{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1af8b99c",
   "metadata": {},
   "source": [
    "# Fitting distribution\n",
    "\n",
    "Author: Marija Delic, 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11453582",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fitter import Fitter\n",
    "from scipy import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a45df375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from fitter import Fitter, get_common_distributions, get_distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af1f1ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "suv_filename = \"../data/suv_percentilesSLOthenUWM.mat\"\n",
    "flags_filename=\"../data/flags_combined.mat\"\n",
    "normal_range_filename =\"../data/normal_range.mat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a2a1241",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Loading all data\n",
    "\"\"\"\n",
    "suv_dict = io.loadmat(suv_filename)\n",
    "flags_dict = io.loadmat(flags_filename)\n",
    "normal_range_dict = io.loadmat(normal_range_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb8fde87",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    " Extracting only data associated to lungs\n",
    "\"\"\"\n",
    "lung_suv = suv_dict['lung_SUVperc_COMBINED'][0:58,:]\n",
    "flags = flags_dict['flags'][0:58,3]  # 0 == NC, 1 == AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ba61636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58, 17, 100)\n",
      "(58,)\n"
     ]
    }
   ],
   "source": [
    "print(lung_suv.shape)\n",
    "print(flags.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d7b11d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "NC: 53\n",
      "AE: 5\n"
     ]
    }
   ],
   "source": [
    "print(flags)\n",
    "print(\"NC:\", np.count_nonzero(flags==0))\n",
    "print(\"AE:\", np.count_nonzero(flags==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fb4074a",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 95  # percentile of interest\n",
    "X = np.nanmax(lung_suv[:,:,p], axis=1)  # taking SUV max and ignoring NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2df8bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_NC = X[flags == 0]\n",
    "X_AE = X[flags == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29b7c7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a4f215e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7be835f5b3e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    " Plotting stuff\n",
    "\"\"\"\n",
    "plt.hist(X_NC,density=True,label=\"NC\")\n",
    "plt.hist(X_AE,density=True,label=\"AE\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b64ee380",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-14 20:33:19.475 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted anglit distribution with error=64.147876)\n",
      "2025-03-14 20:33:19.562 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted arcsine distribution with error=117.749167)\n",
      "2025-03-14 20:33:19.600 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted argus distribution with error=103.851466)\n",
      "2025-03-14 20:33:19.736 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted cauchy distribution with error=70.967112)\n",
      "2025-03-14 20:33:19.754 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted burr distribution with error=61.891249)\n",
      "2025-03-14 20:33:19.763 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted burr12 distribution with error=62.131548)\n",
      "2025-03-14 20:33:19.811 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted chi distribution with error=63.407239)\n",
      "2025-03-14 20:33:19.814 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted bradford distribution with error=95.091169)\n",
      "2025-03-14 20:33:19.891 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted cosine distribution with error=132.880897)\n",
      "2025-03-14 20:33:19.900 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted betaprime distribution with error=63.047044)\n",
      "2025-03-14 20:33:19.904 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED _fit distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:19.910 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted beta distribution with error=63.641649)\n",
      "2025-03-14 20:33:19.913 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted chi2 distribution with error=125.592492)\n",
      "2025-03-14 20:33:19.940 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted dweibull distribution with error=59.582768)\n",
      "2025-03-14 20:33:19.951 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted dgamma distribution with error=57.25687)\n",
      "2025-03-14 20:33:19.956 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted expon distribution with error=100.995868)\n",
      "2025-03-14 20:33:19.991 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponnorm distribution with error=62.329817)\n",
      "2025-03-14 20:33:20.015 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted erlang distribution with error=63.46758)\n",
      "2025-03-14 20:33:20.043 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted alpha distribution with error=63.551371)\n",
      "2025-03-14 20:33:20.068 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted fatiguelife distribution with error=63.49226)\n",
      "2025-03-14 20:33:20.138 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted foldcauchy distribution with error=71.006471)\n",
      "2025-03-14 20:33:20.146 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted foldnorm distribution with error=63.075166)\n",
