method_mass_reconstruction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-28T14:35:10.873046Z",
     "start_time": "2022-11-28T14:35:10.866065Z"
    }
   },
   "source": [
    "# Библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:38:19.427661Z",
     "start_time": "2023-07-19T08:38:18.543235Z"
    },
    "cell_style": "center"
   },
   "outputs": [
    {
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       "   1       2.153930\n",
       "   2       1.613667\n",
       "   3       1.101869\n",
       "   4       1.008112\n",
       "             ...   \n",
       "4  2574    3.975918\n",
       "   2575    2.940794\n",
       "   2576    3.112251\n",
       "   2577    4.095870\n",
       "   2578    2.372703\n",
       "Name: pred, Length: 14932, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "from scipy.integrate import quad\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from pathlib import Path\n",
    "from numpy import linalg as LA\n",
    "from iminuit import Minuit\n",
    "from scipy.stats import beta\n",
    "from scipy import interpolate\n",
    "import matplotlib as mpl\n",
    "from scipy.optimize import leastsq, curve_fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['xtick.direction'] = 'in' \n",
    "\n",
    "plt.rcParams['xtick.major.size']=10\n",
    "plt.rcParams['xtick.minor.size'] = 5\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "# plt.rcParams['xtick.minor.visible'] = 'True'\n",
    "\n",
    "plt.rcParams['ytick.major.size'] = 10\n",
    "plt.rcParams['ytick.minor.size'] = 5\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams.update({'font.size':20})\n",
    "plt.rc('xtick', labelsize=20)\n",
    "plt.rc('ytick', labelsize=20)\n",
    "\n",
    "file_predictions=Path.cwd()/'predictions_NN_lnA_norm.csv'\n",
    "\n",
    "rhomax=170\n",
    "p0max=2000\n",
    "chi2max=10000\n",
    "\n",
    "color5=['blue','orange','green','cyan','gray']\n",
    "m_name=['p','He','N','S','Fe']\n",
    "\n",
    "df_pred=((((pd.read_csv(file_predictions,header=0,sep=',')).dropna()).iloc[:,1:]))\n",
    "p=df_pred['pred'][df_pred['m_name']==m_name[0]]\n",
    "p.index=np.arange(len(p))\n",
    "He=df_pred['pred'][df_pred['m_name']==m_name[1]]\n",
    "He.index=np.arange(len(He))\n",
    "N=df_pred['pred'][df_pred['m_name']==m_name[2]]\n",
    "N.index=np.arange(len(N))\n",
    "S=df_pred['pred'][df_pred['m_name']==m_name[3]]\n",
    "S.index=np.arange(len(S))\n",
    "Fe=df_pred['pred'][df_pred['m_name']==m_name[4]]\n",
    "Fe.index=np.arange(len(Fe))\n",
    "\n",
    "#=============================================================================\n",
    "df_together=pd.concat([p,He,N,S,Fe],keys=[0,1,2,3,4])\n",
    "df_together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Определение масс p, N, Fe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:38:21.242904Z",
     "start_time": "2023-07-19T08:38:21.224950Z"
    }
   },
   "outputs": [],
   "source": [
    "mp=1.001\n",
    "mHe=4.0026\n",
    "mC=12\n",
    "mN=14.007\n",
    "mS=32.064\n",
    "mCa=40.08\n",
    "mFe=55.845\n",
    "\n",
    "mass_toFe=[1.0078,4.0026,6.941,9.01218,10.81,12.011,14.0067,15.999,18.9984, \\\n",
    "           20.179,22.989,24.305,26.98,27.98,30.97376,32.06,35.453,39.948,39.0983,40.08, \\\n",
    "           44.956,47.9,50.942,51.99,54.938,55.847]\n",
    "mass_name=['p','He','Li','Be','B','C','N','O','F','Ne','Na','Mg','Al','Si','P','S', \\\n",
    "           'Cl','Ar','K','Ca','Sc','Ti','V','Cr','Mn','Fe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:38:21.745569Z",
     "start_time": "2023-07-19T08:38:21.719639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "r=df_together\n",
    "N_samples=5  \n",
    "print(N_samples)\n",
    "\n",
    "if N_samples==5:\n",
    "    m=np.array([mp,mHe,mN,mS,mFe])\n",
    "    ln_m=np.log(m+1)\n",
    "    m_name=['p','He','N','S','Fe']\n",
    "    color7=['blue','orange','red','purple','green']\n",
    "\n",
    "min_common,max_common=[],[]\n",
    "for i in range(N_samples):\n",
    "    min_common.append(min(r[i]))\n",
    "    max_common.append(max(r[i]))\n",
    "min_common,max_common=min(min_common),max(max_common)\n",
    "\n",
    "# Устанавливаю размер бина, в котором будут считаться кол-ва событий с определенным \n",
    "# нормализованным радиусом от 0 до 1\n",
    "glob_step_bins = 0.04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Функции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:38:22.790023Z",
     "start_time": "2023-07-19T08:38:22.743149Z"
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   "outputs": [],
   "source": [
    "# Гистограмма для рисования по трем ядрам\n",
    "def hist_3(r,add_name,xlabel='ln(A)',one=True,norm=True,step_bins=glob_step_bins,save=False):\n",
    "    if norm==True: bins=np.arange(0,1,step_bins)\n",
    "    else: bins=np.arange(min_common,max_common,step_bins)\n",
    "    if one==True:\n",
    "        fig=plt.figure(figsize=(8,4))   \n",
    "        for i in range(N_samples):\n",
    "            plt.hist(r[i],bins,alpha=0.6,label=m_name[i],color=color7[i])\n",
    "        plt.ylabel('count in bin '+str(step_bins))\n",
    "        plt.xlabel(xlabel)\n",
    "        plt.legend()\n",
    "        if save==True: plt.savefig('distribution_R'+add_name+'.png',bbox_inches='tight')\n",
    "    else:\n",
    "        fig,ax=plt.subplots(nrows=len(r),figsize=(10,3*len(r)))   \n",
    "        for i in range(N_samples):\n",
    "            ax[i].hist(r[i],bins,alpha=0.6,label=m_name[i],color=color7[i])\n",
    "            ax[i].legend(loc='centre left',bbox_to_anchor=(1,1))\n",
    "            ax[i].xaxis.set_minor_locator(AutoMinorLocator())\n",
    "            ax[i].tick_params(which = 'major',    #  Применяем параметры к основным делениям\n",
    "               length = 10,    #  Длинна делений\n",
    "               width = 3,     #  Ширина делений\n",
    "               color = 'black',    #  Цвет делений\n",
    "               pad = 5,    #  Расстояние между черточкой и ее подписью\n",
    "               labelsize = 18,    #  Размер подписи\n",
    "               bottom = True,    #  Рисуем метки снизу\n",
    "               left = True,    #  слева\n",
    "               direction='in'\n",
    "               )    #  Поворот подписей\n",
    "            ax[i].tick_params(which = 'minor',    #  Применяем параметры к основным делениям\n",
    "               length = 5,    #  Длинна делений\n",
    "               width = 3,     #  Ширина делений\n",
    "               color = 'black',    #  Цвет делений\n",
    "               pad = 5,    #  Расстояние между черточкой и ее подписью\n",
    "               labelsize = 18,    #  Размер подписи\n",
    "               bottom = True,    #  Рисуем метки снизу\n",
    "               left = True,    #  слева\n",
    "               direction='in'\n",
    "               )    #  Поворот подписей         \n",
    "        plt.ylabel('count in bin'+str(step_bins))\n",
    "        plt.xlabel(xlabel)\n",
    "        plt.legend(loc='centre left',bbox_to_anchor=(1,1))\n",
    "        if save==True: plt.savefig('distribution_R'+add_name+'.png',bbox_inches='tight')\n",
    "\n",
    "# Бета-распределение\n",
    "def beta_dist(x,a,b):\n",
    "    return (x**(a-1)*(1-x)**(b-1))\n",
    "def integral_beta(x,a,b):\n",
    "    return quad(beta_dist, min(x), max(x), args=(a,b))[0]\n",
    "def beta_pdf(x,a,b):\n",
    "    return beta_dist(x,a,b)/integral_beta(x,a,b)\n",
    "\n",
    "# Находим y - значения кол-ва радиусов в бине для каждого ядра, а затем делим на суммарное число событий\n",
    "# и получаем плонтость распределения на отрезке от 0 до 1\n",
    "def find_y_core(rn,x,step_bins=glob_step_bins):\n",
    "    xn=x-step_bins/2  # Начинаем с min(x) \n",
    "    y=np.zeros((N_samples,len(xn)))\n",
    "#     print(y.shape)\n",
    "    for part in range(N_samples):     \n",
    "        for i in range(len(xn)):\n",
    "            if i==len(xn)-1:   \n",
    "                y[part][i]=np.sum((rn[part]>xn[i]))\n",
    "            else:  \n",
    "                y[part][i]=np.sum((rn[part]<xn[i+1])&(rn[part]>xn[i]))\n",
    "        func_interp=interpolate.interp1d(x,y[part])\n",
    "        Ip=quad(func_interp,min(x),max(x))[0]\n",
    "        y[part]/= Ip \n",
    "    return y  # Плотность распределения на х = (0,1)\n",
    "def find_ab_beta_for_cores(x,y):\n",
    "    a,b=[],[]\n",
    "    for i in range(N_samples):\n",
    "        opt, cor = curve_fit(beta_pdf, x, y[i], p0=(8,16),maxfev=10**5)\n",
    "        ai,bi=opt\n",
    "        a.append(ai)\n",
    "        b.append(bi)\n",
    "    return a,b   # коэффициенты аппроксимации Бета-распределения a = [for p, for N, for Fe]\n",
    "\n",
    "# Аппроксимация линейной функцией\n",
    "def appro_ab(x,y,print_error=False):\n",
    "    x = x.reshape((-1,1)) \n",
    "    model = LinearRegression()\n",
    "    model.fit(x, y)\n",
    "    p0,p1=model.intercept_, model.coef_[0]  # a+bx\n",
    "    y_pred=p0+p1*x.reshape(-1)\n",
    "    if print_error == True:   print('error', ((y_pred - y)**2).sum())\n",
    "    return p0,p1\n",
    "# Аппроксимация квадратичной функцией\n",
    "def sq(x,p0,p1,p2):\n",
    "    return p0+p1*x+p2*x**2\n",
    "def appro_c(x,y,print_error=False):\n",
    "    # Квадратичная аппроксимация\n",
    "    opt, cor = curve_fit(sq, x, y, p0=(1,1,1),maxfev=10**6)\n",
    "    p0,p1,p2=opt\n",
    "    y_pred=sq(x,p0,p1,p2)\n",
    "    if print_error == True:    print('error', ((y_pred - y)**2).sum())\n",
    "    return p0,p1,p2\n",
    "\n",
    "def c_r(p0,p1):\n",
    "    c=1/(ln_m[-1]**2)*(min_manual[-1]-p1*ln_m[-1]-p0)\n",
    "    return c\n",
    "def sq_r(x,p0,p1):\n",
    "    c=c_r(p0,p1)\n",
    "    return p0+p1*x+c*x**2\n",
    "def appro_c_r(x,y,print_error=False):\n",
    "    opt, cor = curve_fit(sq_r, x, y, p0=(1,1),maxfev=10**6)\n",
    "    p0,p1=opt\n",
    "    y_pred=sq_r(x,p0,p1)\n",
    "    if print_error == True:    print('error', ((y_pred - y)**2).sum())\n",
    "    c=c_r(p0,p1)\n",
    "    return p0,p1,c\n",
    "\n",
    "def c_rm(p0,p1):\n",
    "    c=1/(ln_m[-1]**2)*(max_manual[-1]-p1*ln_m[-1]-p0)\n",
    "    return c\n",
    "def sq_rm(x,p0,p1):\n",
    "    c=c_rm(p0,p1)\n",
    "    return p0+p1*x+c*x**2\n",
    "def appro_c_r_max(x,y,print_error=False):\n",
    "    opt, cor = curve_fit(sq_rm, x, y, p0=(1,1),maxfev=10**6)\n",
    "    p0,p1=opt\n",
    "    y_pred=sq_rm(x,p0,p1)\n",
    "    if print_error == True:    print('error', ((y_pred - y)**2).sum())\n",
    "    c=c_rm(p0,p1)\n",
    "    return p0,p1,c\n",
    "\n",
    "\n",
    "def find_manual_border(min_manual,max_manual):\n",
    "    min_per,max_per=np.zeros(N_samples),np.zeros(N_samples)\n",
    "#     print(min_per)\n",
    "    for i in range(N_samples):\n",
    "#         print(i, min_per[i])\n",
    "        min_per[i]=(min_manual[i]-min(r[i]))/(max(r[i])-min(r[i]))\n",
    "        max_per[i]=(max_manual[i]-min(r[i]))/(max(r[i])-min(r[i]))\n",
    "#         print(min_manual[i]-min(r[i]),(max(r[i])-min(r[i])),min(r[i]),max(r[i]))\n",
    "    return min_per,max_per\n",
    "# min_per,max_per=find_manual_border(min_manual,max_manual)\n",
    "\n",
    "def fcn(rtoa):\n",
    "    rtoa['A'] = rtoa['A'].astype('float')\n",
    "    df_mass=rtoa.groupby('real_A').agg({'A':['mean']})\n",
    "    # -1 потому что не учитываем вклад жедлеза\n",
    "    return sum(((df_mass['A']['mean'].to_numpy()[1:-1]-df_mass['A'].index[1:-1])/df_mass['A'].index[1:-1])**2) \n",
    "def mean_A(rtoa):\n",
    "    rtoa['A'] = rtoa['A'].astype('float')\n",
    "    rtoe=rtoa.dropna()\n",
    "    df_mass=rtoa.groupby('real_A').agg({'A':['mean']})\n",
    "    # -1 потому что не учитываем вклад жедлеза\n",
    "    return df_mass['A']['mean']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Класс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:38:23.462833Z",
     "start_time": "2023-07-19T08:38:23.391026Z"
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   "source": [
    "#В класс обязательно входит r, радиус с которым мы будем работать.\n",
    "# r,min_common,max_common,min_manual,max_manual,\n",
    "class find_A:\n",
    "    def __init__(self, r, add_name,chek=False,manual=False,step_bins = glob_step_bins, norm=True):\n",
    "        self.r=r\n",
    "        self.min_r=min_common\n",
    "        self.max_r=max_common\n",
    "        self.x=np.arange(0.001,0.999,step_bins) + step_bins/2\n",
    "        self.manual=manual\n",
    "        if norm==True:\n",
    "            self.rn=self.norm_individ()\n",
    "        else:\n",
    "            self.rn=self.norm_common()\n",
    "        #  Нашли y - плотность распределния наших смоделированных данных     \n",
    "        self.y=find_y_core(self.rn,self.x)\n",
    "        # Нашли аппроксимации Бета-распределения для каджого ядра\n",
    "        self.a,self.b=find_ab_beta_for_cores(self.x,self.y)\n",
    "        self.chek=chek\n",
    "        self.add_name=add_name\n",
    "        direct_out_for_graph = Path.cwd() / 'graphs'\n",
    "        direct_out_for_graph.mkdir(exist_ok=True)\n",
    "        direct_out_for_graph_mass = direct_out_for_graph / ('graph_mass'+self.add_name)\n",
    "        direct_out_for_graph_mass.mkdir(exist_ok=True)\n",
    "        self.direct_out_for_graph_mass = direct_out_for_graph_mass\n",
    "        \n",
    "    def norm_common(self):\n",
    "        rn=(self.r-self.min_r)/(self.max_r-self.min_r)\n",
    "        return rn\n",
    "    \n",
    "    # Если стоит ручной режим, то нужно будет заранее в global определить \n",
    "    # max_per и т.д.\n",
    "    def norm_individ(self):\n",
    "        r_norm=[]\n",
    "        for i in range(N_samples):\n",
    "            r_norm.append((self.r[i]-np.min(self.r[i]))/(self.r[i].max()-self.r[i].min()))\n",
    "        r_norm=np.array(r_norm)\n",
    "        \n",
    "        if self.manual == True:\n",
    "            r_norm2=[]\n",
    "            for i in range(N_samples):\n",
    "                r_norm2.append(r_norm[i][(r_norm[i]<max_per[i])&(r_norm[i]>min_per[i])])\n",
    "                r_norm2[i]=(r_norm2[i]-np.min(r_norm2[i]))/(r_norm2[i].max()-r_norm2[i].min())\n",
    "            r_norm=np.array(r_norm2)\n",
    "        \n",
    "        return r_norm\n",
    "\n",
    "    def find_Beta_param(self,A):\n",
    "        # Фитируем эти коэффициенты a,b,c , чтобы узнать какие они будут у промежуточных ядер\n",
    "\n",
    "        p0_a,p1_a,p2_a=appro_c(ln_m,self.a)\n",
    "        p0_b,p1_b,p2_b=appro_c(ln_m,self.b)\n",
    "        a_beta=sq(np.log(A+0.1),p0_a,p1_a,p2_a)\n",
    "        b_beta=sq(np.log(A+0.1),p0_b,p1_b,p2_b)\n",
    "\n",
    "        return a_beta,b_beta   # Коэффициенты Бета-распределения для заданной массы A\n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    def plot_appro_Beta(self,save=False):\n",
    "        fig,(ax1,ax2)=plt.subplots(nrows=1,ncols=2,figsize=(15,4))\n",
    "        df_ab=pd.DataFrame({'ln_m':ln_m,'a':self.a,'b':self.b}).sort_values(by=['ln_m'])\n",
    "        \n",
    "        #================================ a\n",
    "#         ax1.scatter(ln_m,self.a, label='a',marker='o',s=80)\n",
    "        # plt.yscale('log')\n",
    "        ax1.set_xlabel('ln(A+0.1)')\n",
    "        ax1.set_ylabel('parameters Beta a')\n",
    "#         print(self.a,self.b)\n",
    "        a_appro,b_appro=[],[]\n",
    "        for i in range(N_samples):\n",
    "            a_beta,b_beta=self.find_Beta_param(m[i])\n",
    "            a_appro.append(a_beta)\n",
    "            b_appro.append(b_beta)\n",
    "#             print(a_beta,b_beta)\n",
    "            ax1.scatter(ln_m[i],self.a[i],marker='o',s=80,color=color7[i])\n",
    "            ax2.scatter(ln_m[i],self.b[i],marker='o',s=80,label=m_name[i],color=color7[i])\n",
    "            \n",
    "        ax1.plot(ln_m,a_appro)\n",
    "        ax2.plot(ln_m,b_appro)\n",
    "        #================================== b\n",
    "#         ax2.scatter(ln_m,self.b,marker='o',s=80)\n",
    "#         ax2.set_yscale('log')\n",
    "        ax2.set_xlabel('ln(A+0.1)')\n",
    "        ax2.set_ylabel('parameters Beta b')\n",
    "        ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "        if save==True: plt.savefig(self.direct_out_for_graph_mass/Path('aprro_beta_param_ab_v3'+self.add_name+'.png'),bbox_inches='tight')\n",
    "   \n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF        \n",
