{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60d4d348",
   "metadata": {},
   "source": [
    "# Script for analysis of droplet time lapse experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e1d47c",
   "metadata": {},
   "source": [
    "# Load important libaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d19f4f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import scipy\n",
    "import matplotlib.gridspec as gridspec\n",
    "#plt.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbbc212",
   "metadata": {},
   "source": [
    "# Help functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9497e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in data from each field of view\n",
    "\n",
    "def split_data_in_images(sphere1,geod_elon1,vol1,l1):\n",
    "    #sphere1 = sphericity\n",
    "    #geod_elon1 = elongation\n",
    "    #vol1 = volume\n",
    "    #l1 = indices of entries (start with 2)\n",
    "    \n",
    "    #get split points\n",
    "    data_len_im=[]\n",
    "\n",
    "    data_split=[]\n",
    "    data_split.append(0)\n",
    "    #split points between images in each data stack\n",
    "    for i in range(len(l1)):\n",
    "        if l1[i]==2 and i>2:\n",
    "            data_len_im.append(l1[i-1])\n",
    "            data_split.append(i)\n",
    "\n",
    "    data_split.append(len(l1)) \n",
    "    \n",
    "    #split data\n",
    "    sphere_im_list_1=[]\n",
    "    \n",
    "    elon_im_list_1=[]\n",
    "    \n",
    "    vol_im_list_1=[]\n",
    "\n",
    "    for i in range(len(data_split)-1):\n",
    "        sphere_im_list_1.append(sphere1[data_split[i]:(data_split[i+1])])\n",
    "\n",
    "        elon_im_list_1.append(geod_elon1[data_split[i]:(data_split[i+1])])\n",
    "        \n",
    "        vol_im_list_1.append(vol1[data_split[i]:(data_split[i+1])])\n",
    "        \n",
    "    #mask wrong data (entries where sphericity is larger than 1 and volume greater than 500 are likely artefacts)  \n",
    "    sphere_im_list_1_m=[]\n",
    "    elon_im_list_1_m=[]\n",
    "    vol_im_list_1_m=[]\n",
    "    \n",
    "    for i in range(len(sphere_im_list_1)):\n",
    "        #sphericity\n",
    "        sph_e=np.asarray(sphere_im_list_1[i])\n",
    "        sph_e=np.ma.masked_greater(sph_e,1)\n",
    "\n",
    "        #elongation\n",
    "        el_e=np.asarray(elon_im_list_1[i])\n",
    "        el_e=np.ma.array(el_e,mask=np.ma.getmask(sph_e))\n",
    "        \n",
    "        #volume\n",
    "        vol_e=np.asarray(vol_im_list_1[i])\n",
    "        vol_e=np.ma.array(vol_e,mask=np.ma.getmask(sph_e))\n",
    "        \n",
    "        vol_e=np.ma.masked_greater(vol_e.compressed(),500)\n",
    "        \n",
    "        sph_e=np.ma.array(sph_e.compressed(),mask=np.ma.getmask(vol_e))\n",
    "        \n",
    "        el_e=np.ma.array(el_e.compressed(),mask=np.ma.getmask(vol_e))\n",
    "\n",
    "        sphere_im_list_1_m.append(sph_e.compressed())\n",
    "        elon_im_list_1_m.append(el_e.compressed())\n",
    "        vol_im_list_1_m.append(vol_e.compressed())\n",
    "        \n",
    "    #print number of objects in each image\n",
    "    len_obj_m=[]\n",
    "    for i in range(len(sphere_im_list_1_m)):\n",
    "        print(\"number of objects in image\",i+1,\"=\",len(sphere_im_list_1_m[i]))\n",
    "        len_obj_m.append(len(sphere_im_list_1_m[i]))\n",
    "\n",
    "    mean_num_obj=np.mean(len_obj_m)\n",
    "\n",
    "    std_num_obj=np.std(len_obj_m)/np.sqrt(len(len_obj_m))\n",
    "\n",
    "    print(\"mean number of objects\", mean_num_obj,\"+/-\", std_num_obj)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #generate list with all entries \n",
    "    sphere_im_list_1_all=[]\n",
    "    elon_im_list_1_all=[]\n",
    "    vol_im_list_1_all=[]\n",
    "\n",
    "    for i in range(len(sphere_im_list_1_m)):\n",
    "        for j in range(len(sphere_im_list_1_m[i])):\n",
    "            sphere_im_list_1_all.append(sphere_im_list_1_m[i][j])\n",
    "            elon_im_list_1_all.append(elon_im_list_1_m[i][j])\n",
    "            vol_im_list_1_all.append(vol_im_list_1_m[i][j])\n",
    "\n",
    "    tot_num_obj=len(sphere_im_list_1_all)    \n",
    "    print(\"total number of objects =\",tot_num_obj)\n",
    "    \n",
    "    return sphere_im_list_1_m, elon_im_list_1_m, vol_im_list_1_m, sphere_im_list_1_all,elon_im_list_1_all, vol_im_list_1_all, mean_num_obj, std_num_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac507ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering sphericity by volume \n",
    "\n",
    "def sph_filter(vol_all_le,sph_all_le,vol_b1,vol_b2):\n",
    "    #vol_all_le = volume \n",
    "    #sph_all_le = sphericity\n",
    "    #vol_b1,vol_b2 = lower and upper boundary \n",
    "    \n",
    "    sph_all_le2=[]\n",
    "    \n",
    "    for i in range(len(vol_all_le)):\n",
    "        sph_e=[]\n",
    "        for j in range(len(vol_all_le[i])):\n",
    "\n",
    "            if vol_b1< vol_all_le[i][j] < vol_b2:\n",
    "                sph_e.append(sph_all_le[i][j])\n",
    "        sph_all_le2.append(sph_e)\n",
    "    return sph_all_le2\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d9a384",
   "metadata": {},
   "source": [
    "# Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39255680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sphericity at different time points\n",
    "\n",
    "l1_t0 = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t0= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t10 = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t10= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t20 = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t20= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t30 = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t30= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "\n",
    "l1_t40 = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t40= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "# Control\n",
    "\n",
