bentrevett/Poisson.ipynb

## Poisson.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265252859812191058636308480000000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poisson(lambd, start=0, end=30):\n",
    "    P = []\n",
    "    for r in range(start, end):\n",
    "        P.append(np.e ** -lambd * lambd ** r / math.factorial(r))\n",
    "    return P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = poisson(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe171dab50>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(P)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"265252859812191058636308480000000"
	]
	},
	"execution_count": 1,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"import math"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [],
	"source": [
	"def poisson(lambd, start=0, end=30):\n",
	" P = []\n",
	" for r in range(start, end):\n",
	" P.append(np.e ** -lambd * lambd ** r / math.factorial(r))\n",
	" return P"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [],
	"source": [
	"P = poisson(15)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7fbe171dab50>]"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"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(P)"
	]
	}
	],
	"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": "3.7.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}