    "    def plot_norm(self,init=0,end=N_samples,data=False,fit=True,appro=True, save=False):\n",
    "        fig, ax = plt.subplots(figsize=(13,6))\n",
    "        plt.ylabel('count in bin 0.04')\n",
    "        plt.xlabel('normalized R')                   \n",
    "        #==========================================================================================\n",
    "        for i in range(init,end):\n",
    "            # Рисуем реальные значения гистограммы приведенной к плотности распределения\n",
    "            if data==True: \n",
    "                ax.plot(self.x,self.y[i], label='pdf '+m_name[i],color=color7[i],marker='o')\n",
    "                ax.legend(loc='centre left',bbox_to_anchor=(1,1))\n",
    "                if save==True:\n",
    "                    plt.savefig(self.direct_out_for_graph_mass/Path('data_norm_v3'+self.add_name+'.png'),bbox_inches='tight')\n",
    "            if fit==True:\n",
    "                # Рисуем фитированные по данным распределения \n",
    "                ax.plot(self.x, beta_pdf(self.x,self.a[i],self.b[i]), '-', lw=4, alpha=0.6, \\\n",
    "                        label='fit pdf'+m_name[i],color=color7[i])\n",
    "                ax.legend(loc='centre left',bbox_to_anchor=(1,1))\n",
    "                if save==True:\n",
    "                    plt.savefig(self.direct_out_for_graph_mass/Path('fit_norm_v3'+self.add_name+'.png'),bbox_inches='tight')                \n",
    "            if appro==True:\n",
    "                mass=m[i] # H\n",
    "                a_beta,b_beta=self.find_Beta_param(mass)\n",
    "                # Рисуем аппроксимированные распределения\n",
    "                ax.plot(self.x, beta_pdf(self.x,a_beta,b_beta), linestyle='--',lw=5,\\\n",
    "                        alpha=0.6, label='appro pdf '+m_name[i],color=color7[i])\n",
    "                ax.legend(loc='centre left',bbox_to_anchor=(1,1))           \n",
    "                if save==True:\n",
    "                    plt.savefig(self.direct_out_for_graph_mass/Path('appro_norm_v3'+self.add_name+'.png'),bbox_inches='tight')\n",
    "        \n",
    "        ax.legend(loc='centre left',bbox_to_anchor=(1,1))\n",
    "        plt.ylabel('pdf')\n",
    "        plt.xlabel('normalized R')\n",
    "        if save==True:\n",
    "            plt.savefig(self.direct_out_for_graph_mass/Path('total_norm_distr_v3'+self.add_name+'.png'),bbox_inches='tight')\n",
    "    \n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  \n",
    "    # Находим зависимость от массы начала и конца распределения на известных ядрах\n",
    "    def fit_border(self,graph=False, save=False):   \n",
    "        # Зависимость начала и конца распределения от массы, точнее логарифма массы\n",
    "        min_value,max_value=[],[]\n",
    "        for i in range(N_samples):\n",
    "            min_value.append(min(r[i]))\n",
    "            max_value.append(max(r[i]))\n",
    "            \n",
    "        if self.manual==True:\n",
    "            min_manual_bord=min_manual.copy()\n",
    "            max_manual_bord=max_manual.copy()\n",
    "            min_a,min_b,min_c=appro_c_r(ln_m,min_manual)   \n",
    "            max_a,max_b,max_c=appro_c_r_max(ln_m,max_manual)            \n",
    "        else:    \n",
    "            min_a,min_b,min_c=appro_c(ln_m,min_value)\n",
    "            max_a,max_b,max_c=appro_c(ln_m,max_value)\n",
    "        \n",
    "        if graph==True:\n",
    "            fig=plt.figure()   \n",
    "            if self.manual==True:\n",
    "                for i in range(N_samples):\n",
    "                    plt.scatter(ln_m[i],min_manual[i],label=m_name[i],color=color7[i])\n",
    "                    plt.scatter(ln_m[i],max_manual[i],color=color7[i])                 \n",
    "            else:\n",
    "                for i in range(N_samples):\n",
    "                    plt.scatter(ln_m[i],min_value[i],label=m_name[i],color=color7[i])\n",
    "                    plt.scatter(ln_m[i],max_value[i],color=color7[i])        \n",
    "            plt.plot(ln_m,min_a+min_b*ln_m+min_c*ln_m**2, label='start R')\n",
    "            plt.plot(ln_m,max_a+max_b*ln_m+max_c*ln_m**2, label='end R')\n",
    "            plt.xlabel('ln(A+0.1)')\n",
    "            plt.ylabel('R')\n",
    "            plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "            if save==True:\n",
    "                plt.savefig(self.direct_out_for_graph_mass/Path('fit_border_v3'+self.add_name+'.png'),bbox_inches='tight')\n",
    "        return min_a,min_b,min_c, max_a,max_b,max_c\n",
    "        \n",
    "    def find_r_start_end(self,A):\n",
    "#         # ИЗМЕНЕНО!!!!!!!!!!!!\n",
    "        if self.chek==True:\n",
    "            min_a,min_b,min_c, max_a,max_b,max_c=min_an[k],min_bn[k],min_cn[k], max_an[k],max_bn[k],max_cn[k]\n",
    "        else:  min_a,min_b,min_c, max_a,max_b,max_c=self.fit_border()\n",
    "#         print(min_a,min_b,min_c, max_a,max_b,max_c)                          \n",
    "        r_start=sq(np.log(A+0.1),min_a,min_b,min_c)\n",
    "        r_end=sq(np.log(A+0.1),max_a,max_b,max_c)\n",
    "        return r_start,r_end\n",
    "    def find_r_distr(self,A):\n",
    "        r_start,r_end=self.find_r_start_end(A)\n",
    "#         print(r_start,r_end)\n",
    "        r_distr=self.x*(r_end-r_start)+r_start\n",
    "        return r_distr    \n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    #FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "    def plot(self,appro=False,fcn=False,save=False):\n",
    "        fig, ax = plt.subplots(figsize=(8,4))\n",
    "        if self.manual==True:\n",
    "            for i in range(N_samples):\n",
    "                R_actual=self.x*(max_manual[i]-min_manual[i])+min_manual[i]             \n",
    "                ax.plot(R_actual, beta_pdf(self.x,self.a[i],self.b[i])/(max_manual[i]-min_manual[i]), \\\n",
    "                        linestyle='-',lw=4,  alpha=0.6, label='fit pdf '+m_name[i], color=color7[i])\n",
    "        else:\n",
    "            for i in range(N_samples):\n",
    "                R_actual=self.x*(max(r[i])-min(r[i]))+min(r[i])  \n",
    "#                 if i==\n",
    "#                 ax.plot(R_actual, beta_pdf(self.x,self.a[i],self.b[i])/(max(r[i])-min(r[i])), \\\n",
    "#                         linestyle='-',lw=4,  alpha=0.6, label='fit pdf '+m_name[i], color=color7[i])\n",
    "        #==========================================================================================\n",
    "        if appro==True:\n",
    "            for i in range(N_samples):\n",
    "                A=m[i]\n",
    "                r_start,r_end=self.find_r_start_end(A)\n",
    "#                 print(r_start,r_end)\n",
    "                if self.chek==True: a_beta,b_beta=find_param(A)   # ИЗМЕНЕНО!!!!\n",
    "                else:  a_beta,b_beta=self.find_Beta_param(A) \n",
    "                \n",
    "                f_interpolate=interpolate.interp1d(self.find_r_distr(A),beta_pdf(self.x,a_beta,b_beta))\n",
    "                Integral_below=quad(f_interpolate,min(self.find_r_distr(A)),max(self.find_r_distr(A)))[0]\n",
    "#                 print(Integral_below/(r_end-r_start))\n",
    "                ax.plot(self.find_r_distr(A), beta_pdf(self.x,a_beta,b_beta)/(r_end-r_start), \\\n",
    "                        linestyle='-',lw=4,  alpha=0.6, label=m_name[i],color=color7[i])\n",
    "        #==========================================================================================\n",
    "        plt.legend()\n",
    "        plt.ylim(0,None)\n",
    "        # plt.xscale('log')\n",
    "        plt.ylabel('PDF')\n",
    "        plt.xlabel('ln(A)')\n",
    "        plt.savefig('Beta_R_initial_'+self.add_name+'.png')\n",
    "        \n",
    "        if fcn==True: \n",
    "            plt.title('k = '+str(k)+', j = '+str(j)+', l = '+str(l)+', fcn = '+str(df_fcn['fcn'].loc[l+j*5+k*5*5]))\n",
    "        if save==True:\n",
    "            plt.savefig(self.direct_out_for_graph_mass/Path('R_init_'+self.add_name+'.png'),bbox_inches='tight')        \n",
    "        \n",
    "    def interpolate_distr(self):\n",
    "        some_R=pd.DataFrame()\n",
    "        f_arr=[]\n",
    "        for i in range(len(mass_toFe[:])):\n",
    "            A=mass_toFe[i]\n",
    "            if self.chek==True: a_beta,b_beta=find_param(A)   # ИЗМЕНЕНО!!!!\n",
    "            else:  a_beta,b_beta=self.find_Beta_param(A) \n",
    "            r_i1=self.find_r_distr(A)\n",
    "            beta_i1=beta_pdf(self.x,a_beta,b_beta)\n",
    "\n",
    "            f_inter=interpolate.interp1d(r_i1,beta_i1/(max(r_i1)-min(r_i1)))\n",
    "            if i==0: min_possible_r=min(r_i1)\n",
    "            if i==len(mass_toFe)-1: max_possible_r=max(r_i1) \n",
    "            f_arr.append(f_inter)\n",
    "\n",
    "        return f_arr,min_possible_r,max_possible_r    # Возвращаю масив с интерполяциями для 26 ядер\n",
    "    def from_R_to_A(self):\n",
    "        R_to_A=pd.DataFrame(index=np.arange(len(df_together)),columns=['R','A','type_part'])\n",
    "        add_toi=0\n",
    "        f_arr,min_possible_r,max_possible_r=self.interpolate_distr()\n",
    "        for j in range(N_samples):\n",
    "            print(j)\n",
    "            for i in range(len(self.r[j][:])):\n",
    "                real_i=i+add_toi\n",
    "                R_to_A['R'].loc[real_i]=self.r[j][i]\n",
    "                R_to_A['type_part'].loc[real_i]=m_name[j]\n",
    "                if R_to_A['R'].loc[real_i] >= max_possible_r: \n",
    "                    A_res=m[0]\n",
    "                elif R_to_A['R'].loc[real_i] <= min_possible_r :\n",
    "                    A_res=m[N_samples-1] \n",
    "                else:    \n",
    "                    y_df=pd.DataFrame(index=np.arange(len(mass_toFe)),columns=['A','y','mass'])\n",
    "                    y_df['A']=mass_toFe\n",
    "                    summ=0\n",
    "                    for part in range(len(mass_toFe[:])):\n",
    "                        try: \n",
    "                            summ+=1\n",
    "                            y_interp=f_arr[part](R_to_A['R'].loc[real_i])\n",
    "                            y_df['y'].loc[part]=y_interp\n",
    "                        except:\n",
    "                            y_df['y'].loc[part]=0\n",
    "                        y_df['mass'].loc[part]=mass_name[part]\n",
    "                    y_max=max(y_df['y'])\n",
    "                    A_res=float(y_df['A'][y_df['y']==y_max])\n",
    "                R_to_A['A'].loc[real_i]=A_res\n",
    "#                 if (real_i+1)%500==0: print(real_i)\n",
    "            add_toi+=len(self.r[j])   \n",
    "        return R_to_A \n",
    "    \n",
    "    def minimize(self):\n",
    "        \n",
    "        R_to_A=cl.from_R_to_A()\n",
    "        for i in range(N_samples):\n",
    "            R_to_A['real_A'][R_to_A['type_part']==m_name[i]]=m[i]\n",
    "        fcn(R_to_A)\n",
    "        \n",
    "    def mass_distribution(self):\n",
    "        x=mass_name\n",
    "        y=np.zeros((N_samples,len(mass_toFe)))\n",
    "        for j in range(N_samples):\n",
    "            for i in range(len(mass_toFe)):\n",
    "                y[j][i]=np.count_nonzero(R_to_A['A'][(R_to_A['A']==mass_toFe[i])&(R_to_A['type_part']==m_name[j])])\n",
    "\n",
    "        max_len=max(len(r[0]),len(r[1]),len(r[2])) #,len(r[3]),len(r[4]))\n",
    "        coef=np.zeros(N_samples)\n",
    "        for i in range(N_samples):\n",
    "            coef[i]=max_len/len(r[i])\n",
    "\n",
    "        x1=np.arange(1,27)\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(15,8))\n",
    "        bottom=0\n",
    "        for j in range(N_samples):\n",
    "            width=0.68\n",
    "            if j!=0: bottom+=y[j-1]*coef[j-1]\n",
    "            ax.bar(x1, y[j]*coef[j], bottom=bottom,width = width,color=color7[j],label=m_name[j],alpha=0.6)\n",
    "        ax.legend(loc='center left',bbox_to_anchor=(1,0.5))\n",
    "        ax.set_xticks(np.arange(1,len(mass_name)+1))\n",
    "        ax.set_xticklabels(mass_name, fontsize=20) #, mass_name, size='small')\n",
    "\n",
    "        plt.savefig(self.direct_out_for_graph_mass/Path('mass_distr_above_'+self.add_name+'.png'),bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "    def mass_lineal(self):\n",
    "        fig,ax=plt.subplots(figsize=(10,4))\n",
    "        mA=mean_A(R_to_A)\n",
    "        for i in range(N_samples):\n",
    "            plt.scatter(np.array(mA)[i],[1],color=color7[i],s=80,label=m_name[i])\n",
    "            plt.axvline(np.array(mA.index)[i],color=color7[i])\n",
    "        plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "        plt.savefig(self.direct_out_for_graph_mass/Path('mean_A'+self.add_name+'.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Восстановление массы "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:40:53.419167Z",
     "start_time": "2023-07-19T08:38:24.227597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl=find_A(r,add_name='_5_10-20_q1',manual=False)\n",
    "add_name='_9_10-20_q1'\n",
    "cl.plot(save=True,appro=True)\n",
    "R_to_A=cl.from_R_to_A()\n",
    "R_to_A['real_A']=0\n",
    "for typ in range(N_samples):\n",
    "    R_to_A['real_A'][R_to_A['type_part']==m_name[typ]]=m[typ]\n",
    "print(round(fcn(R_to_A)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Гистограмма выхода нейронной сети на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:40:54.244388Z",
     "start_time": "2023-07-19T08:40:53.481088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_3(r,norm=False,step_bins=0.2,save=True,add_name='1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Массовое распределение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:40:55.072243Z",
     "start_time": "2023-07-19T08:40:54.307262Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl.mass_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Параметризация распределений Бета-распределением"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:40:55.618913Z",
