170492.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd \n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt('170492.txt', skiprows=1)\n",
    "data1 = data[:,0]\n",
    "N = len(data1)\n",
    "KS = {}\n",
    "AD = {}\n",
    "Chi2 = {}\n",
    "AIC = {}\n",
    "BIC = {}\n",
    "para = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions For Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 870,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ks_test(data,distribution,args):\n",
    "    stat_value , p_value = stats.kstest(data,distribution,args)\n",
    "    significance = 0.05\n",
    "    if(p_value < significance):\n",
    "#         print(\"KS_test : H0 is rejected : P_value(=\",\"%.3f\" % p_value,\") < α(=\",significance, \")\")\n",
    "        return p_value ,stat_value\n",
    "    else:\n",
    "#         print(\"KS_test : H0 is accepted : P_value(=\",\"%.3f\" % p_value,\") > α(=\",significance, \")\")\n",
    "        return p_value , stat_value\n",
    "    \n",
    "def anderson_test(data, distribution):\n",
    "    significance = 0.05\n",
    "    data = sorted(data)\n",
    "    if(distribution=='pearson3'):\n",
    "        A_star = stats.anderson(data,distribution)\n",
    "        A_star,temp1,temp2 =A_star\n",
    "    else:\n",
    "        A_star , C_alpha , alpha_val = stats.anderson(data,distribution)\n",
    "        \n",
    "    if ((A_star) >= 0.00 and (A_star) < 0.200):\n",
    "            P = 1 - np.exp(-13.436 + 101.14*(A_star) - 223.73*((A_star)**2))\n",
    "    if ((A_star) >= 0.200 and (A_star) < 0.340):\n",
    "        P = 1 - np.exp(-8.318 + 42.796*(A_star) - 59.938*((A_star)**2))\n",
    "    if ((A_star) >= 0.340 and (A_star) < 0.600):\n",
    "        P = np.exp(0.9177 - 4.279*(A_star) - 1.38*((A_star)**2))\n",
    "    if ((A_star) >= 0.600):\n",
    "        P = np.exp(1.2937 - 5.709*(A_star) + 0.0186*((A_star)**2))    \n",
    "    if(distribution =='pearson3'):\n",
    "        if(P  > significance):\n",
    "#             print(\"Anderson_test:  H0 is accepted : P(=\",\"%.3f\" % P,\") > α(=\",significance, \")\")\n",
    "            return P, A_star \n",
    "        else:\n",
    "#             print(\"Anderson_test: H0 is rejected : P(=\",\"%.3f\" % P,\") < α(=\",significance, \")\")\n",
    "            return P, A_star\n",
    "        \n",
    "    else:\n",
    "        if(A_star  > C_alpha[2]):\n",
    "#             print(\"Anderson_test:  H0 is rejected : A*(=\",\"%.3f\" % A_star,\") > Cα(=\",C_alpha[2], \") &  P_value =\",\"%.3f\" % P  )\n",
    "            return P, A_star , C_alpha[2] \n",
    "        else:\n",
    "#             print(\"Anderson_test: H0 is accepted : A*(=\",\"%.3f\" % A_star,\") < Cα(=\",C_alpha[2], \") &  P_value =\", \"%.3f\" % P )\n",
    "            return P, A_star , C_alpha[2]\n",
    "        \n",
    "        \n",
    "def chi2(dataset,dist):\n",
    "    lower_val = []\n",
    "    upper_val = []\n",
    "    p = []\n",
    "    e = []\n",
    "    qc = pd.qcut(dataset,q = 20,precision=0)\n",
    "    n = qc.value_counts().to_list()\n",
    "    ppp = qc.categories\n",
    "    for k in range(len(qc.categories)):\n",
    "        lower_val.append(ppp[k].left)\n",
    "        upper_val.append(ppp[k].right)\n",
    "        if(dist=='norm'):\n",
    "            loc,scale = stats.norm.fit(dataset)\n",