    "l1_t0c = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t0c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t10c = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t10c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t20c = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t20c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t30c = np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t30c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "l1_t40c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(0))\n",
    "sphere_t40c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(-1))\n",
    "\n",
    "\n",
    "# Elongation at different time points\n",
    "elon_t0= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t10= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t20= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t30= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t40= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "# Control\n",
    "\n",
    "elon_t0c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t10c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t20c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t30c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "elon_t40c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(6))\n",
    "\n",
    "\n",
    "# Volume at different time points\n",
    "vol_t0= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t10= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t20= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t30= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t40= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Amphiphile_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "# Control\n",
    "\n",
    "vol_t0c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t0.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t10c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t10.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t20c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t20.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t30c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t30.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n",
    "vol_t40c= np.loadtxt(\"Daten_Timelapse_Droplets/50_degrees/S0_Control_elo_vol_t40.csv\",skiprows=1,delimiter=\",\",usecols=(-2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d481d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0\n",
      "number of objects in image 1 = 3382\n",
      "number of objects in image 2 = 3088\n",
      "number of objects in image 3 = 2793\n",
      "number of objects in image 4 = 2890\n",
      "number of objects in image 5 = 3382\n",
      "number of objects in image 6 = 2564\n",
      "number of objects in image 7 = 3024\n",
      "mean number of objects 3017.5714285714284 +/- 105.27434657430663\n",
      "total number of objects = 21123\n"
     ]
    }
   ],
   "source": [
    "print(\"t0\")\n",
    "sp_t0,el_t0,vol_t0,sp_t0_all,el_t0_all,vol_t0_all,ms0,mstd0 = split_data_in_images(sphere1=sphere_t0,geod_elon1=elon_t0,vol1=vol_t0,l1=l1_t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63db1c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t10\n",
      "number of objects in image 1 = 731\n",
      "number of objects in image 2 = 577\n",
      "number of objects in image 3 = 621\n",
      "number of objects in image 4 = 530\n",
      "number of objects in image 5 = 591\n",
      "number of objects in image 6 = 646\n",
      "number of objects in image 7 = 414\n",
      "mean number of objects 587.1428571428571 +/- 34.601774504117834\n",
      "total number of objects = 4110\n"
     ]
    }
   ],
   "source": [
    "print(\"t10\")\n",
    "sp_t10,el_t10,vol_t10,sp_t10_all,el_t10_all,vol_t10_all,ms10,mstd10 = split_data_in_images(sphere1=sphere_t10,geod_elon1=elon_t10,vol1=vol_t10,l1=l1_t10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "019cec9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t20\n",
      "number of objects in image 1 = 105\n",
      "number of objects in image 2 = 175\n",
      "number of objects in image 3 = 182\n",
      "number of objects in image 4 = 209\n",
      "number of objects in image 5 = 155\n",
      "number of objects in image 6 = 144\n",
      "number of objects in image 7 = 74\n",
      "mean number of objects 149.14285714285714 +/- 16.2799846705897\n",
      "total number of objects = 1044\n"
     ]
    }
   ],
   "source": [
    "print(\"t20\")\n",
    "sp_t20,el_t20,vol_t20,sp_t20_all,el_t20_all,vol_t20_all,ms20,mstd20 = split_data_in_images(sphere1=sphere_t20,geod_elon1=elon_t20,vol1=vol_t20,l1=l1_t20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85630e9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t30\n",
      "number of objects in image 1 = 58\n",
      "number of objects in image 2 = 26\n",
      "number of objects in image 3 = 44\n",
      "number of objects in image 4 = 60\n",
      "number of objects in image 5 = 102\n",
      "number of objects in image 6 = 95\n",
      "number of objects in image 7 = 52\n",
      "mean number of objects 62.42857142857143 +/- 9.505867453051584\n",
      "total number of objects = 437\n"
     ]
    }
   ],
   "source": [
    "print(\"t30\")\n",
    "sp_t30,el_t30,vol_t30,sp_t30_all,el_t30_all,vol_t30_all,ms30,mstd30 = split_data_in_images(sphere1=sphere_t30,geod_elon1=elon_t30,vol1=vol_t30,l1=l1_t30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4c69872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t40\n",
      "number of objects in image 1 = 34\n",
      "number of objects in image 2 = 44\n",
      "number of objects in image 3 = 42\n",
      "number of objects in image 4 = 30\n",
      "number of objects in image 5 = 36\n",
      "number of objects in image 6 = 30\n",
      "number of objects in image 7 = 23\n",
      "mean number of objects 34.142857142857146 +/- 2.5589265242027213\n",
      "total number of objects = 239\n"
     ]
    }
   ],
   "source": [
    "print(\"t40\")\n",
    "sp_t40,el_t40,vol_t40,sp_t40_all,el_t40_all,vol_t40_all,ms40,mstd40= split_data_in_images(sphere1=sphere_t40,geod_elon1=elon_t40,vol1=vol_t40,l1=l1_t40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b17e6f18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0 control\n",
