find best fit distribution-electric-1-eco2.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": [],
   "source": [
    "import os\n",
    "\n",
    "# Load data from statsmodels datasets\n",
    "data = pd.Series(sm.datasets.elnino.load_pandas().data.set_index('YEAR').values.ravel())\n",
    "data\n",
    "\n",
    "location = os.getcwd()+'/Covid-19_Data/Raw Data/'\n",
    "owid_location = 'owid-covid-data.csv'\n",
    "\n",
    "#data = pd.read_csv(location+owid_location)\n",
    "#data = pd.read_csv(owid_location)\n",
    "#data = data[(data.location != 'International') & (data.location != 'World')]\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Humidity    Temperature     MQ139    TVOC   eCO2   Detector\n",
      "Time                                                                \n",
      "0:00:00       38.2            21.9      69       0     400       OFF\n",
      "0:00:02       38.2            21.9      69       0     400       OFF\n",
      "0:00:04       38.2            21.9      69       0     400       OFF\n",
      "0:00:06       38.2            21.9      68       0     400       OFF\n",
      "0:00:08       38.3            21.9      68       0     400       OFF\n"
     ]
    }
   ],
   "source": [
    "series = pd.read_csv('electrical_1.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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = series.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        400\n",
       "1        400\n",
       "2        400\n",
       "3        400\n",
       "4        400\n",
       "        ... \n",
       "1135    6640\n",
       "1136    7190\n",
       "1137    6750\n",
       "1138    5858\n",
       "1139    6100\n",
       "Name:  eCO2 , Length: 1140, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.iloc[:,5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "new_cases_per_million = data['new_cases_per_million']\n",
    "new_cases_per_million = new_cases_per_million.dropna()\n",
    "new_cases_per_million\n",
    "new_cases_per_million = new_cases_per_million[ new_cases_per_million >= 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        400\n",
       "1        400\n",
       "2        400\n",
       "3        400\n",
       "4        400\n",
       "        ... \n",
       "1135    6640\n",
       "1136    7190\n",
       "1137    6750\n",
       "1138    5858\n",
       "1139    6100\n",
       "Name:  eCO2 , Length: 1140, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = series.iloc[:,5]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1140.000000\n",