    "            p.append( stats.norm.cdf(upper_val[k],loc,scale) - stats.norm.cdf(lower_val[k],loc,scale ))\n",
    "        if(dist=='expon'):\n",
    "            loc,scale = stats.expon.fit(dataset,floc=0)\n",
    "            p.append(stats.expon.cdf(upper_val[k],loc,scale) - stats.expon.cdf(lower_val[k],loc,scale ))\n",
    "        if(dist=='lognorm'):\n",
    "            shape,loc,scale = stats.lognorm.fit(dataset,floc=0)\n",
    "            p.append( stats.lognorm.cdf(float(upper_val[k]),shape,loc,scale) - stats.lognorm.cdf(lower_val[k],shape,loc,scale ))\n",
    "        if(dist=='gamma'):\n",
    "            shape,loc,scale = stats.gamma.fit(dataset,floc=0)\n",
    "            p.append(stats.gamma.cdf(upper_val[k],shape,loc,scale) - stats.gamma.cdf(lower_val[k],shape,loc,scale ))\n",
    "        if(dist=='gumbel_r'):\n",
    "            loc,scale = stats.gumbel_r.fit(dataset)\n",
    "            p.append( stats.gumbel_r.cdf(upper_val[k],loc,scale) - stats.gumbel_r.cdf(lower_val[k],loc,scale ))\n",
    "        if(dist=='pearson3'):\n",
    "            skew,loc,scale = stats.pearson3.fit(dataset)\n",
    "            p.append(stats.pearson3.cdf(upper_val[k],skew,loc,scale) - stats.pearson3.cdf(lower_val[k],skew,loc,scale ))\n",
    "        \n",
    "        e.append(p[k]*N)\n",
    "    stat_value , p_value = stats.chisquare(n,e,ddof = 2)\n",
    "    significance = 0.05\n",
    "    if(p_value < significance):\n",
    "#         print(\"Chi2_test : H0 is rejected : P_value(=\",\"%.3f\" % p_value,\") < α(=\",significance, \")\")\n",
    "        return p_value ,stat_value\n",
    "    else:\n",
    "#         print(\"Chi2_test : H0 is accepted : P_value(=\",\"%.3f\" % p_value,\") > α(=\",significance, \")\")\n",
    "        return p_value,stat_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(data1):\n",
    "    mu, std = stats.norm.fit(data1)\n",
    "    args = mu , std\n",
    "    logLik = np.sum(stats.norm.logpdf(data1,mu,std) ) \n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    p = stats.norm.pdf(x, mu, std)\n",
    "    para[\"norm\"] = args\n",
    "    AD[\"norm\"] = anderson_test(data1,'norm')\n",
    "    KS[\"norm\"] = ks_test(data1,'norm',args)\n",
    "    Chi2[\"norm\"] = chi2(data1,'norm')\n",
    "    AIC[\"norm\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"norm\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p , args, mu , std\n",
    "def expon(data1):\n",
    "    loc , scale = stats.expon.fit(data1,floc=0)\n",
    "    args = loc , scale\n",
    "    mean, var, skew, kurt = stats.expon.stats(loc,scale,moments='mvsk')\n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(0, xmax, 100)\n",
    "    p = stats.expon.pdf(x,loc,scale)\n",
    "    logLik = np.sum(stats.expon.logpdf(data1,loc,scale) ) \n",
    "    para[\"expon\"] = args\n",
    "    AD[\"expon\"] = anderson_test(data1,'expon')\n",
    "    KS[\"expon\"] = ks_test(data1,'expon',args)\n",
    "    Chi2[\"expon\"] = chi2(data1,'expon')\n",
    "    AIC[\"expon\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"expon\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p, args, mean , var**0.5\n",
    "def lognorm(data1):\n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    args = stats.lognorm.fit(data1,floc=0)\n",
    "    shape, loc, scale = args\n",
    "    mean, var, skew, kurt = stats.lognorm.stats(shape,loc,scale,moments='mvsk')\n",