      "2025-03-14 20:33:20.167 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted crystalball distribution with error=62.494043)\n",
      "2025-03-14 20:33:20.183 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted fisk distribution with error=62.373276)\n",
      "2025-03-14 20:33:20.201 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted f distribution with error=63.533486)\n",
      "2025-03-14 20:33:20.226 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gamma distribution with error=63.467576)\n",
      "2025-03-14 20:33:20.252 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponpow distribution with error=74.722514)\n",
      "2025-03-14 20:33:20.269 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genhalflogistic distribution with error=112.93178)\n",
      "2025-03-14 20:33:20.304 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genextreme distribution with error=64.825608)\n",
      "2025-03-14 20:33:20.314 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genlogistic distribution with error=61.962151)\n",
      "2025-03-14 20:33:20.333 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gennorm distribution with error=65.216291)\n",
      "2025-03-14 20:33:20.366 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gibrat distribution with error=91.576204)\n",
      "2025-03-14 20:33:20.435 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponweib distribution with error=63.849318)\n",
      "2025-03-14 20:33:20.449 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gumbel_l distribution with error=78.674518)\n",
      "2025-03-14 20:33:20.464 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gumbel_r distribution with error=66.371286)\n",
      "2025-03-14 20:33:20.475 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfcauchy distribution with error=97.060677)\n",
      "2025-03-14 20:33:20.488 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gengamma distribution with error=63.723087)\n",
      "2025-03-14 20:33:20.498 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halflogistic distribution with error=90.270268)\n",
      "2025-03-14 20:33:20.506 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genpareto distribution with error=89.583493)\n",
      "2025-03-14 20:33:20.510 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfnorm distribution with error=87.364496)\n",
      "2025-03-14 20:33:20.526 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted hypsecant distribution with error=62.754794)\n",
      "2025-03-14 20:33:20.557 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genhyperbolic distribution with error=62.424139)\n",
      "2025-03-14 20:33:20.557 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted geninvgauss distribution with error=63.558485)\n",
      "2025-03-14 20:33:20.563 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED irwinhall distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:20.580 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invgamma distribution with error=63.538562)\n",
      "2025-03-14 20:33:20.605 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gompertz distribution with error=82.367909)\n",
      "2025-03-14 20:33:20.646 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invgauss distribution with error=63.503425)\n",
      "2025-03-14 20:33:20.654 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genexpon distribution with error=68.913661)\n",
      "2025-03-14 20:33:20.658 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted johnsonsu distribution with error=62.564347)\n",
      "2025-03-14 20:33:20.678 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gausshyper distribution with error=94.289833)\n",
      "2025-03-14 20:33:20.710 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ksone distribution with error=101.864313)\n",
      "2025-03-14 20:33:20.736 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kappa3 distribution with error=75.523347)\n",
      "2025-03-14 20:33:20.746 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted laplace distribution with error=70.136116)\n",
      "2025-03-14 20:33:20.747 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invweibull distribution with error=66.377578)\n",
      "2025-03-14 20:33:20.758 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kstwo distribution with error=135.865512)\n",
      "2025-03-14 20:33:20.760 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfgennorm distribution with error=81.25525)\n",
      "2025-03-14 20:33:20.777 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy_l distribution with error=120.059332)\n",
      "2025-03-14 20:33:20.786 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy distribution with error=103.345517)\n",
      "2025-03-14 20:33:20.799 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted logistic distribution with error=61.387552)\n",
      "2025-03-14 20:33:20.803 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted jf_skew_t distribution with error=63.664289)\n",
      "2025-03-14 20:33:20.813 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lognorm distribution with error=63.524129)\n",
      "2025-03-14 20:33:20.817 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted laplace_asymmetric distribution with error=64.678523)\n",
      "2025-03-14 20:33:20.862 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loglaplace distribution with error=70.584247)\n",
      "2025-03-14 20:33:20.884 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loguniform distribution with error=87.289922)\n",
      "2025-03-14 20:33:20.889 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lomax distribution with error=101.463778)\n",