     "start_time": "2023-07-19T08:40:55.137063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y0cYiqo9W92ofnjAJE4vzF+vG1heuVz0XFD6hPAcKhaL/sHKlVo7ozJQp2mv8eEhOdn+eM2YzLF8O2dnwxhscLXIqMSwtIymqjYwR7qsLPNFua+9W9MhbRl05irKdZTRWOBIo9/1jH4sfWUxYRFg3Z3rHvLx5vHX0rS6VxOrmag5VHWJC+gS/11acXyjPgUKh6B9s2QIbN+rHpk2D73wH5s/3zjDoRAi47DJqrrydynP6ksGxscWIJ5+EsjIPJ7vyxZkvdPH7GHOMT6WCYeYwptwxRee1aKpq4ujbR7s5y3viI+NdRJHWFa4LyNqK8wtlHCgUitBz5gz885/6sfR0uOMO7UbvA1JKjh4TMHWqlqwIpCZaSUuyQlWVVslw8KBX6xjLFxflLyIy3DedgpQRKeQvzteNnfz4JHWFdR7O6B2LC/Shhf2V+7vyEBTBpaCgACklL7zwQqi34jfKOFAoFKGlpUWT0bM4Ze6bzVo7v+hon5etOlRFzfEaiI2BaVMhMZGxBa0OW6O1VUt8/PRTTWHRA/sr91PR6FC8MwkTFw27yOd9AYy7bhzRyY7vTUrJ3pf26gSafGVY0jDyEh0lmlJKNhRt8HtdxfmFMg4UCkXokBJefBEqK/Xjt90Gub5LDEspObrGyVVvNpP+tSWkXDnHOBFee03LdfBgIHx84mPd8QU5F5AU5V0PBk+ER4W7VCnUl9RzetNpv9YFTXjJWNa46fQm1a1R0SuUcaBQKELHJ5+AsUPdwoUwd65fy1bsrXDpujhm2Ti4/Xa48UbXUMXata77AE6fO82xmmO6sUuGe1e+2BPpE9PJna03gArXFQakumBWziyizQ7PRGN7IztLd/q9ruL8QRkHCoUiNBw/DqtX68eGDgU/RWKkXXJkzRHdWNa0LE2ESAi4+GL47ne78hC6WL0arPqna2OuwZi0MTqXvb+MWzZOJ8TUUNbA2S9dutT3moiwCJeESZWYqOgNyjhQKBR9T309PP+8XvUwJkbLMzCb/Vq6dEcpDaWOdspCCMZcPUY/aeJEePhhfWOmqipY7yhXrG2pZXvJdt1pPUkl95aopCgXCeWi9UUeZvcOo+bBydqTFJ8r9jBbodCjjAOFQtG32O2aYWDsaX/nnZCW5t/SNrtLWWDOrBzis+NdJ+flwaJF+rF33ulq1rS2cC126TBeMuIymJQ+ya/9uaNgcYHuuGxXGW0NbX6vmxGXwbgh43RjRq0GhcITyjhQKBR9y5o1cEwfx+fyy2Gy/zLCZ744Q1OlQ49AmASjrxrt+YSrrtKHF5qb4d13abO2sbFIr7lw8fCLe62q6A2pY1KJTY/tOrbb7BR/HpgnfGNi4tYzW2m2NLufrFA4oYwDhULRd+zbBx98oB8bMwauucbvpe1WO8fe0RsdQ+cP1d14XYiPhyuu0I+tXcvmA+/rbqKxEbHMyTVUOgQIIYSL96BoQ1FAEhMnZ0wmOdohHtVua+eLM1/4va5i8KOMA4VC0TdUV8Pf/64fS0yEu+/Wx/59pGhjES1nHT0ZTOEmRn1lVM8nLl2qV1+02djw6V91UxbnLyYiLMLvPXoid24upnDHz6C5ppmqg1V+r2sSJhbl60Mn6wrXqX4Lih5RxoFCoQg+FosmdNTs5NI2meCee7RmSX5ibbNy/L3jurGCxQU6oSGPmM1w3XVdh2WmZkorvoSGem2bARA96omI2AiXLpFFGwKTmLhg6ALCTI6+DRWNFRytCYxcs2LwoowDhUIRfFauhNMGgZ9ly2CUF0/2XlC4rpC2ekcSX1hEmEsHxG6ZNQvyNUnjneaOJ/aTJwHJmLQxJET6b8D0hDG0ULGvQucJ8ZWEyASmZ03XjamyxuAhhOgxN6WgoAAhBIWFhX2zKR9QxoFCoQgu+/e7b6h0cWDKAm0WGyc+PKEbG7ZkGJEJveh9IATccAMAO80dLZTr66G6mhlZMwKyz55IGpZEQq7DCJFSBkQxEVzLGveW76W2pTYgaysGJ8o4UCgUrkgJrVXQfq7nuT2t8847+jE/GyoZqdhbQXtTe9dxeFQ4Iy4d0fuFRo+mfFIBpWGOagfTqUKmDQl8+aI73CUmnt50GrvN/34LI1NGkh3vCFvYpZ1Npzf5va5i8KKMA4VCoaf5DBz+H9j3n7Dnx7D7R3D0D1D8JpzdBa3V3TYq0nHkCBhdp3ff7VdDJSPFm/Vlf0PnDyUi1rfkwZ2z8nRGy5jGKOI27/Brf70hZ1YO4ZHhXcet51qp2FvRzRne4a7fwoaiDarfQj/kyJEjLF++nLy8PCIjI8nIyODWW2/l6NG+zRMJ73mKQqE4L7C2QMnbUPEZ4HTzt9TDuQPaq5OwGIjJg9g8iBkKsUMhKgOE4Xnjvff0x1OmdMX2A0FrXStVh/RZ/XnzfJc33tlyErKyoLQUgBmWNHj3Xa3XQ2w3JZEBIjwqnJzZObpkxML1hWRNz/J77dm5s1l9eDWt1lYA6tvq2VO+h5nZM/1eWxEYPvjgA5YtW4bFYuGrX/0qI0eO5MyZM6xevZp3332XtWvXMn369J4XCgDKOFAoznekhJptULxKMwS8wdYMDUe1VyemCIjJhZh8yFgMpc2uYkdXXhm4faOJHjmX5SXmJeri9r2horGCkvoSGJoPFZWYbDamWlKhvVkzcm68MVDb7paCxQU646D6SDWNFY3EZcT5tW5UeBRzcufokhE3F28OvnGw7d7grh9oZj0XkGUeffRRj+/V1dW5jNXW1nLLLbcQExPDhg0bGD9+fNd7Bw8eZPbs2dx9993s2rUrIPvrCWUcKBTnM82lUPQKNBxz/74IB+ml69neDo0ntVflevjMIFk8bhwUFPi1XWeklC4hBb+8BmUdXQvNZhiax+jjtcTLjvDE2rWweLGWLxFkEnITSB6eTO1JR8Lg6Y2nGX/D+G7O8o5F+Yt0xsHhqsM0tTcRGxF8r8j5xmOPPdar+S+99BJ1dXU8++yzOsMAYMKECdxzzz08/fTTHDp0yOX9YKCMA4XifMTWBiXvQPkngJuEt4hUyL8ZkiZCSzk0n4am09BcrH21t3a/fnk9bF0PCWMgquOGGmCvQd2pOhorGruOTWEmcmbl+LyerqVxdg4zipOhMzfRZoM33tAaQ/UBBYsLdMZB8eZixlwzhjBzWDdn9UxOQg5Z8VmUNZQBWmLinvI9zB863691Fa50JzRVUFBAUZFex2LLli0A7N27163X4ViHF+7w4cPKOFAoFAFGSqjdBUWvgcXVtYkIh6xLIesK6FQEjMnWXmlzHGu0VTkMhc6vVkcnRDafBiTUHwVph4nzA6Zp0EnxFr3XIGNyBhFxviUiVjRWcKb+TNexCAtj2le+CS+tdEzatQtOnIARPlRC9JKsGVkcfO1gVxVGe1M7ZTvLyJ2T6/faM7Jm8E6Do4JkV9kuZRz0A2pqagB4/vnnu53X2NjY7fuBQhkHCsX5QkuFFkKoP+z+/cQJmrcgqgfXuRDanKh0SOnQAJBSMzZq98DOv8Gxmo7JUgtZzLo2YKWLoGkblG4v1Y3lzvX9xtkVUuhgTOoY4udcBOu/AOcnvFWr4Mc/Duj34o4wcxi5c3M5+cnJrrHC9YWBMQ6yZ/DOMYdxcLj6MM2WZmLMMX6v7ZYAxfAHO4mJiYDmOZgcgCZk/qJKGRWKwY6tXStDPPCYe8MgIhlG3gujH+jZMPCEENo6GRfBiWG6Ygcy4iB6C5R97NvabijfU46lxdJ1HBkfSfpE3/MBdCEFtBuoszBSFydPwk793GCRv0hf1VF7spb6M14mjHZDVlwWmXGZXcc2u4095Xv8XlfhH3PmaJ65jUbBsBChjAOFYjDTfAb2Pwpl74O0Gd40QdZlMOkxSJkemKfh6mo4VAWJ4x1ljfOGamsXr4KS97o/30vObDmjO86ZnYMpzLePs8qmSn1IQQimZU7TDkaP1sovnXnjDbAGXx8gLiOOtLFpurHC9YV+ryuE0IwfJ4zGkaLvufPOO0lKSuKxxx5j27ZtLu/b7XbWrVvXZ/tRxoFCMVixNMCxZ6G9xvW9+DEw6RHIWwZhvZAZ7okPPwS7HSJTIWECpMXBmFTH+yVr4Mwa70WU3OBW22CuH1UKhhvj6NTRxEc6VVpcf72+a2R1tVa90AcYFRNLtpZgbfXfMDH2WugMLShCR2pqKqtWraK1tZU5c+ZwySWX8IMf/IAHH3yQG264gby8PC6//PI+248yDhSKwYi0w4m/QrtBP9+cCCPuhrE/hGj/hXV01NXB5s2O48hkuOlBV+Oj9D3Ni+CjgRBIbQNwzTdw6aWQkaGVMTrz3nvQ1ESwyZiSQVRiVNextc1KybYSv9fNic8hIy6j69hmt7GvYp/f6yr8Y+nSpezbt4/77ruPwsJCVqxYwV/+8hcOHDjAkiVLePXVV/tsLyohUaEYjJS865pfkDoHCm6BsCj35/jLxx/r3e2pqbDkJmieBUef0Zc/ln8CdquWANmLcEagtQ0qmyopPudYTwjBtKxprhO/8hXYsgVaO76H5mZNOfGmm3y+tjeYwkwMXTCUY+86dCgK1xcydOHQHjv/dYcQghlZM3jvuCPMs7N0J3Ny5/i1X0X3JYyddNeNsaCggGeffTaAO/IN5TlQKAYbdQeh9F39WNxIGPaN4BkGjY2wYYN+7LLLICwM4kdonoowQzZ85To49Q/Ny+ElQdU2AEaljHLfnjk+3lWnYeNGzUgIMkZDoP5MPXWn3JSh9hJj3sGhqkO0WPxvEa0YHCjjQKEYTLSd1cIJzuUC4fEw8h4w+Seg0y2ffgrtjs6IJCbCvHmO47gCGPsghBskgKs/h5N/99pACKS2AbgJKWR30555yRJISnIct7fD1q0+X9tbopOjyZicoRsLRGJiTnwO6bGOCg+r3creir1+r6sYHCjjQKEYLNit8OVzYHOOhQvNMIhI8nia37S0uCboXXKJJkPsTGwejH0IzIYn85pt8OXz2v67wZ22gT8hhaqmKpeQgjFRT4fZDAsX6sc2bPArudJbjGWNpTtKdW2qfcFd1cKusr7R7Vf0f5RxoFAMFk6/Dk2F+rHcazQJ42Cybp1mIHQSGwuLFrmfG5MNYx/WNBGcqd2lGTZ2i/vzcK9tMGTCEJ+3bfQajEwZ6T6k4MyCBfrKhdJS+PJLn/fgLUMmDCEm1RGWsVvtLuWcvmBMvjxYebCra6Pi/EYZBwrFYKB6mxbDdyZpMmQFufSpvV0LKTizdClEdlMeGZ2hGQiR+hp+6vbByRc8nhZIbQNwI3xkrFJwR1KSq+6BMdciCAghXLwHhesLvUp+647chFyGxDoMLKvdyt5yFVpQKONAoRj4tJRB4T/0Y5FpMPzOoMv8snEjNDj1VIiKgosu6vm8qDQY9zBEGlQNz+6As7tdpgda26C6uZrT5053HfcYUnDGWNa4a5f+ZxAk8ubn6Yyhpsomak/UdnNGz3RWLThj9Kgozk+UcaBQDGRsrXB8hdYuuRMRrskhhwdJK78TqxU++kg/duGFEOPldSOSNQMhyqC3UPSq9n05UbylWK9tMNRPbYNS15BCYlSidyePHQtDnMIZVqtW5hhkIuMjyZyaqRsr3VHqYbb3GPMOVGhBAco4UCgGLlJqpYCt5frx/Jshdmjwr79liyZ81InZDBdf3Ls1IhJh5LfQfRRZ6qD4ja5DKaVLSMGfRETwQvioO4Rwzanoo8TE7AuydcelO0uRdv+um5eQR1qMI8RjtVvZX7HfrzUVAx9lHCgUA5XKdZob3pm0uTBkQfCvbbfDBx/oxxYu1PQAektMttbjwZnK9dCodSR0q21wge/aBtXN1RTVOTotehQ+6o558yDcSUOuqgoOe+h2GUDSJ6YTHuW4blt9GzXH3chj9wK3vRZUaOG8RxkHCsVApPGkVp3gTHQO5N8a/DwDgO3btR4DnYSFwaWX+r5ezlcM+QcSTr0MdpuLIqK/2gbGcr0RySNIiuplqWdcHMwweBv6IDExzBxG5pQghBYMnpMDlQdos7b5va5i4KKMA4VioGFphC//rO+yaIrS8gzCfL9peo2U8P77+rG5cyE52f18bzCZoeA2/VhLCbbijyjZru8l4HdIwV17Zl8whhb27tWHWYJE9kx9aKF8d7nfodzG8ogAACAASURBVIWhiUNJjXE0yLLYLKrXwnmOMg4UioGEp4ZKw5drJYJ9wZ49UFbmOBZCk0r2l8SxWv8HJ8rXfYi1yRFS8FfboKa5hsK6Qt2Y11UKRkaMgGynG7XdDps2+bw3bxkyfgjmGIfAVFtDG9VHq7s5o2dU1YLCiDIOFIqBRMm7UH9IP5Z5MaT0MmbuK+68BhdcAOnp7uf3lqE3QFhs12HxXhM0fNmlBu23toHhhjcixYeQQidCuJY1btyoGQlBxBRucq1a2B74qgUVWji/UcaBQjFQ8NRQKXdZ3+3h1CkoKtKPXXFF4NY3x2sGAtBSL6guDNe8JG2axoE/2gbgo/BRd8yeDRFOoZy6Otgf/Ex/Y2ihbHcZdqt/Rkl+Yr5LaGF/papaOF9RLZsVCjfY7VBRoQkA2mxaKXvn185/exoHGDYMxowJYG5ge11oGioZ2bhRfzx5st61HgjS5kL1F5zZXOioDmw8QeLIfL+0DQIaUugkOhpmzdKHE9avd1VRDDBpY9OIiI3o6q9gabZQfaSa9Im+e3A6haA+PvFx19jO0p3MzJ7p937PJzo7aA4dOpSjR48SFeXaCbWgoICioiIsFgvh4f3zNtxvdiWEyAUeBy4HUoEy4E3gMSmlTzJgQohFwFo0D8mvpJT/GaDtKgYp7e1aq4CPPvJf9C4jQxMLnDtXEw70izNv9n1DJSMtLVqVgjOeeij4gxDI/Fs5c+D3jjG7hbzhXwJLfV7WpUohZQTJ0X4kUXayaJHeODh0SKvkSEvzfI6fmMJMZE3Pomijw4tTsr3EL+MANE+Ks3Gwv3I/bdY2IsO7kcNWuOX06dM8/fTT/OQnPwn1VnyiX4QVhBAjgJ3AncA24H+Bk8D3gS1CiNRuTve0ZjzwIhD8huuKAY/VqjUW/PnP4d//DowabkUFvPoq/PjH2teKCh8XaiqG6i/0Y33RUMnI1q1gcWqMlJwMEyYE5VJ15ZE0tjp6CZjCICdnN9Qf93nN3eV6WWa/Qwqd5OdDQYHjWEpXD0sQcKla2FOOzWLzMNs7CpIKdAaTxWbhQOUBv9Y8H0lOTiYlJYUnnniC6mr/kkVDRb8wDoD/B6QD35NSXiul/ImUcgmakTAG+JUPa/4fkAg8EbhtKgYbNpv2Of6f/6ndwOvrA3+NtjbN8HjkEfi//4N9+3qRsyZlh56BUzghKstVNCjYSOlax2/sUBhAijcXQ0wehGlSzBkjLUTESK2HRA+tnd3R0NbAydqTurFeCx91h9GD8vnnmsUZRFJHpxIZ73iit7ZaXfpP9BZ3VQuqjXPviYmJ4Re/+AX19fU89thjod6OT4Q8rCCEGA5cChQCfzS8/UvgW8DtQoiHpJRNeIEQ4ho0L8Tt9IPvUdH/sNvhiy/g3Xf1Wj7OREZqoYHwcE3jp/Nr57+N451fa2q0Xjw2Nw9xhw5pr7Q0rQ3B/Pk9tCKo2w8NR/VjQ28A0cd2fWEhlDjpDQihbT4I2G12ynaVaddIGAW1e8mb1NE7orUCSt+H3K/2as0DlQd0vRlyE3JJiU4J3KZnzoTXX3e0rm5ogN27tUqOICFMgqwZWRSuK+waK91e6iKS1FtmZM/gk5OfdB3vq9hHu62diL7Q0BhE3H///Tz77LM899xzPPDAA4wePTrUW+oV/eHGuaTj60dSSt3zlJSyQQjxOZrxMAf41HiyESFEOvA88KaU8mUhxPIA71cxgJESduyAt9/27OY3m2HJEk3wLy7Ot+ucO6d5JNavd++NqK6GVavgrbe0hPclS9zk9dltULxKP5YwDhKD48rvFneJiP6IHnVD9ZHqrkQ7zAmYk7MZMtxJmrjsA0i9AKK9vwkaBX0mZ0wOxFYdREbCnDmai6iTDRuCahyAFlpwNg4q9lVgs9gIM/uepDosaRjJ0cnUtmipXu22dg5UHvA9efPee33eS0h47rmALGM2m3nyySe58cYb+clPfsLq1asDsm5f0R/CCp2B02Me3u8MMnprdv0Z7fv6dm82sXPnToQQPb4UAxMptQe5xx+Hv/zFvWEQHg5Ll8Kvfw3LlvluGAAkJsJVV8ETT8Ddd8Pw4e7ntbdr993HHoPf/U4LOXQ94FZt1J6UuxAdXoM+/jt0l4i4cGHQLle2s0x3nLVwHqYopyoFaYXCl71udGS1WzlYdVA3NiUzCNUERs2DY8f0YlFBIGVkClFJjmxXa5uVyv2Vfq3prn21sQRU4R033HADc+fO5Y033mBTHwhkBZL+YBx09kk95+H9zvEe07KFEHcB1wD3SSl9Tf9SDCKk1G64v/oVrFgBpW60YsLCtM/1X/0KbroJEnyvlnMhPFx7ePyP/4Cf/UyrXPBUuXTsGPzxj/Dii2BpbYGSt/UT0uZBTG7gNuctW7dqVkwnQUxEtFvtlO3W31Cz5wyDoTfpJzYch+rNXq15rOaYTswnITKB/MT8bs7wkawsGDVKPxbkxEQhBNkzDJ0ag9BrYX/lfiw2i4fZiu743e9+B8BDDz2kC231d/qDcdATnY9J3f5UhRAFwNPA61LK13p7kRkzZiCl7PGlGDjU1MBvf6vdcIuLXd83mbSw+X/9F9x6KyQFuSowPx+WL4cnn4Rrr/Xsld+yBd5a8T6tjQ7ZYEyRWoVCX+Mu8z6IiYjVR6qxNDtuQhGxEaSNSYOUma7hlNOrwNJzWYkxpDApY1LwvIBG78GWLXrDKghkzcjSHVfsq8Da5l8y5PDk4TrlyDZrm6pa8JG5c+dyww03sG3bNl57rde3ppDRH3IOOj0DiR7eTzDM88TfgBbgvkBsSjGwqarS3PS1bhQyhNB0a666KnCqv70hPl4TFbzsMq1NwWefwXGnCr3Y8BpSLZ+yezeMG9dhtGRdChGe/osEkcJCOHPGcRzERERwferNmp6FMHXcyPNvhf2PguwwHmzNcPo1GPFNj+tJKV2MgykZQRQomjZN+wV31sI2N2tJLvPmBe2SycOTiU6OpqVWS4a0WWxU7Kvwq611Z2jhs1OfdY3tKtvlW4VHgGL4A5knn3ySNWvW8NOf/pTrrrsu1Nvxiv7gOehMxfaUU9Dpp/OUk9DJdLRyyCohhOx8AX/veP/nHWNv+rddRX+nO8Ng5kz45S/hrrtCYxg4YzLB9Onw8MOavkJGR9+kqWlvYBJWLBZNibewNAmZcUloNmn0GkyaFLRERLvVTvmect2YrpY/Kg1yr9afVLNNk5X2QGlDKTXNNV3H4aZwxqaNDch+3RIe7moIrF8fvOvREVqYGfjQgjHvYH/lfqw+lJEqYMSIEdx3332cOnWKP/zhD6Hejlf0B+OgM733UiH09VkdQkbz0TwCXxhPNPAS8Fc3r87i7D0dxx+7PVsxKKishP/5H1fDYMIE+MUv4J57tNBwf2PoUC0nYeG0UxTEO5L/pISVn1/DX1+IDLZ32pU+TkSsOlSFpcURUoiMjyR1tEH/LGMpRBvyLor+BTb3Pxyj12Bs2tjgq/0ZNQ8KC+H06aBe0mgcVB6oxNrq3418RMoI4iPju45bLC0cr/FdhOp855FHHiEpKYlf/epXNDqHDPspITcOpJQngI+AAuB+w9uPAbHAS84aB0KIsUIInfkvpfyelPJu4wuH5+DdjjGjloJikFBZqXkM6ur043PmwHe/C7khyOXrDVGRktsWvq6J7XV40mvb8jhZP5ft2+E3v/GsyRAU3CUiTpwYtMt1G1LoxBQGw27HkYoEtFVD2Ydu13QJKQSjSsFIWpprwqZRQCrAJOYnEpPmEMxw54XpLSZhcin53FO+x681z2dSUlL42c9+Rm1tLTU1NT2fEGJCbhx0cB9QCTwjhHhTCPGEEOIz4Ido4YSfG+Yf7ngpFIBnw2DuXLjjjqDlzwWW2t2IphMMHardW8LDYWfVDXTeCM+c0SoqDh3qfpmA0MeJiDaLjfK93YQUnIkrgPQL9WPln4BVr5HW0NbAqbpTurFJ6ZP83KmXGL0H27ZBa2vQLhes0MLUzKm64z3le1Rith9873vfo8BZarsf0y8+Mju8BzOBF4DZwEPACOAZYK6Usv+bWYqQUVGhhRKMhsG8efCNbwwQw8BuheJ/dx2mpsCkhZMJS9bHx5ub4Zln4IMPvC7z942ioj5NRKw6VKVzg0cmRJIyshsFw7xrISzWcWxv1QwEJ/ZX7tfdyIYmDg1MoyVvmDxZX/7S1qZJcgYRo3FQdahKV/nhC+PSxunCMHWtdRSdK+rmDIWUkjPO/3eciIyM5NSpU13Vb/21IyP0E+MAQEpZLKW8U0qZJaWMkFLmSym/L6U862aukFJ6VYskpXyhY77qyDgIqajQPAbnDLUsA8owAKhcp7nHuzARP/56fvITLWnRGSnhjTfgz3/W7jlBwegGD2IiIngZUnAmLMq1v0T5p2BxxHL3lu/VvR1wVcTuMJlc8zM2bAiqRZeQm0BchkO5y27zP7RgDjMzYYg+RKJCC+cHXn10CiE+E0J8wzCWIIQYGpxtKRQ9U17u3jCYP18zDAaMoKW1CUre1Y+lL4LoTCIj4Vvf0hQbjd/Prl2aZkKlf4J4rvRxIqLNYqNir16zzGNIwZmMCyHckTCHvQ3KPwK0boKHqvTxlz41DsA1DFNSAidPep7vJ8EKLRjLF3eX7fYwUzGY8Pa56kK0hEFnfgiccpmpUPQB3RkGt98+gAwD0AwDm1NncVMU5FzVdSiEponw/e9DbKz+1NJSTe55//4A7mfbtj5NRKw8UKkT7YlKjOo+pNBJWKSr96BiLVgaOFpzlHanCoakqCSGJvbxs0xSkhZecObzz4N6SZfQwuEq2hv9K3OZmD4Rk1MhWXljOeWN/nkkFP2fgeJ0VSi6KCvTDANjQ6MFCwagYdBaqYUUnMm+AszxLlPHjdPKHY1VFy0tmgqkc88fn+nj1szgJqQwI8t7BcP0xWB20ru2t0PZh32ritgdRo/Lzp1BVUyMz44nPsvxtyPt0kWOurfEmGMYkzZGN6ZCC4MfZRwoBhRlZfD737saBgsXwte/PsAMA4Di1SCdejtHpEDmUo/T09K0Pg2zZ+vHpYRXXw3Ag2kfJyLa2jU1P2e8Cil0EhYBWZfrhmT5WvaV6sMifR5S6GT8eK0LVyetrVoHsCASlNBCpj60oIyDwY8yDhQDhtJS9x6DRYvgttsGoGFQfxxqDTeK3OvAZO72tIgIuPNOrUmU8YH+H//QHk59po8TESv2V2BrdxhHUUlRJA/v5fXSF4HZURlwpqWO2rOOOIs5zMy4tHF+79UnTCZXS27LlqBeMvsCvXFQc7SGtnr/MleN+hCnak9R11rnYbZiMKCMA8WAoLRU8xg0GPrsLFqkNU0acIaBlFC8Sj8WWwCpF3h1uhBae+nvflff5VFK+Otf4aBnRWHPtLb2aSIiuD7VZs/M7r3732TWQjEd7Gs4Cy1lWoIiWjmeOax7gyuozJ2rPz5yxL22d4CIy4gjIdcRapFSUrbLv9BCUlQSw5KH6caM1SCKwUVvjIOpQohvdL6AqQBCiNudxw1zFAq/8WQYLF48QA0DgJrt0FSoHxt6Y6+/mQkTtGoGZw+CzQZ/+pO+mZNX9LEiorXNSuV+famFsf2w1wxZABGax2FfQw1gh2atFWfIQgqdZGdr+tidSKn9rIN5yT4SRFIMXnpjHFyDJkXc+boaTbrtBcO480uh8IvWVi3ZzmgYXHgh3HLLADUM7BY484Z+LHkaxI/0abkpU7RW0M5YLPDss1oKgVe4S0ScPz+oiYiV+yuxWRwhheiUaJKG+dg32xQO2VdyztJOYUvHH0tLOdjbQm8cgKv34Isvgqp5YOzIePbLs7TW+afQaDQOjlQfodnS7GG2YqDjrTzTi0HdhULhgVdfde0ncOGFcPPNA9QwAE2sp91J20uEQd4yv5acPVurWnjlFcdYayv83//Bj37kRbMpd4mICxb4taeecAkpzPAhpOBM2jz273/eacBOvmgmMSoEra6NXHABvP462O3acVmZ9jMPkpRuTFoMSflJ1BVpeQFSSkp3ljJ86XCf18yMyyQzLrOrjNEu7RyoPMCsnFkB2bOif+GVcSClvDPYG1EojOzY4Zq7NX/+ADcMrM1Q+r5+LP1CiPK/f/SFF2oGwptOTcmbmuDppzUDIS2tm5P7OBHR2mql8oAhpNCbKgV3mMLZZ9L/HCebGqGtBiJTPZzUR8THaz/TvU5x+i1bgmYcgPbz7DQOQDPG/DEOQPMefPDlB13He8r3KONgkKISEhX9ktpa+Oc/9WNZWQM4lNBJ+adaH4BOwmIg5ysBW/6KKzTBJGfq6uB//9e190QXIUhErNhXoQspxKTGkJjv3xO+xWbhUHOrJq3cwZT4ZCh9z691A4YxtLB9O1j9a6vcHVkz9O6i2pO1tJxt8WtNY2jhQOUBLDb/+jco+ic+GwdCiHwhxEwhxAwlo6wIJHY7/O1vWpOhTsLD4ZvfBHMIk879xtoMFZ/qx7Iug/BY9/N95LrrtGRNZ6qrtRBDU5ObE/pYERECVKVg4Ej1ESx2K8TkA5BsjiQ3KhaqNkNrlV9rB4RJk/QSl01NAZa21BOTGuNSFupv1UJBUgFJUY68kDZrG0eqj/i1pqJ/0ivjQAiRJoT4vRCiDDgJbAW2AaeEEKVCiN8KIbzQPVUoPPPxx3DsmH7suusgLy80+wkY5Z+CzenJLSxW6w8QYITQPCzG8vrSUs1A0HUODkEiorXVSuVBfUjB+JTrC12qiFHpEBbNpLiUDoPD3j+8B+HhWu6BM8HWPDBUf5Tu9K9qQQjhonmgqhYGJ15/AgghRgE7gO8DGYANqASqOv6dCTwI7BBC+BfYUpy3FBXpY+agyQYv9SwaODBw6zW4ROcCDyRCwB13aJUMzhQVaVUMFovTQHGx/sQgKiIClO8px261dx3HpMWQONS/kIKU0kkyWUBMPlMSnPIMqrdoUtWhxhha2L/ftRQngLgLLTTX+FdhYFRL3FuxF7u0e5h9/mGz2Xj++edZvHgxKSkpmM1m0tPTmTx5MnfffTdvvfVWqLfoFd52ZTQB/wSGAuuBi4G4jvbKmUA8cCmwAa1B08tB2a1iUNPWpgn42J0+Z2JjtTK9AZ1nAFDxmRuvwUVBvWRYGNxzD4wdqx8/fhxWrOgId2/cqH9z4kRICa7zz/j0mnNBjt8hheL6Yp1iX0RsDmNSnfsBSCh5x69rBIT8fMjMdBzb7a75HgEkOjk64KGFUamjiDZHdx03tDVwsjZ43SYHEjabjauuuopvfetb7Nu3jyuvvJKHHnqIa6+9liFDhvCvf/2Lp556KtTb9ApvPQeXAjOB14ClUsrPpJRdQUopZZuU8hNgCbAKmC2EuCTgu1UMal5/HSr0MvvcfrvW3G5AY23RQgrOZF4cNK+BM2Yz3HcfDNOL23HgALz051bktr5NRLQ0W6g6qI//BzSk0MG4IeMx512rn1SzTVNODCVCuHoPgh1aCLAgUrgpnEnpk3Rjqo2zxiuvvMIHH3zAlClTKCws5OWXX+bJJ5/kz3/+M59++inV1dX813/9V6i36RXeGgfXA23AA1J6Vu7oeO+7gAW4wf/tKc4X9uxxfYhduBCmTXM/f0BRsVbfkjksBjKX9NnlIyPhgQcgR6+LQ80H2zl+sM2hxZOUpCXNBZHyveXYbQ7XUGx6rE7q11eMxsGUjCmQMgOinW+MUmuPHWpmz9a7wk6f1hJCgkTWdL3xVVdY539oIUs1YnLH5s2bAVi+fDmJia6hspiYGC66KLgew0DhrXEwHfhcStljyq+UshLY1HGOQtEj587BSy/pxzIy4MYbQ7OfgGJrhfKP9WN95DVwJjYWfvADSHeSARhVvpHyMjhV2DEwb15QExEhOFUKda11FNXppSAnZUzSbsA5X9VPPrsDmoN3I/aK5GTXWE8QvQfRydGkjNCHisp2+udBGT9kPOEmh0xOdXM1VnvwyjIHCqmpWp7LMWNG9QDEW4XEPLQbvrccBG7p/XYU5xtSwgsv6EvsTCatbDEyMmTbChzuvAYZfec1cCYhQTMQfvtboLiYlAbthnqmGGJiBJlBVkRsb2qn6pD++cJv4SNgf4W+HLAgqYCEyA5vRPI0iM6Flk71Rwklb8Ooe/2+rl/MnQuHDzuOv/hCK8kJknGWPTObsyccqpylO0oZcekIn9eLCo9i3JBxup+9J72Dt+992+frhIKvPvfVnid5YNmyZfzmN79hxYoVNDQ0cN111zFjxgzy8/MDuMO+wdu/xASgN/0569CSFBWKbvnsMzh0SD92zTVa3taAx9YKZUavwVIIj3Y/vw9ITYUf/hAm1ult/Y3V4zhRF1wVwfI95Ui7IyoZlxlHfLb/HxMuIQXnUjshIPdq/Qm1u6D5DCFl6lS99VtfrzcWAkzW9Cydh6auqI7m6sBWLbTb2j3MPH+YNm0aL7/8MhkZGbz88stcf/31FBQUkJqaynXXXcfbbw8cQ8lb4yACrVzRW+wd5ygUHjlzBlav1o+NGgWXXhqa/QScirVgc3KJhEWHzGvgTEZyO1dnbNU9pB7NWMiKFUHtJOziyg5ESKHd1s7hav1N1aXRUtJkiDHotJWE+EM6MhJmzNCPBTG0EJUURcpIfWjBX82DyRmTdb8/q92K1aZCCzfddBOnT5/mww8/5Be/+AVXXXUVdrudN998k6uvvpo77riDblL3+g298WH1/+9GMWCwWLSyRWf12OhouOuuoIe9+wZbm6vXIGMphMeEZj/O7NxJgrmFMR2Vfq0R8ZSkTKa+XuuA2R6EB8D2xnaqDhtCCr62Z3biSPURnTs7OTqZnHhD5qVb78EeaPK2ZWWQMFYt7NmjNccIEsaqEH/zDuIj4xmZou8k2mxVXRoBzGYzl156KY8//jhvv/021dXVrFy5ktjYWF566SXWrFkT6i32iLc5BwCPCiEeDdZGFOcX//63a4L2178e9BL7vqNind5rYIrSQgr9gY6ykCFDtFyPD+Q87B3JZcXF8OKLcPfdgdWWKNtdpgspxGfFBySksLd8r+54SsYU996IxIkQWwBNhY6xkndg9P1+78FnRo3S4jw1NdqxxaJ1GwtSOWnW9CwOrjzY9dRaV1RHU1UTsUN8l++emjmV4zXHu46bLc2OfI8O/InhDxbCwsK46aab2L9/P//93//NZ599xrXXXtvziSGkN89oopcvhcItBw7A2rX6sTlzYObM0Own4NjaoPwj/VhmP/EalJbCiRNdh/n5ELlUn4i4Ywe8/77xRP8o2VaiOw5EIqJd2tlboTcOXEIKnQgBOQbvQd2+0HoPhND+8J0JZmgh0TW04K/3wNiIqc3ahs3emwj0+UV8vGYQD5qwgpTS5MMrLNibVww8Ghq06gRn0tK0XgCDhsr1YG10HPcnr8EmfSKiGDOaW76fTrbhXr1mjb67sD+01LZw9vhZ3VjOrBwPs73nxNkTNLQ5pIejwqMYkzbG8wmJ4yHOkKEf6p4LRuPgxAmoCl6TqEALIqXFpJGbkKsba7EGLzTS33nllVf4+OOPsdtd5aTLy8t5/vnnAVi0aFFfb63XDIbormKAIKWmZ+AsJd9ZthjVt2X/wcPWBmVGr8GSgHde9AmLRSuZc2bhQqKi4P779Q0DQcsJKdE/8PtE6fZS3ZNSUkESsen+/zyMwjuTMibpau9dEAKyDe2xa/dAcwC+SV9JT4cRBoPF+DsKIMaqhXPF52iqdNeq03uM3oNmy/mbd7B161YuvfRScnJyuPHGG/nxj3/Mj3/8Y2688UaGDx/Ol19+yTXXXMMNN/R/jcDedmUcKoS4XgixTAgx0HvkKfqYDRtgn77qjK98BYYPpjZdlevB6mz9RGmiR/2BPXv0ghKxsV0SlGlp8O1v65NB29q0BMXGRvzCGFIIhNdASsnucr1k7/QsL3TXEsdruQfOlAY4htJb3MkpB8ntHJkQScqowFYtGI2DVmvreduI6aGHHuLZZ59lzpw57Nu3jxUrVvD000+zadMmLrzwQv7xj3+wevVqv6t0+oLedGX8H7Q2za8Br6O1af5tsDamGFycPQurVunHhg+HK68MzX6Cgq3d1WuQcVH/8BqAqz71nDla84UORo+Gm2/WT6mpgeeeA5uPYeSGsgbOFZ/rOhZCBCTf4Ez9GWqaa7qOw03hTBgyoecThYBswx/d2R3QUuF+fl8wc6bWzrmTmhqtO1aQMP78/c07yE3IJTXGoZEhpaTV0trNGYOXvLw87r//ft544w2OHj1KfX097e3tlJWV8d577/H1r38d0wApx/K2K+OtaO2YBXAEONrx7weFEIMpWqwIEqtW6UvkoqK0cMIA+X/iHVUbDF6DSK0tc3+gshKOHtWPuVFEXLwYjOHQY8dg5UrfLmv0GqSNTSMq0f8YkjGkMH7IeCLDvZTUTJqsqSZ2IaHsA7/35DPR0ZookjNBTEzMmuYaWmis8N09JIRw8R40WfwLVShCj7cfzd8ErMDFUsoJUsrxwGVoYkffDNbmFIODI0dg50792PXXa67sQYOtHco+1I/1J6+BIRGRESNwyULs4Gtf07wIzqxfr716g5QyKCEFwCWkYGwE1C1CQPYV+rHqL6C1OgA78xFjaGHnTi2uEwQiEyJJHa1Xw/TXe2BUS2yxtpy3oYXBgrfGwWTgTSllVwFaR4vmNcBUj2cpzntsNnj1Vf1Yfr7bh9aBTdVGsNQ7jk2RkNlPvAZWK3R0i+uim19AeDjce69Wgu/Mq69qXgRvqSvUS/Sawk1kTsv0fgEPVDVVUVLvMDqEEJ5LGD2RMh2iMpwG7K7GXV8yfrzW/KKTtjYtRyRIuFQt+Jl3MDJlJCbhuJ1IKWmxnL9VC4MBb42DZLRQgpEjQFLgtqMYbKxdC2WGh5Jbbhlk4QS7xdUtnXEhmONCsh0X9u3Tl4hERblK9xqIi9MqGJzl/+12WLECqr18wC7ZqvcaZEzKwBxt9jDbe4xe8gyeJgAAIABJREFUg1Epo4iL6OXPWphccw+qN0N7EPWju8Nk6lPNg8xpmbrQQv2Zer9DCxHhesX887lqYTDg7Ue0CXDXcsuCEjxSeODcOTD2GZk/H4YNC81+gkal0WsQ0X+8BuAaUpg926uWlzk5mpy1M01NWgVDaw/5ZtIuXWroAxVSMOYb9Cqk4EzKBRDpFNuSVlfJ677EaBwcORK0ZheR8ZGkjglsaCEyTP83pUILAxvVW0ERNFav1t9EoqO1rrSDCndeg/QLwdxPmpLW1Li2veyFPO/UqVqXTGdKS+Fvf+u+2q76SDVtDY6YeXhUOOmT0r2+rifOtZ7jxNkTujFjMpzXmMIg63L9WOUGvaHXl+TkQJ5ThbiUsHVr0C4XaEGkcFM4YSaH9p0KLQxsemMcPCqEsDm/gEcAjOMdL9We6zzmxAlXLZdrroH4fnLPDBhVm8DiKNXDFAFZ/ait5Oef6+/i+fn6G5AXXHGFaxRi71546y3P5xgTEbOmZxFm9l801SiXnJ+UT0q0Hw050uaC2SkyKi1Q/onv6/lLH2oeZE7NRJicQgsl9TSUNXRzRs/EmvUJuKpqYeASzN4KgymqrOgFdju88op+LCdHK5MbVNgtUGr0GizuP14Du10zDpzxIRNUCFi+3NWmeO892L3bdb7NYqNst95FHbAqhTL9BX32GnRiCoesy/RjFevAGqKb2qxZ+oSc8nI4dSool4qMjyRtjL5kyN/QQoxZ3z+kxdKiei0MUILZW0EZB+cpGzdq3f2cGXRJiABVn4OlznEszP3La3DgANQ57S8iQrv5+EBEhJagaPT8/P3vrgmnFfsqsLY6HIeRCa43IV9otjRzpPqIbsxYQucT6Qsh3Okbs7dB+Wf+r+sL8fEwaZJ+zCheFUACXbUQERbhImF9PvdaGMgMto9rRYhpbIQ339SPzZqldacdVNgtrrK7GYvBnOB+figwJiJecIFfTSySk+E734Ewp+hAWxv86U/Q4vT576JtcEGOzn3tK/sr9usS3DLiMsiM8780EpMbo67iM7CFSOXPmBOyfbv+BxxAjKGFhtIGGkp9Dy0IIVxDC+0qtDAQUcaBIqC8+SY0O1UwRUZqgkeDjsoNbrwGl3me39fU1bk2suhFIqInRoyAm27Sj1VUaB4EKcHSbKHyQKXu/aBVKWROC5xGffoiCHO6qdmaoWKt5/nBZMIEzRLrxGKBbduCcqmIuAjSxuq9Ov56D2Ii9KGFVmurCi0MQJRxoAgYRUWuD6tXXQVJg00Jw9buQdegH3kNNm/WJ7Ll5EBBQUCWXrwY5s3Tj+3dC++/D2W7y7BbHU/3semxJOYn+n1Ni83CgcoDujGfSxjdEeamrXb5J1qXzb7GZNJqfp3ZuDFoiYmB7rVgNpkxh+n1LJTmwcBDGQeKgCClloTo/PmVkQFLloRuT0Gjcp2rGmJ/8hpI6WqlLVyoZRYGACHg1lu1wgdn3noLdq5xlUsOxNP9oapDtNsczTmSopLIT8zv5gwfyLhI66LZibVRU74MBfPn639fxcVw+nRQLuUSWijzP7RgTExUxsHAQxkHioCwZYtrUvXNN+ubzQ0KbK3ueyj0lwoFgMOHNX2DTsxmTfgogJjNmsRynLMwYWsr2z+oodUpPB6skMLUzKmBb3sbHqP9Lp0p+0jLL+lrUlK08IIzQUpMjIiNYMi4Iboxf0MLxrwDFVoYeCjjQOE3zc2a4JEz06ZpcvGDjoq12hNlJ6ao/lWhAK43kenTISbG/Vw/SE2Fe+5xPOCaq0qxWiSHDoHdBkn5ScRl+C8hbZd2F32DgIYUnMlcqmlVdGI5B1WbPc8PJsYckW3bgtaMyZ0gkvQjjGEOO39DC0KIbl8vvPBCqLfoFYPtuU4RAt5+Wy/dbzbDjTeGbj9Bw9qiPUk6k3lx/+m8CNovYq/+RhqIRERPjB0Ly5bBv/8NEVVaSKGpCY4dh2VfC4zX4HjNcV3Ge4w5hlEpQSp/McdryYnOQkhlH8CQBZqiYl8yaRIkJmo65KAZBtu3B6VrWebUTEwvm7DbtHyRxvJGGkobSMjxPY8m1hxLnc2RtNtkaSI+sh952ILML3/5S7fjU43tufspyjhQ+MWZM1pzJWcuv9y1o9+goOJTLYu9k7AY1yS2ULNli9YKs5OMDBg5MqiXvOQSOLGvkVMbHDeCqirB8aZsRgRgfWOjpckZk3UyvQEn81JNCEl2aDW0n4WarTBkXrenBZywMC3z832nktmNG4NiHJhjzKSNS9NVmpRsKyHhOt+NgxhzDHWtjr+JNmsbVrvVRQdhsPLoo4+Gegt+ocIKCp+RUmvj6+x9TEuDy/pRbl7AsDa7NuXJvESLU/cXpHQNKSxYELBERE8IAQuHlxDr5ECxJqay5sOoXrV4doeUMnCNlrwlIlHzFDhT+j6EoomQ0RAoLHRVGAsQxtDCmS/OIO3+hRYiwlSnxp5oamri17/+NVOmTCE2Npa4uDjmzZvHypUrQ7ovZRwofGbHDjh+XD92001aWGHQUf4x2J1EccJiIbOflWIcPQqVThoDYWGuWv1BQEpJ5e4Sxo93JKBa0nOw2+HPf/avseDpc6epbXEsYA4zM35IHySzZF2G7uOxrRJqdgT/ukbS0mDcOP2YsRIlQGRNzyI80vFU31rXStWhKr/WNFYtKEEkPbW1tcyfP5+f//znmM1m7rrrLu644w4qKiq4+eabQ+p9OD/8O4qA09YGq1bpxyZOhMmTQ7OfoGJphPJP9WNZl2m18f2Jjw2ejWnT+qTT1bmiczRVNhEVDWPGwMHDJixpWYCWArFiBTz8sG9GozGkMDF9osvTaFCITNGaMlU79aYofQ9SLwi6J8aFRYu0CpROtm7VlMUiAvtzCI8MJ3tmNqc/d5RMnv78NOkTfe+mGRsRy3fe/Y5+zByLSfTf59LnvvpcQNZxd2MvKChg+fLlXccPPPAAe/fu5Xe/+x0PPvhg13hLSwtXX301jz/+ONdffz2TjJLafYAyDhQ+8e67etn+8HD42tf6/nOzTyj/SNPb7yQ8XhM96k+UlWm9FJzpI5EJZ7nklFQYuyidrXaHJVBYqIWfbr+992sHvNFSb8i+HKo309WtvrUMavdASpDDGkYmT9aMvM6s35YW2LkzKF6hvPl5OuOgYm8FbQ1tRMZH+rReZxtn5zJGq93aNwZeiHnsscdcxhYvXtxlHFRWVvLKK68wZ84cnWEAEB0dzZNPPsnMmTN55ZVXlHGgGBhUVMAnhq62l1wC6b4/YPRfLPWuMrrZl0OYbx+WQcP4Cxk2DIYPD/plpV1Ssl0vfHT53bm0fKFXb960SRNo7E3hRHljOeWN5V3HJmFickYfuqai0iF1lpaM2Enpu5A8tW+t4PBwzRD4yKlSZuPGoBgHycOTicuIo7FCK9e12+yUbCth+FLf/5bCTeHnpXHQUynotm3bsNvtSCndehnaOspWDzt7jfoQZRwoekVnEqJzQnxyMlxxRej2FFTKPgS7Q5kPcwIMWRS6/bijvh6++EI/dsklfXIDqz5aTVu9w6sSHhVOxuR07hwHTzyhT4F45RVNxdlbm8XoNRiTNsYlhh10sq/QGwfNxXDuICRN7Nt9LFigNw5OnIDSUsjO9nyODwghyJufx+HVjhtS8efFDFsyzGfRqXBTOG04/kZs0oZd2vt1aKEvqOkQKtu6dStbt271OK+xsdHje8FEGQeKXrF/Pxw6pB+74QatwdKgo70OKtbrx7KvhP721LN+PVgdLZJJTdXyDfoAYwfGrGlZhJnDiDFrHRyfeALaO2wrmw2eew5+/nNI8KJCzl2jpT4nOguSp0PtLsdYyduQOKFvvQcZGTB6NLryj02bXLtgBYDcObkcefNIV6VCfUk954rOkVTgW5OU57/6POWN5bRZHQZCUlQSiVH+99wYyCQmat//j370I5566qkQ78aVfmO6CSFyhRB/E0KUCiHahBCFQoinhRDJPZ8NQohYIcRtQoh/CSGOCCGahBANQogdQoiHhBD97BN94GG1wuuv68fGjIEZM0Kzn6BT+gFIJ+nciGTXErdQY7HAunX6saVLteY9QcZmsVG2S9+kx1kuOTsb7rhDf05dnVbB8P/be+/4OKs73/99ZtSL1SzL6nIvuGBc6cY4tunYhJLCAoFACFlDwu7Nbu5NbrL3tzebvdkAISQLSQgEklBCsSHGNsG429gYG3CvsiVbtixZlmTV0cz5/XEkZs48MyqjRzMj6bxfr3mN58xTjjwzz/M93/L5urtQ0q1pqqH0fKk2NnX41N5MN3TyrtdfN5Sq3INwc5Wfx2rrVvX520xCWoIlCdE3DyEULG2cXaZqYfbs2Qgh2NBHsti9JSqMAyHEKGAHcB+wDXgCOAo8CmwRQnRHUudK4GVgIbAbeBr4C5AP/Bz4UAgRZenl/Yt163Q3sRADOAmxtcbadCfvenBEWZ3m1q3g63ZMTLR29OsjKndX0tbs9VjEp8Zb2v/OmAEL/NSlDx2Crkq4/b0GIzNGkp4QofaeyYUqz8CX8mXh1z24+GI0MYmGBti5M/j2vaDo8iLt9cltJ3G3ht4bwT8c5HK7tEZag5Hc3Fzuuusutm7dyk9/+lPcASzmw4cPc/z48QjMLkqMA+DXwDBgqZTyVinlv0gp56GMhHHAv3fjGKeBrwO5Usovtx/jQWAs8AlwGfBI30x/4NPQAO++q49deaWKIQ9ITq7wKuQBxGXB0DAr5HWFlNbyxSuvhITw2MD+IYW8mXlad78OFi9WMsu+rFtndXj4EqjRUkTJvwXw+duaK6Bqa9DN+4TYWGsSYh+tOodNHqZVKLQ1t1GxM/RWzk6Hk4QY/XtpBJHgN7/5DTNnzuQHP/gBEydO5P777+df/uVfuOeee5g5cyZjxoxh+/btEZlbxI0DIcRIYAFQCjzj9/b/BhqAu4UQnQrYSyl3SSn/JKVs9RuvB/6r/eVcO+Y8GHnnHdVgqYOEBLj55sjNp09pqYazfkIz+TdAtMm+fv65Kh3pwOEIW/lia0MrZz47o40F68DocMADD1gltV99Ffbvt27f0NrAwWpdWrHPVRG7IilP6R74cnJ5+Ds2+ismHjyofwdswuF0UDCnQBsr29w7ZUZLaKG1oVfNnQYCaWlpbNiwgaeeeorMzEz++te/8uSTT7J27VrS0tJ48sknmRehvvcRNw6Ajr98tZS6n679xr4JSALm9OIcHb/gtk63MgSkokKt9Hy5/vqw6OtEhpN/A3y+ivHZkNWbr18f4e81mDFDlY6EgRMbT+Bp8/4fJWcnd5qwlpoK3/62nrjaoaB41k+E77Mzn+HxuRTkpeYxLDkK6mTzbwThYyC21kDluuDb9wW5udZeGX2kmFh4WaH2ump/FY1Voa/2E2MTtddtnjZckWiH3cdIKXtk9MTHx7N06VK2bNlCbW0tzc3NHD9+nL///e88+uijZGZm9uFsgxMNxsG49udgKuwdAr1je3GOb7Q/rwy2wY4dO7pstWl7//h+wl//qi7kHQwdqnLeBiTNlVC1RR/LvzH8Hfm64sQJLI0LvvSlsJxaeiTH1+lx0OKrirv8fRQUwDe+oY81NMAzz0CzjzL11nLdXR/xkEIH8Vkw7Gp97OQKcDcH3r6v8BeL2LxZr1axidS8VDJG6MZm2ZbQvQeBQgtGTjl6iQbjoKOepTbI+x3jIWUjCSG+AywCdgHPh3KMwczevVbhvdtu82roDzj8vQYJOUoIJ9rwFz0aOxaKigJvazNnPj9DY7V3BemMdVJ4eWEne3i5+GK49VZ9rKICfvtbZYBWNVaxv0qPNczMn9nrOdtG3nXg8HF/uBusDbn6munTIcknwe/CBdjVN9UT/p9r2aayXjVjSo7TQwuNrsZBH1qIVqLBOOiKjuVIj79BQoglwJOoZMXbpJRBfVjTp0//wh3U2WMw4fFYSxfHjAlbCX34aTqtC94A5N8E0SbWUlMD/klKYfIaAJR+WKq9zp+VT1xy9yuFFy2CmX73+9274a23YEuZ7rUZkTGCvFR7hX56RWwq5PqVX5x+H1z1YZxDLMyerY/1UWghf2Y+zliv16yppomqA1UhHy/Jr4tpm6dt0FctRCvRcNXr8AwEU8QY4rddtxBC3Aq8AlQCc6WUR0Ob3uBlwwYlwtaBEHD77QO0dBHg5LtoNmhiHmRGoYjDmjV6nCcnB8KkvV5fUc/ZfXqSQMk1JT06hhBK/6C4WB9ftdrD6x9t0sYuLwxPWWaPGD4fYlK8rz0tqilTOPEPLezbB1Wh37SDEZMQQ+70XG3sxMbQNQ8cDocl9+BCa2QUAA2dEw3GwYH252A5BWPan7vdGV4IcTvwOnAGuFpKeaCLXQx+NDbC8uX62Jw51gv6gKHxFJzza8kbjV6D5mZr+VqYpJIBSteWaq8zR2WSVthzpbvYWJWgmOaz6/mYA+zYW0Nd+yI81hnLjLwZvZhtH+FMsAojVa5TVS7hIj9f9c/wpY+8B/6aB6d3naa1IfTVfkpsiva6wdWg9V4wRAfRcOXr6GqzQAj9SiyESAUuB5qAbhUVCyG+ihI/OoUyDA51sYshAO+9p2vrxMVZY8UDipPvoHsNCiAjCuMnmzaprnwdpKQoqy0MtDW3Ub6lXBvrqdfAl/R0ZSB05K+cjtuIx6PyXFpaYXrudMsqM2oYdrXSvuhAuqF8efDt+wJ/78GmTV1LT4ZA5phMkrO9uQKeNo9F46InJMYm4vRJ8JVSGsXEKCTixoGU8giwGijBKlL0EyAZ+KOU8otvjxBivBDCT1YFhBD3AC8BJ4CrTCghNCor4YMP9LFFi9TFfEByoVTXzgcouDn64icej/WDmTtXLcPDQNmWMtpafBQRh8STOy23kz26pqREhRhcooHqWJVU19oKe/bAzOFRGFLowBGjviO+VH+kPFDhYsYMXfCqrk5vhWkTQghLWWPZptCrFoQQpMTp3oP6lvpBl9MV7UTcOGjn26jcgF8KId4WQvxUCLEG+C4qnPA//bbf1/74AiHENahqBAfKG3GfEOLHfo/H+vwvGQC8+aa162IY893Ci5Rw/C/6WHIxpIexNXB32bkTqn1c1zExyjgIA1JKSyJiydUlOGJ6fwmZNQtGXLENj48Mibsum83vjCGq7xdZs1ReyhdIKH87fOePj1f/eb70UWih4NICrVS1tqyW2jJrGlh3b/CpcbpISpunjea2MJeEDlDsMrKiwjho9x7MAF4AZgOPA6OAXwKXSim7E8wrxvv3fAOlruj/MMZBFxw8aJVrX7xYhRUGJFWbVSMdXwoWR5/XIJBU8pw5YVOiqtpfxYUz3jiTcAiKrrSxdLJwk6agmNN6GTt2CFaEOc+vRwgHFPjF2s5/CvVHwjcH/9DCnj26AWkTiRmJZE/M1sb8vQdOpxNXNxtBOR1OS7+F+tYwVnwMYFwuF05n73VZosI4AJBSlkkp75NS5kop46SUxVLKR6WU5wJsK6SUwm/shY7xTh4lYfuD+iEeD7z2mj42YoR1cTJgaGuEsjf1sYxLIG1CZObTGUePwrFj+tj8+WE7vb/XIG96Hglp9vRwKKsto7yujPHjVF8hgSCnVUkVL18On3zSxQEiSfoUSBmpj5W/RdhcHkVFepawlH2nmOineVD+UTlul9fFmJqaSl1dXbePlxqvG7ZNriba3EbEtrfU1dWRasOiIWqMA0Pk2boVyvxCiXfcEX2LaNsoXw5tPlmXIhaKbo/cfDrD32swaZKS0g0DjVWNlj4KvUlE9GdTmSpfdDrhoomQ47iIeOlV5vvDH6zfy6hBCChYoo/VH4LaPeGbg3+/hbVrdclJmxg+dbimZ+FqdHHmU+/3IjMzk5qaGqqqqmhtbe3SvR3vjCfWqefLGO9BaEgpaW1tpaqqipqaGlsklweqzp2hhzQ3KxEaX2bOhJEjA2/f72kog8q1+lje9RAfGR3zTjl71qqAF07Ro3Wl2oU+rTCNjJH29HBwuV18VO4VnkpIgKW3Xs7qF71SDq2t8Otfww9+EKX9PIaMgbRJUOsjJVr2FqRdFB7LetYs9ePt6IzW2AgffgjXXWfraRwxDvJn53NsjdeDVba5jLwZKu8iPj6eoqIizp07R2lpacAWxP40tzVrEsoO4SA9IX3QStX3BqfTSWpqKkVFRcT7NjEJEWMcGABYtUolO3cQG6tyDQYkXyQh+qxs4rMhN0qzLj/4QHdTFxTAuHHBt7cRd6vbInpTck2JbRfvT898qrXuTY5L5qZZUxjqgpdf9m537hz85jfwve9FqXR3wa26cdBUrnQzssIg/ZyQoJqdvPOOd+z991WHThtuEr4UXV6kGQdn956lqaaJxAxVchofH09ubi653fRqtbS18P2/f58ml7c8975p9zGnIAobnQ0yTFjBwLlzVq/1/PnWFrsDhuptcMEvaaz4TnCEpySwRzQ0qPp1X8IoenRy+0lcjd4ks7jkuKCtmUNh4wk9Pj6nYA4xjhiuvBKuuUbf9sgReP55XRwyakguhEw/Q6D8bfCEKYY+bx4k+mhCNDSo8ILNDCkYQnqxt6ZZStmrVs7xMfFcVniZNra2dG3IxzPYhzEODLz5JvgmGQ8ZonQNBiTuZij7qz6WPgXSwyM/3GPWr1d+9Q7S01V9exgIVL5YeHmhprXfG6obqy1Nlnzlku+4Ayb45Ybu2AF//nP48v16RMHNaJfUlio4uyno5raSlGRtlbp6NbS02H4qi+bB5rJelc/NLZmrvT5Wc4zj548H3tgQNoxxMMg5etTaw+fWW3VtlQHFyXfB5RM/ETFQdEfk5tMZbW0qduzLvHlh86vXHK3RatmFEJTMLbHt+FvKt2g3leL0YvKHeL0SDgc8+KBqHeHLhg2wbJlt07CPhGEwzK+08NS74Lb/Bh2QefP0H+6FC7Bune2nyZ+Vr+lbNFY1Un0w9PLJYcnDmJg9URsz3oPIY4yDQYyU1tLFwkK49NLIzKfPaaqA034Kg7kLICE78PaR5uOPodZHaCY+3lrX3of4ew1ypuSQlJUUeOMeIqVkc9lmbSxQk6WkJHjsMSXE5ct771m7VkcFeTeoqpcOXHVwZk14zp2crAwEX1av1j1PNhCbFGtRxuyNYiJYvQfbT23XEhUN4ccYB4OY7dutpfN33KFWbAMOKeH4K4BPwDouE3Ltzei2DbcbVq7Uxy67TN0tw0BzbTOnduhSwHaWLx6oPkB1o3e1GeuMZWZ+4OS9zEx49FF17/Pl9ddh8+aAu0SOuDTVtdGXilXQFqYb3fz5ehJifb0KTdmMv+ZBxScVvWrGNDlnMllJ3iQnl9v1RYmrITIMxNuAoRu0tqpcA1+mTYOxwXpj9ndqPoE6Pb5N0R3gjFLpx7VroaLC+1qIsIoeHV9/HOnxuvxTclIYOn6obcffdEK/8E8bPs2imOdLbi4sXWpNvn/pJfj0U9umZQ+5C8Dp87e4m5SBEA6Sk62ZnKtW6UlFNjB0/FASM70JkG6Xm6Pvh97KxiEcXF18tTa2rnQdHhmN2aeDA2McDFLeew9qaryvnU5YsiT49v0adwuceF0fGzIBMi6OzHy6or5eL0sDFesZat/NuTM8bR6Or9cTwuwsX2x0NfJJhS57eHlR102WSkr0Lo6gKheee07JfkcNMUmQ65fRe3oNtNYE3t5uvvQl3Yqqq7O2+e4lQghGXKO3jD625hitF0L3HlxedDkxDu+HW9VYxZ7KMIpJGTSMcTAIOXXK6rGeNw+GDYvMfPqcU+/5XZgdUHxX9Eo/Llumt2VOSAir6ETFzgpa6rxJdDHxMRReWtjJHj1j+8nttPmU+GUlZTEuq3u6DePHwwMP6B9dWxs88wwcj6YE95xrINanjal0QWmYyixSUuBqfRXeF96D4quLiU/1GiFtLW0cWR16X4mUuBRm5OmVOCYxMXIY42CQIaUSl/GtFU9LgxtuiNyc+pTmSjjtJ+IwfD4kDo/MfLqirMyqjX/DDaq+NEz4JyIWXFpATIJ9FRL+seTLCi/rkVdi2jS4+259rLkZfvlLOHMm8D5hxxkH+TfqY+c/U22dw8GCBXor7/Pnbe+5EBMfw6iFo7SxYx8eo6U+9OqMa0boIZE9Z/dwtuFsyMczhI4xDgYZGzYoMRlf7rpL108ZMHQkIUofIZrYdMiPUktISnjlFX11mZNjzUDvQ2pP1HLuiN7rzM7yxfK6cq2GXQhhEcHpDpdfbg2DXbgATz6ph8siSvblkKLfPDn+KrRaWx3bTmqq1XuwcqVys9hIydUlxA/xeg/crW6OrArde1CSXkJxureRlJSSdcftL8c0dI0xDgYRtbXWJMQpU9RKbEBy/jNrA5yiL4MzSkUcPv4YDh/Wx+64I6x6waVrS7XX2ROySc21r6GBf/nihKETyEwMrZ/FwoXq4cu5c8pAuHAh8D5hRThgxD16aaO7EUpfDk94YeFCq/fAX22zlzjjnIxeNFobK11bqoWleop/WeOmE5toddtbjmnoGmMcDCJee00PZcfHw1e+Er2h917hccEJPxGH1DGQGR51wR7T0gJvvKGPTZqkHmGitaGVk9tOamN2li+2edrYWr5VG+tOImJnLF6svAi+nD4NTz/dJ40Je05iDhTeqo+FK7wwZAhcdZU+9t57tnsPiq8q1tp3u11uDq883MkenTMzbybJcd661UZXI9tPbu9kD0NfYIyDQcLu3Wph6sstt6ga8gFJxSolX/sFUZ6EuGqVtXzkjvAqN57YeAK3y9tJLzEzkZzJOZ3s0TM+O/OZJmyTFJvE1JypvTqmEPD1r1u9X6WlqlGTzffB0MiZF7nwwoIFuueppga2bLH1FM5YJ6Ov070Hx9cfp/l8aNZZrDOWK4r0NtQfln7YK4lmQ88xxsEgoKVF6dH7UlxsLYceMDRXwSm/coycuZBUEJHpdEl1tVKy8+Xaa626wX1IS32LZbVXMrcE4bDPmPKqmjoKAAAgAElEQVRvsjS7YDaxzt43u3I44P77VSWDL/v3w+9/HwWNmiIZXkhPt6pq9oH3oOiKIhLS7fMeXFV8lZakWlZbxrHzxzrZw2A3xjgYBLz7rrr/dNCx2hqQSogAZa+r0rEOYlIh/6bIzacr/vpXa+erMJePHFh2QOu+GBMfQ9HlRbYdv6aphr1n92pjgeSSQyU2Fh5+WBm9vnzyCfzpT1HQqCmS4YWFC3XvQXU1bN0afPsQcMY6GXP9GG3s+IbjNNU0Bdmjc4YmDWXyML0ZmilrDC8D9fZgaKeszKpBf+21UGTfdT+6OL8HanbpY4VLlDBNNHLggLqD+bJ4cVg7X9WeqOXExhPa2NgbxxKXYp965PtH39fcwoVphRSm2aedAOq/bOlSq8Nl40blOYu4ByFoeOF83543IwOu0N30vPeekui2kaLLizTVRE+bp1feA//ExB2ndlDXUhd4Y4PtGONgAOPxKHlZ34tiZibcfHPk5tSnuOqh9CV9LHkEDI3STlIejypd9KWkJKydr6SU7H5lt3bjTslJYcS8EZ3s1TOqGqssqz7/mLJdpKTAd79rbdS0fr1SUrRZB6hnBA0vhMG1sXChymPpoKoKPrLXa+GIcVi8Byc2nKDpXGjeg4nZExmW7FVma/O08fb+t3s1R0P3McbBAGbtWqtq3Fe+YtWnHxBIDxz5nZ8SooCSKC7HWL9eyVX6cuedYZ3vyW0nLboGF91xkdaSt7cs278Mt8e7Ss1MzLQ1pOBPRobq5JiSoo/v3AlPPQWNjX126q6JVHghM9Na1rFihe3ulMJLC7XOnR63h0PvHQrpWEII5o3QNT42ndhE6fnS3kzR0E2McTBAqamBt/2M7EsuUboGA5Kyt6yNlYZfC8nFgbePNA0NSibZlzlzYOTIsE2hraWNfW/u08ZypuQwbJJ9OtrHzx9n28lt2tgt42+xJRGxM4YPh8cft3oQDh2Cn/9clfxHjEiFFxYt0hONzp6FbduCbx8CjhgHY27w8x5sPEFjdWgW2VXFV5GbqreH/svnfzGVC2HAGAcDlFdeUVUKHSQkqEXpgKT6Yzjtl+2fOhYKoriT1PLl+hI2Pj6s/RMADr93WCs3c8Q4uOj2i2w7vpSSN/fpqlsFQwqYlT/LtnN0Rl4efP/7qqOjLydPws9+pvQQIkKkwgtZWartty9/+5vt3oOCOQUkDfV6D6RHcmhFaN4Dp8PJnRfpF67S86VsKbe3HNNgxRgHA5Bdu9TDlyVLVFXTgKPxFBx7UR+LTYfR3wSHM/A+kaa8HNb5ScJef31YP6CGygaOvK/L3I6cP5LkYclB9ug5e8/uZX+V7s1ZMmEJDhG+y05GBvzzP8Mov4X6uXPwn/8JR0PvMtw7IhVeuO463XtQWQnb7RUYcjgdjL1R7/1etrmMhrMNQfbonAnZE5iWqwtZvLnvTZpcoeUyGLqHMQ4GGM3N8Je/6GMjR1qF0gYEbY1w6Dfg8ZFWFTEw5lsQG75GRT1CSnj1VX2FmJ0N8+eHdRp7Xt+Dp827YkxIT7Akk/WGQF6D8UPHMzF7om3n6C7JySpJcaqf3lJDA/ziF/D552GfkiIS4YWhQ1X4ype+8B7MLtAMzd54DwBun3i7Foqqb6nn3YPv9mqOhs4xxsEA4+239Xiqw6E0DaI1Jy9kpIQjz0NLpT5efBek2Jdpbzs7d8LBg/rY7beHtX9C5e5Kznymty+csGQCMfH2zeGjkx9RXleujS2ZsKRH3RftJDYWvvUta0WfywW//jVs3hx4vz4lUuGF66/XvQdnziiFThsRDmHxHpRvLaehMjTvQVZSFgtH6Y001hxbQ0V9RchzNHSOMQ4GEKWlqkLBlwULID8/ErPpY06+C7V+S77sK2DYlYG3jwZcLiV45MvEiWHNEvW0edjzmt6MKnNUJvmz7PuSuNwulu3Xky1n5s/Uuu1Fgg5D2V9fyuOBF19Upf9hz3OLRHghOxtm+eV9LF9ubdfaS/Jn5pOS4y0ZkR7Jwb8d7GSPzlk0ehFZSVlfvPZID6/sfsUkJ/YRxjgYILjdStPA93cydCjceGPwffotNZ/BKT+XYnIJFH8lItPpNqtW6VKVDofqnxDG1fSxNce4cMbbslAIwaS7Jtm6ol9bupZzTd7ySKfDya3jb+1kj/AhhNL5+OpXrf/tb7+tIj5hF0uKRHhh8WIVb+nA41Fa0zbWeQqHYOxNuvfg5Ecnte9fT4h1xnL7xNu1sf1V+9l1eleQPQy9wRgHA4QPPlB5br58/et6x9YBQXMlHH1eH4tJVXkGjvC55ntMVRWs9Ov3cM011lT6PqS5tpmD7+ort6IrikgrSrPtHI2uRlYcWqGNzS2Zy9Ckobadww6uvhoeesgazfnwQ/jd78LcsCkS4YX0dLjvPn2sutq6wugledPztJbfUkrLd7AnXDz8YsYP1ZtovL73ddPSuQ8wxsEAoLoa3nlHH5s9GyZMiMx8+gx3i0pAdPtmKTtg9IMQlxF0t4jT2Ai/+pUuz5eaGna3zv639tPW4r3rxSbGMu6WcbaeY+XhlTS6vKvPhJgErh9zva3nsItp0+DRRyExUR/fsUOJJTWFMxk+EuGFyZOVlrovn3wCGzbYdopAuQentp+ivqI+tOMJwV2T7tIqXqobq1l12N6cCYMxDvo9Uird+FYfwzkpSeW4DSikhGN/hCY/RcGi22DI2MD7RANtbap3cIVf4tQtt6gPKkzUHKuhbEuZNjbu5nHEp9onl1nTVMMHRz/QxhaNXkRKXEqQPSLP2LGq1NG/ivTgwQiIJQUKL5T+CepCz/LvkiVLrI1WXnvNqtzZC3Kn55Kap3sP9r6+N+RcgdzUXIty4qojq6hurA6yhyEUjHHQz9m8GXbv1se+/GW1MB1QnH4fzn2sj2XOhJxrA28fDUipMt38qxNmzLCmzffpNFT/BF9S81IpvtreBMHlB5bT5vF6JtIT0rl2ZBR/Pu3k58P/+B/Whk3l5fBv/2bti9VnBAoveFrh4NNwoY8EGWJi4Jvf1DXVXS7ViKLVHle9EIJxN+seqso9lb0KL9w49kZS470XOZfbxet7Xw/5eAYrxjjoxxw8qNrR+jJmjFUErd9Tux/K9Jp5EgtgxN3RXaO5bJlVnnb0aLj33rDOu3xLOedL9SXwpDsn4XDa9/M/WXfSolp307ibiHPa19mxL8nKUmqK/urVDQ3w7LPwhz+EKcyQmAPFd+hjnhbY/xQ0HA+8T28ZNgy+9jV9rKJCeRBsYvjFw8kclamNHXz3oKWktrskxiayZIKugLqzYif7zu4LsoehpxjjoJ9SUaG81b5dV2NjB6CmQUs1HH4O8HFBOpNUAqIzijtIbdigauN8ycmBb387rFmiriaXpX9C7iW5DB1vb4Lgm/ve1NzEuam5XFbYv6zU5GTVsGnyZOt7W7cqL4K/E6hPGHYVFPjlH3iaYf+T0FAWeJ/eMnu2VRxpwwaVgGEDQggu+eYlljDWJ7//JGTtg0sLLmVEhq5p8uqeV7UmX4bQMcZBP6S+XuW3+Vcd3XuvajgzYPC44PCz4Pa9eAgYdT8kZEdsWl2ye7dKBPElNRWWLtXLx8LAwXcP0lLvbbLhjHUy8cv2qhQerD7I7ko9bLF4/OKwyiTbRXy8st+WLNE7HIOSXP7FL5RURZ+3fs67DvJv0sfcjbD/CWg82Tfn/OpXlRfBl5deUpU2NpCYkcj0B6cjHN7VS1tzG9t/vV1LlO0uHcmJvlTUV/Bh6Ye9nqvBGAf9DpcLnnnG+ntdvFiFsgcMUkLpn62u1PybIX1SZObUHU6cUPFa32L52Fj4zneU8EQYqT5YzbE1x7SxUQtHaS11e4uUkjf2vqGNjc4czZSc/tv+0+GAhQvhBz9QzZt8kRLefx9++lNr6bDt5N0AudfpY+4GZSA09YEyYHw8PPigXt/Z1KRqO932rMazxmZZjNP6ino+ffHTkBIUS9JLuLxIb0X9zoF3qGup69U8DcY46FdICc8/D8f06z1XXKEuZgMGTxuUvgxVfpq26VPViipaOXcOnn5ab4cphEr4KikJ61Rqjtaw7VfbkB7vBTcxM5HRC0fbep4dFTsoPV+qjd028baIySTbSUGBMhC+9CVrqO7kSWUgrF7dh6JJQkDBLTB8gT7eVg/7fwFNocXrO6WwEG67TR87dkwpKNrEiHkjLIqcp3ac4uj7oSVdLh6/mMRYbz1qc1szb+17q1dzNBjjoF/x5pvWzOmJEwOrvfVbXHXqwnd2oz6ekAMj74veP7SxURkGdX4rljvvtHb86WNqT9Ty0S8/srhqJ905CWecfZ0qXW4Xb+9/WxubljuNkRkjg+zR/4iNVdU/3/2u6vDoS1sbvPGGCjVU91UVnRBQuESVOfrS8TtpPmv/Oa+5xirpvWoV7LMn2U8IwdS7pzKkQG+Otu/NfVTt73kIIzU+lZvG6iGYzWWbLWW1hp5hjIN+wvr1apXiS16e8gL6x0b7LQ3HYc//hQt+Gu+OBBjzMMQkBt4v0rS1wX//t7U2fP58daENI/Wn6tn65FZcTXpQfOJtExl+sX0JKW6Pm+d2PMfZBu/NySEcLB6/2LZzRBPjxsGPfmTN2QM4dEglK27Z0keChkJA0R2Q7dda1XVeGQgtNlsmQsA99+jiDx1uS3/jN0SccU5mPjyT2CRvcq6Ukh3P7aCxuucSznNL5pKbqquNvrbnNdaVrguyh6ErjHHQD9izx9qGecgQ+Md/tKq79Vuqt8Pe/wetNfp4fDZM/D4khk9muEdIqZK2DhzQxy+5RC05w0hDZQNbnthCa4Nenz72xrGMWjAqyF49R0rJC7te4LMzn2njVxRdQU5KTpC9+j9JSUpx+MEHrXmlzc3wwguq7PFCaK0DOkcIKPkqDNXj67SeUwaC/++mt6SkwP336566ujr1R9pkASUNTeKSBy7RQlCtDa18/N8f43b1LMfB6XByz9R7tLbOAH/+/M9sOrHJlvkONoxxEOWUl6sLjm9cMy5O5bdlZgbfr98gPUrD4MjvQPqlgA+ZABf9KyTlBd43GnjnHVXn5svIkfCNb4Q1BNJY3ciWX2yhpa5FGx+1YJRFvrY3SCn5y+6/sO2krt+Ql5rH4gkD02vgz/TpyoswMUDRx86d8JOfwEcf9UEughAw4uuQ5ee+aKmCfb+wv1HT2LHWFpZ79qiMTJsYdtEwi0BS7YlaPv/z5z1OUByRMYJHZj5CjF+PlZc+e4mPyvtQhnqAYoyDKOb8+cD5bQ88AMWR7X5rD22NcPAZqAigi55zLYxbCjHhLf3rEZs2wd/+po8NGwaPPBJWLYPm881s+cUWmmp0lZ6SuSVMWDLB1uTAZQeWWVy12cnZPDbnMZJiwycHHWnS01Vl6le+Yv2o6+qUB/7HP1ahBluNBOGAkfcodVBfWipVFYPL5iz9G25Qymq+vPWWre2dR183muFT9ZBX2eYyjq/vuejThOwJPDzzYZwOb6xVSskfdv2Bj0993MmeBn+McRCltLQoLQN/bfc77gh7flvf0HQG9v4H1PppP4sYJSFbfIe6EEYrn30GL7+sj6WkqFhPSvh6CbTUt7DliS00Vulx2sLLCm1vxbz6yGreO6QLO6UnpPPYnMdIS7Cvs2N/QQiYOxf+1/8KbKyfOaO88D/8obIjbev0KBwqOTfjEn28+XS7gWBjXMPhUOEF3z4gHg888QRs3GhLiEEIwbRvTCMlR//d7Hl1D+eOnAuyV3AmDZvEQ9Mf0nQ2pJT8/pPfm/bOPSCKr76DF48HfvtbKPMTQ5s3Tz36Ped3w96fQrNfKVZsGkx4HLKjWFmvsVEZBc88Y9UyeOQRq4hMH9La0MrWJ7dy4bR+M8ibkcfUu6faahhsOL7BomeQHJfMY3Mei7p2zOFm+HAlvXzjjepe6k9VFfzxj8pIWL/eJiPB4VRiYOl+K4WmU3DgSWi2R7gIUGUa996rj7lcKtfm979XCRe9JCYhhhkPzyAm3hsS8Lg97Hh2B821PT/+1OFTeeCSB7TfgEd6eG7HcxbBLkNgRKidsQYaM2bMkB9/HHm3k5Twyiuwdq0+PmUKPPxw4ItPv0FKqFgN5W+hySEDJJeoioS49EB7Rgc7d6rM0NpafVwIeOgh1QM4TLQ1t7HliS2WngnDpw5n+kPTbe2b8PGpj/ndJ7/TYsDxMfF879LvUZJeYtt5BgKVlbByZefhhPR0WLRI6ZP0OvrkaVNtzAN54IZ/SemC2CUz/vbbVklwUAbxgw8qjYReUvFJBR8/q1+HM0dlMus7s7TKhu6y7eQ2nt/5vPbdjXHE8J1Z32FC9kDrad9zhBA7pJQB5fOMcdBOtBgHf/87vO7XXKyoCP7pn/TGaf0Oj0u1XK7eZn0va45KtHKEL07fI86fVxbbzp2B37/rrrCWLLpb3Xz0y4+oPqSXsGVPzGbWI7NwxNhnGOyu3M0z257BI713uhhHDI/OeZSxWVHcKjvCVFcrI2HTpuDigkOGKPGyq65SScYh43Gp3J26ADoEsWlQsBiGzrEnQXbrViUN3qInvhITo/rEX311r8+z7619HF55WBuLHxLPlK9NCakcd3PZZl7c9aI2FuuMZenspYP+O2yMg24QDcbBzp2qMsH3I8nIgH/9V0jrzyHd5rNw5LcBusoJKPqySj6MRnEjKVVc9Y03Arfky8mBu++2Jmz1IW6Xm+3PbOfsPl38JmtMFrOXzrZV5OhQ9SGe+ugpXG5vFYlDOHh45sP9Wh45nNTUKCNh48bg4YTUVKXCOHduLxYA7lblQajbG/j95BIovhNSbBCoOn1aSYSfDNDjYdo0+Id/0HMUeoj0SD765UeW7zhA3vQ8Jt01ifghPfuPWn98PX/6TG9hGx8Tz6OzH2VUpn1lvv0NYxx0g0gaB1Kq0qeXX9YbuiQkqD7z+fnB941apIT6g3BmDdR8iiWM4EyC0d+EtMBNgDpihRH7fp45oz6QQG34HA7lF77++rBVJUgpOb3rNAeWH6D+VL32XsaIDOY8NoeYhJgge/ecE7Un+K/N/0VzmzfeK4TgvovvY3bBbNvOE/HPOUycP69EzNavD960KTlZiSxdfLHq7N3jEKKUULVVhe1ctYG3yZqlFBfjMgK/311cLtXSef36AOfIUmEGH8nwnn7OrQ2tbH1iK7Vl1r8jLjmOi+68iPxZ+T3Kq1lzbA2v7n5VG0uISeCRWY8MWg+CMQ66QSSMAylh714li+zfxMXhUInvgWqpoxp3qwodnFkDTUG6xyXkwthvQ0Lw5L2I3TTcbnUVf/fdwEu9khK1MgqTxSalpPLzSg4sPxDwQplWmMal37s0pHhsME5fOM3PN/+c+hbdCPnK5K8wt2SubeeBwWMcdFBXp2QC1q6F1tbg2yUnqzyjqVPVNaBHHgV3C5x6D06/DzLAd1jEQt4i1bPB2Zt4BvDxxyox0T8p0eFQPRquVV7BUD5nT5uHQysOcei9Q1qPkA5yJucw+WuTSczovhLc6iOrLYm1oCocbh53M8XpA6FGvPsY46AbhNs4KC1V3upg/eG//nW48sqwTaf3tJyDyrVQudGvxbIf6VNUlrUzodPDReSmcfy4SisP1G4vLg5uvVXlFoQhK1RKSdX+Kg4sO0DNscDqd6m5qVz6+KXEp9qTjFLdWM3qI6vZVLZJCyUA3Dr+Vq4bY3/Tq8FmHHRQXw8ffABr1ljD9/7ExsKECcqjMGWKCkN0i+YqKHsDaj4J/H5cBhTeBpkzehfWq6xU5VUnTljfmzIF7r0X0V7eG8rnXFdex6d//JTzx60iTzEJMUy8bSJFVxZ124uw4tAKlu1fFvC9KTlTuGncTRSlFfV4nv0RYxx0g3AZB2fOqKRf/wZKHTid6h60YEHg96MKKaH+cHvoYBfQidpLYgEMn9/txKiw3TQ8Hjh6FLZvh3XrAtdtX3QRfO1ryl0aBqoPVnNg+QFLwmEHQgjyZ+Uz8faJthgGp+pPsfLwSraf3K4lHnawYNQClkxY0iedFgercdBBQ4PXSAiU1uKPEEqA8+KLlVchpztq1XUH4fir0BSkx3TKKJWPkNyLVXNHF6o1a6zvZWRw0c9+xj7AE+LnLD2SI+8f4cDyA3jarN/RrLFZTL17KsnDuiea9s6Bd3j34LtB3586fCo3jb2JwrTeV2BEM/3COBBCFAD/BiwCsoAK4G3gJ1LKbguHCyEygR8BtwK5QDWwEviRlDJoB/a+Ng5qa5WneuPG4CVOM2fCLbdAdnafTcMePC7VC+HMGmgs62RDARkXq4TD1NE9Wp306U3j/HklA7t7t+o0F+yqnJysuirOmhWWhMmaozXsX7a/0850edPzGHvTWFJzu7t8DM6Rc0dYeXilpUeCL1cWX8nXJn+tz1owD3bjoIOWFvWV3LULPv9cyWl0h9xcZSSMHq0asWVmBvmqSg+c3QTly1TLZwsCUsdA6lj1nDIytJDDrl3w4ouWP+DZ557jAvD4888rb8LEiSE1hrlw5gKf/vFTzh22iiM5Y52Mu2UcJXNLcMZ2nZi7v2o/yw8s58i54GqP03KncePYGykYUtDjufYHot44EEKMAjYDw4BlwH5gFnANcAC4XErZZesxIURW+3HGAmuA7cB44BagErhUShmwaXhfGQdNTSqE/fe/B48xTpwIixerksWoxN0CTRVKYKWxTOUUtHWiwuZMgmFXwrCrIT601batNw23W3kHdu9Wj0BhA39mz1alWd324YZGW3Mb54+f58jqI1Turgy63fCpwxl701jSCntXtiKlZM/ZPaw8vJJD1YeCbpeRmMF1o6/jquKr+swwAGMcBMLthsOH1X3200971g46Pl4ZDHl53ue8PFX1JATQ1gSn/qYMe9lZcyMHpJR4DYaUUd3vilpdrcIMx459MfTsc88B8NCDD7Yf3qF6N0yerIyFHoiHSSk5vu44+97cZ2lLDuCIcZAxIoOssVlkjc0iY2RG0CoeKSX7qvbxzoF3OFoT8NYAwCW5l3DD2BsGnJHQH4yDVcACYKmU8mmf8V8A3wWelVJ+qxvHeRZ4EHhCSvk9n/GlwFPAKinlokD72m0ctLWppKMVK5TrMBDFxcoomBAtWhweFzSdVkaA76Olm2priXnKS5A1q9eJTr26aUipvAN79ypjYO/e7qu4ZWaqEMKkST0/b6dTkjSda6KuvI66sjrqyuuoLau1yB7709GYJr0kdHEoj/RQ01TDkZojrDq8ivK64MbR8JThLBy9kFn5sywNbPoCYxx0jpSqYnDXLvXwV03tLr5GQ14eFGZXUsTrJLZ81k2nmICkAq9nIXUMxHYiE+52q/hpe595i3HgT06O11AYPbpbfegbqxv5/E+fU7knuFEN4HA6SC9JJ2tsFpljMskcnakpMYL6/u09u5flB5ZTer406LFS41MpGFJAfmo+hWmF5Kfmk5uaG5bfSl8Q1caBEGIkcAQoBUZJ6Q16CiFSUeEFAQyTUgbNdBNCJANnUYHvXCllvc97jvZzlLSfw2Ii2mUceDywbRssXx7c4s/OVnkF06f3sbdaSrU68LSAp1VVEnzx7xZwN3o9Ak2noLkSS8lhlwjImAo589SFw6Y/yHLTkFK5YerrVYymrk79u64u8KMnGrUpKSqv4KKLVJ12DxVppJR42jy4W91fPFyNLupP1itjoP3hagpSwxaAoeOGMu6WcWSO6rr1ppSSC60XqG6q5mzDWaoaq7R/n2s6FzCXwJeS9BIWjV7ExcMv7lNPgT/GOOgZ584pb8KuXSqZubdNnfKS9zJz+BtkJ5cTE6OSH2Ni8P47FmJ9X8eoMacDRFIuIrlYNUdzJkJMkvIaxiR5Xx+rgI3befJbj5BIJ8aBL4mJMG6ckpJMTvY+kpL018nJSCEo31rOntf24Grs3u9LOARpRWlkjc1iSP4QYhJjiE2MJSYhBme8k4ONB1lxfAUn6k8g6Pq34BAOhqcM/8JYKBhSQHZyNnHOuC8eTuEM6++qu0S7cfAA8FvgOSnlQwHe7/AqzJdSftDJceYD7wOrpZQLA7zf4VV4QEr5e//37TAODhyAV18NrA0CShHthhtUFYLFMD65AhqOtSfEdXwmPjfGrsakW93wNUOglU6TBHtDvYBNbSqXoGMF0d3vkpTqquZ2q4fHY3n97//n/+AA/vWf/1m919RkW+ea/ccTqIsbhszNVcupzAzwuQhIKTUbqeM34mnz4HHpRkDHw47fkRs3G4s3kj0lm6TsJDzSg0d6cEv3F//+YszjRiJxe9zUttTS0tZFynsQJmRPYNHoRYzLGheRi5cxDkKnsVE5xg4ehFOnoKKi+7kKOpLkmGpykg4xLFE9UmM7X413IIR6OByB/93xetvWrcRWt3FF3ngSys8RW+c/UQGWG7FA+t6ehSArC+IySrzdWhMSIDmZFmcSBw87OHNaelOIBN5jdny3vQfzvp+Xq+Iu2v+I5ET8CbYnb6c6thqH04GIEd7Z+E71i6HA72WMzCA+MV4zFjTDwaeDZMcxfH+L/mNDk4Zy16S7sIPOjINo8IV0NPMOUtTHIZRxMBYIahx08zi0H8fCjh07unVx7Owidv58YMMgPl7JpM6f30m9ckMpnA+eGBZZBMRnQ1I+JOZCUiFcSIfXfgYc63LvUPiilY9/H4NQSU1VyR2TJlGzpomqY+2tbSvaoMKqxBYuhBAkD0smrSiN4bOHs6J0BfWyXmXI9OE5pw2fxqLRiwZdXfdAIilJ5crOmqVeS6l+LhUVXmPh1Cn16LwSQtDQNpSjdUM5WncpAInO8wxrNxZyEg+RFncq4J5Sem39TnGk4cqG06OHwuihxF5oIrniHMkV1SSerUUEua5arsitgMcn7t/cDM3NxFPN5CEweQg0Nguqa2PU43wMjc1dlB5nWr1zAkFxSzFFLUWUxpSyK34XZ2LO4KazPI3ApBen45EemtuaNVGxUAlX3mo6m60AAAeXSURBVEM0GAcdGVbB7gId410FXe06TqcIIXaEst/TT3e9jcHKt9pjlYaBTTS6XA19wQYbjrHfhmP03eHCwY/4kV2HCro6iAbjoCs6rhq99Tt2ehwppbk6GQwGg8EAREMD4I4VfbAarSF+2/X1cQwGg8FgGNREg3FwoP05WOeLjpZ3wXIJ7D6OwWAwGAyDmmioVhgFHKbzUkYHkN1FKWMKKo0rpFJGg8FgMBgMioh7DqSUR4DVqBv3I35v/wRIBv7oaxgIIcYLIcb7HecC8FL79j/2O8532o+/yhgG0Y0QokAI8bwQ4pQQokUIUSqEeFII0cses4ZoQAiRJYR4QAjxlhDisBCiSQhRK4TYKIS4v92QNwxAhBB3CyFk++OBSM/H0DkR9xxAQPnkfcBslHzyQeAyX/lkIYQEaxJhAPnkbcAEvPLJl7UbI4YoxC4ZbUP0IoT4FvAblEfwQ+AEkAMsQeULvQHcLqPhwmSwDSFEIfA54ARSgG9KKX8X2VkZOiMqjAP44ssTrPHSOb9tAxoH7e9lAv8bvfHSe3TReMkQeeyS0TZEL0KIeSjv3t/8QojDUcZ8IfBlKeUbEZqiwWaEqlN9HxgBvAn8E8Y4iHqixjgwDG7sktE29F+EED8A/h34lZTyHyM9H4M9CCEeBZ4A5gLzUIs3YxxEOSa+Z4gW5rU/r/Y1DADak0s3AUnAnHBPzBA2OsTx7dHJNkQcIcQE4D+Ap6SU6yM9H0P3McaBIVrolfy1oX8jhIgB/qH95cpIzsVgD+2f6UuovJIfRHg6hh7SHxQSDYODsMhfG6KW/wAmASuklKsiPRmDLfwImAZcIaXstLuDIfowngNDf8EuGW1DlCGEWAo8jqpOuTvC0zHYgBBiFspb8F9Syi2Rno+h5xjjwBAtGPnrQYgQ4hHgKWAvcI1/ZZKh/+ETTjgI/DDC0zGEiDEODNGCkb8eZAghHgN+BexGGQanIzwlgz2koH7HE4BmH+EjiapUAPht+9iTEZuloVNMzoEhWviw/XmBEMIRoJTxcqAJ2BqJyRnsRQjxfVSewS7gS1LKqghPyWAfLcDvg7x3CSoPYSNqQWBCDlGKMQ4MUYGU8ogQYjVKBOkR4GmftztktJ81Ggf9HyHED1GCZzuABSaUMLBoTz4MKI8shPgxyjh40egcRDfGODBEE99GySf/UghxLVYZ7f8ZwbkZbEAIcQ/KMHADG4ClSkBPo1RK+UKYp2YwGHwwxoEhamj3HszAK6N9PUoZ8ZcEkNE29EtGtD87gceCbLMOeCEsszEYDAEx8skGg8FgMBg0TLWCwWAwGAwGDWMcGAwGg8Fg0DDGgcFgMBgMBg1jHBgMBoPBYNAwxoHBYDAYDAYNYxwYDAaDwWDQMMaBwWAwGAwGDWMcGAwGg8Fg0DDGgcFg6BZCiJL2Tnov2HS8b7Qfb5YNx3pcCOESQoy3Y24Gw2DHGAcGgyHsCCFSgP8PeEdKua2T7b7m0/J3QSeH/DVQCfzc5qkaDIMSYxwYDIZIsBTIRbVt7owHAenz74C0dwJ8CrhBCHGZLTM0GAYxxjgwGAxhRQjhBL4FHJJSbu5ku3HAVcAHwCfAzUKInE4O/TKq2+O3bZyuwTAoMcaBwWAIGSHEC+0u/xIhxENCiM+FEM1CiDNCiOeEEGkBdvsSUAi82sXhv9n+/AdUl8ZY4N5gG0spT6HaQH9ZCDGkp3+LwWDwYowDg8FgB//Z/vgUeAY4ibq5vxVg2/ntzxuDHUwIEQfcA9S1H+PPQCvwgBBCdDKPTUA8yuNgMBhCxBgHBoPBDuYAk6WUX5dSPg7MQq3irwlQjXBF+/PHnRxvCTAUeEVK2SSlrAbeBUYD8zrZb3v7szEODIZeYIwDg8FgB/8mpTzR8UJK2YYKB4AyFHwpAlztN/xgdCQfvuAz1vHvbxKc0z7nMBgMIWKMA4PBYAeBvABl7c8ZfuNZQE2wAwkhRgNzgQNSyi0+b70HnAEWCyGGBtn9XPtzsPcNBkM3MMaBwWCwg/MBxtran51+401AQifH+iYg0L0GHd6Il4E4gicmJvqcw2AwhIgxDgwGQ7ipBIYIIWL932gfu7f95U99BJCkEEICj7e/Fyy0kOVzDoPBECIxkZ6AwWAYdHwGjAHGAbv93rsFGAYcIHg1wzXAWCHE1VLKdX7vdcgn77JprgbDoMQYBwaDIdysBW5DVTj4GwcdiYg/klK+FmhnIcT9wO/at/U3Dua0P39oy0wNhkGKCSsYDIZw8zZKyXCh76AQYgRKA6GqfZtgvALUA7cJITJ99ncA16ISGf2NDoPB0AOMcWAwGMKKlLIceAe4SQjhW8nwACoR8SUpZWsn+zegDIR4lFBSB/OBfOC/bZ+0wTDIEFLKrrcyGAwGG2lvjrQJ+J6U8gmbjvkGcDUwSkpZa8cxDYbBijEODAZDRBBCvIZSMhwppWzs5bEuRjVnWiql/JUd8zMYBjMmrGAwGCLFP6FCACNsOFYu8ENMSMFgsAXjOTAYDAaDwaBhPAcGg8FgMBg0jHFgMBgMBoNBwxgHBoPBYDAYNIxxYDAYDAaDQcMYBwaDwWAwGDSMcWAwGAwGg0Hj/wepbGgL9qIoHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl.plot(appro=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Аппроксимация границ распределений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:14.963053Z",
     "start_time": "2023-07-19T08:41:14.814432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.4444716118440308,\n",
       " -0.06372601642494828,\n",
       " 0.16307468616684456,\n",
       " 4.374547492776035,\n",
       " -0.13921385491143534,\n",
       " 0.09866942924955954)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl.fit_border(graph=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Аппроксимация параметров Бета-распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:16.352138Z",
     "start_time": "2023-07-19T08:41:16.116317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl.plot_appro_Beta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выборочные средние (точки) относительно истинных масс (линии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:18.511254Z",
     "start_time": "2023-07-19T08:41:18.273826Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,3))\n",
    "\n",
    "mA=mean_A(R_to_A)\n",
    "for i in range(N_samples):\n",
    "    plt.scatter(np.array(mA)[i],[1],color=color7[i],s=80,label=m_name[i])\n",
    "    plt.axvline(np.array(mA.index)[i],color=color7[i],linewidth=2)\n",
    "plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "plt.savefig('mean_A_9all_500_convert',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Аппроксимация зависимости истинных масс от выборочных средних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:19.961496Z",
     "start_time": "2023-07-19T08:41:19.429430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error 4.699543139652115\n",
      "error general 0.7663482299759072\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mA=mean_A(R_to_A)\n",
    "p0,p1,p2=appro_c(np.array(mA),(np.array(mA.index)-np.array(mA)),print_error=True)\n",
    "y=p0+p1*np.array(mA)+p2*(np.array(mA))**2\n",
    "# print('error general', sum(abs(y+np.array(mA)-np.array(mA.index))/np.array(mA.index)))\n",
    "print('error general', sum(abs(y+np.array(mA)-np.array(mA.index)))/5)\n",
    "\n",
    "# plt.rcParams.update({'font.size':20})\n",
    "# plt.rc('xtick', labelsize=20)\n",
    "# plt.rc('ytick', labelsize=20)\n",
    "\n",
    "\n",
    "fig=plt.figure(figsize=(5,4))\n",
    "for i in range(N_samples):\n",
    "    plt.scatter(np.array(mA)[i],np.array(mA.index)[i]-np.array(mA)[i],color=color7[i],s=80,label=m_name[i])\n",
    "df_y=pd.DataFrame({'y_compute':np.array(mA),'y':y})\n",
    "# print(df_y['y_compute'].to_numpy())\n",
    "# df_y.sort_values(by=['y_compute'])\n",
    "# plt.ylim(-22,12)\n",
    "df_y=df_y.sort_values(by=['y_compute'])\n",
    "plt.plot(df_y['y_compute'].to_numpy(),df_y['y'],label='fit')\n",
    "plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "plt.xlabel('<M>')\n",
    "plt.ylabel(r'$A_{true}$ — <M>')\n",
    "\n",
    "plt.savefig('mean_A_5_900',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:21.412782Z",
     "start_time": "2023-07-19T08:41:21.170298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "\n",
    "mA=mean_A(R_to_A)\n",
    "for i in range(N_samples):\n",
    "    plt.scatter(np.array(mA)[i],np.array(mA.index)[i],color=color7[i],s=80,label=m_name[i])\n",
    "plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "plt.xlim(0,60)\n",
    "plt.ylim(0,60)\n",
    "plt.ylabel(r'$A_{true}$')\n",
    "plt.xlabel('<M>')\n",
    "plt.savefig('mean_A_5_convert',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оцененные массы известных типов ядер"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:23.557570Z",
     "start_time": "2023-07-19T08:41:23.290108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error 4.699543139652115\n",
      "error general 3.831741149879536\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mA=mean_A(R_to_A)\n",
    "p0,p1,p2=appro_c(np.array(mA),(np.array(mA.index)-np.array(mA)),print_error=True)\n",
    "y=p0+p1*np.array(mA)+p2*(np.array(mA))**2\n",
    "# print('error general', sum(abs(y+np.array(mA)-np.array(mA.index))/np.array(mA.index)))\n",
    "print('error general', sum(abs(y+np.array(mA)-np.array(mA.index))))\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,3))\n",
    "\n",
    "plt.rcParams.update({'font.size':20})\n",
    "plt.rc('xtick', labelsize=20)\n",
    "plt.rc('ytick', labelsize=20)\n",
    "mA=mean_A(R_to_A)\n",
    "for i in range(N_samples):\n",
    "    plt.scatter(np.array(mA)[i]+y[i],[1],color=color7[i],s=80,label=m_name[i])\n",
    "    plt.axvline(np.array(mA.index)[i],color=color7[i],linewidth=2)\n",
    "plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "plt.savefig('mean_A_5_convert_',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оцененные массы относительно истинных масс для двух моделей ядерногоо взаимодействия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:30.683137Z",
     "start_time": "2023-07-19T08:41:30.365998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_name='5'\n",
    "fig,ax=plt.subplots(figsize=(6,5))\n",
    "sum_err=0\n",
    "xx=np.linspace(0,60,100)\n",
    "yy=xx\n",
    "plt.plot(xx,yy,color='black')\n",
    "for i in range(N_samples):\n",
    "    plt.scatter(m[i],np.array(mA)[i]+y[i],label=m_name[i],color=color7[i],s=80)\n",
    "    sum_err+=np.array(mA)[i]+y[i]-m[i]\n",
    "plt.legend(loc='lower right') #loc='center left', bbox_to_anchor=(1,0.5))\n",
    "# plt.xlabel('Истинные массы')\n",
    "# plt.ylabel('Оцененные массы')\n",
    "plt.xlabel(r'$A_{true}$')\n",
    "plt.ylabel(r'$A_{est}$')\n",
    "plt.savefig('mean_A_real_1_'+add_name,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Поиск средних масс по всей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:32.870715Z",
     "start_time": "2023-07-19T08:41:32.465653Z"
    }
   },
   "outputs": [],
   "source": [
    "cl=find_A(r,add_name='_5_q1q2_10-20_',manual=False)\n",
    "f_arr,min_possible_r,max_possible_r =cl.interpolate_distr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:32.947533Z",
     "start_time": "2023-07-19T08:41:32.932550Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def extract_data(probab):\n",
    "    p=df_pred['pred'][df_pred['m_name']==m_name[0]]\n",
    "    mask=np.random.choice(a=[True, False], size=(len(p)), p=[probab[0], 1-probab[0]])\n",
    "    p2n=p[mask]\n",
    "    p2n.index=np.arange(len(p2n))\n",
    "    \n",
    "    He=df_pred['pred'][df_pred['m_name']==m_name[1]]\n",
    "    mask=np.random.choice(a=[True, False], size=(len(He)), p=[probab[1], 1-probab[1]])\n",
    "    He2n=He[mask]\n",
    "    He2n.index=np.arange(len(He2n))\n",
    "    \n",
    "    N=df_pred['pred'][df_pred['m_name']==m_name[2]]\n",
    "    mask=np.random.choice(a=[True, False], size=(len(N)), p=[probab[2], 1-probab[2]])\n",
    "    N2n=N[mask]\n",
    "    N2n.index=np.arange(len(N2n))\n",
    "    \n",
    "    S=df_pred['pred'][df_pred['m_name']==m_name[3]]\n",
    "    mask=np.random.choice(a=[True, False], size=(len(S)), p=[probab[3], 1-probab[3]])\n",
    "    S2n=S[mask]\n",
    "    S2n.index=np.arange(len(S2n))\n",
    "    \n",
    "    Fe=df_pred['pred'][df_pred['m_name']==m_name[4]]\n",
    "    mask=np.random.choice(a=[True, False], size=(len(Fe)), p=[probab[4], 1-probab[4]])\n",
    "    Fe2n=Fe[mask]\n",
    "    Fe2n.index=np.arange(len(Fe2n))\n",
    "    \n",
    "    df_together=pd.concat([p2n,He2n,N2n,S2n,Fe2n],keys=[0,1,2,3,4])\n",
    "    return df_together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:33.230293Z",
     "start_time": "2023-07-19T08:41:33.208352Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def from_R_to_A_mix(f_arr,min_possible_r,max_possible_r,r_mix):\n",
    "    R_to_A_mix=pd.DataFrame(index=np.arange(len(df)),columns=['R','A','type_part'])\n",
    "    add_toi=0\n",
    "    for j in range(N_samples):\n",
    "        print(j)\n",
    "        for i in range(len(r_mix[j][:])):\n",
    "            real_i=i+add_toi\n",
    "            R_to_A_mix['R'].loc[real_i]=r_mix[j][i]\n",
    "            R_to_A_mix['type_part'].loc[real_i]=m_name[j]\n",
    "            #         print(R_toA(R_to_A['R'].loc[i]))\n",
    "            if R_to_A_mix['R'].loc[real_i] >= max_possible_r:  #   error!!!!!!!!!!!!!!!!!!\n",
    "                A_res=m[0]\n",
    "            elif R_to_A_mix['R'].loc[real_i] <= min_possible_r :\n",
    "                A_res=m[N_samples-1] \n",
    "            else:    \n",
    "    #             y_interp_arr=[]\n",
    "                y_df=pd.DataFrame(index=np.arange(len(mass_toFe)),columns=['A','y'])\n",
    "                y_df['A']=mass_toFe\n",
    "                summ=0\n",
    "                for part in range(len(mass_toFe[:])):\n",
    "                    try: \n",
    "                        summ+=1\n",
    "                        y_interp=f_arr[part](R_to_A_mix['R'].loc[real_i])\n",
    "    #                     print(y_interp)\n",
    "    #                     y_interp_arr.append(y_interp)\n",
    "                        y_df['y'].loc[part]=y_interp\n",
    "                    except:\n",
    "                        y_df['y'].loc[part]=0\n",
    "                try:\n",
    "                    y_max=max(y_df['y'])\n",
    "\n",
    "                except:\n",
    "                    y_max=max(y_df['y'])\n",
    "    #             print('R', R_to_A['R'].loc[real_i], y_df['y'])    \n",
    "    #                 print(y_max)\n",
    "    #             print(y_df['A'][y_df['y']==y_max])\n",
    "                A_res=float(y_df['A'][y_df['y']==y_max])\n",
    "        #         print(summ)\n",
    "        #         print(y_interp_arr)\n",
    "\n",
    "    #         print(R_to_A['R'].loc[real_i], A_res)\n",
    "            R_to_A_mix['A'].loc[real_i]=A_res\n",
    "    #         if (real_i+1)%200==0: print(real_i)\n",
    "        add_toi+=len(r_mix[j])   \n",
    "    return R_to_A_mix # return R_to_A_mix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:33.618890Z",
     "start_time": "2023-07-19T08:41:33.606900Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def find_aver_A(R_to_A_mix):\n",
    "    Ni=np.zeros(26)\n",
    "    for i in range(26):\n",
    "        Ni[i]=np.count_nonzero(R_to_A_mix['A'][(R_to_A_mix['A']<(mass_toFe[i]+0.1))&(R_to_A_mix['A']>(mass_toFe[i]-0.1))])\n",
    "    # Ni\n",
    "    m_aver=sum(mass_toFe*Ni)/np.count_nonzero(R_to_A_mix['A'])\n",