       "mean      682.624561\n",
       "std       913.293695\n",
       "min       400.000000\n",
       "25%       436.000000\n",
       "50%       447.000000\n",
       "75%       471.000000\n",
       "max      7190.000000\n",
       "Name:  eCO2 , dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.031e+03, 4.100e+01, 1.900e+01, 1.000e+00, 4.000e+00, 7.000e+00,\n",
       "       2.000e+00, 4.000e+00, 9.000e+00, 3.000e+00, 1.000e+00, 1.000e+00,\n",
       "       4.000e+00, 9.000e+00, 4.000e+00])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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11dx///3ZuHFjnnrqqYyMjHzo/u12++d+7MHBwQwODs7fPnHixEKXyQIt1mve19dnfg1mvs1lts1mvs1lts1mvs11vc92zZo1F9y+4MuAe3t709vbO3+29M4778yrr76arq6uzMzMJElmZmayYsWK+f2np6fn799qtdLT07PQpwcAAKDBFhyrN998c3p7e3P06NEkyQsvvJBbbrkl/f39GR8fT5KMj49ny5YtSZL+/v5MTEzk7NmzmZqayrFjx7Jhw4ZFOAQAAACa5pK+Dfj+++/P/v37c+7cuaxatSoPPfRQ2u12hoeHMzY2lr6+vgwNDSVJ1q1bl61bt2ZoaCidnZ154IEHfBMwAAAAF3RJsfrxj388e/bs+cD2Rx999IL779ixIzt27LiUpwQAAOA64NQmAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5Sy/1Aebm5rJ79+709PRk9+7dOX36dIaHh3P8+PGsXLkyu3btyvLly5Mkhw8fztjYWDo7O7Nz585s3rz5Up8eAACABrrkM6v/9E//lLVr187fHhkZyaZNm7J///5s2rQpIyMjSZI33ngjExMT2bdvXx555JE8+eSTmZubu9SnBwAAoIEuKVanp6fz7LPP5q677prfNjk5mYGBgSTJwMBAJicn57dv27Yty5Yty6pVq7J69eocOXLkUp4eAACAhrqkWP32t7+de++9Nx0dHfPbTp48me7u7iRJd3d3Tp06lSRptVrp7e2d36+npyetVutSnh4AAICGWvBnVp955pl0dXVl/fr1efHFFz9y/3a7/XM/9ujoaEZHR5Mke/bsSV9f30KXedm9ebUXcJks1mu+dOnS0vPj0phvc5lts5lvc5lts5lvc5nthS04Vl966aV8//vfz3PPPZfZ2dmcOXMm+/fvT1dXV2ZmZtLd3Z2ZmZmsWLEiSdLb25vp6en5+7darfT09FzwsQcHBzM4ODh/+8SJEwtdJgu0WK95X1+f+TWY+TaX2Tab+TaX2Tab+TbX9T7bNWvWXHD7gi8Dvueee3Lo0KEcOHAgX/3qV/OpT30qDz/8cPr7+zM+Pp4kGR8fz5YtW5Ik/f39mZiYyNmzZzM1NZVjx45lw4YNC316AAAAGuyS/3TN/7Z9+/YMDw9nbGwsfX19GRoaSpKsW7cuW7duzdDQUDo7O/PAAw+ks9OfeQUAAOCDFiVWP/nJT+aTn/xkkuQXf/EX8+ijj15wvx07dmTHjh2L8ZQAAAA0mFObAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKWbrQO544cSIHDhzIT3/603R0dGRwcDCf//znc/r06QwPD+f48eNZuXJldu3aleXLlydJDh8+nLGxsXR2dmbnzp3ZvHnzYh0HAAAADbLgWF2yZEl+//d/P+vXr8+ZM2eye/fu/Nqv/Vr+9V//NZs2bcr27dszMjKSkZGR3HvvvXnjjTcyMTGRffv2ZWZmJo899li++c1vprPTyV0AAAB+1oJLsbu7O+vXr0+S3HDDDVm7dm1arVYmJyczMDCQJBkYGMjk5GSSZHJyMtu2bcuyZcuyatWqrF69OkeOHFmEQwAAAKBpFnxm9X+amprKq6++mg0bNuTkyZPp7u5O8n7Qnjp1KknSarWycePG+fv09PSk1Wpd8PFGR0czOjqaJNmzZ0/6+voWY5mXxZtXewGXyWK95kuXLi09Py6N+TaX2Tab+TaX2Tab+TaX2V7YJcfqu+++m7179+a+++7LjTfe+KH7tdvtn/sxBwcHMzg4OH/7xIkTl7RGLt5iveZ9fX3m12Dm21xm22zm21xm22zm21zX