      "2025-03-14 20:33:20.897 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loggamma distribution with error=63.282334)\n",
      "2025-03-14 20:33:20.899 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED multivariate_normal distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:20.902 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted maxwell distribution with error=64.551249)\n",
      "2025-03-14 20:33:20.943 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted moyal distribution with error=70.172314)\n",
      "2025-03-14 20:33:20.949 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted nakagami distribution with error=63.858936)\n",
      "2025-03-14 20:33:21.085 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted mielke distribution with error=61.068543)\n",
      "2025-03-14 20:33:21.097 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kstwobign distribution with error=68.071393)\n",
      "2025-03-14 20:33:21.104 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted johnsonsb distribution with error=63.570136)\n",
      "2025-03-14 20:33:21.112 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norm distribution with error=62.494042)\n",
      "2025-03-14 20:33:21.124 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted pareto distribution with error=100.995868)\n",
      "2025-03-14 20:33:21.140 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powerlaw distribution with error=114.111038)\n",
      "2025-03-14 20:33:21.156 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ncf distribution with error=63.667487)\n",
      "2025-03-14 20:33:21.221 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted pearson3 distribution with error=63.467929)\n",
      "2025-03-14 20:33:21.259 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norminvgauss distribution with error=62.774409)\n",
      "2025-03-14 20:33:21.321 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted reciprocal distribution with error=87.289922)\n",
      "2025-03-14 20:33:21.352 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rdist distribution with error=105.633376)\n",
      "2025-03-14 20:33:21.408 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powernorm distribution with error=63.706962)\n",
      "2025-03-14 20:33:21.410 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rel_breitwigner distribution with error=64.676165)\n",
      "2025-03-14 20:33:21.420 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rayleigh distribution with error=68.650651)\n",
      "2025-03-14 20:33:21.424 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED rv_histogram distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:21.443 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted semicircular distribution with error=96.900468)\n",
      "2025-03-14 20:33:21.473 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rice distribution with error=63.95261)\n",
      "2025-03-14 20:33:21.476 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED rv_continuous distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:21.496 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted skewcauchy distribution with error=68.089104)\n",
      "2025-03-14 20:33:21.512 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ncx2 distribution with error=116.466509)\n",
      "2025-03-14 20:33:21.558 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted nct distribution with error=62.190382)\n",
      "2025-03-14 20:33:21.597 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted skewnorm distribution with error=63.198772)\n",
      "2025-03-14 20:33:21.597 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kappa4 distribution with error=98.227243)\n",
      "2025-03-14 20:33:21.619 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted trapezoid distribution with error=123.620771)\n",
      "2025-03-14 20:33:21.694 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted triang distribution with error=77.610454)\n",
      "2025-03-14 20:33:21.701 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted trapz distribution with error=123.620771)\n",
      "2025-03-14 20:33:21.740 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted uniform distribution with error=100.978125)\n",
      "2025-03-14 20:33:21.767 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powerlognorm distribution with error=63.418964)\n",
      "2025-03-14 20:33:21.768 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted vonmises distribution with error=62.002246)\n",
      "2025-03-14 20:33:21.778 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncexpon distribution with error=92.437831)\n",
      "2025-03-14 20:33:21.778 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED vonmises_fisher distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:21.800 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted wald distribution with error=89.680928)\n",
      "2025-03-14 20:33:21.820 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncweibull_min distribution with error=92.278674)\n",
      "2025-03-14 20:33:21.839 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy_stable distribution with error=60.595428)\n",
      "2025-03-14 20:33:21.840 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncnorm distribution with error=92.068708)\n",
      "2025-03-14 20:33:21.862 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted weibull_max distribution with error=64.825475)\n",
      "2025-03-14 20:33:21.867 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted weibull_min distribution with error=67.786048)\n",