    "    p = stats.lognorm.pdf(x,shape,loc,scale)\n",
    "    logLik = np.sum(stats.lognorm.logpdf(data1,shape,loc,scale) )\n",
    "    para[\"lognorm\"] = args\n",
    "    AD[\"lognorm\"] = anderson_test(data1,'lognorm') \n",
    "    KS[\"lognorm\"] = ks_test(data1,'lognorm',args)\n",
    "    Chi2[\"lognorm\"] = chi2(data1,'lognorm') \n",
    "    AIC[\"lognorm\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"lognorm\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p, args, mean , var**0.5\n",
    "def gamma(data1):\n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    args = stats.gamma.fit(data1,floc=0)\n",
    "    shp, locs, scle = args\n",
    "    mean, var, skew, kurt = stats.gamma.stats(shp,locs,scle,moments='mvsk')\n",
    "    p = stats.gamma.pdf(x,shp,locs,scle)\n",
    "    logLik = np.sum(stats.gamma.logpdf(data1,shp,locs,scle) ) \n",
    "    para[\"gamma\"] = args\n",
    "    AD[\"gamma\"] = anderson_test((data1),'gamma')\n",
    "    KS[\"gamma\"] = ks_test(data1,'gamma',args)\n",
    "    Chi2[\"gamma\"] = chi2(data1,'gamma')\n",
    "    AIC[\"gamma\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"gamma\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p, args, mean , var**0.5\n",
    "def gumbel_r(data1):\n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    args = stats.gumbel_r.fit(data1)\n",
    "    loc, scale = args\n",
    "    mean, var, skew, kurt = stats.gumbel_r.stats(loc,scale,moments='mvsk')\n",
    "    p = stats.gumbel_r.pdf(x,loc,scale)\n",
    "    logLik = np.sum(stats.gumbel_r.logpdf(data1,loc,scale))\n",
    "    para[\"gumbel_r\"] = args\n",
    "    AD[\"gumbel_r\"] = anderson_test(data1,'gumbel_r')\n",
    "    KS[\"gumbel_r\"] = ks_test(data1,'gumbel_r',args)\n",
    "    Chi2[\"gumbel_r\"] = chi2(data1,'gumbel_r')\n",
    "    AIC[\"gumbel_r\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"gumbel_r\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p, args, mean , var**0.5\n",
    "def pearson3(data1):\n",
    "    xmax, xmin = np.max(data1) + 200 , np.min(data1) - 200\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    args = stats.pearson3.fit(data1)\n",
    "    skew ,loc, scale = args\n",
    "    mean, var, skew, kurt = stats.pearson3.stats(skew,loc,scale,moments='mvsk')\n",
    "    p = stats.pearson3.pdf(x,skew,loc,scale)\n",
    "    logLik = np.sum(stats.pearson3.logpdf(data1,skew,loc,scale) ) \n",
    "    para[\"pearson3\"] = args\n",
    "    AD[\"pearson3\"] = anderson_test(data1,'pearson3')\n",
    "    KS[\"pearson3\"] = ks_test(data1,'pearson3',args)\n",
    "    Chi2[\"pearson3\"] = chi2(data1,'pearson3')\n",
    "    AIC[\"pearson3\"] = -2*logLik + 2*len(args)\n",
    "    BIC[\"pearson3\"] = -2*logLik + np.log(N)*len(args)\n",
    "    return p, args , mean , var**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def Plot():\n",
    "    plt.figure(figsize=(20,12))\n",
    "    plt.subplot(2,3,1)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    xmin, xmax = plt.xlim()\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    plt.plot(x, norm(data1)[0], 'k', linewidth=2)\n",
    "    title = \"Normal: mu = %.2f,  std = %.2f\" % (norm(data1)[2],norm(data1)[3])\n",
    "    plt.title(title)\n",
    "\n",
    "    plt.subplot(2,3,2)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    plt.plot(x, expon(data1)[0], 'k', linewidth=2)\n",