+2zXrFlzwe2X9IHRc+fOZe/evfnMZz6TO+64I0nS1dWVmZmZJMnMzExWrFiRJOnt7c309PT8fVutVnp6ei7l6QEAAGioBcdqu93OoUOHsnbt2nzhC1+Y397f35/x8fEkyfj4eLZs2TK/fWJiImfPns3U1FSOHTuWDRs2XOLyAQAAaKIFXwb80ksv5emnn86tt96aP/uzP0uSfOlLX8r27dszPDycsbGx9PX1ZWhoKEmybt26bN26NUNDQ+ns7MwDDzzgm4ABAAC4oAXH6q/+6q/m7//+7y/43x599NELbt+xY0d27Nix0KcEAADgOuHUJgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADKEasAAACUI1YBAAAoR6wCAABQjlgFAACgHLEKAABAOWIVAACAcsQqAAAA5YhVAAAAyll6tRdATee/8sVFeZw3F+VRFteSb333ai8BAAD4CM6sAgAAUI5YBQAAoByxCgAAQDliFQAAgHLEKgAAAOWIVQAAAMoRqwAAAJQjVgEAAChHrAIAAFCOWAUAAKAcsQoAAEA5YhUAAIByll7tBcCVdv4rX7zaS7gslnzru1d7CQAAsGicWQUAAKAcsQoAAEA5YhUAAIByxCoAAADliFUAAADK8W3AAHCd8u3owEL42cGV4swqAAAA5TizCnAVVH9X+s0F3s+70gDAYrnisfr888/nqaeeytzcXO66665s3779Si8BuIZUjzquD037/3Chb0YAF2exf3b4t3t5Xc2f9ZdzttfyG8lXNFbn5uby5JNP5i/+4i/S29ubr3/96+nv788tt9xyJZcBADRY095c+G/X8i+cAAtxRWP1yJEjWb16dX7pl34pSbJt27ZMTk6KVVgEV+OXM+/w8r81NRKggp/n35efy0CTXNFYbbVa6e3tnb/d29ubH/3oRx/Yb3R0NKOjo0mSPXv2ZM2aNVdsjRft/3z/aq8AAACgca7otwG32+0PbOvo6PjAtsHBwezZsyd79uy5EsviMtq9e/fVXgKXkfk2l9k2m/k2l9k2m/k2l9le2BWN1d7e3kxPT8/fnp6eTnd395VcAgAAANeAKxqrv/Irv5Jjx45lamoq586dy8TERPr7+6/kEgAAALgGXNHPrC5ZsiT3339/Hn/88czNzeW3fuu3sm7duiu5BK6wwcHBq70ELiPzbS6zbTbzbS6zbTbzbS6zvbCO9oU+SAoAAABX0RW9DBgAAAB+HmIVAACAcq7oZ1a59h08eDDPPvtsurq6snfv3iTJ6dOnMzw8nOPHj2flypXZtWtXli9fniQ5fPhwxsbG0tnZmZ07d2bz5s1Jkh//+Mc5cOBAZmdnc/vtt2fnzp0X/DNGXFknTpzIgQMH8tOf/jQdHR0ZHBzM5z//eTNugNnZ2fzlX/5lzp07l/Pnz+fOO+/M3XffbbYNMjc3l927d6enpye7d+822wb5oz/6o3zsYx9LZ2dnlixZkj179phvg7z99ts5dOhQXn/99XR0dOTBBx/MmjVrzPcad/To0QwPD8/fnpqayt13352BgQGzvRhtuAgvvvhi+5VXXmkPDQ3Nb/vOd77TPnz4cLvdbrcPHz7c/s53vtNut9vt119/vf2nf/qn7dnZ2fabb77Z/uM//uP2+fPn2+12u7179+72Sy+91J6bm2s//vjj7WefffaKHwsf1Gq12q+88kq73W6333nnnfbDDz/cfv311824Aebm5tpnzpxpt9vt9tmzZ9tf//rX2y+99JLZNsj3vve99hNPPNH+67/+63a77Wdzkzz00EPtkydP/sw2822Ov/3bv22Pjo622+33fz6fPn3afBvm/Pnz7S9/+cvtqakps71ILgPmotx2223z7/78t8nJyQwMDCRJBgYGMjk5Ob9927ZtWbZsWVatWpXVq1fnyJEjmZmZyZkzZ/KJT3wiHR0d+exnPzt/H66u7u7urF+/Pklyww03ZO3atWm1WmbcAB0dHfnYxz6WJDl//nzOnz+fjo4Os22I6enpPPvss7nrrrvmt5lts5lvM7zzzjv54Q9/mM997nNJkqVLl+amm24y34Z54YUXsnr16qxcudJsL5LLgLlkJ0+eTHd3d5L3Y+fUqVNJklarlY0bN87v19PTk1arlSVLlqS3t3d+e29vb1qt1pVdNB9pamoqr776ajZs2GDGDTE3N5evfe1r+clPfpLf+Z3fycaNG822Ib797W/n3nvvzZkzZ+a3mW2zPP7440mS3/7t387g4KD5NsTU1FRWrFiRgwcP5rXXXsv69etz3333mW/D/Nu//Vt+/dd/PYmfzRdLrHLZtD/kryJ92HbqePfdd7N3797cd999ufHGGz90PzO+tnR2duZv/uZv8vbbb+cb3/hG/uu//utD9zXba8czzzyTrq6urF+/Pi+++OJH7m+2157HHnssPT09OXnyZP7qr/4qa9as+dB9zffacv78+bz66qu5//77s3Hjxjz11FMZGRn50P3N99pz7ty5PPPMM7nnnnv+v/uZ7YWJVS5