find best fit distribution-cartoon-1-humidity-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution Fitting with Sum of Square Error (SSE)\n",
    "## find best fit by computing the least/minimum residual sum of squares "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "#import statsmodels as sm\n",
    "import statsmodels.api as sm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (16.0, 12.0)\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "# Create models from data\n",
    "def best_fit_distribution(data, bins=200, ax=None):\n",
    "    \"\"\"Model data by finding best fit distribution to data\"\"\"\n",
    "    # Get histogram of original data\n",
    "    y, x = np.histogram(data, bins=bins, density=True)\n",
    "    x = (x + np.roll(x, -1))[:-1] / 2.0\n",
    "\n",
    "    # Distributions to check\n",
    "    DISTRIBUTIONS = [\n",
    "        st.norm,\n",
    "        st.powerlaw,\n",
    "        #st.powerlognorm,\n",
    "        st.powernorm,\n",
    "        st.weibull_min,\n",
    "        st.weibull_max,\n",
    "        st.dweibull,\n",
    "        st.erlang,\n",
    "        st.expon,\n",
    "        st.exponnorm,\n",
    "        st.exponweib,\n",
    "        st.logistic,\n",
    "        st.loggamma,\n",
    "        st.loglaplace,\n",
    "        #st.johnsonsb,\n",
    "        #st.johnsonsu,\n",
    "        st.skewnorm,\n",
    "        st.pareto,\n",
    "        st.pearson3,\n",
    "        st.beta,\n",
    "        st.lognorm,\n",
    "        st.gengamma\n",
    "        \n",
    "        \n",
    "    ]\n",
    "\n",
    "    # Best holders\n",
    "    best_distribution = st.norm\n",
    "    best_params = (0.0, 1.0)\n",
    "    best_sse = np.inf\n",
    "\n",
    "    # Estimate distribution parameters from data\n",
    "    for distribution in DISTRIBUTIONS:\n",
    "\n",
    "        # Try to fit the distribution\n",
    "        try:\n",
    "            # Ignore warnings from data that can't be fit\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.filterwarnings('ignore')\n",
    "\n",
    "                # fit dist to data\n",
    "                params = distribution.fit(data)\n",
    "\n",
    "                # Separate parts of parameters\n",
    "                arg = params[:-2]\n",
    "                loc = params[-2]\n",
    "                scale = params[-1]\n",
    "\n",
    "                # Calculate fitted PDF and error with fit in distribution\n",
    "                pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n",
    "                sse = np.sum(np.power(y - pdf, 2.0))\n",
    "\n",
    "                # if axis pass in add to plot\n",
    "                try:\n",
    "                    if ax:\n",
    "                        pd.Series(pdf, x).plot(ax=ax)\n",
    "                    end\n",
    "                except Exception:\n",
    "                    pass\n",
    "\n",
    "                # identify if this distribution is better\n",
    "                if best_sse > sse > 0:\n",
    "                    best_distribution = distribution\n",
    "                    best_params = params\n",
    "                    best_sse = sse\n",
    "                    print(\"distribution: \" +str(distribution))\n",
    "                    print(\"best_params: \" +str(best_params))\n",
    "                    print(\"best_sse: \" +str(best_sse))\n",
    "\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "    return (best_distribution.name, best_params)\n",