    "#     print(m_aver)\n",
    "\n",
    "    Nri=np.zeros(N_samples)\n",
    "    for i in range(N_samples):\n",
    "        Nri[i]=np.count_nonzero(R_to_A_mix['type_part'][R_to_A_mix['type_part']==m_name[i]])\n",
    "    # Nri\n",
    "    A_aver_real=sum(m*Nri)/np.count_nonzero(R_to_A_mix['A'])\n",
    "#     print(A_aver_real)\n",
    "    return m_aver,A_aver_real,Ni,Nri    #return m_aver, A_aver_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:34.131969Z",
     "start_time": "2023-07-19T08:41:34.113990Z"
    }
   },
   "outputs": [],
   "source": [
    "probab_c=[ [1,0.8, 0.1, 0.02,0.01 ],\n",
    "          [1,0.1, 0.1, 0.02,0.01  ],\n",
    "          [0.9,1, 0.2, 0.1,0.01 ],\n",
    "          [0.6,1, 0.3, 0.2,0.1],\n",
    "          [0.6,0.8, 1, 0.3,0.4],\n",
    "          [0.6,0.5, 1, 0.8,0.6],\n",
    "         [1,0.03, 0.03, 0.003,0.003],\n",
    "         [0.6,1, 0.1, 0.1,0.1],\n",
    "         [0.6,0.6, 1, 0.03,0.03],\n",
    "         [0.3,0.4, 0.5, 1,0.1],\n",
    "         [0.9,0.5, 0.01, 0.02,0.01],\n",
    "         [0.9,0.2, 0.01, 0.02,0.01],\n",
    "         [0.9,0.2, 0.1, 0.2,0.1],\n",
    "         [0.2,0.2, 0.2, 0.2,0.6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:34.476612Z",
     "start_time": "2023-07-19T08:41:34.469631Z"
    }
   },
   "outputs": [],
   "source": [
    "prob_all=[]\n",
    "for i in range(100):\n",
    "    prob_loc=[np.random.random()*((100-i)/100),np.random.random()*((100-i)/100)*0.8,np.random.random()*((100-i)/100)*0.8,\n",
    "             np.random.random()*(i/100)*0.8,np.random.random()*(i/100)]\n",
    "    prob_all.append(prob_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T08:41:35.757457Z",
     "start_time": "2023-07-19T08:41:35.738503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05343027, 0.62052418, 0.46641719, 0.003     , 0.003     ],\n",
       "       [0.74873126, 0.19063177, 0.07771291, 0.00582806, 0.00874551],\n",
       "       [0.85611431, 0.01793769, 0.52864113, 0.00857807, 0.00661057],\n",
       "       [0.31467334, 0.417625  , 0.51781371, 0.02163894, 0.00787803],\n",
       "       [0.54807141, 0.16987513, 0.33662703, 0.02102757, 0.02065159],\n",
       "       [0.35921201, 0.68519424, 0.20768425, 0.02744114, 0.03205859],\n",
       "       [0.25621508, 0.2968637 , 0.10489997, 0.01331045, 0.05675168],\n",
       "       [0.70310952, 0.65709355, 0.68180157, 0.02127981, 0.04524105],\n",
       "       [0.06789245, 0.48080562, 0.38665674, 0.01007166, 0.04400369],\n",
       "       [0.57741488, 0.59452693, 0.7164393 , 0.01547262, 0.0116151 ],\n",
       "       [0.39272683, 0.19467934, 0.71709973, 0.0061193 , 0.02676488],\n",
       "       [0.78797049, 0.09848544, 0.28228862, 0.05152282, 0.02177342],\n",
       "       [0.52671909, 0.31075641, 0.43248381, 0.02361433, 0.0137823 ],\n",
       "       [0.45228309, 0.53431198, 0.04209941, 0.06628585, 0.03994621],\n",
       "       [0.26452553, 0.44389748, 0.06194883, 0.05934111, 0.06329558],\n",
       "       [0.40430911, 0.29811837, 0.35454848, 0.02494701, 0.06058655],\n",
       "       [0.56470473, 0.52587121, 0.39137402, 0.04637924, 0.0827689 ],\n",
       "       [0.19021695, 0.36860221, 0.51867234, 0.0790497 , 0.09575504],\n",
       "       [0.12995212, 0.21746112, 0.22523377, 0.06810263, 0.10875343],\n",
       "       [0.20164366, 0.35083902, 0.34324943, 0.0439001 , 0.08410122],\n",
       "       [0.10949107, 0.13876061, 0.45573663, 0.0920032 , 0.1353264 ],\n",
       "       [0.64707005, 0.51530509, 0.4397076 , 0.10004915, 0.09584838],\n",
       "       [0.25626141, 0.60920002, 0.54536361, 0.11535916, 0.05984119],\n",
       "       [0.42688262, 0.1810718 , 0.55219806, 0.02088086, 0.14092423],\n",
       "       [0.06127243, 0.53043007, 0.36420818, 0.1253765 , 0.14853419],\n",
       "       [0.72576307, 0.24491436, 0.10400218, 0.10703031, 0.19681573],\n",
       "       [0.57819015, 0.00466183, 0.19452519, 0.06570303, 0.0181446 ],\n",
       "       [0.45720498, 0.37662778, 0.38150079, 0.20337833, 0.00671059],\n",
       "       [0.70204084, 0.19144015, 0.41470802, 0.20579586, 0.15901231],\n",
       "       [0.60346356, 0.49301102, 0.38982316, 0.18616774, 0.10387792],\n",
       "       [0.09984551, 0.30377814, 0.003     , 0.20219211, 0.02013413],\n",
       "       [0.36185836, 0.54814811, 0.11092351, 0.21239119, 0.2732258 ],\n",
       "       [0.59489943, 0.46617908, 0.07896561, 0.12541999, 0.22757721],\n",
       "       [0.56823978, 0.02172421, 0.04703106, 0.18334826, 0.26304364],\n",
       "       [0.003     , 0.47739864, 0.43657435, 0.13425551, 0.2595316 ],\n",
       "       [0.29028795, 0.28681891, 0.48127794, 0.1998496 , 0.19640123],\n",
       "       [0.63169883, 0.39659794, 0.48270358, 0.02886637, 0.28100264],\n",
       "       [0.6052738 , 0.38662148, 0.28659661, 0.13735454, 0.29533801],\n",
       "       [0.10260887, 0.08998743, 0.07825462, 0.19315817, 0.13736266],\n",
       "       [0.11835996, 0.24995258, 0.25858337, 0.28438612, 0.18252862],\n",
       "       [0.50424342, 0.12786166, 0.26589238, 0.12480511, 0.30928524],\n",
       "       [0.26762438, 0.40574022, 0.38003234, 0.13026857, 0.2378152 ],\n",
       "       [0.22919176, 0.34403207, 0.37962432, 0.3281445 , 0.15353573],\n",
       "       [0.53315784, 0.332013  , 0.37655667, 0.21584346, 0.06746818],\n",
       "       [0.2257869 , 0.04176494, 0.16153677, 0.03855071, 0.07517758],\n",
       "       [0.06076567, 0.08397067, 0.27551688, 0.22963384, 0.21766445],\n",
       "       [0.02492638, 0.16749045, 0.42636733, 0.04494516, 0.07064789],\n",
       "       [0.40675802, 0.07123781, 0.02375911, 0.15629952, 0.34133091],\n",
       "       [0.20800134, 0.32853691, 0.08957598, 0.22856637, 0.03879769],\n",
       "       [0.49436346, 0.09269687, 0.40457088, 0.30765038, 0.40842131],\n",
       "       [0.25459194, 0.16066924, 0.09457104, 0.16465694, 0.42873543],\n",
       "       [0.1932062 , 0.0125074 , 0.06387522, 0.08126169, 0.418851  ],\n",
       "       [0.36579413, 0.03077044, 0.16287305, 0.01817468, 0.22498949],\n",
       "       [0.27114626, 0.0030629 , 0.24143809, 0.30769855, 0.01595281],\n",
       "       [0.39126846, 0.33288041, 0.19230365, 0.33357146, 0.30474988],\n",
       "       [0.27672459, 0.17249969, 0.08140524, 0.14190558, 0.16763323],\n",
       "       [0.02701271, 0.06736215, 0.16898171, 0.13519092, 0.17000987],\n",
       "       [0.26485183, 0.00380375, 0.13412065, 0.38765496, 0.53707088],\n",
       "       [0.29313499, 0.02468843, 0.09919307, 0.074478  , 0.03294463],\n",
       "       [0.003     , 0.27089388, 0.01145725, 0.46982561, 0.09695063],\n",
       "       [0.2424109 , 0.12037362, 0.04532177, 0.46005196, 0.02652317],\n",
       "       [0.30878564, 0.30582052, 0.15345528, 0.36487837, 0.4167072 ],\n",
       "       [0.0558037 , 0.28757116, 0.17695095, 0.00990965, 0.57549583],\n",
       "       [0.06733449, 0.23618857, 0.16617044, 0.33379528, 0.24190397],\n",
       "       [0.03903939, 0.05229381, 0.16503134, 0.50516122, 0.63006716],\n",
       "       [0.08461331, 0.04702251, 0.15895546, 0.17290935, 0.19629557],\n",
       "       [0.33776433, 0.05780815, 0.18455839, 0.08164288, 0.27866992],\n",
       "       [0.26801542, 0.01455738, 0.24101873, 0.01589524, 0.54578118],\n",
       "       [0.30471082, 0.160471  , 0.07023552, 0.47772857, 0.64168375],\n",
       "       [0.08534569, 0.01088055, 0.18734339, 0.46589175, 0.08679484],\n",
       "       [0.19555431, 0.04068158, 0.07757466, 0.45533951, 0.19891318],\n",
       "       [0.21900386, 0.0298751 , 0.07864572, 0.28784539, 0.24946812],\n",
       "       [0.05641662, 0.0280608 , 0.14573045, 0.38778962, 0.46118567],\n",
       "       [0.11714992, 0.0986944 , 0.18246629, 0.3711238 , 0.19189044],\n",
       "       [0.18016016, 0.08469139, 0.18067049, 0.2742819 , 0.59104791],\n",
       "       [0.09460867, 0.18170145, 0.15145343, 0.18792765, 0.37322675],\n",
       "       [0.14981994, 0.08177497, 0.12334886, 0.48864139, 0.39091214],\n",
       "       [0.05464125, 0.03020716, 0.09371161, 0.48624691, 0.65659606],\n",
       "       [0.02756524, 0.04816236, 0.0238288 , 0.17001236, 0.46195486],\n",
       "       [0.19466291, 0.14756873, 0.15800935, 0.51178065, 0.69778641],\n",
       "       [0.10127125, 0.14310031, 0.11552393, 0.32210258, 0.75308436],\n",
       "       [0.11341882, 0.13084483, 0.08973795, 0.39664196, 0.21261138],\n",
       "       [0.13098877, 0.06996645, 0.11127703, 0.39130302, 0.44645023],\n",
       "       [0.09459932, 0.06489455, 0.01700077, 0.38992224, 0.79760732],\n",
       "       [0.0662834 , 0.00647406, 0.09146449, 0.52622139, 0.27582083],\n",
       "       [0.10167115, 0.07354148, 0.00318405, 0.30907086, 0.07514993],\n",
       "       [0.07705712, 0.10829478, 0.08633256, 0.57280606, 0.61731399],\n",
       "       [0.03039972, 0.08586327, 0.07714562, 0.06400093, 0.51643342],\n",
       "       [0.003     , 0.07125577, 0.0902381 , 0.38102662, 0.68047746],\n",
       "       [0.003     , 0.07798048, 0.08430568, 0.037776  , 0.48997681],\n",
       "       [0.08382884, 0.03453709, 0.00758259, 0.14388913, 0.8769377 ],\n",
       "       [0.08047415, 0.02722414, 0.01352467, 0.02545697, 0.58820651],\n",
       "       [0.01500435, 0.02803061, 0.00393309, 0.31527856, 0.25116628],\n",
       "       [0.01570627, 0.026277  , 0.04296033, 0.04017295, 0.44079114],\n",
       "       [0.05302631, 0.01539127, 0.04013466, 0.5590434 , 0.72010234],\n",
       "       [0.01559328, 0.00546761, 0.00461128, 0.37620967, 0.14593035],\n",
       "       [0.00743918, 0.01343981, 0.003     , 0.28867408, 0.21482676],\n",
       "       [0.00303089, 0.01418503, 0.01723231, 0.04320728, 0.35524374],\n",
       "       [0.00631149, 0.003     , 0.00361195, 0.3602184 , 0.33674386],\n",
       "       [0.003     , 0.00336494, 0.00602793, 0.18354897, 0.92275436]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_all=np.array(prob_all)\n",
    "prob_all[prob_all<0.003]=0.003\n",
    "prob_all[prob_all>0.997]=0.997\n",
    "prob_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Составление выборок с разными итоговыми средними массами из данных о двух моделях взаимодействия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:21:15.451395Z",
     "start_time": "2023-07-19T08:41:39.741226Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# =  0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.875094870365206 7.851248396989129 [519. 732. 321. 216. 122. 136. 143. 181. 100. 118.  90.  75.  73.  67.\n",
      "  62.  58.  75.   0.  36.   0.  10.  36.  22.  37.  25. 333.] [ 180. 2015. 1378.    7.    7.]\n",
      "# =  1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "9.753687236533958 2.864830327868852 [1.174e+03 8.190e+02 3.050e+02 1.570e+02 7.800e+01 1.140e+02 9.400e+01\n",
      " 9.700e+01 5.100e+01 6.000e+01 5.600e+01 5.300e+01 3.700e+01 2.300e+01\n",
      " 3.400e+01 2.700e+01 3.400e+01 0.000e+00 1.900e+01 1.000e+00 4.000e+00\n",
      " 1.400e+01 1.000e+01 1.800e+01 7.000e+00 1.300e+02] [2544.  624.  212.   10.   26.]\n",
      "# =  2\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "13.453452609265733 5.8035387237762235 [1290.  960.  386.  206.  123.  156.  144.  156.   85.   86.   82.   76.\n",
      "   56.   61.   71.   54.   69.    0.   41.    0.   10.   31.   14.   36.\n",
      "   22.  361.] [2929.   72. 1532.   22.   21.]\n",
      "# =  3\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.377724306365714 7.731478518894242 [719. 825. 323. 204. 129. 155. 149. 173. 100. 117.  92.  70.  58.  68.\n",
      "  67.  54.  68.   0.  36.   0.  12.  37.  26.  42.  33. 386.] [1046. 1318. 1496.   60.   23.]\n",
      "# =  4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "13.88493971983991 6.490813150371641 [910. 765. 293. 141.  87. 126. 115. 122.  68.  97.  67.  54.  56.  38.\n",
      "  61.  42.  56.   0.  29.   0.   9.  25.  10.  32.  13. 282.] [1868.  539.  978.   56.   57.]\n",
      "# =  5\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "13.831016320754717 5.953120566037736 [974. 952. 376. 220. 117. 151. 154. 172.  95. 112.  80.  78.  59.  64.\n",
      "  55.  50.  77.   0.  42.   1.  13.  29.  15.  31.  19. 304.] [1250. 2257.  580.   72.   81.]\n",
      "# =  6\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "14.628563361196658 8.219442234931808 [542. 522. 189. 113.  57.  60.  63.  83.  52.  50.  34.  39.  38.  33.\n",
      "  38.  27.  44.   0.  22.   0.   6.  14.  14.  14.  13. 206.] [840. 957. 280.  36. 160.]\n",
      "# =  7\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.104354996268098 7.154431258396776 [1459. 1428.  564.  338.  184.  229.  240.  270.  150.  167.  150.  142.\n",
      "  110.  104.  113.   82.  114.    0.   68.    0.   18.   69.   37.   51.\n",
      "   43.  569.] [2415. 2095. 1997.   66.  126.]\n",
      "# =  8\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.661610292510446 9.562455094824815 [462. 626. 269. 168. 114. 106. 118. 148.  75.  99.  84.  65.  63.  55.\n",
      "  53.  43.  71.   0.  31.   0.   7.  31.  24.  37.  19. 343.] [ 239. 1573. 1162.   30.  107.]\n",
      "# =  9\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.389784580803937 6.980681345365053 [1.249e+03 1.301e+03 5.180e+02 3.160e+02 1.930e+02 2.270e+02 2.120e+02\n",
      " 2.460e+02 1.240e+02 1.680e+02 1.330e+02 1.240e+02 9.900e+01 9.300e+01\n",
      " 1.140e+02 7.800e+01 1.090e+02 0.000e+00 5.900e+01 1.000e+00 1.800e+01\n",
      " 4.600e+01 3.600e+01 5.600e+01 3.300e+01 5.420e+02] [1977. 1913. 2131.   45.   29.]\n",
      "# =  10\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.510110481132077 9.094576839622642 [764. 779. 358. 215. 122. 149. 171. 180. 120. 119. 104.  80.  72.  83.\n",
      "  89.  76.  82.   0.  46.   0.  13.  47.  29.  48.  31. 463.] [1395.  623. 2123.   25.   74.]\n",
      "# =  11\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "12.8153115884653 5.8186011241446725 [1234.  878.  337.  160.  113.  145.  116.  133.   83.   83.   69.   58.\n",
      "   48.   50.   62.   29.   53.    0.   29.    0.    8.   44.   18.   22.\n",
      "   20.  300.] [2714.  333.  839.  147.   59.]\n",
      "# =  12\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "14.820721248803828 6.701302870813397 [972. 868. 363. 197. 111. 147. 149. 163.  95. 101.  99.  83.  64.  58.\n",
      "  66.  48.  67.   0.  45.   0.  11.  47.  15.  28.  19. 364.] [1798. 1005. 1269.   68.   40.]\n",
      "# =  13\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "12.765924775796867 6.083962938951917 [1000.  837.  313.  190.  110.  124.  108.  126.   86.   81.   67.   69.\n",
      "   50.   48.   47.   26.   59.    0.   28.    0.    6.   23.   21.   20.\n",
      "   14.  249.] [1591. 1678.  120.  208.  105.]\n",
      "# =  14\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "14.881325669626998 8.254819680284191 [662. 613. 230. 147.  71.  87.  86. 115.  54.  73.  56.  52.  42.  36.\n",
      "  35.  31.  51.   0.  35.   0.   3.  25.  15.  18.  12. 266.] [ 898. 1409.  188.  160.  160.]\n",
      "# =  15\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.696275904027422 8.518259411596688 [786. 729. 283. 166. 100. 115. 125. 120.  76.  82.  69.  80.  52.  53.\n",
      "  61.  45.  61.   0.  32.   1.   8.  34.  18.  39.  27. 339.] [1358.  912. 1008.   74.  149.]\n",
      "# =  16\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.061122684719535 7.976227852998066 [1167. 1117.  439.  234.  156.  160.  188.  205.  115.  127.  115.   97.\n",
      "   75.   69.   91.   54.   83.    0.   42.    0.   17.   39.   31.   37.\n",
      "   27.  485.] [1943. 1700. 1181.  142.  204.]\n",
      "# =  17\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "19.46189420682469 12.640353581661891 [535. 665. 333. 186. 119. 133. 159. 193. 112. 116.  93.  86.  78.  73.\n",
      "  83.  65.  80.   0.  54.   1.  16.  43.  24.  47.  45. 500.] [ 659. 1169. 1519.  234.  258.]\n",
      "# =  18\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "20.16843449361702 14.398729021276596 [348. 409. 183. 125.  68.  89.  81.  89.  52.  75.  58.  49.  46.  35.\n",
      "  46.  48.  61.   0.  31.   0.   8.  19.  17.  32.  14. 367.] [450. 757. 683. 206. 254.]\n",
      "# =  19\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.54439953885029 10.845979532533166 [556. 604. 255. 168. 100. 114. 128. 141.  94.  91.  63.  55.  58.  58.\n",
      "  55.  48.  51.   0.  35.   1.  18.  31.  24.  29.  20. 369.] [ 701. 1142. 1003.  120.  200.]\n",