ZV1dXZmZm0t3dnZmZmaxYsSLJ++/8TE9Pz+/XarXS09Pzge3T09Pp6em54uvmws6dO5e9e/fmM5/5TO64444kZtw0N910U2677bY8//zzZtsAL730Ur7//e/nueeey+zsbM6cOZP9+/ebbYP89xy6urqyZcuWHDlyxHwbore3N729vfNn1O68886MjIyYb4M899xz+eVf/uXcfPPNSfxOdbF8ZpVL1t/fn/Hx8STJ+Ph4tmzZMr99YmIiZ8+ezdTUVI4dO5YNGzaku7s7N9xwQ15++eW02+08/fTT6e/vv5qHwP/Tbrdz6NChrF27Nl/4whfmt5vxte/UqVN5++23k7z/zcAvvPBC1q5da7YNcM899+TQoUM5cOBAvvrVr+ZTn/pUHn74YbNtiHfffXf+8u533303//7v/55bb73VfBvi5ptvTm9vb44ePZrk/c823nLLLebbIP/zEuDE71QXq6N9vZ9b5qI88cQT+cEPfpC33norXV1dufvuu7Nly5YMDw/nxIkT6evry9DQ0PyXMP3DP/xD/uVf/iWdnZ257777cvvttydJXnnllRw8eDCzs7PZvHlz7r///uvnK7gL+4//+I88+uijufXWW+fn8aUvfSkbN24042vca6+9lgMHDmRubi7tdjtbt27N7/3e7+Wtt94y2wZ58cUX873vfS+7d+8224Z48803841vfCPJ+5eM/sZv/EZ27Nhhvg3yn//5nzl06FDOnTuXVatW5aGHHkq73TbfBnjvvffy4IMP5u/+7u/mP1bl3+7FEasAAACU4zJgAAAAyhGrAAAAlCNWAQAAKEesAgAAUI5YBQAAoByxCgAAQDliFQAAgHL+L1a77PQujGR+AAAAAElFTkSuQmCC\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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NDwAAwCq27J3S5557Llu3bl28PDY2lv7+/hw9ejT9/f0ZGxtLkpw6dSpTU1M5cuRIHnnkkRw7dizz8/PXfXAAAABWv2VF6ezsbF588cXceeedi9dNT09ncHAwSTI4OJjp6enF6/fu3Zv169dn8+bN2bJlS06ePNmG0QEAAFjtlnX47i9+8YvcfffdefvttxevO3/+fHp6epIkPT09uXDhQpKk1Wpl586di/fr7e1Nq9V633OOj49nfHw8STIyMpJms3n1q/gIvFE9QJus9PcdVoquri6fFwCANlgySv/whz+ku7s727dvzyuvvLLkEy4sLCzrhYeGhjI0NLR4+dy5c8t6HNeX9x2Wp9ls+rwAAFylvr6+D7xtySh99dVX88ILL+SPf/xjLl68mLfffjtHjx5Nd3d35ubm0tPTk7m5uWzcuDFJ0mg0Mjs7u/j4VquV3t7e67AMAAAA1polzyn97ne/m6eeeipPPPFEfvCDH+QLX/hCHnzwwQwMDGRycjJJMjk5md27dydJBgYGMjU1lUuXLmVmZiZnzpzJjh072rsKAAAAVqVl/0mY/2vfvn0ZHR3NxMREms1mhoeHkyTbtm3Lnj17Mjw8nM7Ozhw4cCCdnf4cKgAAAO/XsbDck0Db7PTp09UjfKgr991VPUJbrHvm2eoRYFVwTikAwNX7sHNKbWECAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQRpQCAABQpmupO5w7dy5PPPFE/v73v6ejoyNDQ0P5+te/nrfeeiujo6M5e/ZsNm3alIMHD2bDhg1JkhMnTmRiYiKdnZ3Zv39/du3a1e51AAAAsAotGaXr1q3L9773vWzfvj1vv/12Dh06lC9+8Yv5zW9+k/7+/uzbty9jY2MZGxvL3XffnVOnTmVqaipHjhzJ3NxcHnvssTz++OPp7LQpCwAAwHstWYo9PT3Zvn17kuSTn/xktm7dmlarlenp6QwODiZJBgcHMz09nSSZnp7O3r17s379+mzevDlbtmzJyZMn27gEAAAAVqv/avtyZmYmr7/+enbs2JHz58+np6cnyf+G64ULF5IkrVYrjUZj8TG9vb1ptVrXcWQAAADWiiUP3/23d955J4cPH84999yTG2644QPvt7CwsKznGx8fz/j4eJJkZGQkzWZzuaOUeKN6gDZZ6e87rBRdXV0+LwAAbbCsKL18+XIOHz6cr3zlK7n11luTJN3d3Zmbm0tPT0/m5uaycePGJEmj0cjs7OziY1utVnp7e9/3nENDQxkaGlq8fO7cuWtaCFfH+w7L02w2fV4AAK5SX1/fB9625OG7CwsLeeqpp7J169Z84xvfWLx+YGAgk5OTSZLJycns3r178fqpqalcunQpMzMzOXPmTHbs2HGtawAAAGANWnKn9NVXX83zzz+fz3zmM/nRj36UJPnOd76Tffv2ZXR0NBMTE2k2mxkeHk6SbNu2LXv27Mnw8HA6Oztz4MAB37wLAADAf9SxsNyTQNvs9OnT1SN8qCv33VU9Qluse+bZ6hFgVXD4LgDA1bumw3cBAACgXUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZUQpAAAAZbqqB6DWlfvuqh6hLdY982z1CAAAwDLYKQUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKCMKAUAAKBMV/UA0A5X7rureoS2WPfMs9UjAADAdWWnFAAAgDKiFAAAgDIO34VVxGHJAACsNXZKAQAAKCNKAQAAKCNKAQAAKCNKAQAAKCNKAQAAKCNKAQAAKCNKAQAAKCNKAQAAKNNVPQDAWnXlvruqR2ibdc88Wz0CALBG2CkFAACgjCgFAACgjCgFAACgjHNKAfivrdXzZZ0rCwAfPTulAAAAlBGlAAAAlHH4LlBuNRwK+kb1AADANVsNv3NcrdV8CoqdUgAAAMrYKQUAgKu0VnfeVvOuG6uPnVIAAADKiFIAAADKOHwXAFiVHDYJsDaIUgCAFURsAx83bYvSl156KcePH8/8/HzuvPPO7Nu3r10vBQDACrdWY3ut8vPio9SWKJ2fn8+xY8fyk5/8JI1GIw899FAGBgZy0003tePlAOC6WKu/hNmhAmAla8sXHZ08eTJbtmzJjTfemK6uruzduzfT09PteCkAAABWsbbslLZarTQajcXLjUYjr7322nvuMz4+nvHx8STJyMhI+vr62jHK9fM/L1RPAAD8//zfDLAmtGWndGFh4X3XdXR0vOfy0NBQRkZGMjIy0o4Rrrunn366eoQya2ntq2ktK3XWlTBXxQyHDh36yF8TVqKV8G8A187P8dp9XN/Dtbbu1bSelTrr9ZirLVHaaDQyOzu7eHl2djY9PT3teKmPzJe//OXqEcqspbWvprWs1FlXwlwrYQb4uPL5Wxv8HK/dx/U9XGvrXk3rWamzXo+5Ohb+07bmNbpy5Uq+//3v59FHH01vb28eeuihPPjgg9m2bdv1fimAj8ShQ4dWzZEdAACrSVvOKV23bl3uvffe/OxnP8v8/Hxuv/12QQqsakNDQ9UjAACsSW3ZKQUAAIDlaMs5pQAAALAcohQAAIAyohQAAIAybfmiI4C17tSpU3nuuefy5ptvpr+/P1/72teqRwIAWJV80RHAvzz55JN58cUX093dncOHDy9e/9JLL+X48eOZn5/PnXfemX379i3eNj8/n6effjr3339/wcQAAKufw3cB/uWrX/1qHn744fdcNz8/n2PHjuXhhx/O6Ohofve73+XUqVNJkhdeeCGPPvpo+vv7K8YFAFgTRCnAv3z+85/Phg0b3nPdyZMns2XLltx4443p6urK3r17Mz09nSQZGBjIT3/60/z2t7+tGBcAYE1wTinAh2i1Wmk0GouXG41GXnvttbzyyiv5/e9/n8uXL+dLX/pS4YQAAKubKAX4EP/ptPuOjo7cfPPNufnmmwsmAgBYWxy+C/AhGo1GZmdnFy/Pzs6mp6encCIAgLVFlAJ8iM997nM5c+ZMZmZmcvny5UxNTWVgYKB6LACANcOfhAH4l5///Of505/+lDfffDPd3d359re/nTvuuCMvvvhifvnLX2Z+fj633357vvWtb1WPCgCwZohSAAAAyjh8FwAAgDKiFAAAgDKiFAAAgDKiFAAAgDKiFAAAgDKiFAAAgDKiFAAAgDKiFAAAgDKiFAAAgDL/Dx1uIlBbHdvjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_loghist(x, bins):\n",
    "  hist, bins = np.histogram(x, bins=bins)\n",
    "  logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))\n",
    "  plt.hist(x, bins=logbins)\n",
    "  plt.xscale('log')\n",
    "\n",
    "plot_loghist(data, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 400.        ,  852.66666667, 1305.33333333, 1758.        ,\n",
       "       2210.66666667, 2663.33333333, 3116.        , 3568.66666667,\n",
       "       4021.33333333, 4474.        , 4926.66666667, 5379.33333333,\n",
       "       5832.        , 6284.66666667, 6737.33333333, 7190.        ])"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribution: <scipy.stats._continuous_distns.norm_gen object at 0x0000017E83E60A00>\n",
      "best_params: (682.6245614035088, 912.89303927192)\n",