    "    title = \"Exponential: mu = %.2f,  std = %.2f\" % (expon(data1)[2],expon(data1)[3])\n",
    "    plt.title(title)\n",
    "\n",
    "    plt.subplot(2,3,3)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    plt.plot(x, lognorm(data1)[0], 'k', linewidth=2) \n",
    "    title = \"Lognormal: mu = %.2f,  std = %.2f\" % (lognorm(data1)[2],lognorm(data1)[3])\n",
    "    plt.title(title)\n",
    "\n",
    "    plt.subplot(2,3,4)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    plt.plot(x, gamma(data1)[0], 'k', linewidth=2) \n",
    "    title = \"Gamma: mu = %.2f,  std = %.2f\" % (gamma(data1)[2],gamma(data1)[3])\n",
    "    plt.title(title)\n",
    "\n",
    "    plt.subplot(2,3,5)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    plt.plot(x, gumbel_r(data1)[0], 'k', linewidth=2) \n",
    "    title = \"Gumbel: mu = %.2f,  std = %.2f\" % (gumbel_r(data1)[2],gumbel_r(data1)[3])\n",
    "    plt.title(title)\n",
    "\n",
    "    plt.subplot(2,3,6)\n",
    "    plt.hist(data1, bins=25, density=True, alpha=0.6, color='g')\n",
    "    xmin,xmax = plt.xlim()\n",
    "    x = np.linspace(xmin, xmax, 100)\n",
    "    plt.plot(x, pearson3(data1)[0], 'k', linewidth=2) \n",
    "    title = \"Pearson3: mu = %.2f,  std = %.2f\" % (pearson3(data1)[2],pearson3(data1)[3])\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding design discharge for return period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf2x(data1,dist):\n",
    "    data2 =  sorted(data1)\n",
    "    \n",
    "    if(dist=='norm'):\n",
    "        mu, std = stats.norm.fit(data1)\n",
    "        temp = stats.norm.cdf(data2, mu, std)\n",
    "    if(dist=='expon'):\n",
    "        mu , std = stats.expon.fit(data1,floc=0)\n",
    "        temp = stats.expon.cdf(data2, mu, std)\n",
    "    if(dist=='lognorm'):\n",
    "        args = stats.lognorm.fit(data1,floc=0)\n",
    "        shape, loc, scale = args\n",
    "        temp = stats.lognorm.cdf(data2,shape, loc, scale)\n",
    "    if(dist=='gamma'):\n",
    "        args = stats.gamma.fit(data1,floc=0)\n",
    "        shp, locs, scle = args\n",
    "        temp = stats.gamma.cdf(data2,shp, locs, scle)\n",
    "    if(dist=='gumbel_r'):\n",
    "        args = stats.gumbel_r.fit(data1)\n",
    "        loc, scale = args\n",
    "        temp = stats.gumbel_r.cdf(data2, loc, scale)\n",
    "    if(dist=='pearson3'):\n",
    "        args = stats.pearson3.fit(data1)\n",
    "        skew ,loc, scale = args\n",
    "        temp = stats.pearson3.cdf(data2, skew ,loc, scale)\n",
    "    cnt = 0\n",
    "    for i in temp:\n",
    "        cnt = cnt+1\n",
    "        if(i>49/50):\n",
    "            break\n",
    "    cnt = cnt-1\n",
    "    temp_cdf = data2[cnt] - (data2[cnt]-data2[cnt-1])*(temp[cnt]-0.98)/(temp[cnt]-temp[cnt-1])\n",
    "    return temp_cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Print(dist,distribution):\n",
    "    print(\"Distribution :\",'\\x1b[1;31m'+distribution+'\\x1b[0m')\n",
    "    print(\"Mean = \",\"%.4f\" % dist(data1)[2])\n",
    "    print(\"Standard Deviation = \", \"%.4f\" %dist(data1)[3])\n",
    "    print(\"Parameters = \", [float(\"{0:.4f}\".format(n)) for n in dist(data1)[1]])\n",
    "    if(dist == pearson3):\n",
    "        print(\"Anderson Test:\",\"Accept\" if AD[distribution][0]>0.05 else \"Reject\", \"\\n    statistical Value (A2*) = \",\"%.4f\" %AD[distribution][1],\"\\n    p Value = \",\"%.4f\" %AD[distribution][0])\n",