      "2025-03-14 20:33:21.884 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted wrapcauchy distribution with error=101.064515)\n",
      "2025-03-14 20:33:21.976 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncpareto distribution with error=97.748045)\n",
      "2025-03-14 20:33:22.007 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted tukeylambda distribution with error=112.153074)\n",
      "2025-03-14 20:33:22.612 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted vonmises_line distribution with error=132.880897)\n",
      "2025-03-14 20:33:24.545 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted recipinvgauss distribution with error=63.487826)\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/integrate/_quadpack_py.py:1260: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/integrate/_quadpack_py.py:1260: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "2025-03-14 20:33:51.512 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED studentized_range distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:52.409 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted t distribution with error=62.430767)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dgamma</th>\n",
       "      <td>57.256870</td>\n",
       "      <td>425.526074</td>\n",
       "      <td>431.436950</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.096016</td>\n",
       "      <td>0.677014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dweibull</th>\n",
       "      <td>59.582768</td>\n",
       "      <td>414.337885</td>\n",
       "      <td>420.248761</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.098857</td>\n",
       "      <td>0.642004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>levy_stable</th>\n",
       "      <td>60.595428</td>\n",
       "      <td>646.645060</td>\n",
       "      <td>654.526228</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.095316</td>\n",
       "      <td>0.685610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mielke</th>\n",
       "      <td>61.068543</td>\n",
       "      <td>389.794907</td>\n",
       "      <td>397.676075</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.092708</td>\n",
       "      <td>0.717457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logistic</th>\n",
       "      <td>61.387552</td>\n",
       "      <td>406.729837</td>\n",
       "      <td>410.670420</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>0.627755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sumsquare_error         aic         bic  kl_div  ks_statistic  \\\n",
       "dgamma             57.256870  425.526074  431.436950     inf      0.096016   \n",
       "dweibull           59.582768  414.337885  420.248761     inf      0.098857   \n",
       "levy_stable        60.595428  646.645060  654.526228     inf      0.095316   \n",
       "mielke             61.068543  389.794907  397.676075     inf      0.092708   \n",
       "logistic           61.387552  406.729837  410.670420     inf      0.100015   \n",
       "\n",
       "             ks_pvalue  \n",
       "dgamma        0.677014  \n",
       "dweibull      0.642004  \n",
       "levy_stable   0.685610  \n",
       "mielke        0.717457  \n",
       "logistic      0.627755  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_NC)\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c87fdec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-14 20:33:53.065 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lognorm distribution with error=63.524129)\n",
      "2025-03-14 20:33:53.082 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norm distribution with error=62.494042)\n",
      "2025-03-14 20:33:53.142 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gamma distribution with error=63.467576)\n",
      "2025-03-14 20:33:53.231 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted burr distribution with error=61.891249)\n",
      "2025-03-14 20:33:53.270 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted beta distribution with error=63.641649)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>burr</th>\n",
       "      <td>61.891249</td>\n",
       "      <td>371.295200</td>\n",
       "      <td>379.176368</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.099767</td>\n",
       "      <td>0.630798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm</th>\n",
       "      <td>62.494042</td>\n",
       "      <td>445.292821</td>\n",
       "      <td>449.233404</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.148212</td>\n",
       "      <td>0.176081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>63.467576</td>\n",
       "      <td>352.535206</td>\n",
       "      <td>358.446082</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.134092</td>\n",
       "      <td>0.271326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognorm</th>\n",
       "      <td>63.524129</td>\n",
       "      <td>344.814300</td>\n",
       "      <td>350.725175</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.130771</td>\n",
       "      <td>0.298377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>63.641649</td>\n",
       "      <td>348.827907</td>\n",
       "      <td>356.709074</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.135396</td>\n",
       "      <td>0.261207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sumsquare_error         aic         bic  kl_div  ks_statistic  \\\n",
       "burr           61.891249  371.295200  379.176368     inf      0.099767   \n",
       "norm           62.494042  445.292821  449.233404     inf      0.148212   \n",
       "gamma          63.467576  352.535206  358.446082     inf      0.134092   \n",