      "best_sse: 0.0002659612460595635\n",
      "distribution: <scipy.stats._continuous_distns.powerlaw_gen object at 0x0000017E83F0BC40>\n",
      "best_params: (0.3271401905658069, 399.9999999999999, 7321.781088319998)\n",
      "best_sse: 0.00021897731378702233\n",
      "distribution: <scipy.stats._continuous_distns.powerlognorm_gen object at 0x0000017E83F0BF40>\n",
      "best_params: (0.07495130328190312, 0.4512434293707195, 395.25875353721074, 12.880472364657598)\n",
      "best_sse: 0.00012355998590593981\n",
      "distribution: <scipy.stats._continuous_distns.dweibull_gen object at 0x0000017E83E97190>\n",
      "best_params: (0.4175213998239733, 444.99999999999994, 336.1610236854952)\n",
      "best_sse: 0.00012079188791618212\n",
      "distribution: <scipy.stats._continuous_distns.exponweib_gen object at 0x0000017E83E97AC0>\n",
      "best_params: (154.733129695906, 0.18817484573675652, 395.35685854807934, 0.008624828482886341)\n",
      "best_sse: 0.00011267029929785147\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "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 an Electrical Device 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 an Electrical Device Fire with best fit distribution \\n' + dist_str)\n",
    "ax.set_xlabel(u'eCO2')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## log normal distribution only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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.lognorm\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(\"log normal 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log normal distribution: <scipy.stats._continuous_distns.lognorm_gen object at 0x0000017E83EE8430>\n",
      "best_params: (1.3384520314401045, 398.7347355782563, 66.33516304397119)\n",
      "best_sse: 0.00012593272006109185\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 12,
     "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, density=True, alpha=0.5) #, color=plt.rcParams['axes.prop_cycle'][1]\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'COVID-19 New Cases Per Millions.\\n All Fitted Distributions')\n",
    "ax.set_xlabel(u'New Cases Per Millions')\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'COVID-19 New Cases Per Millions with best fit distribution \\n' + dist_str)\n",
    "ax.set_xlabel(u'New Cases Per Millions')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats._continuous_distns.lognorm_gen at 0x17e83ee8430>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}