    "    else:\n",
    "        print(\"Anderson Test:\",\"Accept\" if AD[distribution][2]>AD[distribution][1] else \"Reject\", \"\\n    statistical Value (A2*) = \",\"%.4f\" %AD[distribution][1],\"\\n    Critical Value (α) = \",\"%.4f\" %AD[distribution][2],\"\\n    p Value = \",\"%.4f\" %AD[distribution][0])\n",
    "    print(\"KS Test:\",\"Accept\" if KS[distribution][0]>0.05 else \"Reject\", \"\\n    P value = \",\"%.4f\" %KS[distribution][0],\"\\n    Stat value = \",\"%.4f\" %KS[distribution][1])\n",
    "    print(\"Chi_square Test:\",\"Accept\" if Chi2[distribution][0]>0.05 else \"Reject\", \" \\n    P value = \",\"%.4f\" %Chi2[distribution][0],\" \\n    Stat value = \",\"%.4f\" %Chi2[distribution][1])\n",
    "    print(\"AIC = \",\"%.4f\" %AIC[distribution])\n",
    "    print(\"BIC = \",\"%.4f\" %BIC[distribution])\n",
    "    print(\"\\n\\n\")\n",
    "    \n",
    "def Best_print():\n",
    "    print(\"Best fitted Distribution: \")\n",
    "    print( \"According to AD test:\",AD_accept[0][0])\n",
    "    print( \"According to KS test:\",KS_accept[0][0])\n",
    "    print( \"According to Chi2 test:\",Chi2_accept[0][0])\n",
    "    print( \"According to AIC Value:\",AIC[0][0])\n",
    "    print( \"According to BIC Value:\",BIC[0][0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 875,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution : \u001b[1;31mnorm\u001b[0m\n",
      "Mean =  2623.4709\n",
      "Standard Deviation =  972.2060\n",
      "Parameters =  [2623.4709, 972.206]\n",
      "Anderson Test: Accept \n",
      "    statistical Value (A2*) =  0.5532 \n",
      "    Critical Value (α) =  0.7590 \n",
      "    p Value =  0.1539\n",
      "KS Test: Accept \n",
      "    P value =  0.7638 \n",
      "    Stat value =  0.0678\n",
      "Chi_square Test: Accept  \n",
      "    P value =  0.5285  \n",
      "    Stat value =  15.9341\n",
      "AIC =  1663.7012\n",
      "BIC =  1668.9116\n",
      "\n",
      "\n",
      "\n",
      "Distribution : \u001b[1;31mexpon\u001b[0m\n",
      "Mean =  2623.4709\n",
      "Standard Deviation =  2623.4709\n",
      "Parameters =  [0.0, 2623.4709]\n",
      "Anderson Test: Reject \n",
      "    statistical Value (A2*) =  18.3595 \n",
      "    Critical Value (α) =  1.3330 \n",
      "    p Value =  0.0000\n",
      "KS Test: Reject \n",
      "    P value =  0.0000 \n",
      "    Stat value =  0.3514\n",
      "Chi_square Test: Reject  \n",
      "    P value =  0.0000  \n",
      "    Stat value =  126.9073\n",
      "AIC =  1778.4507\n",
      "BIC =  1783.6610\n",
      "\n",
      "\n",
      "\n",
      "Distribution : \u001b[1;31mlognorm\u001b[0m\n",
      "Mean =  2662.4573\n",
      "Standard Deviation =  1236.4680\n",
      "Parameters =  [0.4419, 0.0, 2414.7593]\n",
      "Anderson Test: Reject \n",
      "    statistical Value (A2*) =  1.8496 \n",
      "    Critical Value (α) =  0.7590 \n",
      "    p Value =  0.0001\n",
      "KS Test: Accept \n",
      "    P value =  0.2165 \n",
      "    Stat value =  0.1038\n",
      "Chi_square Test: Accept  \n",
      "    P value =  0.1560  \n",
      "    Stat value =  22.7955\n",
      "AIC =  1684.3367\n",
      "BIC =  1692.1522\n",
      "\n",
      "\n",
      "\n",
      "Distribution : \u001b[1;31mgamma\u001b[0m\n",
