Distribution_Fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Distributions to a Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "continuous_distributions = \"continuous.csv\"\n",
    "dists_unindexed = pd.read_csv(continuous_distributions)\n",
    "dist_list = dists_unindexed.distribution.tolist()\n",
    "dists = dists_unindexed.set_index(dists_unindexed.distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "file_names = ['453488','456615','459659','463721','465734','436464','437789','439070','441615']\n",
    "file_path = \"\"   #Path to your data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def save_figure(path,name):\n",
    "    plt.savefig(path+name+\".png\",\n",
    "                dpi=200,\n",
    "                facecolor='none',\n",
    "                edgecolor='none'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_nearest(array,value):\n",
    "    idx = (np.abs(array-value)).argmin()\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def dist_fit(path,name,dist_name,binsize):\n",
    "    #If your data is in an excel spreadsheet save it as a delimited text file (.csv formatted)\n",
    "    filename = path+name+\".txt\"\n",
    "    df = pd.read_csv(filename,names=[\"Hs\",\"File\"])           \n",
    "    title = \"Node #: \"+str(name)+\"\\n\"\n",
    "    \n",
    "    #Initialize figure and set dimensions\n",
    "    fig = plt.figure(figsize = (18,6))\n",
    "    gs = gridspec.GridSpec(2,2)\n",
    "    ax1 = fig.add_subplot(gs[:,0])\n",
    "    ax3 = fig.add_subplot(gs[:,1])\n",
    "    ax1.set_title(title,fontsize=20)\n",
    "    \n",
    "    #Remove the plot frame lines. They are unnecessary chartjunk.\n",
    "    ax1.spines[\"top\"].set_visible(False)  \n",
    "    ax1.spines[\"right\"].set_visible(False) \n",
    "    ax3.spines[\"top\"].set_visible(False)  \n",
    "    ax3.spines[\"right\"].set_visible(False) \n",
    "    \n",
    "    # Ensure that the axis ticks only show up on the bottom and left of the plot.  \n",
    "    # Ticks on the right and top of the plot are generally unnecessary chartjunk.  \n",
    "    ax1.get_xaxis().tick_bottom()  \n",
    "    ax1.get_yaxis().tick_left()  \n",
    "    ax3.get_xaxis().tick_bottom()  \n",
    "    ax3.get_yaxis().tick_left()  \n",
    "    \n",
    "    # Make sure your axis ticks are large enough to be easily read.  \n",
    "    # You don't want your viewers squinting to read your plot.  \n",
    "    ax1.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "                    labelbottom=\"on\", left=\"on\", right=\"off\", labelleft=\"on\",labelsize=14)\n",
    "    ax3.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "                    labelbottom=\"on\", left=\"on\", right=\"off\", labelleft=\"on\",labelsize=14)\n",
    "\n",
    "    # Along the same vein, make sure your axis labels are large  \n",
    "    # enough to be easily read as well. Make them slightly larger  \n",
    "    # than your axis tick labels so they stand out.  \n",
    "    ax1.set_xlabel(\"Wave Height (m)\", fontsize=16)  \n",
    "    ax1.set_ylabel(\"Frequency of occurence\", fontsize=16)  \n",
    "    ax3.set_xlabel(\"Wave Height (m)\", fontsize=16) \n",
    "    ax3.set_ylabel(\"Probability Hs < x\", fontsize=16)\n",
    "    #set x-axis limits\n",
    "    ax1.set_xlim(0,3)\n",
    "    ax3.set_xlim(0,3)\n",
    "    #Setting .... variables\n",
    "    size    = len(df.Hs)\n",
    "    max_val = 2*max(df.Hs)\n",
    "    x0 = np.arange(0,max_val,max_val*0.0001)\n",
    "    x1 = np.arange(0,3,binsize)\n",
    "    y1 = df.Hs\n",
    "    #Plot histograms\n",
    "    EPDF = ax1.hist(y1, bins=x1, color='w')\n",
    "    ECDF = ax3.hist(y1, bins=x1, color='w', normed=1, cumulative=True)\n",
    "    #Fitting distribution\n",
    "    dist  = getattr(scipy.stats, dist_name)\n",
    "    param = dist.fit(y1)\n",
    "    pdf_fitted = dist.pdf(x0, *param[:-2], loc=param[-2], scale=param[-1])*size*binsize\n",
    "    cdf_fitted = dist.cdf(x0, *param[:-2], loc=param[-2], scale=param[-1])\n",
    "    \n",
    "    #Checking goodness of fit\n",
    "    #ks_fit = stats.kstest(pdf_fitted,dist_name) # Kolmogorov-Smirnov test: returns [KS stat (D,D+,orD-),pvalue]\n",
    "    #print(ks_fit)\n",
    "    \n",
    "    #Finding location of 0.002 and 0.01 exceedence probability events \n",
    "    FiveHundInd = find_nearest(cdf_fitted,0.998)\n",
    "    OneHundInd  = find_nearest(cdf_fitted,0.990)\n",
    "    #Plotting pdf and cdf    \n",
    "    ax1.plot(x0,pdf_fitted,linewidth=2,label=dist_name)\n",
    "    ax3.plot(x0,cdf_fitted,linewidth=2,label=dist_name)\n",
    "    #update figure spacing\n",
    "    gs.update(wspace=0.1, hspace=0.2)\n",
    "    #adding a text box\n",
    "    ax3.text(-1.7,0.7,\n",
    "             dist_name.upper()+\" distribution\\n\"\n",
    "             +\"\\n\"\n",
    "             +\"0.2% - value: \" + str(\"%.2f\" %x0[FiveHundInd])+ \" meters\\n\"\n",
    "             +\"1.0% - value: \" + str(\"%.2f\" %x0[OneHundInd]) + \" meters\",\n",
    "             fontsize=14\n",
    "            )\n",
    "\n",
    "    print(dists.loc[dist_name,'description']+\"\\n\")\n",
    "    print('Distribution parameters: '+str(param)+\"\\n\\n\")\n",
    "    \n",
    "    plt.show()\n",
    "    return name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "#mpl.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mpl.style.use('seaborn-notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A generalized extreme value continuous random variable.\n",
      "\n",
      "Distribution parameters: (0.096722889323678979, 0.38716118882974093, 0.22310119769768522)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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RDnJ6AVMRERHxmf/gTFHN77FORgvgoLV2b0YuoL8bRBxnzsCBA7Bvn/exfz8cOuQkL44e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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd144492d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'453488'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(dist_fit, path=file_path, name=file_names, dist_name=dist_list,binsize=[0.05,0.55,0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "   ###References:\n",
    "   Python <br\\>\n",
    "   http://stackoverflow.com/questions/6615489/fitting-distributions-goodness-of-fit-p-value-is-it-possible-to-do-this-with/16651524#16651524 <br\\>\n",
    "   http://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-ones-with-scipy-python <br\\><br\\>\n",
    "   Extreme wave statistics <br\\>\n",
    "   http://drs.nio.org/drs/bitstream/handle/2264/4165/Nat_Hazards_64_223a.pdf;jsessionid=55AAEDE5A2BF3AA06C6CCB5CE3CBEBAD?sequence=1 <br\\><br\\>\n",
    "   List of available distributions can be found here <br\\>\n",
    "   http://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions<br\\><br\\>\n",
    "   Goodness of fit tests <br\\>\n",
    "   http://statsmodels.sourceforge.net/stable/stats.html#goodness-of-fit-tests-and-measures <br\\>\n",
    "   http://docs.scipy.org/doc/scipy/reference/stats.html#statistical-functions <br\\>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
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    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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