MercurySizeComparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class fish(object):\n",
    "    def __init__(self, status, ppm, weight, unc, uncy):\n",
    "        self.status = status\n",
    "        self.ppm = ppm\n",
    "        self.weight = weight\n",
    "        self.unc = unc\n",
    "        self.uncy = uncy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#sample1: swai basa fillet, Vietnam, farmed\n",
    "ppm = 0.035\n",
    "#weight in kg\n",
    "weight = 2\n",
    "unc = 1\n",
    "sample1 = fish('farm', 0.035, 2, 1, 0.005)\n",
    "\n",
    "#sample2: Hamachi Fillet from Japan, wild\n",
    "ppm2 = 0.234\n",
    "weight2 = 6.8\n",
    "unc2 = 2.3\n",
    "sample2 = fish('wild', 0.234, 6.8, 2.3, 0.042)\n",
    "\n",
    "#sample3: Cardinal snapper, Mexico, wild \n",
    "ppm3 = 0.024\n",
    "weight3 = 0.85\n",
    "unc3 = 0.4\n",
    "sample3 = fish('wild', 0.024, 0.85, 0.4, 0.0037)\n",
    "\n",
    "#sample4: Shortraker steak, Canada, wild\n",
    "ppm4 = 0.22\n",
    "weight4 = 20 \n",
    "unc = 3\n",
    "sample4 = fish('wild', 0.22, 20, 3, 0.0278)\n",
    "\n",
    "#sample7: Grass Carp steak, Taiwan, farmed \n",
    "ppm5 = 0.09\n",
    "weight5 = 19 \n",
    "unc5 = 16\n",
    "sample7 = fish('farm', 0.09, 19, 16, 0.012)\n",
    "\n",
    "#sample8: Grass Carp steak, Tawian, farmed\n",
    "ppm6 = 0.064\n",
    "weight6 = 19\n",
    "unc6 = 16\n",
    "sample8 = fish('farm', 0.064, 19, 16, 0.0093)\n",
    "\n",
    "#sample9: Chilean Sea Bass, Korea, wild \n",
    "ppm7 = 0.09\n",
    "weight7 = 40\n",
    "unc7 = 0.13\n",
    "sample9 = fish('wild', 0.09, 40, 0.13, 0.0114)\n",
    "\n",
    "#sample10: Wild Isle Salmon, Scotland, farmed\n",
    "#the fact that it's farmed might influence mercury build up\n",
    "ppm8 = 0.0085\n",
    "weight8 = 13\n",
    "unc8 = 7\n",
    "sample10 = fish('farm', 0.0085, 13, 7, 0.0015)\n",
    "\n",
    "#sample11: Mackerel, India, wild\n",
    "ppm9 = 0.066\n",
    "weight9 = 0.295\n",
    "unc9 = 0.155\n",
    "sample11 = fish('wild', 0.066, 0.295, 0.155, 0.0104)\n",
    "\n",
    "#sample12: Norwegian Basa, Norway, wild\n",
    "ppm10 = 0.046\n",
    "weight10 = 2\n",
    "unc10 = 1\n",
    "sample12 = fish('wild', 0.046, 2, 1, 0.0067) \n",
    "\n",
    "#sample13: Golden Pompano, China, farmed\n",
    "ppm11 = 0.02\n",
    "weight11 = 1.4\n",
    "unc11 = 0.4\n",
    "sample13 = fish('farm', 0.02, 1.4, 0.4, 0.00305)\n",
    "\n",
    "#sample14: Chilean Sea Bass, Arctic, wild\n",
    "ppm12 = 0.15\n",
    "weight12 = 40\n",
    "unc12 = 0.13\n",
    "sample14 = fish('wild', 0.15, 40, 0.13, 0.0197)\n",
    "\n",
    "#sample15: HD Shrimp, Venezuela, farmed\n",
    "ppm13 = 0.037\n",
    "weight13 = 0.06\n",
    "unc13 = 0.028\n",
    "sample15 = fish('farm', 0.037, 0.06, 0.028, 0.0062)\n",
    "\n",
    "#sample16: Pink Grouper steak, Mexico, wild\n",
    "ppm14 = 0.75\n",
    "weight14 = 5.67\n",
    "unc14 = 3.4\n",
    "sample16 = fish('wild', 0.075, 5.67, 3.4, 0.0958)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 6\n"
     ]
    }
   ],
   "source": [
    "wildppm = []\n",
    "wildweight = []\n",
    "wildunc = []\n",
    "wilduncy = []\n",
    "farmppm = []\n",
    "farmweight = []\n",
    "farmunc = []\n",
    "farmuncy = []\n",
    "samples = [sample1, sample2, sample3, sample4, sample7, sample8, sample9,\n",
    "          sample10, sample11, sample12, sample13, sample14, sample15, sample16]\n",
    "for s in samples:\n",
    "    if s.status == 'wild':\n",
    "        wildppm.append(s.ppm)\n",
    "        wildweight.append(s.weight)\n",
    "        wildunc.append(s.unc)\n",
    "        wilduncy.append(s.uncy)\n",
    "    else:\n",
    "        farmppm.append(s.ppm)\n",
    "        farmweight.append(s.weight)\n",
    "        farmunc.append(s.unc)\n",
    "        farmuncy.append(s.uncy)\n",
    "print(len(wildppm), len(farmppm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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hBgCgguXZw33u9VXasfvQ5PpIrix4OL1c52REKfua+QyKzJctW6YvfOELevWrX61bb711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\nAQAAgAyRcAMAAAAZIuEGAAAAMvT/ASY1mRGk9tmZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10627d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl_fig = plt.figure(figsize=(12,5))\n",
    "ax = mpl_fig.add_subplot(111)\n",
    "p1 = plt.errorbar(wildweight, wildppm, yerr=wilduncy, xerr=wildunc, fmt='o', ecolor='blue', capthick=1, capsize=2, label='wild')\n",
    "p2 = plt.errorbar(farmweight, farmppm, yerr=farmuncy, xerr=farmunc, fmt='x', ecolor='red', capthick=1, capsize=2, label='farm')\n",
    "\n",
    "plt.legend()\n",
    "plt.ylabel('Mercury ppm')\n",
    "plt.title('Fish weight vs. Mercury concentration ')\n",
    "plt.xlabel('Avg weight of fish species')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}