       "lognorm        63.524129  344.814300  350.725175     inf      0.130771   \n",
       "beta           63.641649  348.827907  356.709074     inf      0.135396   \n",
       "\n",
       "         ks_pvalue  \n",
       "burr      0.630798  \n",
       "norm      0.176081  \n",
       "gamma     0.271326  \n",
       "lognorm   0.298377  \n",
       "beta      0.261207  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_NC, distributions=['gamma',\n",
    "                          'lognorm',\n",
    "                          \"beta\",\n",
    "                          \"burr\",\n",
    "                          \"norm\"])\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6a1a7ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-14 20:33:54.040 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norm distribution with error=62.494042)\n",
      "2025-03-14 20:33:54.041 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted cauchy distribution with error=70.967112)\n",
      "2025-03-14 20:33:54.043 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lognorm distribution with error=63.524129)\n",
      "2025-03-14 20:33:54.053 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powerlaw distribution with error=114.111038)\n",
      "2025-03-14 20:33:54.059 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted uniform distribution with error=100.978125)\n",
      "2025-03-14 20:33:54.061 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rayleigh distribution with error=68.650651)\n",
      "2025-03-14 20:33:54.110 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gamma distribution with error=63.467576)\n",
      "2025-03-14 20:33:54.155 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted chi2 distribution with error=125.592492)\n",
      "2025-03-14 20:33:54.170 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted expon distribution with error=100.995868)\n",
      "2025-03-14 20:33:54.249 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponpow distribution with error=74.722514)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>norm</th>\n",
       "      <td>62.494042</td>\n",
       "      <td>445.292821</td>\n",
       "      <td>449.233404</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.148212</td>\n",
       "      <td>0.176081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>63.467576</td>\n",
       "      <td>352.535206</td>\n",
       "      <td>358.446082</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.134092</td>\n",
       "      <td>0.271326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognorm</th>\n",
       "      <td>63.524129</td>\n",
       "      <td>344.814300</td>\n",
       "      <td>350.725175</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.130771</td>\n",
       "      <td>0.298377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rayleigh</th>\n",
       "      <td>68.650651</td>\n",
       "      <td>294.459176</td>\n",
       "      <td>298.399759</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.155178</td>\n",
       "      <td>0.139927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cauchy</th>\n",
       "      <td>70.967112</td>\n",
       "      <td>297.308600</td>\n",
       "      <td>301.249184</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.132246</td>\n",
       "      <td>0.286136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sumsquare_error         aic         bic  kl_div  ks_statistic  \\\n",
       "norm            62.494042  445.292821  449.233404     inf      0.148212   \n",
       "gamma           63.467576  352.535206  358.446082     inf      0.134092   \n",
       "lognorm         63.524129  344.814300  350.725175     inf      0.130771   \n",
       "rayleigh        68.650651  294.459176  298.399759     inf      0.155178   \n",
       "cauchy          70.967112  297.308600  301.249184     inf      0.132246   \n",
       "\n",
       "          ks_pvalue  \n",
       "norm       0.176081  \n",
       "gamma      0.271326  \n",
       "lognorm    0.298377  \n",
       "rayleigh   0.139927  \n",
       "cauchy     0.286136  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_NC, distributions = get_common_distributions())\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c50d9de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-14 20:33:55.051 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED _fit distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:55.077 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted anglit distribution with error=266.75092)\n",
      "2025-03-14 20:33:55.121 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted argus distribution with error=268.707736)\n",
      "2025-03-14 20:33:55.145 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted arcsine distribution with error=241.001093)\n",
      "2025-03-14 20:33:55.168 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted alpha distribution with error=257.364705)\n",
      "2025-03-14 20:33:55.172 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted cauchy distribution with error=250.337238)\n",
      "2025-03-14 20:33:55.210 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted beta distribution with error=241.722912)\n",
      "2025-03-14 20:33:55.215 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted cosine distribution with error=266.21987)\n",
      "2025-03-14 20:33:55.333 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted bradford distribution with error=251.268831)\n",
      "2025-03-14 20:33:55.347 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted burr12 distribution with error=241.650093)\n",
