdz9+Bi/gyeJKosj5Nvo7qF9O74IPn3X0aisRve6vyMHZ5W7diMm72n8FHyewHb8N/iFwnxf4w/CcjrMWwhcQQ8BqKq4/Jsa6bMIEYrDFhB68kVsGNyRTdd80ZrPnxyBfiJ+iDlB6CaORO4Ejickc9qG7cA/wsfE0jhl8C5wOfw58rDNbAJ/xsuTIw/iFvg/gR/9DX8We1T2fZW4LaUAvJE25NZRXPnzr0eH3wJRp/tuM1pP26lOhG3Tl1P8XvePB7HW+vp+HNhw1uvXsx62wycgpvSvRcfwR3AxwF+gPdClnZZxnfwKYvvxmcnCb8ArQQ+jT8rL2r83orH8Mkyp+PHsD/+uwwC38Iz/v1XavCObow9XJp3/amnnhqiDVKYnG0bKJdyoyh9+MVtK2n34UWYhl9E11HwOXynpb7VeqMAAAJaSURBVHnRPQ52djaTNhJdlO2kzzEuSje3CyOIgaggqBkdW9pUF/fhLF68eLBXsYJgvBMtbRDUjBBtENSMEG0Q1IwQbRDUjLzntD9maKq+VPamWKLdXrK7mRVdOxkEtWFMUl1KutXMxjTVZRVlBsFYEN3jIKgZIdogqBljJdpLxqicqssMglFnTO5pgyDoHdE9DoKa0RPRSjpQ0nWS7pa0TNJ5LfY5QdJ6SUuz7aM9KPckSfdKWi7pAy0+l6SLss/vkHREt2UGQdX0amneAPA3ZnZbtgb3N5J+amZ3DdvvBjM7pRcFSurDFxO/HHdjuEXSVcPKPBm3qJwHHA18Mfs3CGpLT1paM3vIzG7L/r8Rd6TbvxexO3AUsNzM7jOzbcC3GZlOYwFwqTk3A9MlpbrfB8FOQc/vaSXNwXOx/KrFx8dKul3SjySlGEE3sz/wQNPrVYy8UBTZJwhqRU+dKyRNAxYB7zGz4X5BtwEHmdkTkubjmdG6SfnXKj3F8KHwIvsEQa3oWUsraRIu2G+Z2ZXDPzezDWb2RPb/q4FJkrqZ17wKz9DW4ADcCbDsPkFQK3o1eiw8dd/dZnZhm332y/ZD0lFZ2amm2QC3APMkzZXUD5wGXDVsn6uAM7JR5GOA9WaW6skbBDsFveoeHwe8GbhTUsPe8kN4jk7M7GLgNcDZkgZwo67TrIuZHWY2IOlc4BrcUe9rZrZM0llNZV4NzAeW4362Z6aWFwQ7CzEjKghqRsyICoKaEaINgpoRog2CmhGiDYKaEaINgpoRog2CmhGiDYKaEaINgprx/wEt2/lj5S8sQwAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 288x352 with 4 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"plot(data)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": [
	"z”"
	]
	}
	],
	"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": "3.7.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 4
	}