      "# =  20\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "23.429160504017528 17.602681154127097 [283. 416. 198. 130.  86.  98. 104. 122.  67.  93.  71.  58.  52.  49.\n",
      "  66.  55.  71.   0.  31.   0.  11.  45.  26.  40.  31. 535.] [ 362.  455. 1316.  261.  344.]\n",
      "# =  21\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.532559698086711 8.493531332280146 [1.302e+03 1.176e+03 4.640e+02 2.650e+02 1.640e+02 1.740e+02 1.880e+02\n",
      " 2.410e+02 1.350e+02 1.380e+02 1.180e+02 1.040e+02 8.900e+01 7.900e+01\n",
      " 1.000e+02 6.200e+01 1.010e+02 0.000e+00 5.700e+01 1.000e+00 1.900e+01\n",
      " 4.600e+01 3.200e+01 3.400e+01 4.100e+01 5.670e+02] [2253. 1640. 1315.  259.  230.]\n",
      "# =  22\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "18.03881771615008 10.199928670473083 [792. 936. 409. 249. 162. 182. 169. 221. 132. 149. 121. 104.  93.  98.\n",
      "  91.  64. 102.   0.  56.   0.  14.  56.  35.  46.  39. 584.] [ 915. 1897. 1620.  317.  155.]\n",
      "# =  23\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "18.991488420542634 12.162115067829458 [764. 747. 301. 185. 124. 135. 134. 165.  82. 114.  93.  83.  74.  85.\n",
      " 100.  55.  82.   0.  53.   1.  14.  38.  26.  51.  32. 590.] [1427.  585. 1681.   59.  376.]\n",
      "# =  24\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "21.102700330843117 14.817577267876201 [465. 641. 288. 174. 106. 139. 148. 167. 110. 129.  88.  84.  78.  80.\n",
      "  82.  61.  83.   0.  50.   0.  13.  42.  35.  42.  38. 605.] [ 210. 1721. 1080.  330.  407.]\n",
      "# =  25\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.12848899146088 11.12371719363028 [1167.  847.  335.  160.  100.  125.  113.  140.   89.   86.   75.   70.\n",
      "   60.   55.   70.   45.   75.    0.   41.    0.   13.   43.   21.   26.\n",
      "   28.  549.] [2483.  731.  297.  306.  516.]\n",
      "# =  26\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "13.192401721194882 6.44000106685633 [845. 617. 220. 108.  65.  82.  82. 102.  51.  49.  49.  44.  30.  30.\n",
      "  40.  37.  35.   0.  26.   0.   7.  25.  15.  18.  15. 220.] [2002.   15.  585.  163.   47.]\n",
      "# =  27\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.32029339057684 9.154402818141788 [926. 901. 378. 245. 128. 161. 150. 189.  99. 125. 113.  89.  74.  70.\n",
      "  68.  58.  92.   0.  56.   0.  14.  48.  26.  42.  27. 463.] [1561. 1251. 1145.  576.    9.]\n",
      "# =  28\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.779475499522444 11.887482101241643 [1.158e+03 9.680e+02 3.980e+02 2.280e+02 1.150e+02 1.690e+02 1.740e+02\n",
      " 1.820e+02 1.280e+02 1.270e+02 1.140e+02 9.600e+01 7.500e+01 8.500e+01\n",
      " 9.500e+01 5.700e+01 1.010e+02 0.000e+00 4.600e+01 1.000e+00 2.500e+01\n",
      " 6.500e+01 2.900e+01 6.400e+01 3.800e+01 6.970e+02] [2431.  623. 1209.  585.  387.]\n",
      "# =  29\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.38802931356979 10.181187153696841 [1218. 1148.  456.  251.  163.  177.  206.  204.  124.  158.  122.  111.\n",
      "   90.   87.  104.   79.  113.    0.   53.    0.   19.   50.   35.   50.\n",
      "   37.  612.] [2105. 1601. 1147.  519.  295.]\n",
      "# =  30\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "19.009345277630878 13.160048863035431 [328. 363. 134.  82.  39.  62.  70.  75.  60.  50.  36.  44.  43.  31.\n",
      "  36.  30.  47.   0.  22.   0.  14.  19.  18.  15.  13. 260.] [313. 964.   9. 555.  50.]\n",
      "# =  31\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "19.792319899387575 15.535029571303586 [862. 818. 338. 207. 111. 134. 131. 159. 106. 119.  94.  94.  76.  81.\n",
      "  77.  74. 100.   0.  61.   0.  18.  38.  30.  45.  40. 759.] [1210. 1727.  339.  598.  698.]\n",
      "# =  32\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "16.607421013299557 11.744654042643024 [1142.  949.  352.  205.  113.  148.  128.  159.   98.  112.   90.   79.\n",
      "   91.   68.   64.   68.   81.    0.   40.    0.   19.   48.   23.   34.\n",
      "   29.  597.] [2039. 1502.  242.  382.  572.]\n",
      "# =  33\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "19.67211399293286 17.65904322732627 [839. 561. 228. 109.  59.  84.  94. 106.  61.  74.  62.  55.  58.  43.\n",
      "  65.  50.  61.   0.  47.   0.   9.  38.  16.  34.  27. 616.] [1928.   88.  146.  558.  676.]\n",
      "# =  34\n",
      "0\n",
      "1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "24.223342134510517 18.840832511036094 [337. 579. 281. 187. 109. 120. 132. 171. 109. 123. 111.  93.  80.  91.\n",
      "  90.  88. 111.   0.  45.   0.  14.  65.  31.  61.  46. 777.] [  13. 1535. 1267.  387.  649.]\n",
      "# =  35\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.126001038251367 16.227884016393446 [652. 646. 321. 200. 125. 143. 157. 171. 106. 125. 113.  93.  87.  90.\n",
      "  91.  87.  94.   0.  63.   0.  23.  60.  34.  60.  53. 798.] [ 987.  896. 1445.  545.  519.]\n",
      "# =  36\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "18.08572843013024 12.372427314325941 [1.191e+03 1.066e+03 4.380e+02 2.430e+02 1.360e+02 1.900e+02 1.790e+02\n",
      " 2.190e+02 1.180e+02 1.340e+02 1.150e+02 1.130e+02 9.600e+01 9.300e+01\n",
      " 1.050e+02 7.200e+01 1.120e+02 0.000e+00 5.500e+01 1.000e+00 2.000e+01\n",
      " 5.800e+01 3.700e+01 5.800e+01 5.000e+01 7.830e+02] [2177. 1265. 1435.   84.  721.]\n",
      "# =  37\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "18.478629458646616 14.07488060150376 [1153. 1030.  365.  207.  132.  159.  149.  191.  105.  119.  110.  108.\n",
      "   80.   81.   96.   83.   90.    0.   56.    0.   16.   53.   33.   58.\n",
      "   52.  794.] [2087. 1243.  808.  400.  782.]\n",
      "# =  38\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "26.26735836103152 23.186523782234953 [197. 210. 112.  61.  40.  48.  55.  71.  46.  51.  36.  35.  48.  41.\n",
      "  51.  41.  46.   0.  27.   0.  15.  28.  16.  27.  26. 417.] [348. 295. 242. 525. 335.]\n",
      "# =  39\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "24.77933529018535 20.35259775144333 [382. 443. 218. 129.  85. 102.  91. 141.  96.  97.  78.  72.  83.  78.\n",
      "  87.  74.  95.   0.  42.   0.  21.  38.  31.  57.  34. 717.] [410. 837. 754. 815. 475.]\n",
      "# =  40\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "21.33196819995193 17.522976015380916 [811. 664. 272. 155.  90. 132. 118. 143.  89.  97.  75.  87.  57.  83.\n",
      "  88.  77.  88.   0.  55.   0.  23.  50.  37.  45.  39. 786.] [1727.  432.  799.  375.  828.]\n",
      "# =  41\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "20.53528743763115 15.659638983446023 [675. 761. 319. 164. 126. 150. 134. 181.  95. 151.  84.  87.  78.  80.\n",
      "  95.  62. 100.   0.  49.   1.  18.  54.  33.  59.  34. 699.] [ 907. 1306. 1106.  363.  607.]\n",
      "# =  42\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.31801422894986 17.04334981078524 [569. 628. 297. 198. 110. 132. 154. 180. 105. 169. 112.  96.  89.  79.\n",
      " 102.  75. 102.   0.  54.   0.  24.  60.  38.  52.  41. 762.] [ 750. 1055. 1097.  939.  387.]\n",
      "# =  43\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.33962241845003 10.672951381674629 [990. 912. 386. 226. 149. 150. 160. 186. 113. 142.  97.  89.  75.  69.\n",
      "  87.  60.  99.   0.  55.   0.  20.  59.  33.  47.  40. 569.] [1837. 1080. 1091.  632.  173.]\n",
      "# =  44\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "17.907943047966 12.630791742562232 [354. 301. 138.  66.  55.  53.  50.  51.  24.  45.  35.  34.  23.  26.\n",
      "  32.  18.  32.   0.  22.   0.   3.  23.  10.  20.  17. 215.] [778. 135. 468.  81. 185.]\n",
      "# =  45\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "28.530643612121214 25.69328056565657 [169. 243. 158.  94.  76.  79.  74.  95.  71.  88.  76.  58.  68.  65.\n",
      "  72.  56.  71.   0.  37.   1.  12.  41.  21.  50.  33. 667.] [202. 269. 807. 666. 531.]\n",
      "# =  46\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.754300571161046 14.956999719101125 [190. 327. 156.  99.  74.  79.  93. 104.  60.  66.  74.  57.  49.  56.\n",
      "  60.  36.  48.   0.  31.   1.  12.  29.  15.  31.  33. 356.] [  99.  544. 1216.  119.  158.]\n",
      "# =  47\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "23.826922393980848 22.49671313269494 [585. 426. 155.  95.  59.  80.  64.  77.  51.  71.  50.  54.  40.  50.\n",
      "  63.  40.  67.   0.  31.   0.  16.  43.  25.  36.  36. 710.] [1326.  222.   65.  444.  867.]\n",
      "# =  48\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "18.50602425208258 12.55498457080768 [504. 503. 236. 129.  73.  92.  85. 113.  70.  79.  61.  70.  49.  46.\n",
      "  49.  39.  48.   0.  20.   0.  18.  32.  17.  26.  20. 382.] [ 711. 1039.  273.  641.   97.]\n",
      "# =  49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "23.62308410958904 20.189943483365948 [ 819.  734.  324.  197.  110.  140.  138.  190.  117.  146.  122.  110.\n",
      "   91.   92.  119.   84.  129.    0.   63.    0.   20.   83.   42.   58.\n",
      "   55. 1127.] [1727.  322. 1202.  862.  997.]\n",
      "# =  50\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "26.229103247030153 25.59184282668291 [480. 423. 199. 116.  60.  89.  81. 108.  52.  84.  65.  64.  57.  74.\n",
      "  88.  49.  83.   0.  43.   0.  14.  54.  32.  54.  36. 878.] [ 904.  500.  288.  495. 1096.]\n",
      "# =  51\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "30.38775356102004 32.09258369763206 [278. 235. 105.  52.  33.  43.  48.  71.  47.  51.  40.  32.  37.  39.\n",
      "  56.  46.  49.   0.  27.   0.  19.  38.  19.  26.  33. 772.] [ 679.   48.  167.  229. 1073.]\n",
      "# =  52\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "20.330988637820518 17.694922435897436 [558. 405. 172.  90.  59.  66.  75.  82.  51.  55.  39.  51.  39.  28.\n",
      "  49.  31.  48.   0.  23.   0.  10.  26.  15.  26.  23. 475.] [1258.   99.  469.   62.  608.]\n",
      "# =  53\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "21.822522167373698 16.104046818357116 [418. 379. 163.  94.  58.  88.  86. 118.  65.  73.  71.  70.  53.  55.\n",
      "  71.  53.  75.   0.  37.   0.  14.  43.  16.  34.  30. 429.] [934.  14. 720. 882.  43.]\n",
      "# =  54\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.159971400945423 18.52146927374302 [768. 729. 296. 190. 112. 136. 133. 194. 108. 130. 107. 100.  99. 104.\n",
      "  95.  60. 118.   0.  58.   0.  18.  47.  34.  54.  41. 923.] [1321. 1070.  560.  940.  763.]\n",
      "# =  55\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "20.512078429073856 16.80519406017976 [517. 460. 162.  98.  48.  58.  81.  73.  62.  64.  47.  48.  50.  38.\n",
      "  56.  37.  61.   0.  28.   0.  11.  28.  25.  28.  22. 457.] [938. 561. 242. 389. 429.]\n",
      "# =  56\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "30.305161545338443 27.757975223499358 [ 81. 152.  78.  54.  48.  52.  58.  60.  35.  56.  42.  37.  38.  32.\n",
      "  56.  38.  46.   0.  27.   0.   7.  34.  19.  26.  26. 464.] [ 82. 207. 477. 370. 430.]\n",
      "# =  57\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "30.86614770212766 31.529261968085105 [ 404.  330.  171.  112.   58.   83.   95.  128.   73.   90.   80.   89.\n",
      "   90.  101.  100.   88.  111.    0.   51.    0.   22.   62.   43.   77.\n",
      "   47. 1255.] [ 900.   15.  377. 1073. 1395.]\n",
      "# =  58\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "15.835666430644222 10.273375623911782 [468. 317. 128.  66.  40.  49.  45.  71.  32.  38.  32.  32.  29.  22.\n",
      "  33.  29.  35.   0.  18.   0.   4.  17.   7.  10.   5. 196.] [1027.   92.  296.  212.   96.]\n",
      "# =  59\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "27.369443533251733 24.656084863321095 [173. 290. 149. 104.  47.  88.  83.  92.  59.  77.  64.  68.  67.  78.\n",
      "  73.  70.  90.   0.  36.   0.  20.  38.  23.  39.  30. 593.] [   8.  850.   33. 1285.  275.]\n",
      "# =  60\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.54262283152783 18.006512065745238 [441. 381. 161. 100.  71.  79.  85. 101.  58.  81.  60.  59.  66.  57.\n",
      "  62.  56.  84.   0.  37.   0.  21.  44.  23.  38.  25. 487.] [ 836.  383.  135. 1259.   64.]\n",
      "# =  61\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "25.592762912371132 23.21090201890034 [ 629.  648.  279.  169.   94.  128.  128.  180.  108.  112.   91.   96.\n",
      "   97.  115.  105.   70.  110.    0.   57.    0.   26.   73.   45.   60.\n",
      "   60. 1176.] [1010. 1008.  442. 1085. 1111.]\n",
      "# =  62\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "29.922920227560052 30.58945176991151 [2.89e+02 3.50e+02 1.81e+02 1.10e+02 6.10e+01 7.30e+01 8.60e+01 1.15e+02\n",
      " 6.10e+01 7.80e+01 7.30e+01 7.30e+01 6.60e+01 6.30e+01 6.00e+01 6.00e+01\n",
      " 8.10e+01 0.00e+00 5.80e+01 1.00e+00 1.80e+01 4.70e+01 3.10e+01 4.60e+01\n",
      " 5.30e+01 1.03e+03] [ 182.  919.  515.   31. 1517.]\n",
      "# =  63\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "27.353626826072716 24.797415460203077 [264. 367. 190. 117.  82.  93.  95. 109.  92. 105.  81.  56.  67.  75.\n",
      "  84.  59.  85.   0.  47.   0.  18.  53.  33.  53.  46. 782.] [212. 774. 500. 930. 637.]\n",
      "# =  64\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "35.65181808673469 37.40549545918367 [ 115.  209.  158.  100.   69.   96.   97.  160.   90.  125.   99.  115.\n",
      "  102.  114.  115.  107.  134.    0.   74.    0.   30.   88.   49.   99.\n",
      "   66. 1509.] [ 130.  177.  495. 1483. 1635.]\n",
      "# =  65\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "28.465014241486067 27.01039040247678 [167. 235. 104.  69.  26.  57.  62.  72.  39.  61.  42.  54.  51.  52.\n",
      "  53.  44.  55.   0.  31.   0.   9.  39.  17.  35.  27. 537.] [309. 151. 461. 497. 520.]\n",
      "# =  66\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "22.734651055555556 19.9730325 [565. 421. 191.  98.  66.  79.  69. 103.  50.  63.  55.  51.  44.  41.\n",
      "  55.  44.  69.   0.  39.   0.  16.  44.  23.  37.  25. 632.] [1197.  186.  539.  230.  728.]\n",
      "# =  67\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "28.651582333439595 29.031435766656042 [408. 365. 155.  95.  69.  81.  76. 107.  60.  52.  61.  72.  60.  52.\n",
      "  68.  60.  77.   0.  38.   0.  15.  60.  36.  47.  41. 982.] [ 934.   50.  702.   39. 1412.]\n",
      "# =  68\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "29.36027250159676 30.059813072173725 [ 519.  493.  233.  134.   88.  119.  114.  176.  103.  128.   98.  110.\n",
      "  100.   99.  122.  113.  136.    0.   67.    0.   29.   71.   47.   72.\n",
      "   56. 1470.] [ 992.  520.  214. 1295. 1676.]\n",
      "# =  69\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "29.070794858828485 25.124527180783815 [157. 230. 138.  77.  49.  67.  65. 111.  61. 105.  62.  61.  62.  71.\n",
      "  64.  72.  90.   0.  34.   0.  25.  44.  21.  45.  37. 625.] [ 300.   40.  580. 1266.  187.]\n",
      "# =  70\n",
      "0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "28.024894204258388 26.58538679177192 [300. 302. 143.  88.  55.  82.  82. 108.  67.  94.  63.  62.  52.  62.\n",
      "  80.  57. 100.   0.  36.   0.  31.  40.  25.  50.  33. 759.] [ 654.  133.  207. 1247.  530.]\n",
      "# =  71\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "26.812419715302493 25.894478924476076 [340. 300. 141.  81.  57.  62.  68.  89.  54.  70.  60.  63.  53.  56.\n",
      "  63.  41.  67.   0.  36.   0.  16.  45.  25.  34.  34. 674.] [773.  82. 230. 819. 625.]\n",
      "# =  72\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "34.880439902534114 35.571245159194284 [ 120.  183.  125.   63.   54.   76.   83.  117.   72.   96.   84.   88.\n",
      "   86.   95.   98.   86.  107.    0.   55.    0.   25.   57.   37.   58.\n",
      "   49. 1164.] [ 206.   91.  467. 1129. 1185.]\n",
      "# =  73\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "28.368839463573757 25.804459731786874 [243. 270. 155.  99.  81.  79.  83. 119.  68. 102.  67.  62.  62.  65.\n",
      "  76.  70.  84.   0.  36.   0.  30.  63.  27.  47.  34. 737.] [ 382.  304.  513. 1039.  521.]\n",
      "# =  74\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "31.292063012240558 31.835869345396485 [3.430e+02 3.340e+02 1.620e+02 1.210e+02 8.300e+01 9.000e+01 8.300e+01\n",
      " 1.340e+02 8.800e+01 1.050e+02 6.900e+01 9.100e+01 8.400e+01 8.900e+01\n",