      "2025-03-14 20:33:55.358 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted chi distribution with error=238.764954)\n",
      "2025-03-14 20:33:55.363 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted expon distribution with error=254.404064)\n",
      "2025-03-14 20:33:55.380 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted chi2 distribution with error=237.348868)\n",
      "2025-03-14 20:33:55.411 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted dgamma distribution with error=248.000469)\n",
      "2025-03-14 20:33:55.502 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted betaprime distribution with error=257.694481)\n",
      "2025-03-14 20:33:55.538 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted dweibull distribution with error=246.666009)\n",
      "2025-03-14 20:33:55.581 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponnorm distribution with error=254.407014)\n",
      "2025-03-14 20:33:55.666 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted crystalball distribution with error=264.747351)\n",
      "2025-03-14 20:33:55.717 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted foldnorm distribution with error=259.753435)\n",
      "2025-03-14 20:33:55.749 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted f distribution with error=257.500771)\n",
      "2025-03-14 20:33:55.798 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted burr distribution with error=257.521037)\n",
      "2025-03-14 20:33:55.834 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted erlang distribution with error=240.029394)\n",
      "2025-03-14 20:33:55.949 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genextreme distribution with error=257.672109)\n",
      "2025-03-14 20:33:55.958 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted fisk distribution with error=231.478144)\n",
      "2025-03-14 20:33:55.977 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponpow distribution with error=229.805636)\n",
      "2025-03-14 20:33:56.062 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genhalflogistic distribution with error=267.838794)\n",
      "2025-03-14 20:33:56.072 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted fatiguelife distribution with error=251.184445)\n",
      "2025-03-14 20:33:56.237 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gennorm distribution with error=237.73208)\n",
      "2025-03-14 20:33:56.310 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gamma distribution with error=234.91893)\n",
      "2025-03-14 20:33:56.353 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted foldcauchy distribution with error=249.70505)\n",
      "2025-03-14 20:33:56.373 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gumbel_l distribution with error=268.620083)\n",
      "2025-03-14 20:33:56.391 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gumbel_r distribution with error=258.554032)\n",
      "2025-03-14 20:33:56.406 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted geninvgauss distribution with error=239.582352)\n",
      "2025-03-14 20:33:56.423 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfcauchy distribution with error=255.71737)\n",
      "2025-03-14 20:33:56.435 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gausshyper distribution with error=245.12087)\n",
      "2025-03-14 20:33:56.533 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gibrat distribution with error=257.61481)\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py:3935: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = integrate.quad(llc, x0, x1,\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py:3930: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  intgrl = (integrate.quad(llc, x0, mean,\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py:3932: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  + integrate.quad(llc, mean, x1,\n",
      "2025-03-14 20:33:56.575 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genhyperbolic distribution with error=245.164678)\n",
      "2025-03-14 20:33:56.602 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halflogistic distribution with error=256.245072)\n",
      "2025-03-14 20:33:56.622 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfnorm distribution with error=258.5619)\n",
      "2025-03-14 20:33:56.669 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted hypsecant distribution with error=258.762999)\n",
      "2025-03-14 20:33:56.693 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gengamma distribution with error=235.771945)\n",
      "2025-03-14 20:33:56.703 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genpareto distribution with error=252.761329)\n",
      "2025-03-14 20:33:56.719 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED irwinhall distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:56.744 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genlogistic distribution with error=258.528863)\n",
      "2025-03-14 20:33:56.760 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invgauss distribution with error=257.7781)\n",
      "2025-03-14 20:33:56.826 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invweibull distribution with error=257.67325)\n",
      "2025-03-14 20:33:56.845 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted exponweib distribution with error=228.925939)\n",
      "2025-03-14 20:33:56.874 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted invgamma distribution with error=257.694761)\n",
      "2025-03-14 20:33:56.944 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ksone distribution with error=264.198258)\n",
      "2025-03-14 20:33:56.990 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted halfgennorm distribution with error=252.407016)\n",
      "2025-03-14 20:33:57.065 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kstwo distribution with error=273.936953)\n",
      "2025-03-14 20:33:57.105 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted laplace_asymmetric distribution with error=255.626631)\n",
      "2025-03-14 20:33:57.109 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kappa3 distribution with error=263.816447)\n",
      "2025-03-14 20:33:57.132 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy_l distribution with error=270.854567)\n",
      "2025-03-14 20:33:57.144 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted jf_skew_t distribution with error=258.245018)\n",
      "2025-03-14 20:33:57.202 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy distribution with error=254.846367)\n",
      "2025-03-14 20:33:57.263 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted genexpon distribution with error=254.403929)\n",
      "2025-03-14 20:33:57.418 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loggamma distribution with error=264.87209)\n",
      "2025-03-14 20:33:57.433 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loguniform distribution with error=266.196831)\n",
      "2025-03-14 20:33:57.448 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted logistic distribution with error=261.415646)\n",
      "2025-03-14 20:33:57.500 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted maxwell distribution with error=262.830979)\n",
      "2025-03-14 20:33:57.543 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted loglaplace distribution with error=242.991951)\n",
      "2025-03-14 20:33:57.590 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lognorm distribution with error=250.31023)\n",
      "2025-03-14 20:33:57.622 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted lomax distribution with error=254.283898)\n",
      "2025-03-14 20:33:57.665 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted moyal distribution with error=257.467214)\n",
      "2025-03-14 20:33:57.671 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED multivariate_normal distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:57.781 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kstwobign distribution with error=260.075598)\n",
      "2025-03-14 20:33:57.811 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted laplace distribution with error=255.026964)\n",
      "2025-03-14 20:33:57.831 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norm distribution with error=264.747351)\n",
      "2025-03-14 20:33:57.884 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted nakagami distribution with error=235.680341)\n",
      "2025-03-14 20:33:57.934 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted mielke distribution with error=nan)\n",
      "2025-03-14 20:33:57.958 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted pareto distribution with error=253.999418)\n",
      "2025-03-14 20:33:58.028 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted johnsonsb distribution with error=247.967691)\n",
      "2025-03-14 20:33:58.074 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted norminvgauss distribution with error=252.821428)\n",
      "2025-03-14 20:33:58.113 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powerlaw distribution with error=228.788255)\n",
      "2025-03-14 20:33:58.159 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted nct distribution with error=257.954933)\n",
      "2025-03-14 20:33:58.186 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted gompertz distribution with error=254.421467)\n",
      "2025-03-14 20:33:58.303 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ncf distribution with error=259.757905)\n",
      "2025-03-14 20:33:58.437 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted johnsonsu distribution with error=257.60842)\n",
      "2025-03-14 20:33:58.487 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted pearson3 distribution with error=244.808457)\n",
      "2025-03-14 20:33:58.529 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rdist distribution with error=265.747431)\n",
      "2025-03-14 20:33:58.577 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted reciprocal distribution with error=266.196831)\n",
      "2025-03-14 20:33:58.650 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rice distribution with error=262.279322)\n",
      "2025-03-14 20:33:58.724 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED rv_continuous distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:58.780 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED rv_histogram distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:58.877 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted semicircular distribution with error=266.89682)\n",
      "2025-03-14 20:33:59.046 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted skewcauchy distribution with error=251.481887)\n",
      "2025-03-14 20:33:59.102 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rel_breitwigner distribution with error=253.6705)\n",
      "2025-03-14 20:33:59.247 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted ncx2 distribution with error=243.782815)\n",
      "2025-03-14 20:33:59.301 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted kappa4 distribution with error=259.128948)\n",
      "2025-03-14 20:33:59.439 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted trapezoid distribution with error=273.102494)\n",
      "2025-03-14 20:33:59.505 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted skewnorm distribution with error=258.562341)\n",
      "2025-03-14 20:33:59.654 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted trapz distribution with error=273.102494)\n",
      "2025-03-14 20:33:59.673 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powerlognorm distribution with error=235.582727)\n",
      "2025-03-14 20:33:59.699 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted uniform distribution with error=263.812567)\n",
      "2025-03-14 20:33:59.702 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted triang distribution with error=268.866815)\n",
      "2025-03-14 20:33:59.723 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted vonmises distribution with error=261.471366)\n",
      "2025-03-14 20:33:59.773 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED vonmises_fisher distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:33:59.794 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncexpon distribution with error=259.128135)\n",
      "2025-03-14 20:33:59.842 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted wald distribution with error=257.730732)\n",
      "2025-03-14 20:33:59.943 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncnorm distribution with error=254.956151)\n",
      "2025-03-14 20:33:59.952 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted powernorm distribution with error=nan)\n",
      "2025-03-14 20:34:00.059 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncweibull_min distribution with error=254.473922)\n",
      "2025-03-14 20:34:00.145 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted truncpareto distribution with error=254.8769)\n",
      "2025-03-14 20:34:00.154 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted weibull_max distribution with error=259.075908)\n",
      "2025-03-14 20:34:00.165 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted rayleigh distribution with error=262.279322)\n",
      "2025-03-14 20:34:00.224 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted tukeylambda distribution with error=265.227373)\n",
      "2025-03-14 20:34:00.249 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted wrapcauchy distribution with error=254.180823)\n",
      "2025-03-14 20:34:00.372 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted weibull_min distribution with error=237.323195)\n",
      "2025-03-14 20:34:02.073 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted vonmises_line distribution with error=259.263683)\n",
      "2025-03-14 20:34:03.383 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted levy_stable distribution with error=251.281979)\n",
      "2025-03-14 20:34:05.811 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted recipinvgauss distribution with error=236.175895)\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/integrate/_quadpack_py.py:1260: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "/home/horvat/venvs/base/lib/python3.12/site-packages/scipy/integrate/_quadpack_py.py:1260: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "2025-03-14 20:34:29.136 | WARNING  | fitter.fitter:_fit_single_distribution:337 - SKIPPED studentized_range distribution (taking more than 30 seconds)\n",
      "2025-03-14 20:34:30.736 | INFO     | fitter.fitter:_fit_single_distribution:333 - Fitted t distribution with error=264.747349)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>powerlaw</th>\n",
       "      <td>228.788255</td>\n",
       "      <td>452.604830</td>\n",
       "      <td>451.433144</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.599354</td>\n",
       "      <td>0.030360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exponweib</th>\n",
       "      <td>228.925939</td>\n",
       "      <td>440.014005</td>\n",
       "      <td>438.451757</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.403842</td>\n",
       "      <td>0.298813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exponpow</th>\n",
       "      <td>229.805636</td>\n",
       "      <td>394.721026</td>\n",
       "      <td>393.549340</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.388485</td>\n",
       "      <td>0.340837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fisk</th>\n",
       "      <td>231.478144</td>\n",
       "      <td>503.287303</td>\n",
       "      <td>502.115617</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.367344</td>\n",
       "      <td>0.408087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>234.918930</td>\n",
       "      <td>449.229349</td>\n",
       "      <td>448.057662</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.530632</td>\n",
       "      <td>0.076795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sumsquare_error         aic         bic  kl_div  ks_statistic  \\\n",
       "powerlaw        228.788255  452.604830  451.433144     inf      0.599354   \n",
       "exponweib       228.925939  440.014005  438.451757     inf      0.403842   \n",
       "exponpow        229.805636  394.721026  393.549340     inf      0.388485   \n",
       "fisk            231.478144  503.287303  502.115617     inf      0.367344   \n",
       "gamma           234.918930  449.229349  448.057662     inf      0.530632   \n",
       "\n",
       "           ks_pvalue  \n",
       "powerlaw    0.030360  \n",
       "exponweib   0.298813  \n",
       "exponpow    0.340837  \n",
       "fisk        0.408087  \n",
       "gamma       0.076795  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_AE)\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4048b123",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}