awra_presentation.ipynb

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    "# Open Science in Water Resources with the Jupyter Notebooks.\n",
    "Jared Dorvinen, Dewberry, Fairfax, VA.\n",
    "Contact: Jared Dorvinen (Email: jdorvinen@dewberry.com)\n",
    "Abstract. With the ever increasing availability of data and ever more complex computational models, the computational and coding\n",
    "needs of water resources professionals is greater now than ever. However, water resources community currently has no ‘lingua franca’\n",
    "for scientific computing and coding is being done in a wide variety of specialized environments computer languages. This has created\n",
    "a situation where the complexity of these computing environments is inhibiting many from being able to easily share their code with\n",
    "colleagues across various countries and organizations. The open source community is addressing this issue by developing innovative\n",
    "programming tools and environments which allow for a universal interface for computing in a variety of languages. Jupyter\n",
    "Notebooks, developed by the Jupyter Project, enable the combination of rich multimedia documentation and interactive code within a\n",
    "standard web browser interface, they facilitate the setup and sharing of customizable computing environments in a variety of\n",
    "programing languages, and eliminate the need for expensive licensing in many scientific computing applications. This presentation\n",
    "introduces Jupyter Notebooks as applied to a variety of problems in water resources engineering. Examples notebooks include\n",
    "applications for coastal and inland water resources projects as well as statistical and data analysis tools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Fitting Distributions to a Dataset "
   ]
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ipywidgets import interact, interact_manual\n",
    "import os\n",
    "import scipy.stats as stats\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>inter</th>\n",
       "      <th>swel</th>\n",
       "      <th>hsig</th>\n",
       "      <th>tps</th>\n",
       "      <th>a_hsig</th>\n",
       "      <th>a_tps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>71</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4.04</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.61</td>\n",
       "      <td>9.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>63</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.54</td>\n",
       "      <td>9.09</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>152</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>3.71</td>\n",
       "      <td>7.69</td>\n",
       "      <td>3.45</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>251</td>\n",
       "      <td>0.09</td>\n",
       "      <td>4.96</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.93</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.57</td>\n",
       "      <td>9.09</td>\n",
       "      <td>3.35</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   length     inter      swel  hsig    tps  a_hsig  a_tps\n",
       "0      34        71      0.26  4.04  10.00    3.61   9.01\n",
       "1      20        63      0.40  5.54   9.09    4.01   9.15\n",
       "2       5       152     -0.34  3.71   7.69    3.45   7.95\n",
       "3      13       251      0.09  4.96  10.00    3.93   9.05\n",
       "4       6        52      0.05  3.57   9.09    3.35   8.50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sample Data File\n",
    "filename = 'https://raw.githubusercontent.com/jdorvi/MonteCarlos_SLC/master/montauk_combined_data.csv'\n",
    "\n",
    "#Read in data\n",
    "df = pd.read_csv(filename, sep = ',').dropna()  \n",
    "df.columns = df.columns.str.strip()\n",
    "df.drop(labels= ['start','end'],axis = 1,  inplace = True)\n",
    "title = str(filename).split('/')[-1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Read in statistical distribution data\n",
    "continuous_distributions = \"https://raw.githubusercontent.com/jdorvi/MonteCarlos_SLC/master/continuous_distributions_.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": 7,
   "metadata": {
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   "source": [
    "def save_figure(path,name):\n",
    "    plt.savefig(path+name+\".png\",\n",
    "                dpi=200,\n",
    "                facecolor='none',\n",
    "                edgecolor='none'\n",
    "               )\n",
    "\n",
    "def find_nearest(array,value):\n",
    "    idx = (np.abs(array-value)).argmin()\n",
    "    return idx\n",
    "\n",
    "def dist_fit(name,dist_name,bins,parameter):\n",
    "    global df\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(parameter, fontsize=16)  \n",
    "    ax1.set_ylabel(\"Frequency of occurence\", fontsize=16)  \n",
    "    ax3.set_xlabel(parameter, fontsize=16) \n",
    "    ax3.set_ylabel(\"Exceedance Probability\", fontsize=16)\n",
    "    #Setting .... variables\n",
    "    size    = len(df[parameter])\n",
    "    max_val = 1.1*max(df[parameter])\n",
    "    min_val = min(df[parameter])\n",
    "    range_val = max_val-min_val\n",
    "    binsize = range_val/bins\n",
    "    x0 = np.arange(min_val,max_val,range_val*0.0001)\n",
    "    x1 = np.arange(min_val,max_val,binsize)\n",
    "    y1 = df[parameter]\n",
    "    #set x-axis limits\n",
    "    ax1.set_xlim(min_val,max_val)\n",