      "number of objects in image 1 = 2715\n",
      "number of objects in image 2 = 2369\n",
      "number of objects in image 3 = 2328\n",
      "number of objects in image 4 = 2296\n",
      "number of objects in image 5 = 2320\n",
      "number of objects in image 6 = 1999\n",
      "number of objects in image 7 = 2164\n",
      "mean number of objects 2313.0 +/- 76.41188068752189\n",
      "total number of objects = 16191\n"
     ]
    }
   ],
   "source": [
    "print(\"t0 control\")\n",
    "\n",
    "sp_t0c,el_t0c,vol_t0c,sp_t0c_all,el_t0c_all,vol_t0c_all,ms0c,mstd0c = split_data_in_images(sphere1=sphere_t0c,geod_elon1=elon_t0c,vol1=vol_t0c,l1=l1_t0c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26176f5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t10 control\n",
      "number of objects in image 1 = 1014\n",
      "number of objects in image 2 = 810\n",
      "number of objects in image 3 = 832\n",
      "number of objects in image 4 = 613\n",
      "number of objects in image 5 = 741\n",
      "number of objects in image 6 = 1030\n",
      "number of objects in image 7 = 735\n",
      "mean number of objects 825.0 +/- 53.09214169916375\n",
      "total number of objects = 5775\n"
     ]
    }
   ],
   "source": [
    "print(\"t10 control\")\n",
    "\n",
    "sp_t10c,el_t10c,vol_t10c,sp_t10c_all,el_t10c_all,vol_t10c_all,ms10c,mstd10c = split_data_in_images(sphere1=sphere_t10c,geod_elon1=elon_t10c,vol1=vol_t10c,l1=l1_t10c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6a0cf26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t20 control\n",
      "number of objects in image 1 = 478\n",
      "number of objects in image 2 = 448\n",
      "number of objects in image 3 = 380\n",
      "number of objects in image 4 = 388\n",
      "number of objects in image 5 = 385\n",
      "number of objects in image 6 = 359\n",
      "number of objects in image 7 = 269\n",
      "mean number of objects 386.7142857142857 +/- 23.37075993255403\n",
      "total number of objects = 2707\n"
     ]
    }
   ],
   "source": [
    "print(\"t20 control\")\n",
    "\n",
    "sp_t20c,el_t20c,vol_t20c,sp_t20c_all,el_t20c_all,vol_t20c_all,ms20c,mstd20c = split_data_in_images(sphere1=sphere_t20c,geod_elon1=elon_t20c,vol1=vol_t20c,l1=l1_t20c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f248bb57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t30 control\n",
      "number of objects in image 1 = 325\n",
      "number of objects in image 2 = 249\n",
      "number of objects in image 3 = 255\n",
      "number of objects in image 4 = 247\n",
      "number of objects in image 5 = 291\n",
      "number of objects in image 6 = 265\n",
      "number of objects in image 7 = 258\n",
      "mean number of objects 270.0 +/- 9.92831448793946\n",
      "total number of objects = 1890\n"
     ]
    }
   ],
   "source": [
    "print(\"t30 control\")\n",
    "\n",
    "sp_t30c,el_t30c,vol_t30c,sp_t30c_all,el_t30c_all,vol_t30c_all,ms30c,mstd30c = split_data_in_images(sphere1=sphere_t30c,geod_elon1=elon_t30c,vol1=vol_t30c,l1=l1_t30c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5de45a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t40 control\n",
      "number of objects in image 1 = 348\n",
      "number of objects in image 2 = 299\n",
      "number of objects in image 3 = 340\n",
      "number of objects in image 4 = 226\n",
      "number of objects in image 5 = 249\n",
      "number of objects in image 6 = 218\n",
      "number of objects in image 7 = 289\n",
      "mean number of objects 281.2857142857143 +/- 18.280930496697177\n",
      "total number of objects = 1969\n"
     ]
    }
   ],
   "source": [
    "print(\"t40 control\")\n",
    "\n",
    "sp_t40c,el_t40c,vol_t40c,sp_t40c_all,el_t40c_all,vol_t40c_all,ms40c,mstd40c = split_data_in_images(sphere1=sphere_t40c,geod_elon1=elon_t40c,vol1=vol_t40c,l1=l1_t40c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5892219e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of droplets\n",
    "\n",
    "t_l=[0,10,20,30,40]\n",
    "\n",
    "\n",
    "num_obj_t=[ms0,ms10,ms20,ms30,ms40]\n",
    "\n",
    "num_obj_std_t=[mstd0,mstd10,mstd20,mstd30,mstd40]\n",
    "\n",
    "num_obj_tc=[ms0c,ms10c,ms20c,ms30c,ms40c]\n",
    "\n",
    "num_obj_std_tc=[mstd0c,mstd10c,mstd20c,mstd30c,mstd40c]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97613037",
   "metadata": {},
   "source": [
    "# Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd592fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert voxel to um^3\n",
    "\n",
    "vox_s=0.1625**3\n",
    "\n",
    "\n",
    "vol_t0c_all=np.asarray(vol_t0c_all)*vox_s\n",
    "vol_t10c_all=np.asarray(vol_t10c_all)*vox_s\n",
    "vol_t20c_all=np.asarray(vol_t20c_all)*vox_s\n",
    "vol_t30c_all=np.asarray(vol_t30c_all)*vox_s\n",
    "vol_t40c_all=np.asarray(vol_t40c_all)*vox_s\n",
    "\n",
    "vol_t0_all=np.asarray(vol_t0_all)*vox_s\n",
    "vol_t10_all=np.asarray(vol_t10_all)*vox_s\n",
    "vol_t20_all=np.asarray(vol_t20_all)*vox_s\n",
    "vol_t30_all=np.asarray(vol_t30_all)*vox_s\n",
    "vol_t40_all=np.asarray(vol_t40_all)*vox_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f5e3b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x250 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(2.5*6*2/3,2.5))\n",
    "\n",
    "gs = gridspec.GridSpec(1, 5, width_ratios=[1,1,1,1,1])\n",
    "\n",
    "pos1=[100,200,300,400,500]\n",
    "\n",
    "pos2=[0.0,0.5,1.0]\n",
    "\n",
    "pos3=[0.0,0.5,1.0,1.5,2.0]\n",
    "\n",
    "ax1= plt.subplot(gs[0,0])\n",
    "s1=np.ones(len(vol_t0c_all)) *0.05\n",
    "s2=np.ones(len(vol_t0_all)) *0.05\n",
    "st=10*1/14.3776\n",
    "\n",
    "ax1.set_yticks(pos3,labels=pos3)\n",
    "ax1.set_xticks(pos2,labels=pos2)\n",
    "ax1.scatter(sp_t0c_all,vol_t0c_all,label=\"t=0, control\",color=\"red\",alpha=0.3,linewidth=0,s=2)\n",
    "ax1.scatter(sp_t0_all,vol_t0_all,label=\"t=0\",color=\"black\",alpha=0.3,linewidth=0,s=2)\n",
    "#ax1.set_aspect(1./ax1.get_data_ratio())\n",
    "ax1.set_aspect(1./ax1.get_data_ratio() *48.309/34.908)\n",
    "ax1.set_ylim(0,2.25)\n",