      " 1.020e+02 8.500e+01 1.010e+02 0.000e+00 5.800e+01 1.000e+00 2.000e+01\n",
      " 7.400e+01 5.600e+01 7.900e+01 4.900e+01 1.257e+03] [ 617.  285.  545.  791. 1520.]\n",
      "# =  75\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "29.048236559063497 28.032467257892872 [254. 313. 171.  98.  60.  70.  77.  99.  63.  87.  58.  59.  70.  72.\n",
      "  72.  59.  73.   0.  48.   0.  13.  39.  29.  45.  29. 861.] [316. 577. 460. 501. 965.]\n",
      "# =  76\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "30.188203048780487 30.18208344947735 [ 270.  328.  170.   98.   72.  108.  110.  120.   87.  116.   87.   76.\n",
      "   85.   93.   96.   75.  117.    0.   47.    0.   28.   64.   45.   59.\n",
      "   45. 1048.] [ 525.  259.  340. 1343.  977.]\n",
      "# =  77\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "37.05552818862691 39.518522662968095 [ 121.  178.  121.   70.   47.   78.   92.  128.   70.  109.   92.   83.\n",
      "   79.   99.  127.  114.  123.    0.   60.    0.   37.   84.   51.   74.\n",
      "   66. 1502.] [ 198.   82.  276. 1354. 1695.]\n",
      "# =  78\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "38.289330951653945 42.334737099236634 [ 68.  94.  60.  37.  26.  42.  34.  63.  46.  56.  34.  50.  36.  54.\n",
      "  71.  43.  64.   0.  29.   0.  13.  41.  24.  39.  44. 897.] [  78.  159.   63.  479. 1186.]\n",
      "# =  79\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "31.400004500521376 32.16924713242961 [ 389.  419.  213.  150.   98.  123.  130.  177.  108.  149.  107.  107.\n",
      "  115.  118.  133.   98.  141.    0.   79.    0.   35.   96.   58.   80.\n",
      "   66. 1606.] [ 672.  470.  447. 1413. 1793.]\n",
      "# =  80\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "33.85065593089221 36.09295884476534 [ 259.  303.  152.   91.   69.   95.   88.  116.   87.  111.   97.   92.\n",
      "   87.   86.  112.   88.  127.    0.   66.    0.   27.   65.   50.   69.\n",
      "   65. 1476.] [ 339.  459.  330.  858. 1892.]\n",
      "# =  81\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "27.694241273743017 25.92773668528864 [247. 298. 139.  98.  60.  71.  82. 124.  63.  87.  69.  71.  75.  76.\n",
      "  78.  67.  80.   0.  43.   0.  11.  58.  26.  46.  39. 677.] [ 377.  440.  261. 1090.  517.]\n",
      "# =  82\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "31.92430417305922 32.5497029150046 [ 249.  290.  138.   92.   60.   87.   91.  105.   72.   82.   65.   88.\n",
      "   79.   84.  104.   70.  104.    0.   60.    0.   24.   60.   36.   67.\n",
      "   52. 1100.] [ 430.  258.  331. 1056. 1184.]\n",
      "# =  83\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "36.88659553455723 40.78195912526998 [ 182.  204.  118.   78.   51.   64.   64.  130.   68.  103.   76.   99.\n",
      "   99.  101.  112.   85.  116.    0.   69.    0.   23.   75.   53.   81.\n",
      "   75. 1578.] [ 305.  221.   50. 1073. 2055.]\n",
      "# =  84\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "33.68194286543345 33.68841334784186 [143. 180.  98.  62.  52.  71.  64. 107.  72. 100.  86.  70.  79.  81.\n",
      "  92.  82.  94.   0.  48.   0.  28.  50.  36.  45.  43. 974.] [ 250.   21.  252. 1503.  731.]\n",
      "# =  85\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "26.83752361533779 24.184366646378578 [198. 185.  94.  60.  39.  53.  38.  53.  35.  52.  45.  33.  41.  43.\n",
      "  50.  41.  46.   0.  15.   0.  12.  26.  22.  27.  23. 412.] [358. 219.  11. 865. 190.]\n",
      "# =  86\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "34.29700115193644 35.82758723932473 [ 186.  279.  153.  121.   71.  106.   96.  141.   99.  134.  101.  100.\n",
      "  108.  102.  111.  102.  134.    0.   65.    0.   42.   68.   47.   88.\n",
      "   80. 1494.] [ 245.  354.  246. 1621. 1562.]\n",
      "# =  87\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "36.32609530080531 40.719699194694456 [106. 128.  81.  58.  31.  42.  40.  69.  43.  52.  44.  47.  51.  40.\n",
      "  59.  50.  68.   0.  37.   0.  13.  44.  24.  42.  36. 906.] [  90.  270.  214.  170. 1367.]\n",
      "# =  88\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "38.37357081147794 41.8590596112311 [  58.  131.  106.   74.   57.   76.   72.  122.   74.   98.   61.   61.\n",
      "   80.   98.   98.   84.  108.    0.   64.    0.   23.   68.   47.   64.\n",
      "   74. 1443.] [  15.  187.  250. 1024. 1765.]\n",
      "# =  89\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "36.664556114409066 41.312140960604424 [ 61. 121.  62.  51.  34.  38.  34.  75.  33.  46.  31.  43.  51.  42.\n",
      "  55.  44.  59.   0.  37.   0.  16.  39.  30.  33.  30. 788.] [   9.  272.  234.  107. 1231.]\n",
      "# =  90\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "38.91330867082376 45.44896238923531 [ 148.  137.   84.   48.   37.   51.   51.   91.   52.   69.   60.   73.\n",
      "   65.   66.   93.   60.  101.    0.   57.    0.   20.   62.   42.   69.\n",
      "   61. 1450.] [ 305.   94.   22.  385. 2241.]\n",
      "# =  91\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "38.225170997493734 44.878915989974935 [113. 119.  55.  33.  21.  35.  26.  54.  39.  40.  35.  48.  36.  43.\n",
      "  55.  39.  64.   0.  46.   0.   6.  44.  22.  37.  35. 950.] [ 242.   94.   46.   76. 1537.]\n",
      "# =  92\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "35.82777376794258 38.76972272727273 [ 35.  91.  70.  49.  31.  46.  36.  63.  45.  51.  41.  37.  50.  55.\n",
      "  57.  44.  53.   0.  26.   0.  15.  34.  23.  41.  38. 641.] [ 45.  94.   9. 876. 648.]\n",
      "# =  93\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "39.47523900464499 45.52589807564698 [ 38.  75.  45.  33.  21.  33.  20.  54.  24.  33.  28.  27.  29.  34.\n",
      "  44.  32.  50.   0.  26.   0.  11.  32.  22.  35.  30. 731.] [  47.   99.  130.  101. 1130.]\n",
      "# =  94\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "37.47403219525066 41.460568654353565 [  95.  167.  120.   93.   54.   97.   82.  147.   87.  106.   89.  102.\n",
      "   96.  107.  108.  102.  136.    0.   66.    0.   38.   85.   47.   81.\n",
      "   70. 1615.] [ 171.   57.  117. 1568. 1877.]\n",
      "# =  95\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "35.578122496798976 36.31886696542894 [ 30.  77.  47.  45.  34.  48.  45.  58.  39.  53.  41.  35.  51.  53.\n",
      "  64.  47.  58.   0.  21.   0.  21.  39.  24.  35.  26. 571.] [  48.   22.   13. 1101.  378.]\n",
      "# =  96\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "36.78729683276451 38.891070443686004 [ 27.  68.  45.  43.  26.  39.  35.  55.  24.  59.  37.  37.  38.  56.\n",
      "  47.  31.  48.   0.  33.   0.  15.  33.  23.  35.  27. 584.] [ 28.  57.  10. 838. 532.]\n",
      "# =  97\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "40.80412294827586 48.565722758620694 [ 17.  44.  21.  19.  16.  19.  25.  31.  24.  22.  20.  31.  32.  32.\n",
      "  35.  36.  37.   0.  25.   0.   7.  24.  17.  25.  20. 581.] [ 14.  49.  50. 128. 919.]\n",
      "# =  98\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "38.37518916709778 42.30472550439731 [ 20.  67.  57.  48.  28.  45.  46.  73.  45.  67.  51.  56.  43.  56.\n",
      "  72.  44.  74.   0.  33.   0.  23.  38.  24.  54.  33. 836.] [  24.    9.   14. 1001.  885.]\n",
      "# =  99\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "42.89424687801516 51.08431929703652 [  14.   47.   49.   35.   33.   46.   41.   85.   52.   72.   63.   80.\n",
      "   76.   69.   99.   69.  103.    0.   60.    0.   25.   69.   37.   73.\n",
      "   62. 1543.] [  19.   11.   16.  485. 2371.]\n"
     ]
    }
   ],
   "source": [
    "df_mass_result=pd.DataFrame(index=np.arange(len(prob_all)),columns=['mass_mean','A_mean_true','Ni','Nri'])\n",
    "probab_min=0.3\n",
    "probab=np.array([probab_min]*N_samples)\n",
    "j=0\n",
    "\n",
    "for i in range(len(prob_all)):\n",
    "    print('# = ', i)\n",
    "    df=extract_data(prob_all[i])\n",
    "    r_mix=df\n",
    "    R_to_A_mix=from_R_to_A_mix(f_arr,min_possible_r,max_possible_r,r_mix)\n",
    "    mass_mean,A_mean_true,Ni,Nri=find_aver_A(R_to_A_mix)\n",
    "    print(mass_mean,A_mean_true,Ni,Nri)\n",
    "    df_mass_result.loc[i]=mass_mean,A_mean_true,Ni,Nri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Аппроксимация сдвига истинной массы от выборочного среднего "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:21:16.179064Z",
     "start_time": "2023-07-19T09:21:15.564095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.75368724 18.03882715 26.32396706 34.60910697 42.89424688] 9.753687236533958 42.89424687801516\n",
      "116.72612158117855\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p0_est,p1_est=appro_ab(np.array(df_mass_result['mass_mean']),np.array(df_mass_result['mass_mean']-\n",
    "                                                                     df_mass_result['A_mean_true']))\n",
    "A_est=df_mass_result['mass_mean']-p0_est-p1_est*df_mass_result['mass_mean']\n",
    "def f_line(p0,p1,x):\n",
    "    return p0+p1*x\n",
    "x_many=np.linspace(df_mass_result['mass_mean'].min(),df_mass_result['mass_mean'].max(),5)\n",
    "print(x_many,df_mass_result['mass_mean'].min(),df_mass_result['mass_mean'].max())\n",
    "err_A=sum(abs(A_est-df_mass_result['A_mean_true']))\n",
    "print(err_A)\n",
    "fig=plt.figure(figsize=(6,5))\n",
    "plt.scatter(df_mass_result['mass_mean'],df_mass_result['mass_mean']-df_mass_result['A_mean_true'],s=80)\n",
    "plt.plot(x_many,f_line(p0_est,p1_est,x_many),label='fit')\n",
    "plt.xlabel(r'$\\overline{M}$')\n",
    "plt.ylabel(r'$\\overline{M}-\\overline{A}_{true}$')\n",
    "plt.legend(fontsize=17)\n",
    "plt.grid()\n",
    "# plt.text(14.5,27,'$MAE = 4.7$')\n",
    "plt.savefig('A_aver_true__.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:28:08.695081Z",
     "start_time": "2023-07-19T09:28:08.682092Z"
    }
   },
   "outputs": [
    {
     "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>A_est</th>\n",
       "      <th>A_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.2463</td>\n",
       "      <td>7.85125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.372218</td>\n",
       "      <td>2.86483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.14438</td>\n",
       "      <td>5.80354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.50467</td>\n",
       "      <td>7.73148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.78776</td>\n",
       "      <td>6.49081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>38.1338</td>\n",
       "      <td>36.3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>39.9367</td>\n",
       "      <td>38.8911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>45.9261</td>\n",
       "      <td>48.5657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>42.3044</td>\n",
       "      <td>42.3047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>49.0426</td>\n",
       "      <td>51.0843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A_est   A_true\n",
       "0    10.2463  7.85125\n",
       "1  -0.372218  2.86483\n",
       "2    5.14438  5.80354\n",
       "3    9.50467  7.73148\n",
       "4    5.78776  6.49081\n",
       "..       ...      ...\n",
       "95   38.1338  36.3189\n",
       "96   39.9367  38.8911\n",
       "97   45.9261  48.5657\n",
       "98   42.3044  42.3047\n",
       "99   49.0426  51.0843\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_A_out=pd.DataFrame({'A_est':A_est,'A_true':df_mass_result['A_mean_true']})\n",
    "df_A_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:28:09.368421Z",
     "start_time": "2023-07-19T09:28:09.348449Z"
    }
   },
   "outputs": [
    {
     "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>A_est</th>\n",
       "      <th>A_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.2463</td>\n",
       "      <td>7.85125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.372218</td>\n",
       "      <td>2.86483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.14438</td>\n",
       "      <td>5.80354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.50467</td>\n",
       "      <td>7.73148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.78776</td>\n",
       "      <td>6.49081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>38.1338</td>\n",
       "      <td>36.3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>39.9367</td>\n",
       "      <td>38.8911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>45.9261</td>\n",
       "      <td>48.5657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>42.3044</td>\n",
       "      <td>42.3047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>49.0426</td>\n",
       "      <td>51.0843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A_est   A_true\n",
       "0    10.2463  7.85125\n",
       "1  -0.372218  2.86483\n",
       "2    5.14438  5.80354\n",
       "3    9.50467  7.73148\n",
       "4    5.78776  6.49081\n",
       "..       ...      ...\n",
       "95   38.1338  36.3189\n",
       "96   39.9367  38.8911\n",
       "97   45.9261  48.5657\n",
       "98   42.3044  42.3047\n",
       "99   49.0426  51.0843\n",
       "\n",
       "[99 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_A_out1=df_A_out.drop(8)\n",
    "df_A_out1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:28:11.022802Z",
     "start_time": "2023-07-19T09:28:11.008811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1672612158117854"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(abs(df_A_out['A_est']-df_A_out['A_true']))/len(df_A_out['A_est'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка восстановления средней массы по всей выборке "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-19T09:28:29.586875Z",
     "start_time": "2023-07-19T09:28:29.290647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MAE\n",
    "MAE=sum(abs(df_A_out['A_est']-df_A_out['A_true']))/len(df_A_out['A_est'])\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(6,5))\n",
    "    \n",
    "plt.scatter(df_mass_result['A_mean_true'],A_est,s=80,color='green')  \n",
    "# plt.axhline(15,color='gray',linewidth=2,linestyle='--',label='истинные массы')\n",
    "# plt.legend(loc='center left',bbox_to_anchor=(1,0.5),fontsize=20)\n",
    "x=np.linspace(min(df_A_out['A_true']),max(df_A_out['A_true']),100)\n",
    "y=x\n",
    "plt.plot(x,y,color='green')\n",
    "plt.xlabel('Истинные средние массы')\n",
    "plt.ylabel('Оцененные средние массы')\n",
    "plt.xlabel(r'$\\overline{A}_{true}$')\n",
    "plt.ylabel(r'$\\overline{A}_{est}$')\n",
    "plt.text(4,42.2,'MAE = '+str(round(MAE,2))+' а.е.м.',fontsize=25)\n",
    "plt.grid()\n",
    "plt.savefig('A_est_true_2_'+add_name,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-19T14:15:51.652342Z",
     "start_time": "2023-06-19T14:15:51.631371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    27.3839\n",
       "71    32.5542\n",
       "72    22.2339\n",
       "73    29.6777\n",
       "74    21.5902\n",
       "75    24.5809\n",
       "76    34.5049\n",
       "77    37.2584\n",
       "78    45.0639\n",
       "79     34.003\n",
       "80    25.7667\n",
       "81    32.0199\n",
       "82    32.2137\n",
       "83    36.2177\n",
       "84    27.8646\n",
       "85    36.6531\n",
       "86    31.6245\n",
       "87    46.0829\n",
       "88    44.4626\n",
       "89    33.0578\n",
       "Name: A_mean_true, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mass_result['A_mean_true'][70:90]"
   ]
  },
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     "end_time": "2023-06-19T14:15:57.254334Z",
     "start_time": "2023-06-19T14:15:57.241368Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70     27.294\n",
       "71    33.4585\n",
       "72    22.6967\n",
       "73    29.7516\n",
       "74     20.921\n",
       "75     23.952\n",
       "76    34.0432\n",
       "77    37.1087\n",
       "78    44.8633\n",
       "79    33.7172\n",
       "80    26.3475\n",
       "81    32.5916\n",
       "82    30.6169\n",
       "83    36.5294\n",
       "84    27.8399\n",
       "85    37.0399\n",
       "86     32.104\n",
       "87    44.9754\n",
       "88    43.6104\n",
       "89    32.6084\n",
       "Name: mass_mean, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_est[70:90]"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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