    "\n",
    "def make_pdf(dist, params, size=10000):\n",
    "    \"\"\"Generate distributions's Probability Distribution Function \"\"\"\n",
    "\n",
    "    # Separate parts of parameters\n",
    "    arg = params[:-2]\n",
    "    loc = params[-2]\n",
    "    scale = params[-1]\n",
    "\n",
    "    # Get sane start and end points of distribution\n",
    "    start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n",
    "    end = dist.ppf(0.99, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n",
    "\n",
    "    # Build PDF and turn into pandas Series\n",
    "    x = np.linspace(start, end, size)\n",
    "    y = dist.pdf(x, loc=loc, scale=scale, *arg)\n",
    "    pdf = pd.Series(y, x)\n",
    "\n",
    "    return pdf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Humidity  Temperature  MQ139  TVOC  eCO2 Detector\n",
      "Time                                                      \n",
      "0:00:00      43.5         21.3     98     0   400      OFF\n",
      "0:00:01      43.5         21.3     97     0   400      OFF\n",
      "0:00:02      43.6         21.4     97     0   400      OFF\n",
      "0:00:03      43.6         21.4     97     0   400      OFF\n",
      "0:00:04      43.7         21.4     98     0   400      OFF\n"
     ]
    }
   ],
   "source": [
    "series = pd.read_csv('carton.csv', header=0, index_col=0, squeeze=True)\n",
    "series = series.drop('Reading ID', axis=1)\n",
    "series = series.drop('Status', axis=1)\n",
    "print(series.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = series.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      21.3\n",
       "1      21.3\n",
       "2      21.4\n",
       "3      21.4\n",
       "4      21.4\n",
       "       ... \n",
       "325    21.2\n",
       "326    21.2\n",
       "327    21.2\n",
       "328    21.2\n",
       "329    21.2\n",
       "Name: Temperature, Length: 330, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.iloc[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      21.3\n",
       "1      21.3\n",
       "2      21.4\n",
       "3      21.4\n",
       "4      21.4\n",
       "       ... \n",
       "325    21.2\n",
       "326    21.2\n",
       "327    21.2\n",
       "328    21.2\n",
       "329    21.2\n",
       "Name: Temperature, Length: 330, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = series.iloc[:,2]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    330.000000\n",
       "mean      21.377879\n",
       "std        0.099368\n",
       "min       21.200000\n",
       "25%       21.300000\n",
       "50%       21.400000\n",
       "75%       21.500000\n",
       "max       21.500000\n",
       "Name: Temperature, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37.,   0.,   0.,   0.,   0.,  96.,   0.,   0.,   0.,   0., 100.,\n",
       "         0.,   0.,   0.,  97.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABletv9AjMzM9m6dWtWrVqVrVu35tChQxkeHs7+/fuzZs2abN68OStWrFiIWQEAAOgybb9S+pOf/CRr166dvT0yMpL169dnx44dWb9+fUZGRtp9CgAAALpUW1H6zDPP5P7778+ll146e9/4+HgGBgaSJAMDAxkfH29vQgAAALpWW5fv3nXXXbnmmmty+PDh2fsOHDiQRqORJGk0Gjl48OCrPnZ0dDSjo6NJkqGhoTSbzXZG6bje3t5FPyM17AZzebp6gA6y8+1zdjCXbj077Hv7nBvt69afr2Rp78e8o/Q3v/lN+vr6sm7dujz00EP/8eMHBwczODg4e3tiYmK+o5wQzWZz0c9IDbvBycjOt8/ZwcnGvrfPucHxTE9PL+r96O/vn/Nj847Shx9+OPfdd19++9vf5ujRozl8+HB27NiRvr6+TE1NpdFoZGpqKitXrpzvUwAAANDl5h2lV199da6++uokyUMPPZR77rknN954Y+6+++6MjY1l06ZNGRsby8aNGxdsWAAAALrLgr9P6aZNm/LAAw/kxhtvzAMPPJBNmzYt9FMAAADQJdp+n9Ikeec735l3vvOdSZI3velN2bZt20J8WQAAALrcgr9SCgAAAK+XKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKBMb/UAS8XTH724eoSOWP7f/1M9AgAAcBLzSikAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABleuf7wImJiezcuTP/+te/0tPTk8HBwVx22WU5dOhQhoeHs3///qxZsyabN2/OihUrFnJmAAAAusS8o3T58uX5+Mc/nnXr1uXw4cPZunVr3v3ud+cXv/hF1q9fn02bNmVkZCQjIyO55pprFnJmAAAAusS8L99tNBpZt25dkuTUU0/N2rVrMzk5mfHx8QwMDCRJBgYGMj4+vjCTAgAA0HUW5HdK9+3bl8cffzznnntuDhw4kEajkeSlcD148OBCPAUAAABdaN6X777syJEj2b59e6699tqcdtppr/txo6OjGR0dTZIMDQ2l2Wy2O0pHPV09QIcs9n/3paC3t9e/I6+qW8+NxNmxEJwdzKVbzw773j7nRvu69ecrWdr70VaUTk9PZ/v27bnkkktywQUXJEn6+voyNTWVRqORqamprFy58lUfOzg4mMHBwdnbExMT7YzCPPl3b1+z2fTvyEnHzrfP2cHJxr63z7nB8UxPTy/q/ejv75/zY/O+fLfVamXXrl1Zu3ZtLr/88tn7N2zYkLGxsSTJ2NhYNm7cON+nAAAAoMvN+5XShx9+OPfee2/OOuusfOELX0iSXHXVVdm0aVOGh4ezZ8+eNJvNbNmyZcGGBQAAoLvMO0rf/va35wc/+MGrfmzbtm3zHggAAICTx4L89V0AAACYD1EKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAGVEKAABAmd5OfeHf/e53ufPOOzMzM5NLL700mzZt6tRTAQAAsER15JXSmZmZ3HHHHbnlllsyPDycX/7yl/n73//eiacCAABgCetIlD766KN561vfmre85S3p7e3NxRdfnPHx8U48FQAAAEtYRy7fnZyczOrVq2dvr169Oo888sgrPmd0dDSjo6NJkqGhofT393dilIXz/+6rnoBFbNHvLzWcG7wGZwevytnBcTg32tTlP19LdT868kppq9X6t/t6enpecXtwcDBDQ0MZGhrqxAgLbuvWrdUjsEjZDeZiNzge+8Fc7AZzsRscz1Lej45E6erVq/PMM8/M3n7mmWfSaDQ68VQAAAAsYR2J0nPOOSf/+Mc/sm/fvkxPT2fv3r3ZsGFDJ54KAACAJawjv1O6fPnyXHfddfnSl76UmZmZfOADH8iZZ57Ziac6YQYHB6tHYJGyG8zFbnA89oO52A3mYjc4nqW8Hz2tV/sFUAAAADgBOnL5LgAAALweohQAAIAyHfmd0sVsYmIiO3fuzL/+9a/09PRkcHAwl112WX71q1/lhz/8YZ588sl8+ctfzjnnnPO6H5skhw4dyvDwcPbv3581a9Zk8+bNWbFixYn+9mhTp/bjBz/4QX7+859n5cqVSZKrrroq733ve0/o90Z72tmNo0eP5rbbbsv09HSOHTuWCy+8MFdeeWUSZ0c36NRuODeWvnZ242UzMzPZunVrVq1aNft2D86N7tCp/XB2LH3t7sYNN9yQN77xjVm2bFmWL18++xaci/rsaJ1kJicnW4899lir1Wq1nn/++daNN97YeuKJJ1pPPPFE68knn2zddtttrUcfffQ/emyr1Wrdfffdrd27d7darVZr9+7drbvvvrvz3wwLrlP78f3vf7/1ox/96MR8E3REO7sxMzPTOnz4cKvVarVefPHF1s0339x6+OGHW62Ws6MbdGo3nBtLXzu78bJ77rmndfvtt7e+8pWvzN7n3OgOndoPZ8fS1+5uXH/99a0DBw782/2L+ew46S7fbTQaWbduXZLk1FNPzdq1azM5OZm3ve1t6e/vn9djk2R8fDwDAwNJkoGBgYyPj3fwu6BTOrUfLH3t7EZPT0/e+MY3JkmOHTuWY8eOpaenJ4mzoxt0ajdY+trZjeSl93m///77c+mll77ifudGd+jUfrD0tbsbc1nMZ8dJd/nu/7Zv3748/vjjOffcc9t+7IEDB9JoNJK8tEgHDx5c0Fk58RZyP5LkZz/7We69996sW7cun/jEJxbP5RL8x+azGzMzM7npppvyz3/+Mx/5yEdy3nnnJXF2dJuF3I3EudFN5rMbd911V6655pocPnz4Ffc7N7rPQu5H4uzoJvP9/9EvfelLSZIPfehDs28Vs5jPjpPuldKXHTlyJNu3b8+1116b00477YQ9lqVhoffjwx/+cL71rW/la1/7WhqNRr73ve91YmxOgPnuxrJly/L1r389u3btymOPPZa//e1vHZySCgu9G86N7jGf3fjNb36Tvr6+2VdL6F4LvR/Oju4x3/+ufPGLX8xXv/rV3HLLLfnZz36WP/zhDx2ccmGclFE6PT2d7du355JLLskFF1ywII/t6+vL1NRUkmRqamr2l8tZejqxH29+85uzbNmyLFu2LJdeemkee+yxhR6bE6Cd3XjZ6aefnne84x353e9+l8TZ0S06sRvOje4w3914+OGHc9999+WGG27I7bffnt///vfZsWNHEudGN+nEfjg7ukM7/11ZtWpVkpfOio0bN+bRRx+dvb1Yz46TLkpbrVZ27dqVtWvX5vLLL1+wx27YsCFjY2NJkrGxsWzcuHHBZubE6dR+vHwAJMmvf/3rnHnmmQsyLydOO7tx8ODBPPfcc0le+murDz74YNauXZvE2dENOrUbzo2lr53duPrqq7Nr167s3Lkzn/vc5/Kud70rN954YxLnRrfo1H44O5a+dnbjyJEjs5d0HzlyJA888EDOOuusJIv77OhptVqt6iFOpD/96U/Ztm1bzjrrrNk/JnHVVVdleno63/3ud3Pw4MGcfvrpOfvss3PrrbdmcnIy3/nOd3LzzTfP+dj3vve9efbZZzM8PJyJiYk0m81s2bLF9ftLUKf241vf+lb+8pe/pKenJ2vWrMmnP/3p2Wv6WRra2Y2//vWv2blzZ2ZmZtJqtXLRRRflYx/7WJI4O7pAp3bDubH0tbMb/9tDDz2Ue+65Z/YtP5wb3aFT++HsWPra2Y2nn3463/jGN5K89Af03ve+9+WKK65IsrjPjpMuSgEAAFg8TrrLdwEAAFg8RCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABlRCkAAABl/j9MvfQ9El0+pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins, _ = plt.hist(data, bins = 15)\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.2 , 21.22, 21.24, 21.26, 21.28, 21.3 , 21.32, 21.34, 21.36,\n",