    "#ax1.set_xlim(-0.0373, 1.0473)\n",
    "ax1.set_yticks(pos3,labels=[], size=11, fontname=\"Arial\")\n",
    "ax1.set_xticks(pos2,labels=pos2, size=11, fontname=\"Arial\")\n",
    "#lim1=ax1.get_xlim()\n",
    "\n",
    "####################################\n",
    "ax2= plt.subplot(gs[0,1])\n",
    "s1=np.ones(len(vol_t10c_all)) *0.05\n",
    "s2=np.ones(len(vol_t10_all)) *0.05\n",
    "\n",
    "\n",
    "ax2.set_yticks(pos3,labels=pos3)\n",
    "ax2.set_xticks(pos2,labels=pos2)\n",
    "ax2.scatter(sp_t10c_all,vol_t10c_all,label=\"t=10, control\",color=\"red\",alpha=0.3,linewidth=0,s=2)\n",
    "ax2.scatter(sp_t10_all,vol_t10_all,label=\"t=10\",color=\"black\",alpha=0.3,linewidth=0,s=2)\n",
    "ax2.set_ylim(0,2.25)\n",
    "#ax2.set_xlim(-0.0373, 1.0473)\n",
    "ax2.set_yticks(pos3,labels=[], size=11, fontname=\"Arial\")\n",
    "ax2.set_xticks(pos2,labels=pos2, size=11, fontname=\"Arial\")\n",
    "#ax2.set_aspect(1./ax2.get_data_ratio())\n",
    "ax2.set_aspect(1./ax2.get_data_ratio() *48.309/34.908)\n",
    "\n",
    "\n",
    "######################################\n",
    "ax3= plt.subplot(gs[0,2])\n",
    "s1=np.ones(len(vol_t20c_all)) *0.05\n",
    "s2=np.ones(len(vol_t20_all)) *0.05\n",
    "\n",
    "\n",
    "ax3.set_yticks(pos3,labels=pos3)\n",
    "ax3.set_xticks(pos2,labels=pos2)\n",
    "ax3.scatter(sp_t20c_all,vol_t20c_all,label=\"t=20, control\",color=\"red\",alpha=0.3,linewidth=0,s=2)\n",
    "ax3.scatter(sp_t20_all,vol_t20_all,label=\"t=20\",color=\"black\",alpha=0.3,linewidth=0,s=2)\n",
    "ax3.set_ylim(0,2.25)\n",
    "#ax3.set_xlim(-0.0373, 1.0473)\n",
    "ax3.set_yticks(pos3,labels=[], size=11, fontname=\"Arial\")\n",
    "ax3.set_xticks(pos2,labels=pos2, size=11, fontname=\"Arial\")\n",
    "#ax3.set_aspect(1./ax3.get_data_ratio())\n",
    "ax3.set_aspect(1./ax3.get_data_ratio() *48.309/34.908)\n",
    "\n",
    "\n",
    "#####################################\n",
    "ax4= plt.subplot(gs[0,3])\n",
    "ax4.set_yticks(pos3,labels=pos3)\n",
    "s1=np.ones(len(vol_t30c_all)) *0.05\n",
    "s2=np.ones(len(vol_t30_all)) *0.05\n",
    "\n",
    "#ax4.set_yticks(pos3,labels=pos3)\n",
    "ax4.set_xticks(pos2,labels=pos2)\n",
    "ax4.scatter(sp_t30c_all,vol_t30c_all,label=\"t=30, control\",color=\"red\",alpha=0.3,linewidth=0,s=2)\n",
    "ax4.scatter(sp_t30_all,vol_t30_all,label=\"t=30\",color=\"black\",alpha=0.3,linewidth=0,s=2)\n",
    "ax4.set_ylim(0,2.25)\n",
    "#ax4.set_xlim(-0.0373, 1.0473)\n",
    "ax4.set_yticks(pos3,labels=[], size=11, fontname=\"Arial\")\n",
    "ax4.set_xticks(pos2,labels=pos2, size=11, fontname=\"Arial\")\n",
    "#ax4.set_aspect(1./ax4.get_data_ratio())\n",
    "#ax4.set_yticks(pos3,labels=pos3)\n",
    "ax4.set_aspect(1./ax4.get_data_ratio() *48.309/34.908)\n",
    "\n",
    "\n",
    "#####################################\n",
    "ax5= plt.subplot(gs[0,4])\n",
    "s1=np.ones(len(vol_t40c_all)) *0.05\n",
    "s2=np.ones(len(vol_t40_all)) *0.05\n",
    "\n",
    "ax5.set_yticks(pos3,labels=pos3)\n",
    "ax5.set_xticks(pos2,labels=pos2)\n",
    "ax5.scatter(sp_t40c_all,vol_t40c_all,label=\"t=40, control\",color=\"red\",alpha=0.3,linewidth=0,s=2)\n",
    "ax5.scatter(sp_t40_all,vol_t40_all,label=\"t=40\",color=\"black\",alpha=0.3,linewidth=0,s=2)\n",
    "\n",
    "ax5.set_ylim(0,2.25)\n",
    "#ax5.set_xlim(-0.0373, 1.0473)\n",
    "ax5.set_yticks(pos3,labels=[], size=11, fontname=\"Arial\")\n",
    "ax5.set_xticks(pos2,labels=pos2, size=11, fontname=\"Arial\")\n",
    "#ax5.set_aspect(1./ax5.get_data_ratio())\n",
    "ax5.set_aspect(1./ax5.get_data_ratio() *48.309/34.908)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticklabels([])\n",
    "ax2.set_yticklabels([])\n",
    "ax2.set_xticklabels([])\n",
    "ax3.set_yticklabels([])\n",
    "ax3.set_xticklabels([])\n",
    "ax4.set_yticklabels([])\n",
    "ax4.set_xticklabels([])\n",
    "ax5.set_yticklabels([])\n",
    "ax5.set_xticklabels([])\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig(\"Daten_Timelapse_Droplets/plots_s0_v1/S0_vol_sph_all_t_v6.png\",format=\"png\",dpi=300)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04525af6",
   "metadata": {},
   "source": [
    "# Plot changes in droplet volume, number and sphericity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e5a38a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "vol_all_lc=[vol_t0c_all,vol_t10c_all,vol_t20c_all,vol_t30c_all,vol_t40c_all]\n",
    "sph_all_lc=[sp_t0c_all,sp_t10c_all,sp_t20c_all,sp_t30c_all,sp_t40c_all]\n",
    "sph_all_le=[sp_t0_all,sp_t10_all,sp_t20_all,sp_t30_all,sp_t40_all]\n",
    "\n",
    "vol_all_le=[vol_t0_all,vol_t10_all,vol_t20_all,vol_t30_all,vol_t40_all]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c488d578",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering droplets with small sphericity\n",
    "sph_all_le2=sph_filter(vol_all_le=vol_all_le,sph_all_le=sph_all_le,vol_b1=0.125,vol_b2=9999)\n",
    "sph_all_lc2=sph_filter(vol_all_le=vol_all_lc,sph_all_le=sph_all_lc,vol_b1=0.125,vol_b2=9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "efdec091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Ym1t8EdLtbfffpv333+fbdu2MXr0aHOHI4QQQohiyMrKirCwMMLCwrK95+XllW3bG29vb3bt2lVU4QlRrGlGvqKjo/H29gYgNTWVxMREvLy8sLe3JyEhgcDAwBwriRrD6Cyqbdu2/PrrryxfvpwzZ86wY8cOvvzyS7y9vWnfvn2BAyppXFxc6NatG5GRkZKACSGEEEIIYaG8vb3x8fHRvvb19S2U6xhdBfH48eO0bNmSv//+m+PHj5OWlsb//vc//P39+frrrwsjxmJPqVQSHx/Pb7/9Zu5QhBBCCCGEEGZkdAIWHBzMhAkTOHDgADY2NgCsW7eOsWPHEhISYvIAS4KuXbtSuXJl2RNMCCGEEEKIUi5fI2ADBgzIdvzdd9/VqRIinrKxsaFv375ER0fz+PFjc4cjhBBCCCGEMBOjEzAbGxuSk5OzHb9w4YLe3diFmlKp5OrVq+zZs8fcoQghhBBCCCHMxOgErHv37nzwwQfcvHkTUJegT0hIYPTo0bzyyismD7Ck8PHxoWHDhkRFRZk7FCGEEEIIIYSZGJ2ALV68mNTUVFxdXfnvv//w8fGhUaNGlC1blkWLFhVGjCWCQqFAqVTyxRdfcPfuXXOHI4QQQgghhDADo8vQOzo6EhcXx969e/nf//5HRkYGjRo14uWXX6ZMGaPzuVKlX79+fPDBB3zyyScMHTrU3OEIIYQQpnf/Ppw5A6mpkJgIXl5gbw/160O5cuaOTgghzC7fuyl37NiRjh07mjKWEs/d3Z3OnTsTGRkpCZgQQoiS6cwZaN48+/HjxyHT/jpCCFFaGZSA1axZE4VCYdAJ//777wIFVNIplUr69u3Ln3/+SZ06dcwdjhBCiMxu3IDYWEhJgZMnoUkTqFABAgLAxcXc0RUP9eurk62EBAgMhOho8PZWHxdCCGFYAqZUKg1OwETuunfvjqOjI1FRUcyePdvc4QghhMgsNladNGQVHQ39+hV9PMVRuXK6I13e3jLyJYQQmRiUgM2aNauQwyg97O3teeutt4iKimLWrFmybk4IISxJQIA62YqLg/BwCAoCX1/1cSGMcPToUXbt2sXPP//M1atXsbKyws3NjRdffJHu3bvTtGlTc4coRMly7px69gKoR+AzP4N6NsOzzxZ9XHrkaw3YkSNHWLFiBadOncLKyormzZszbtw4GjZsaOr4SiSlUsn69es5ePAg7dq1M3c4QpQ4gwYNMvozCoWCDRs2FEI0olhxcXk60hUerk6+ZORLGCEuLo4PPviAw4cP4+PjQ5MmTWjRogXp6elcv36dnTt3Mnv2bNq0acPcuXPx9fU1d8hCFH/nzkHdutmPZ53RcPasRSRhRidgX331FT169OCFF16gU6dOZGRkcPjwYZo3b86ePXto06ZNYcRp+a5cgTVrYPhwcHPLtamvry+1a9cmMjJSEjAhCsGmTZto1aoVNjY2BrV/+PAhR44ckQRMCFEg48aN49tvv2XkyJHs2LGDqlWr6m13/fp1IiIiGDBgAN26deOjjz4q4kiFKGE0I1+aNadZq7Bq1qRq2pmZ0QnY1KlTmThxIvPnz9c5PmHCBIKDgzl8+LDJgitWrlyBkBB47bU8EzCFQsGAAQNYtGgRK1euxMHBoYiCFKL0+Pzzz3F1dTWo7dWrV3F3dy/kiIQQJV316tU5ffo01ta5f72qUqUKwcHBjBkzhhUrVhRRdEIUwJ49cO3a09dxcbrPAK6u0Llz0caVVeY1pxY8umx0Avbnn3/qnd4zfPhwVq1aZZKgSoMBAwYwc+ZMdu7cSf/+/c0djhAlSkREBE5OTgDcuXOHjIwMKlWqlGN7Z2dnIiIiiio8IUQJNW7cOO3PhvQ9tra2TJgwoShCEyL/9uwBf3/974WHqx8au3ebPwkrBoyuANG8eXP27t2b7fgvv/xCgwYNTBJUsXPnDsybZ9RHvLy8aNu2LZGRkYUTkxCl2LVr1/Dx8WHz5s1UrVqVqlWrsm7duhzb29nZoVQqizBCIURJtHjxYho2bGhw3yNEsaAZ+QoKUk/xi46GVavUr1etUr8OCtJtK3Jl9AhYYGAgwcHB/PHHH7Rt25ayZcty7Ngxli1bxvDhw4mKitK2HTBggEmDtVhWVnDwoNEfUyqVDB48mIsXL+Lp6VkIgQlROq1YsYKZM2fy7rvv8vvvv3P9+nXeeust2QBdCFGoPvroI+l7RMmVV1GizCNhIldGJ2BBTzLc5cuXs3z5cp33Fi1apP1Zs86pVKhQAcaPh8mT4dAhg/c7efPNNxk5ciSbN29mypQphRykEKWHtbU1AwcO5PHjx9SqVQt3d3cyMjLMHZYQooSTvkcIYQijpyBmZGQY9EhPTy+MeC2XZr7rokXqyisGqFChAj179iQyMhKVSlWIwQlRurRt25bVq1czfPhwADp37ky3bt3MHJUQoqSTvkcIYYh87QMG8Mcff3Dy5ElsbGzw9vamrr7a+6WJQqF+/vdfWLBAXRHRAEqlkujoaI4ePUrLli0LMUAhSo+IiAju3r2rff3hhx/SokULM0YkhCgNpO8RQhjC6AQsLS2NwMBAPvvsM+0xhULBq6++SkxMDLa2tiYNsNhwc4OZM+H2bQgLg/79oU6dPD/Wvn17PDw8iIyMlARMCBPSVEEEcHBwYMeOHTx48CBbu1IzVVoIUSRKXd9z7tzTvZUSEnSfQb1MwwI2vrV4mcu857PE+40bN4iNjSUlJYWTJ0/SpEkTKlSoQEBAAC4uLoUUuMiPfO0D9vPPP7Nr1y78/PxIT0/n4MGDjBo1ilmzZmXbH6zUcHODWbPg/n348ksYNQq+/fbpyFgOrKys6N+/P2vWrGHp0qWlN4EVopAsWLCAqVOn6p3mW6rWqgohilSp6HvOnQN9M6ACA3Vfnz1r3iTMBMlNocqpzLuRJd5jY2MJzPpnD0RHR9Mvt+IZosgZnYBt27aNdevW0bVrV+2x119/HSsrK0aMGFF6EzCNcuVg+XLo3h0+/xx69szzI0qlkgULFvDVV1/x5ptvFn6MQpQiy5YtY/r06UyePFlucAghikyp6Hs0I1/R0eoNcFNTITERvLzA3l49EhYY+LSdOZgouSlUmcu8+/pCcjKcOgWNG4OjozpZDA/Ps8R7QEAA0dHRxMXFER4eTlBQEL6+vgQEBBTBLyGMYXQClpyczLN67mLUq1eP69evmySoYu+11+CVV2DsWAgIAAeHXJvXr1+fF154gcjISEnAhDCxhw8fEhgYWHK/AAkhLJKp+56MjAxCQkJYv349t2/fpnXr1qxatYo6OSx3ePToETNmzCAqKoo7d+7QokULli9fznPPPWeSeHR4ez+tAO3ra/rzF4QJkps9e/Zw7cn7cU9GzuIyjaC5urrS2RTJW25l3g0o8e7i4qId6QoPD8fX11dGviyU0VUQGzVqxI4dO7Idj4mJoV69eiYJqthTKOCjj+D6dQgNNegjSqWS7777jn///beQgxOidBkwYIBshCqEMNqECRM4ffp0vj9v6r4nNDSU1atXs27dOg4fPoxCoaBLly48fPhQb/ugoCA2bNjA2rVrOX78OJUrV6ZLly46RUIsxblz54iPjyc+Pp6EJ+vHEhIStMfOnTtX8ItokhvN5sFBQerXeSSMe/bswd/fn8DAQAIDAwl/kgiFh4drj/n7+7Nnz56CxyhKDaNHwKZNm8brr7/Or7/+iq+vLwqFgkOHDrFz5062bNlSGDEWTzVrwpQpMHs2KJXqu0O56NOnD+PGjWPr1q2MGzeuiIIUouQLDg6mSZMmbN++nZo1a1KmjO59p3379hl8LmPvQGe2detW+vXrx/nz5/Hy8jL21xBCFLEff/yRpUuX0rx5cwYNGkSfPn1wdnY2+POm7HsePnzIkiVLWLhwoXYJSExMDO7u7uzcuZM+ffrotD9//jwbNmzg66+/1pbB37hxI82aNeP48eN06NDB4GsXtnPnzumtpJ11LdPZs2f1zsAqbJqRL810vuTkZE6dOkXjxo1xdHTUTve7lsf0QCEyMzoB69atG59++ikLFizg66+/RqVS0bhxY7Zv3y7T57KaOBGiouC992Dv3lwLclSqVIlXX32VyMhIScCEMKEhQ4agUql48cUXKVeuXIHOpbkDHRERgYeHB8HBwXTp0oXTp09jY2OT4+f++ecfRowYUaBrCyGK1pEjRzh79iyRkZEsWLCA8ePH8/rrr/POO+/QuXNnFHkU2TJl33PixAlSUlJ0EidnZ2d8fHw4ePBgtgQsNjYWZ2dnunTpotP+/PnzBYqjMKQ8WR8WHR2Nt7c3qampJCYm4uXlhb29PQkJCQQGBmrbmUtu0/nCDZgeKERmRidgYWFhBAYGcuTIkQJf/NatW0yZMoWvv/6a5ORkmjRpwoIFC2jdujWg7nDGjBnDL7/8QuXKlRk9ejQTJkzQft6Qu9F5naNQ2dnBypXw8suwfTu8/XauzZVKJa+99hq//vorTZs2LZoYhSjhDhw4wN69e2nVqlWBzmPsHWiNjIwMAgMDad68uVF3vIUQ5le3bl3mzp3L3Llz+eGHH/jss8/o2bMnlSpV4p133mHYsGF4eHjo/ayp+h6AS5cuAeDp6alz3N3dnQsXLmRrf/bsWWrVqsXOnTuZP38+ly9fxsfHhyVLluCdw4yctLQ00tLStK+Tk5MLHLcxvL298XmyjszX0taRCWFiRq8BmzdvHqmpqSa5eJ8+fThy5Ajbt2/n2LFj+Pj44O/vz5kzZ7h58yadO3embt26/PLLL4SEhDB9+nQiIiK0n89rPrQh5yh0AQHwxhvw/vvqhZ+5ePnll6lSpQqRkZFFFJwQJV/VqlWpUKFCgc+T1x3onMybN4+HDx8yefLkAscghDCPY8eOsXPnTnbu3AlA27Zt+fHHH3n22WdzXH5hqr4H4P79+wDZCnrY2dnp3WMsOTmZv/76i9DQUObPn8+uXbsoW7Ysbdq0yXGq3Pz583FyctI+siZ7QgjTMToBe/HFF9m1a1eBL/znn3+yZ88eVq1aRevWralXrx7Lly/Hw8ODrVu3snbtWmxtbQkPD8fb25t33nmHcePGERYWBjy9Gx0SEkLXrl1p2rQpMTExXL58WdtB5nWOIrN0qTr5mjkz12Zly5alX79+bNmyhUePHhVRcEKUbAsWLGD06NEkJCSQnp6e7/MYewca4OjRoyxevJjo6GisrfOecJCWlkZycrLOQwhhHhcvXmTevHl4e3vTsmVLjh49ysyZM7ly5QqbN29m7969BAcHM3bsWL2fN1XfA2Bvbw+gM0IF8ODBAxz0VFq2sbHh7t27bN++HX9/f1544QW2b98OkONN3smTJ3P37l3t4+LFiwWKWQiRM6MTsPLlyzNx4kRcXFxo1aoVHTp00HkYysXFhW+++YbmzZtrjykUClQqFbdu3eLQoUP4+fnpfGnp0KEDf/zxB9euXTPobnRe5ygynp4wYwasWAEnT+baVKlUcu3aNWJjY4soOCFKtqlTpxIXF0ejRo2wsbHByspK52EoY+9A//fff/Tr14+wsDCDF47LHWghLIeXlxcrVqzglVde4fTp0xw+fJihQ4fqjGr5+Pjk+O/bVH0PPL3xk5SUpHM8KSmJatWqZWtfrVo1rK2tdaYb2tvbU6tWrRzXgdna2uLo6KjzEEIUDqPXgDk6Oppk93ZnZ2edzZwBduzYwV9//UVAQABTp06lcePGOu+7u7sDcOHCBYPuRl+6dCnXc7i6umaLq9DmQI8dC5s2wYgRcPAglNGf+z733HM0adKEyMhIXnnlFdNcW4hSbNq0aSY5T+Y70JqfIec70KNHj6Zu3boMHz7c4GtMnjyZ8ePHa18nJydLEiaEmezcuZNXXnlFb7L077//8swzz/Daa6/x2muv6f28qfoegKZNm+Lo6MiBAweoXbs2AHfu3CE+Pp6RI0dma+/n58fjx4/55ZdfaNGiBQCpqan89ddfOa5XFUIUHaMTsMJaPxUXF8egQYN4/fXXefXVVxk3bpzeO82g/sKT293oW7duAeo71rmdQ5/58+cTEhJS8F8oKxsb9b4T7durKyMOHJhjU6VSyeTJk7l16xaVKlUyfSxClCJKpdIk58l8B1rzBUjzWl/RnI0bN2Jra0v58uUBtFOQGjZsSP/+/Vm9enW2z9ja2sqG0UJYiJ49e3L16lWqVKmiczwxMZFGjRpx7969XD9vqr4H1H3DyJEjmTRpElWqVMHLy4uJEyfi6elJz549SU9P5/r16zg5OWFvb0/r1q3p1KkTAwYMYM2aNVSuXJmZM2dibW1tkpvoQoiCMSgBy22BeVZ+fn5GB/Hll1/St29fXnzxRbZt2wao7zbrm+sM4ODgYNDd6LzOoU+h3oFu1w769lWXp3/tNcghuerbty/BwcHExMQQFBRkmmsLUUpFRUXl+r6hX0aMvQOddePQn3/+mcDAQL799tscq5AJIcxr48aNREdHA6BSqejRo0e2LSaSkpKoWLFinucyVd+jMXv2bB4/fsyQIUNITU3Fz8+P2NhYbGxsSExMpGbNmkRERDDwyQ3enTt3MmnSJHr27Mn9+/fx9fVl//79uLi4GHVdIYTpGZSAtWvXTrs+C9DufZH1NWD0QtOVK1cyZswYevbsSXR0tPbur6enp965zgAeHh7aIhW53Y3O6xz6FPod6MWLoX59mDoVctg3omrVqgQEBBAVFSUJmBAFNDCH0WaFQoGnp6fBX4KMvQOddXNmzbTpGjVq6J3+LIQwv+7du/Pjjz9qv99Uq1ZN5yavQqGgUaNGBo1umarv0bCysiIsLExvITEvLy9tzBoVKlRg1apVrFq1yqjrCJGNZjlOXFzObTTvSfEogxiUgGVesLlv3z5mz57NsmXLeOmllyhbtixHjx5l3LhxfPDBB0ZdPDw8nFGjRjF69GiWLl2qs0u8n58fq1evJj09XTv/eu/evdSrVw9XV1ecnJzyvBud1znMws0NZs+GceNg0CB4/nm9zZRKJb179+aPP/6gXr16RRykECVHRkaGzuuHDx+SmJjIyJEjqVu3rlHnMvYOtBCieKlUqRIbN27Uvv7oo4/yXUrelH2PEGZ16pT6OTw8x8GDbG1FrgxKwGrUqKH9OSwsjPXr19OxY0ftMX9/f1atWoVSqaR///4GXfjs2bOMGTOGHj16MHnyZJ2qhPb29gwaNIiFCxcyePBggoODOXr0KMuWLdOum8jrbjSQ5znM