      "Mean =  2623.4709\n",
      "Standard Deviation =  1054.1761\n",
      "Parameters =  [6.1934, 0.0, 423.5943]\n",
      "Anderson Test: Accept \n",
      "    statistical Value (A2*) =  0.8494 \n",
      "    Critical Value (α) =  2.4120 \n",
      "    p Value =  0.0289\n",
      "KS Test: Accept \n",
      "    P value =  0.6453 \n",
      "    Stat value =  0.0736\n",
      "Chi_square Test: Accept  \n",
      "    P value =  0.4222  \n",
      "    Stat value =  17.4821\n",
      "AIC =  1670.6838\n",
      "BIC =  1678.4993\n",
      "\n",
      "\n",
      "\n",
      "Distribution : \u001b[1;31mgumbel_r\u001b[0m\n",
      "Mean =  2672.7943\n",
      "Standard Deviation =  1159.7227\n",
      "Parameters =  [2150.8574, 904.2321]\n",
      "Anderson Test: Reject \n",
      "    statistical Value (A2*) =  0.9948 \n",
      "    Critical Value (α) =  0.7420 \n",
      "    p Value =  0.0127\n",
      "KS Test: Accept \n",
      "    P value =  0.6187 \n",
      "    Stat value =  0.0750\n",
      "Chi_square Test: Accept  \n",
      "    P value =  0.3674  \n",
      "    Stat value =  18.3443\n",
      "AIC =  1669.9509\n",
      "BIC =  1675.1612\n",
      "\n",
      "\n",
      "\n",
      "Distribution : \u001b[1;31mpearson3\u001b[0m\n",
      "Mean =  2623.4709\n",
      "Standard Deviation =  971.5671\n",
      "Parameters =  [0.3031, 2623.4709, 971.5671]\n",
      "Anderson Test: Accept \n",
      "    statistical Value (A2*) =  0.3580 \n",
      "    p Value =  0.4533\n",
      "KS Test: Accept \n",
      "    P value =  0.9682 \n",
      "    Stat value =  0.0493\n",
      "Chi_square Test: Accept  \n",
      "    P value =  0.6073  \n",
      "    Stat value =  14.8365\n",
      "AIC =  1664.0300\n",
      "BIC =  1671.8456\n",
      "\n",
      "\n",
      "\n",
      "Best fitted Distribution: \n",
      "According to AD test: pearson3\n",
      "According to KS test: pearson3\n",
      "According to Chi2 test: pearson3\n",
      "According to AIC Value: norm\n",
      "According to BIC Value: norm\n",
      "\n",
      "\n",
      "Designed Discharge for 50 year return period = 4630.4628\n"
     ]
    }
   ],
   "source": [
    "Plot()\n",
    "Print(norm,'norm')\n",
    "Print(expon,'expon')\n",
    "Print(lognorm,'lognorm')\n",
    "Print(gamma,'gamma')\n",
    "Print(gumbel_r,'gumbel_r')\n",
    "Print(pearson3,'pearson3')\n",
    "AD_accept = dict((k, v) for k, v in AD.items() if (v[0] >= 0.05))\n",
    "AD_accept = sorted(AD_accept.items(),reverse=True,  key=lambda x: x[1][0])\n",
    "\n",
    "KS_accept = dict((k, v) for k, v in KS.items() if v[0] >= 0.05)\n",
    "KS_accept = sorted(KS_accept.items(),reverse=True,  key=lambda x: x[1][0])\n",
    "KS_best = dict((k, v) for k, v in KS.items() if v==max(KS.values()))\n",
    "\n",
    "Chi2_accept = dict((k, v) for k, v in Chi2.items() if v[0] >= 0.05)\n",
    "Chi2_accept = sorted(Chi2_accept.items(),reverse=True,  key=lambda x: x[1][0])\n",
    "Chi2_best = dict((k, v) for k, v in Chi2.items() if v==max(Chi2.values()))\n",
    "AIC = sorted(AIC.items(),reverse=False,  key=lambda x: x[1])\n",
    "BIC = sorted(BIC.items(),reverse=False,  key=lambda x: x[1])\n",
    "Best_print()\n",
    "print(\"\\n\\nDesigned Discharge for 50 year return period =\",\"%.4f\" %cdf2x(data1,AIC[0][0]))"
   ]
  }
 ],
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