       "       21.38, 21.4 , 21.42, 21.44, 21.46, 21.48, 21.5 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### histogram on log scale. \n",
    "#### Use non-equal bin sizes, such that they look equal on log scale.\n",
    "logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))\n",
    "#logbins = np.geomspace(data.min(), data.max(), 200) \n",
    "plt.subplot(212)\n",
    "plt.hist(data, bins=logbins)\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribution: <scipy.stats._continuous_distns.norm_gen object at 0x000002131FA919A0>\n",
      "best_params: (21.37787878787879, 0.09921685999678506)\n",
      "best_sse: 120623.82767710852\n",
      "distribution: <scipy.stats._continuous_distns.powerlaw_gen object at 0x000002131FB40B20>\n",
      "best_params: (1.1710391122061448, 21.193442612000155, 0.3065574384411546)\n",
      "best_sse: 120144.33401401612\n",
      "distribution: <scipy.stats._continuous_distns.weibull_max_gen object at 0x000002131FAD1CA0>\n",
      "best_params: (0.6352400763319295, 21.500000000000007, 1.0767900788022806)\n",
      "best_sse: 118751.49176528357\n",
      "distribution: <scipy.stats._continuous_distns.exponweib_gen object at 0x000002131FAC8A60>\n",
      "best_params: (0.3471847614569199, 0.5474580086255538, 21.199999999999996, 1.3221984658179902)\n",
      "best_sse: 117959.5998438738\n",
      "distribution: <scipy.stats._continuous_distns.pearson3_gen object at 0x000002131FB407C0>\n",
      "best_params: (-2.5018690129881764, 21.38339984248696, 0.14585916049570877)\n",
      "best_sse: 113599.5660736039\n",
      "distribution: <scipy.stats._continuous_distns.beta_gen object at 0x000002131FA9F3D0>\n",
      "best_params: (0.9466444444986679, 0.2950030955160058, 21.186256527437504, 0.3137434725624966)\n",
      "best_sse: 103053.45783803816\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot for comparison\n",
    "plt.figure(figsize=(12,8))\n",
    "#ax = data.plot(kind='hist', bins=50, normed=True, alpha=0.5) #, color=plt.rcParams['axes.prop_cycle'][1]\n",
    "\n",
    "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5,  \n",
    "               color=list(matplotlib.rcParams['axes.prop_cycle'])[1]['color'])\n",
    "\n",
    "# Save plot limits\n",
    "dataYLim = ax.get_ylim()\n",
    "\n",
    "# Find best fit distribution\n",
    "best_fit_name, best_fit_params = best_fit_distribution(data, 200, ax)\n",
    "best_dist = getattr(st, best_fit_name)\n",
    "\n",
    "# Update plots\n",
    "ax.set_ylim(dataYLim)\n",
    "ax.set_title(u' eCO2 measurement from Carton Fire.\\n All Fitted Distributions')\n",
    "ax.set_xlabel(u'eCO2')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "# Make PDF with best params \n",
    "pdf = make_pdf(best_dist, best_fit_params)\n",
    "\n",
    "# Display\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = pdf.plot(lw=2, label='PDF', legend=True)\n",
    "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n",
    "\n",
    "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n",
    "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fit_params)])\n",
    "dist_str = '{}({})'.format(best_fit_name, param_str)\n",
    "\n",
    "ax.set_title(u'eCO2 measurement from Carton Fire with best fit distribution \\n' + dist_str)\n",
    "ax.set_xlabel(u'eCO2')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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