    "    ax3.set_xlim(min_val,max_val)\n",
    "    ax3.set_ylim(0,1.1)\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(min_val+0.1*range_val,1.1,\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",
    "    \n",
    "    param_names = (dist.shapes + ', loc, scale').split(', ') if dist.shapes else ['loc', 'scale']\n",
    "    param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, param)])\n",
    "    dist_str = '{}({})'.format(dist_name, param_str)\n",
    "    print(dist_str)\n",
    "    plt.show()\n",
    "    print(stats.kstest(y1,dist_name,param,alternative='less'))\n",
    "    print(stats.kstest(y1,dist_name,param,alternative='greater'))\n",
    "    print(stats.kstest(y1,dist_name,param,alternative='two-sided'))\n",
    "    return name"
   ]
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   "execution_count": 8,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A double Weibull continuous random variable.\n",
      "\n",
      "dweibull(c=1.35, loc=3.55, scale=0.35)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+UdKtpB4C65CGDUyi8qwoLZmfpJBvkbrqP527wPYlnXStSJq+ssODIxExU9Ie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d2I2A+4GpgC/AO4LKXk83arr8NrX+K9Xx2bkBOH9YC3yJMtjUopzSq1e9/XTqeufYn3vdoyb2gs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\nZtlae2KOMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3ecf7b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KstestResult(statistic=0.10393989771505538, pvalue=0.0057729514655016079)\n",
      "KstestResult(statistic=0.098864146551981447, pvalue=0.0094137988402592798)\n",
      "KstestResult(statistic=0.10393989771505538, pvalue=0.011545902931003216)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://raw.githubusercontent.com/jdorvi/MonteCarlos_SLC/master/montauk_combined_data.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = list(df.columns) \n",
    "interact_manual(dist_fit, name=filename, dist_name=dist_list, bins=[25,100,5],parameter=params,title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Recurrence Interval Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3f335fc278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3f3057d710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f3f30ea4828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3f30e60e80>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PMekgIobxXg1jzFeAY9bap621F4wxnwOeM8bMAA4BD2fh/iIiOVvxMR2JejQ0RVHyIeUg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\nEeKLZdCEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f30a79f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose data vectors for scatter matrix\n",
    "usecols = ['a_hsig','a_tps']\n",
    "stat_data = df[usecols]\n",
    "pd.tools.plotting.scatter_matrix(stat_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f3f30d09c18>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GrJP0VUnTkkYkPSOpT9KzUY8FAADCieNOe7dIesJa++6Ssr+LYRwA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\nOICEDwCAA0j4AAA4gIQPAIAD/j+6QYJfiwEvhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f30da4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter Plot using 2 variables from dataset\n",
    "\n",
    "x,y = df.length,df.tps\n",
    "\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "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": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Length<br>\n",
    "genexpon(a=1.38, b=0.20, c=0.03, loc=3.00, scale=19.39)<br>\n",
    "KstestResult(statistic=0.090090090087017877, pvalue=0.051121878132879051)<br>\n",
    "genpareto(c=-0.02, loc=3.00, scale=14.16)<br>\n",
    "KstestResult(statistic=0.08997375153682749, pvalue=0.051604871224521176)<br><br>\n",
    "\n",
    "Interim<br>\n",
    "foldcauchy(c=0.63, loc=25.94, scale=137.16)\n",
    "KstestResult(statistic=0.026999638083165967, pvalue=0.99705005373423183)\n",
    "\n",
    "SWEL<br>\n",
    "johnsonsb(a=-0.95, b=1.41, loc=-1.12, scale=1.90)<br> \n",
    "KstestResult(statistic=0.029574404259407161, pvalue=0.9901000531575973)<br><br>\n",
    "\n",
    "HSIG<br>\n",
    "genexpon(a=0.05, b=0.32, c=1.83, loc=3.05, scale=0.36)<br>\n",
    "KstestResult(statistic=0.041869945686266341, pvalue=0.83122014943398015)<br>\n",
    "exponnorm(K=12.76, loc=3.16, scale=0.08)<br>\n",
    "KstestResult(statistic=0.045765702677044495, pvalue=0.74112752318380581)<br><br>\n",
    "\n",
    "TPS<br>\n",
    "recipinvgauss(mu=0.24, loc=5.60, scale=0.74)<br>\n",
    "KstestResult(statistic=0.10435298093410683, pvalue=0.014720986714411577)<br>\n",
    "genextreme(c=-0.08, loc=8.58, scale=1.31)<br>\n",
    "KstestResult(statistic=0.11307745876143599, pvalue=0.0062809593607968672)<br><br>\n",
    "\n",
    "a_hsig<br>\n",
    "genexpon(a=0.22, b=2.34, c=7.13, loc=3.03, scale=1.09)<br>\n",
    "KstestResult(statistic=0.047691926585930133, pvalue=0.70607193161667459)<br>\n",
    "exponnorm(K=8.97, loc=3.11, scale=0.05)<br>\n",
    "KstestResult(statistic=0.050605085052604193, pvalue=0.62055905806032152)<br><br>\n",
    "\n",
    "a_tps<br>\n",
    "recipinvgauss(mu=0.22, loc=5.90, scale=0.61)<br>\n",
    "KstestResult(statistic=0.038004922394594909, pvalue=0.90556531830166975)<br>\n",
    "genextreme(c=-0.08, loc=8.55, scale=1.12)<br>\n",
    "KstestResult(statistic=0.045956458061729433, pvalue=0.73647282485448418)<br><br>\n",
    "    \n",
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    "    "
   ]
  }
 ],
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