5r33ICJCXZDjyBHQs8D3tddew9nZmaioKObOnWuGIIUomWxsbKhbty7Lli3jxRdfZOXKlQZ/1tg70Jm1a9cu1/eFEOZ34cIFPD09USgUhISEcPv2bW7fvq23bfXq1Y06d0H6HlEAeY3eWMLIjaXHqCloFxQEOW2SHRenTs6yFL8rMpo9ihMS9L+vOW6ivYwLyugiHJcuXdI7fa9ixYra4heG+PTTT3n06BGff/45n3/+uc57SqWSTZs2ERsby+jRo/Hx8cHNzY1FixbpDPvndjcawNXVNc9zmIW1tbogh68vrFsH776brYmdnR29e/dm8+bNhIaG6owOCiEK7vLly7i5uZk7DCGEBalZsyZXrlzB1dUVLy8vnSUWGiqVCoVCke+9vaTvKWKGjt6Yc+TG0mPUbEng6wv9+uXcLjz8aduilpiofg4MzLtdTklkETI6AWvZsiVTp04lMjJSW93r1q1bTJw4kbZt2xp8nilTpjBlypRc2zz//PMcPnw4x/dzuxtt6DnM5qWX4J13YMoUeOMNyFJlCdSJ6Jo1a9i/f7/OiKMQwnBZF8KnpaVx/vx5Vq5cyauvvqrzvlQHE6J027dvn7b68P79+wt0Lul7LEReozfmHrmB4hGjpfPyUj9HR4O+QlcJCerkTNPOzIxOwD766CM6duyIh4cHzz77LCqVij/++INnnnmmwJ1VqRMWBl98AZMmQaY55xovvvgizz77LJGRkZKACZFPua3H2rZtm7byqkKhkC9BQpRymW8kt23bljNnzpCSksLzT9ZrL1myhFdeecWgtdnS91gIQ0Zvchm50ewHG5fD9EDN8QLtG1vAGAWgKZbj7Q0+Pnm3MzOjE7CGDRty9uxZtm3bxm+//QbAe++9R58+fShXrpzJAyzRqlSB+fPVUxAHD85210OhUKBUKpk3bx4ff/xxvhcCC1GaZV0IL4QQhoiNjaV79+5MmDBBm4DFxMQwa9YsvvvuO1q3bp3r56XvKRlOPZn2Fx4eTngu0wNPmbn4xJ49e7T1FDRJYeak0dXVlc6dO5slNpGd0QkYgKOjI8OHDzd1LKXTkCGwYYO6IMfx4+r1YZn079+f6dOn89lnn0llNSEMNHv2bCZMmGDwTaH//vuPJUuWMGPGjEKOTAhRXEydOpXg4GBCQkK0x44ePcq0adMIDg7mp59+MmN0oqg0fjLtLygoCF890wPj4uIIDw/XtjOHPXv24O/vn+141qRx9+7dkoRZiHwlYMKErKzUBTleeAE+/hjGjNF5u3r16rRv357IyEhJwIQwUEhICEFBQQYnYPfu3SMkJEQSMCGEVkJCAjt27Mh2fNCgQSxbtqzoAxJm4fhk2p+vry/9cpgeGB4erm1nDpqRL02SmJyczKlTp2jcuDGOjo7aJDFzxXFhXpKAWYIWLdTTEKdPh7feUu8VlolSqUSpVJKYmIiXhSweFMKSqVQqWrRood3/Ly/5rWYmhCi5XF1diY+Pp2bNmjrHT58+TcWKFc0UlRA5yytJFJZDEjBLMXcufPopTJgAW7bovNWzZ09GjBjB5s2bmT59upkCFKL4mDlzprlDEEIUc0qlkhEjRnD79m1atmyJQqHg2LFjTJs2zfzb2QghijWDEjBPT0+OHDmCh4eH0WsrhIEqVoSFC9Wl6YcMgfbttW+VL1+eN998k6ioKKZNm6Z3XxIhxFOSgAkhCmr69OncuHGDkSNH8ujRI1QqFWXLlmXUqFE668KEEMJYBu3ue+PGDZKSkgD12or//vuvUIMqtQYMUFdCfO89ePhQ5y2lUsmff/4pi36FEEKIImBlZcXKlSu5ceMGR48e5cSJE9y5c4fFixdTtmxZvZ/ZuHEjaWlpOsf2799Pt27daNq0KX379uX3338vivCFEBbMoBGwtm3b4ufnR9WqVfNcW/H333+bNMBSpUwZdUEOHx9YtgyCg7VvtW3blurVqxMZGam3Co8QQgghCubChQt4enqiUCi4cOGC9niVKlUAuH79uvZY9erVs31+6NChvPLKK7i6ugLqqnNdunTB39+fgIAAfvnlF1q0aMH333/PSy+9VMi/jRCW58aNG8TGxpKSksLJkydp0qQJFSpUICAgABcXF3OHV2QMSsC2b99OVFQUt2/fJiQkhLfeeovy5csXdmylU5MmMGoUhITA22+DpycAZcqUoX///qxYsYLly5djbyEbyQkhhBAlRc2aNbly5Qqurq54eXnpnfKvUqlQKBR6i/eoVCqd16GhoYwZM4YPP/xQe2z8+PFMmjSJQ4cOmf4XEMLCxcbGEhgYmO14dHR0jgVESiKDEjBnZ2dGjx4NQGJiIjNmzJBNgQtTSAjExMDYsfDZZ9rDAwYMYO7cuezatYvevXubLz4hhBCiBNq3bx+VKlUC1FMHC+rcuXN89NFHOseGDx/OmjVrCnxuIYqjgIAAoqOjtaXxNaXzAwICzB1akTK6CmJERAT//fcfa9as4eTJk5QtW5aGDRvSu3dvs+6BUKI4OsKSJdC3L/zf/8HLLwNQt25dWrVqRWRkpCRgQhghNTWVHTt2kJCQwMSJEzl16hQNGzYsVdMdhBB5a9u2rfbnDz/8kAULFuDt7W3w57OOmNWtW5d79+7pHLtx4wbOzs4FilOI4srFxUU70hUeHp5r6fyilJqaCqj3/8uJ5j1N24IwOgG7ePEibdu25d9//6VevXqkp6ezdu1a5s6dy48//ki1atUKHJQA+vSBdetg5Ej47TewswOelsW9cuUKbln2CxNCZPfvv//y0ksvcfXqVdLS0hg6dCiLFy/m2LFj7N+/36gvV0KI0uPgwYNGT/dXqVTUrFmTevXq0aBBA2xtbQkODubgwYOULVuWn376iREjRtC1a9dCiloIkR+JiYkAeqdH6mtb0HoMBlVBzOz999/H09OTxMRE4uPj+fXXXzl//jzVq1cnOFPRCFFACgV8/DFcuKAuT/9E7969KVu2LFuy7BUmhNDv/fffp2HDhly/fl37ZSoqKoqmTZsyYcIEM0cnhLBUAwcOZNKkSZw+fTpbZcOcJCYmEhMTQ69evUhPT+fKlSscP36cx48fA+rpVw4ODixYsKAwQxcmdPfuXUBdUGXLli2Eh4czYsQIwsPD2bJlC3FxcWaOUJiCl5cXoF6Ldvz4cb2P6OhonbYFYfQI2O7du/n++++1FYEAnnnmGZYsWcLLT6bKCRPx9obx42H+fAgMhFq1cHZ25vXXXycyMpL3339f9gQTIg/79u3jm2++0dm7sGLFiixatIh27dqZLzAhhEX74osv+Oeff/j000/1vq+vCEf16tWpXr06r7zyivbYo0ePtGXrf/rpJxo1aiT/7y5Gfv75Z0B94y4qKirHdprKl6J40tyg9fb2xsfHx6C2BWF0AmZtba33wnZ2dgbfIRJGmD4dtm6F0aPhq69AoUCpVNKtWzf+97//5fmXRIjSLiUlBQcHh2zHVSqV9q60EEJkNWvWrHx/9tGjR6SkpFCpUiWdPcMaN26MSqXiwoULesvYi0LwZASL3bvVz8nJcOoUNG6sXnOfxwjWlClTAGjZsiVOTk7ZikeAOvnq3Llzof0KouQxOgHz9fUlNDSUzZs3azuVR48eMWfOHNnTojA4OKj3BHvjDdi1C15/HX9/f6pWrUpkZKQkYELkwc/Pj48//pjly5cD6kXyjx49Yvbs2bKnnhAiR0ql0ujPPHjwgPfee4/o6GgeP35M8+bNWbVqFS1atNC2uXbtGjVr1tQ7giYKwZMRLKKi1I+c5DCCVa9ePSIjI3WOWVLxCFE8GZ2AhYWF0apVK2rXrk2LFi1QKBQcPXqU5ORkDhw4UAghCnr0UFdCHDMGOnfGulw5+vXrR2RkJIsWLcLGxsbcEQphsRYvXoyfnx8HDhwgLS2NoKAgEhISuHv3Lj/88IO5wxNCWLDvvvuORYsWcebMGQ4fPszGjRupXbs2AwYM0Nt+xowZfP/992zcuBGVSsWSJUto06YNn332mU7hjaz7hRVbmmpwOVWO0xw3QdW4fHsygkXLluDkpB7xCg+HoCDQ3IRzdQVzjmAVcJSu0GWNDywvxmLG6ASsfv36/Prrr3z88cf89ttvqFQq+vbty7vvvkvNmjULI0ahUMCKFdCoEcydC3PnolQqWbJkCd999x2vv/66uSMUwmJ5e3tz8uRJVq1ahaenJxkZGfTu3ZsRI0aYZCGtEKJk2rNnDz169KBPnz4cPnyY9PR0Hj9+zKBBg0hPT+edd97J9pkdO3awbt06/P39AXXhrMDAQN58803+7//+Dz8/PyB7ufpi60nlOPKqHJeY+DTZKWr16kGWESzCw9XxWMoIVgFH6QqdofGB+WIsZoxOwEC9yDQsLMzUsYjc1KkDH3wA8+bBgAE0btyYZs2aERkZKQmYEHlwc3MjNDTU3GEIIYqRmTNnsmDBAsaOHctnn30GwNy5c6lYsSJLlizRm4DduHGDOnXqaF+XLVuWrVu38sorr/D6668TFxdH5cqVi+x3KHSam1jR0erCYVklJKiTM7nZlTtLH6XLGh9YXozFTL4SMGEmkybB5s3qvcF270apVDJx4kRu3rxZsjp0IUzo1q1bLFiwgN9++40HDx5ke3/fvn1miEoIYelOnTrF5s2bsx1/4403mDFjht7PeHt789lnnzFx4kTtMSsrK3bs2EHr1q15+eWX9Z7TEBkZGYSEhLB+/Xpu375N69atWbVqlU7Cl5OtW7fSr18/zp8/b9qRf01RNm9vyG1NugmqxpVolj5Kpy8+sKwYixmj9wETZmRvr56K+P33sGMHb7/9NiqVim3btpk7MiEsVmBgIBs2bMDJyYkaNWpkewghhD5OTk5cvnw52/HffvuNSpUq6f3M9OnTmTJlCl26dOHUqVPa4+XLl+f//u//cHBwyPeWPaGhoaxevZp169Zx+PBhFAoFXbp04eHDh7l+7p9//mHEiBH5uqYQonDICFhx07UrdO8O48bheuYMXbp0ITIykpEjR5o7MiEs0sGDB/nmm29o27atuUMRQhQj/fr1Y8yYMWzYsAGFQsG9e/f47rvvGDlyJL1799b7mVdffZV9+/axdu1aMjIydN6rWrUqhw8fZuzYsXzyySdGxfLw4UOWLFnCwoULtcU8YmJicHd3Z+fOnfTp00fv5zIyMggMDKR58+Yy2i+EBTF6BCwsLEzvHSFRhJYtg9u3ISQEpVLJL7/8wu+//27uqISwSNWqVaNChQrmDkMIUczMmTMHb29vWrRowb1792jWrBndunWjadOmzJ07N8fPtWnThs2bN9O0adNs7zk7O7Np0yb+++8/o2I5ceIEKSkpdOjQQedcPj4+HDx4MMfPzZs3j4cPHzJ58mSjrieEKFxGJ2Dz588n1ZzlRAXUqKHeoHnZMl6pWZOKFStm26NCCKG2ePFi3nvvPfbv309iYiIXLlzQeQghhD6aAhpnz57lk08+Ydu2bfz222/s2rULOzu7Ap3b2CqIly5dAsDT01PnuLu7e4792NGjR1m8eDHR0dFYW+c94SktLY3k5GSdhxCicBg9BbFly5bs2rWL8ePHF0Y8wlDvvw+RkdiOHcvbffoQHR3NvHnzsLKyMndkQlic06dP06lTJ51jKpUKhUIhm6EWpRs3IDYWUlLg5Elo0gQqVICAAHBxMXd0xcOePXDtmvpnzb47mfffkSpkJufs7EzLli21e3dpEp7q1asXWQz3798HwNbWVue4nZ0dt27dytb+v//+o1+/foSFhfHss88aNHNp/vz5hISEmCZgI2hu6ifksJeY5rjc/C/+9uzZw7Un/Vfck34rLlP/5erqSudS0n8ZnYCVL1+eiRMnMm/ePJ599lnss1S2kTnGRcTGBj7+GDp1Qtm+PauSkti7d6927xEhhNq4cePo0KEDw4cPp1y5cuYOp3SLjdW/X1B0tFTRMsSePaCvjw8PVz80du+WJMwEjh49Su/evbONMOV286Z9+/YGj24Z831J810rLS1N53vXgwcPcHBwyNZ+9OjR1K1bl+HDhxt8jcmTJ+vcXE9OTs424lYYEp/sJRaYx15iiYmJ+JprLzFRYHv27NH7HTU8PJzwTP3X7t27S0USZnQC5ujomOMO8KKIdewIvXvzfHg49evWJTIyUhIwIbK4fPkyu3fvlo3iLUFAgDrZyrp/TECAuSMrHjQjX5o/t+RkOHUKGjcGR8enf66adqJA3nvvPTw8PFi6dCkVK1Y06DMdO3Zk5syZ1K9fnxdeeMFksWgSoaSkJGrXrq09npSUpHet2caNG7G1taV8+fIA2mSxYcOG9O/fn9WrV2f7jK2tbbYRtqKgKYsfHR2Nt569xBISEggMDDRt+fwSRjNdNPNoUmaa4+acVqoZ+QoKCsLX15fk5GROnTpF48aNcXR0JC4ujvDwcG27ks7oBCwiIqIw4hD5tWQJivr1UdauzezPPyc5ORlHR0dzRyWExWjVqhW//vqrJGCWwMXl6UiX7B+Tf7n9uWUeCRMF8ttvv3H8+HEaNGhg8GemTZtG+fLlmTFjBt98843JkoamTZvi6OjIgQMHtAnYnTt3iI+P11sF+dy5czqvf/75ZwIDA/n222/1JjnmpBnR8/b2xieXvcSyzrgST2m2PMg6mpRTO3Py9fWlXw79V26xlzT5KkN/5coV1q1bx5kzZ1i2bBk//PADjRo1srh/1KWChweEhBA4YQJTgB07djB48GBzRyWExRg6dCjDhw/n8OHD1K1bl7Jly+q8LyP6Qgh9qlWrlq91R2PHjiU2NpZp06YRHR1tklhsbW0ZOXIkkyZNokqVKnh5eTFx4kQ8PT3p2bMn6enpXL9+HScnJ+zt7bNtzqwp4lGjRg1cXV1NEpOwHI0bNwaeji5lpRld0rQT5md0Avbnn3/SsmVLnJycuHTpEnPmzOGTTz7hnXfeYffu3bz00kuFEafIzahRVIuIoNOFC0RGRkoCJkQmffv2BWDRokXZ3lMoFJKACSH0mjZtGqNGjSIiIoK6desaVblww4YNxMfHmzSe2bNn8/jxY4YMGUJqaip+fn7ExsZiY2NDYmIiNWvWJCIigoEDB5r0usLyaWY+5TW6VKJnSD0pVIPm311qKiQmgpcX2NtDDkVezMXoBOz999+nR48erFu3Tvsfcvv27QwcOJDJkyfzww8/mDxIkYeyZWHVKpR+fgQeOsTff/9NrVq1zB2VEBYh62aoQgiRkzJlyugkWiqVKscpiLlVUHV3d8fd3d2ksVlZWREWFkZYWFi297y8vLRVGvVp165dru8LUeydOaN+Hjo093YWsi+o0QnYTz/9xA8//KDTQVlZWTF16lRatmxp0uCEEdq0oUffvlTYupWo1auZtXChuSMSQgghipWNGzcavUeXEMICdO+ufq5fH8qVU494BQaqCz9plkhVqADPPmu2EDMzOgF7/Pix3jvKd+/elT2ozKzchx/Sa8cOolavZsaCBZQpY/Q+20KUODVr1sz1C9Xff/9dhNEIISxZUUzfS05OZuzYsWzcuLHQryVEqeHiAkOGZD/u7Q25FHcxF6O/oQcEBDBnzhzt0LtCoeDmzZsEBwfTsWNHkwcojPDMMwwYMYLzKSn8uGaNuaMRwiIolUqdR9++fXn++ee5ceMGY8eONXd4QggLtmPHDnx9fXF0dKRixYp07NiR3bt3F+icDx8+ZP/+/SaKUAhRHBk9Avbhhx/Srl07XF1dSU1N5dVXX+Wff/6hUqVKREVFFUaMwghtFi3CKzycyGnT8Bs2DGRUUpRys2bN0nt85cqV/Pjjj4wePbpoAxJCFAsbN25k2LBh9OrVi969e5ORkUFcXBzdunVjx44ddNdMeTKSi4sL58+fN22wQohixegEzN3dnRMnTrBt2zb+97//kZGRQaNGjQgMDCzZ1VWKiTJlyzKgf3+WbtjAiuXLKZdpV3shxFOvvvoqU6ZMMXcYQggLtWDBAhYvXqwzUj527FgWLVrEzJkz852ACSFEvhYJlStXjn79+jFs2DDGjBnD0KFDJfmyIAMmTyYF+HzaNPj3X3OHI4RFOnDgAHZ2duYOQwhhoS5dukS3bt2yHe/Ro0e2jY4z++OPPxgxYgSNGjXC0dERGxsbKlasSOPGjRkxYgRnz54tzLCFEMWA0QlYeno648aNo2LFijRr1owGDRpQsWJFQkNDpcSphahduzatX3yRyMePYeJEc4cjhFl16NBB59G+fXt8fHwYPHgw/fv3N+pcGRkZzJw5Ew8PD8qVK4e/vz9//vlnju1Pnz5Nt27dqFy5Mq6urvTq1YsLFy4U9FcSQhSBjh07EhMTk+14bGxsjnuexsbG8txzz/Hrr7/So0cPwsLCWLNmDfPmzaN79+78+uuv+Pj4yJY9QpRyRk9BnDFjBtu3b2fFihW0bNmS9PR0Dh06xKxZs3j8+DEhISGFEacwknLwYIb9/DOXNm+m2pAh4Odn7pCEMIvq1atnq4JoY2PD2LFjc9ywMiehoaGsXr2aiIgIPDw8CA4OpkuXLpw+fRobGxudtjdv3qRTp060bduWgwcP8uDBAyZMmMDLL79MfHy8jL4JYeFeeuklQkNDOX78OG3btqVs2bIcO3aMbdu2oVQqmT17trbtjBkzAAgODmbUqFEszGErmNDQUIKDg5kwYQLHjh0rkt9DCEuQnJwMQFxcnN73Ncc17Uo6oxOw9evXExERQdeuXbXHnnvuOdzd3Rk5cmS+E7A5c+bw/fffc+DAAe2xEydOMGbMGH755RcqV67M6NGjmTBhgvb9jIwMQkJCWL9+Pbdv36Z169asWrWKOnXqGHyOkqpXr16MGjWKLa6uTBoxAv73P/WGzUKUMps2bTLJeR4+fMiSJUtYuHChtv+LiYnB3d2dnTt30qdPH532n3/+Offv32fTpk3aZGvz5s14enry008/0aFDB5PEJUxozx64dk39s+ZLQuYvC66u0Llz0cdVnJw7Bykp6p8TEnSfNSxoL57crF27lmeeeYYTJ05w4sQJ7XF3d3f27Nmjfa1QKLQJ2NmzZ9mxY0eu5x08eDAff/xxocQshKU6deoUAOHh4YSHh+fZrqQzOgFLS0ujZs2a2Y57e3tz7969fAWxbNkyZsyYgV+mUZqbN2/SuXNnunfvzurVqzly5AgjRoygcuXKvPPOO0Ded6MNOUdJ5eTkRI8ePYg8coTg339H8dFH8P775g5LiCJx8OBBg9v6GTg6fOLECVJSUnQSJ2dnZ3x8fDh48GC2BKxTp0588cUXeke6bt26ZXB8oojs2QP+/tmPh4erHxq7d0sSlpNz56Bu3ezHAwOzHzt71uKTsPxUKqxVqxZ79+6lrr4/hye+/fZbqlWrVpDQhCh2GjduDEBQUBC+vr7Z3o+LiyM8PFzbrqQzOgHTDLtHRkbqTLlZvHgxb7/9tlHnunz5MkOGDOHQoUPUq1dP5721a9dia2tLeHg41tbWeHt7c+7cOcLCwnjnnXcMuhud1zlKOqVSybZt2/jlrbd4ftYs6N0bpNMXpUC7du1QKBR5rktVKBTaPQ3zcunSJQA8PT11jru7u+td1+Xl5YWXl5fOsfnz52NnZ5dj0peWlkZaWpr2dWmZimERNCNfQUHg6wvJyXDqFDRuDI6O6pGw8PCn7UR2mpGv6Gj15qepqZCYCF5eYG+vfi8hQZ2QadoWEzdu3ODgwYNUrVo1x/VfoN72IjAwkEOHDtGhQwc8PT2xtbUlLS2NpKQkDhw4wLZt24iIiCjC6IUwP02xPl9f3xyn/4eHh5eaon4GJWCZ7/g+fvyYH3/8kYMHD/L8889jZWXF//73Py5evMjrr79u1MXj4+OpWLEiJ0+eZPbs2SQmJmrfO3ToEH5+flhbPw2xQ4cOzJ8/n2vXrpGYmJjn3ei8zuHq6mpUvMVNp06dcHd3J9LJiecdHGD8ePjkE3OHJUShK4w9du7fvw+Ara2tznE7OzuDRrQ++ugjVq1axdKlS3Pse+bPny/raM3N1xdyWhuYy7QZkYm3N/j4qH/Wc6fb0oWGhrJ8+XKOHDlCnTp1+Omnn+jatav2hkjHjh3ZtWsX9pqkMpNevXrh4uJCWFgY48aN47///tO+5+DgQOvWrfnmm2/oLKOoQpRqBiVgNWrU0Hldu3Ztnddt27bN18VfffVVXn31Vb3vXbp0KdswpLu7OwAXLlww6G50XufQ9yWoJN2BtrKyIjAwkPXr17Nk8WJsBw1ST5/RN81GiBIka58FkJKSwpkzZyhbtiy1a9emQoUKRp1T82UrLS1N54vXgwcPcHBwyPFzKpWKGTNmMGfOHCZPnqyzp1BWkydPZnymvfuSk5Oz9XFCiMKzdu1a5s2bx7hx47TfEQYNGkT58uU5cuQIFSpU4I033mD+/Pk6RTgya9++Pe3bt0elUnHnzh3u37+Pvb09lSpVKspfRQhhwQxKwMwxVH7//n29d5pB/YXHkLvReZ1Dn5J2B1qpVLJw4UK+cXSkZ9u2MHKkelpNlj8XIUoqlUpFcHAwK1as4NGjR6hUKmxtbRk+fDhLly7NViExJ5pEKCkpSecmVFJSEk2bNtX7mUePHvHOO++wdetWFi1alGcBIFtb22x9lhCi6Kxfv54PP/yQoKAgAI4dO8bZs2cJCwujfv36AEybNo3x48fnmIBpKBQKnJ2d+frrr2nTpo0kYEIILaPXgAH8/vvv/Pbbb9mSGIVCYfS+Ojmxt7fXGYmCp0mTg4ODQXej8zqHPiXtDnSDBg1o0aIFkVFR9Pz4Y3juOVi8GKZONXdoQhSJBQsWsGHDBhYvXoyfnx/p6ekcPHiQkJAQPDw8mGjgXnlNmzbF0dGRAwcOaBOwO3fuEB8fz8iRI/V+pn///uzcuZOtW7dmK9IhhLA8CQkJdOrUSft63759KBQKncrPDRs2NHg/v127dqFUKunbty/R0dEmj1cIUTwZnYAtWLCAKVOm6H3PlAmYp6cnSUlJOsc0rz08PHj06JH2WE53o/M6hz4l8Q70gAEDGD9+PNfXr6fK2LEwd656jUOWAgFClERr165l1apVOglQs2bNqFKlCjNnzjQ4AbO1tWXkyJFMmjSJKlWq4OXlxcSJE/H09KRnz56kp6dz/fp1nJycsLe3Z9OmTcTExLBo0SLatWvH1atXtefStBFCWJ7Mo+KHDh2iSpUqNGzYUHssOTmZcuXKGXSuVatW8dprr/HZZ5/x4Ycflry1509mIxEfr37OWngl6xYEQggAyhj7AU3J+NTUVDIyMnQehlYTM4Sfnx+HDh3SOefevXupV68erq6uOnejNTR3o9u0aWPQOUqLt99+G4VCwdatW2HmTKhUCcaMMXdYQhSJf//9l+effz7b8ZYtW3Lx4kWjzjV79mwGDx7MkCFD8PX1xdramtjYWGxsbLh48SJubm7ExMQAqP+9ARMnTsTNzU3noWkjhLAsjRs31m5jcefOHfbv309AQIBOmx07dhhUKvvcuXPs37+f5cuX06JFC9auXVsoMZvVmTPq56FDoXlzaN1aXeWydWv1a80WBLmsudUsKYmPjyc+Pp64uDi2bNlCXFwc8fHxJEgSJ0ogo0fAHj58SGBgYKGPEg0aNIiFCxcyePBggoODOXr0KMuWLWP16tVA3nejDTlHaeHi4kK3bt2IjIxkzJgxsHQpvPUWfP01vPKKucMTolDVrVuXPXv2ZCsetHv3br3FOnJjZWVFWFgYYWFh2d7z8vLSKXu/e/fu/AUshDCbkSNHMmzYME6ePMlPP/1EWloao0ePBuDKlSts2bKFRYsWsWHDhjzPtWrVKvz9/alRowbvvvsuH3zwAZMnT8bKyqqwf42i0727+rl+fShX7uk2A5qtCCDPjbfPPEnihg4dmuuljC2cJIQlMzoBGzBgAOvWrdP7BcSUXF1diY2NZfTo0fj4+ODm5saiRYtQKpXaNrNnz+bx48cMGTKE1NRU/Pz8tHejDT1HaaFUKunRowenTp2i8ZtvqjcSHT0aOnZ8uj+LECXQ+PHjGT58OOfPn8fX1xeFQsGhQ4dYuXIlCxcuNHd4QggL0rdvXx48eEB4eDhlypThk08+oXnz5oC6SNeaNWuYNGkSgfo2l84kNTWVqKgoNm3aBKjL048dO5adO3fSq1evwv41io6LCwwZkv145q0I8tD9SRJXv359ypUrR0JCAoGBgURHR+P9JImrUKECz1r4xt1CGMPoBCw4OJgmTZqwfft2atasSZkyurMY9+3bl69ANJ1UZs8//zyHDx/O8TO53Y029BylRdeuXalcuTKRkZEsXrwYVq5UbzA6fz7kUclJiOJswIAB3Lp1i7CwMBYtWgSob87Mnj07x+IZQojSa9CgQQwaNCjb8UmTJjFz5kwqV66c5zm2bNlC+fLleeXJLBMbGxsGDhzIihUrSlYCZgIuLi4M0ZPEeXt742NgEidEcWP0GrAhQ4agUql48cUXqVmzJjVq1NB5CMtkY2OjrcL0+PFjqFsXJk6EsDAYNQquXDF3iEKYTK1atZg7dy6XL18GYOzYsVy5coV///2Xq1evcvXq1TxLwgshRGYeHh4GJV8A3377LaNHj9Yp6PHuu+9y6dKlQtkoXghRvBg9AnbgwAH27t1Lq1atCiMeUYiUSiUrVqxg9+7d6pK6U6ZARIR6NEypBDc3c4cohEn4+/vz4YcfMmvWLPz9/Rk6dCivvvoqVapUMXdoQohSYOfOndmO1a5dm7///tsM0QghLI3RI2BVq1aVhZDFlI+PDw0bNiQyMlJ9oFw59SgYQJ8+EBMDJqxkKYS5rF69mitXrrB161asrKx466238PDwYNKkSZw9e9bc4YkCuHHjBlu2bGH16tWMGDGC1atXs2XLFm7cuGHu0IRg+fLlRlWEfvz4MR9++GEhRiRMRdP3xMXFAWirNUrfI/LD6ARswYIFjB49moSEBJOWnReFT6FQoFQq+fLLL7lz5476oJ+f+tnVVZ2ENWwIUVHw+LHZ4hTCFGxsbOjVqxe7du3i8uXLfPDBB+zZswdvb2/8/PyIjIwkNTXV3GEKI8XGxhIYGEhQUBDh4eEEBQURGBhIbGysuUMTggsXLtCgQQNWrlzJtWvXcmx38+ZNPvzwQ7y9vY3eDkOYh6bvCQ8PByA8PFz6HpFvRidgU6dOJS4ujkaNGmFjY4OVlZXOQ1i2wMBAHj16xCeffKL7xsqV8PPPUK+eejpi3bqwbh2kpZknUCFMqEqVKowdO5b4+HhOnjxJ69atmTNnDu7u7uYOTRgpICCA6OhogoKCAAgKCiI6OjrbXk1CmMOSJUvYtGkTn376KR4eHrRq1Yphw4YxdepUJk+ezJAhQ2jWrBlVq1Zl165dbNy4kaVLlxp07oyMDGbOnImHhwflypXD39+fP//8M8f2p0+fplu3blSuXBlXV1d69erFhQsXTPWrljqavkdz4yc8PFz6HpFvRq8BmzZtWmHEIYqIm5sb/v7+REZGMmzYMPW6r5kz1c9ubvDll3DiBMybB8OHqyskTpoEgwdLuXpR7KWnp3P+/HkuXbrEzZs3qVixorlDEkZycXGhX79+gPoOtK+vr/a1EJagVatWHDhwgF9++YUvvviCI0eO8NNPP6FQKPDw8OC1115j48aNNGvWzKjzhoaGsnr1aiIiIvDw8CA4OJguXbpw+vRp7fY7Gjdv3qRTp060bduWgwcP8uDBAyZMmMDLL79MfHw8dnZ2pvyVS4XMfY8QBWV0AlYa99AqaZRKJW+//Tbnzp1T76sxa5Zug+eeg08+gd9/V5epHzMG5s6FCRPUSVn58uYIW4h8++mnn9iyZQuffPIJ9+7do0ePHuzYsYOOHTuaOzQhRAnVokULWrRoYZJzPXz4kCVLlrBw4UJ1ES0gJiYGd3d3du7cSZ8+fXTaf/7559y/f59NmzZpk63Nmzfj6enJTz/9RIcOHUwSlxAif4xOwGbnsWfUjBkz8h2MKBqvv/46jo6OREVFERoamnPDBg1g82b1CNmCBfDBB+qEbPx4eO89cHIquqCFMFJCQgJbtmxh69at/PPPPzRp0oSZM2fSr18/GfkSQhQrJ06cICUlRSdxcnZ2xsfHh4MHD2ZLwDp16sQXX3yhd6Tr1q1bhR6vECJ3RidgEREROq8fPXrEtWvXsLGx4aWXXjJZYKLw2Nvb89ZbbxEVFUVISEi2zbSzqVMH1q+H6dNh4UIICYFFi2D0aPXoWKVKRRO4EAZq1qwZJ0+exNHRkb59+zJ48GDZ0FMIUWxdunQJAE9PT53j7u7uetd1eXl54eXlpXNs/vz52NnZ4acpvpVFWloaaZnWfScnJxcwaiFETowuwnH+/Hmdx6VLl7hx4wadO3fW7vguLJ9SqeTChQv88MMPhn+oRg34+GM4fx7eeUedhNWooR4Zy6XakxBFrWLFikRFRXHlyhU+/vhjSb6EEMXa/fv3AbC1tdU5bmdnx4MHD/L8/EcffcSqVauYP38+rq6uetvMnz8fJycn7SNrsieEMB2jEzB9HB0dCQ0NZcmSJaY4nSgCvr6+1K5d++meYMZwd4cPP4TERBg5Up2UeXnBuHFw+bKpQxXCaPv27aNfv36y0FwIUSLYPymClZalMvGDBw9wcHDI8XMqlYrp06czZswYJk+ezNixY3NsO3nyZO7evat9SHl8IQqPSRIwUE9FvH79uqlOJwqZQqFgwIABfPrpp9y7dy9/J3F1Va8J++cfCA6GTZugVi0YMUJ9TAghhBAFphmNSkpK0jmelJREtWrV9H7m0aNH9O/fn7lz57Jo0SLmzZuX6zVsbW1xdHTUeQghCofRa8CioqJ0XqtUKu7evcuaNWtkDVgxM2DAAGbOnMnOnTsZMGBA/k9UqZK6kuK4cbBqlXp0bN06GDAAJk9WryETQpRue/Y8naocF6f7DOobOp07F31cQhQDTZs2xdHRkQMHDlC7dm0A7ty5Q3x8PCNHjtT7mf79+7Nz5062bt2arUiHEMK8jE7ABg4cmO1Y2bJl8fX1ZdWqVaaISRQRLy8vWrVqxaxZs+jcuTNubm4FO6GTkzrhGj0a1qxRrxHbtAnefhumTFFXVRRClD579oC/f/bj4eHqh8bu3ZKECaGHra0tI0eOZNKkSVSpUgUvLy8mTpyIp6cnPXv2JD09nevXr+Pk5IS9vT2bNm0iJiaGRYsW0a5dO65evao9l6ZNqXPjBsTGZr8BFBAALi7mi0uUSkZPQczIyMj2SEtLY9++fdStW7cwYhSFyN/fn/PnzzNv3jxSUlJMc1IHB3Wp+r//ho8+goMHoVEj6NVLvcmzEKJ00Yx8BQVBdLR6pDwoSP0cHa3+OXM7IUQ2s2fPZvDgwQwZMgRfX1+sra2JjY3FxsaGixcv4ubmRkxMDABbt24FYOLEibi5uek8NG1KndhYCAx8etMnPFz9OjbWvHGJUsnoETBRsrz88suEhISwevVqtmzZwtixYxk1apRp9kmyt1fvFzZ0KERFqdeLNWsGr74K06bBCy8U/BpCiOLD1xf69dP/XuaRMCFENlZWVoSFhREWFpbtPS8vL1Qqlfb17t27izK04iEgQH3DJyUFTp6EJk2gQgX1cSGKmEEJmKE7pisUCvbu3VuggETRsrGxAWDXrl383//9H/Pnz2fJkiWMHDmSsWPHUqVKFVNcBIYMgYEDYds2mDsXWrZUT0maNg3atCn4NYQQQgghcuLikvMNICGKmEFTEGvUqJHr48KFCxw4cIDjx48XdrzCxNzc3Jg5cybPPfccy5cv5/z58wwbNozly5fj5eXF+++/z5UrV0xzMWtr6N8fTp+GmBi4cgX8/KBdO9i7FzLdvRNCCCGEKFVu3IAtW3TXqW3Zoj4uShSDRsAiIiL0Hk9JSWH8+PH8/fffdOrUifXr15s0OFH43NzcmDVrlvZ11apVWbRoEZMmTWL58uV89NFHfPzxxwwZMoRJkyaZZmNGKyt46y1480346isIDYVOneDFF9UjYl27gkJR8OsIIURJkpysfs5cPTIzzXFNOyFE8aJZp6ahKVQUHS2jdyVMvteA7dmzh6FDh3Lnzh1Wr17NsGHDTBmXMDMXFxdCQ0N5//33WblyJUuXLmXt2rUolUomT55MrVq1Cn6RMmXg9dfhtdfUnU5oKLzyinqd2LRp0L27uo0QQgg4dUr9nLV6ZE7thBDFi6xTKzWMTsDu3bvH+PHjWb9+PR07dmTDhg1Ur169MGITFsDZ2Zlp06YxduxYwsPDWbx4MREREfTt25cpU6ZQv379gl9EoYCXX1Z3MAcOqBOxN95QV06cOlVdPdHKquDXEaIku38fzpxR/5yaComJ4OWlLoZTvz6UK2fAKe5z5swZUlNTSUxMxMvLC3t7e+rXr085Az4vClnjxurnoCB1QZOs4uLUiZmmnRCieJF1aqWGUQmYZtTr9u3bhIeHM3z48MKKS1iY8uXLM3HiREaOHMn69esJCwsjOjqaXr16MXXqVJo0aVLwiygU0L69+hEXpy7W8fbbMHOmeh+xvn2hbNmCX0eIkujMGWjeXP97x4+Dj48BpzhDcz3nOH78OD4GfF4UMkdH9XNe1SQ17YQQQlgkgxKwe/fu8f777+uMeplkLZAoduzt7Rk1ahTDhg0jMjKS+fPn07RpU15//XWmTZtGixYtTHMhX1/49lv45Rd1IjZwIMyaBR98oP7Z1tY01xGipKhfX51oASQkqNcRREeDt7f6PYNOUZ/jx4+TkJBAYGAg0dHReHt7m2ak29KZaH3VjRs3iI2NJSUlhZMnT9KkSRMqVKhAQEAALrLZqxBCCAxMwBo1asTFixepVasWvr6+ORblAJgxY4bJghOWy9bWlmHDhvHOO++wdetW5s2bx/PPP8/LL7/MtGnT8NU3PSY/WrSAzz9Xz4WeN0899WbOHAgOVpe2t7c3zXWEKO7Klcs+yuXtbdDI19NTlNMZ6fL29i49I18mWl8VGxtLYOZF9E9ER0fTT6YWCSGKuxs31Ov2M1dqBPUyErnJZDCDpyBWr16dx48fs2nTphzbKBQKScBKmbJly6JUKgkMDGTHjh3MmTOH1q1b0759e6ZNm0b79u1RmKKiYZMmsH27ehRs/nwYN069VqxBA3Vp++bNoWZNcHIq+LWEEBZnz549XLt2Tfs67sn/9OMyjVi5urrSuXPn/F3AROurAgICiI6OJi4ujvDwcIKCgvD19SVAFtELIUoCqdRoEgYlYImJiYUchijurKys6NOnD2+99RZffvklc+bMoWPHjrz00ktMmzaNl19+2TSJWP36EBkJM2bAxInq0bEffnj6fsWK6kRM86hV6+nPNWqAnV3BYxBCFKk9e/bg7++v973w8HDCM41Y7d69O39JmInWV7m4uGhHusLDw/H19S09I1+pqernhISc22je07QVQhQvUqnRJPJdhl4IfcqUKUOPHj3o3r073333HaGhoXTt2pXmzZszbdo0XnvtNcqYorR87drw8cfqu9FvvqmuAHf+vPrx99/q55074cIFePz46efc3bMnZpqHh4dUWxTCAmlGvjSjSQDJycmcOnWKxo0b4+joqB1xyjxKJoqY5matnimYetuaaqq6EKLoSKVGk5AETBQKhUJB165d6dKlC/v37yc0NJQePXrQuHFjpk6dyptvvolVQZMdNzcICXn6umXL7G0eP4bLl3UTs/Pn4a+/4Pvv4cqVp23LllWPkmVNzDQJW+XKskG0EGaU12hSeG5rt0Th8/JSP2uKv+ijKRCjaZsD2RJBCFGU7t+/D0B8fLz2WNb+JyG30X0jSQImCpVCoaBDhw506NCBH3/8kTlz5tCnTx/q1avHlClTePvttylbmKXlra3VSVWNGtCuXfb3U1Phn390k7Pz5+HYMYiJgbt3n7YtX15/Yqb52cGh8H4PIYSwdJqiSIYUf8mjgJJsiVD6aJJuzZdczbMk3aIonHmyj+bQoUPzbFuhQoUCX08SMFFkWrduzf/93/9x7Ngx5s6di1KpZNasWXzwwQcolUpszVFaXrNJbU5ltm/f1k3MNI//+z/1FJq0tKdtq1TRn5jVrAnVq8seZkIIYaBSvSVCKZU16dZUE5WkWxSF7t27A7oJf9b+B9TJ17PPPlvg60kCJorc888/zxdffMGvv/7KvHnzePfddwkNDSU4OJghQ4Zgb0ml5StWVD/0df4ZGXD1avbk7O+/4fBhuHRJ3QagTBmoVk03MXN1hfR09ePx46c/53TMktrUqQP79qmngQqRFxPtsSVKj1K9JUIppUm69U07FaKwubi4MGTIEL3vFUb/IwmYMJumTZsSExPDrFmzmD9/PuPGjWPu3LlMmDCBd999l/Lly5s7xNyVKaMu6uHurn8x+cOHcPFi9uIgp0/Drl1w65Z6iqS1tbr4R+aHKY5ZW6s3rDblOa2s1GvqwsLU6+ckAROGMNEeW8I0ZLNoYYkyJ90m20tUCAslCZgwO29vb6Kiopg5cyYLFixgypQpLFiwgHHjxjFy5EiciuveXjY26mqNtWtnf+/KFVizBoYPL35JzJUr6nL+xS1uYT4m2mNLmIZsFi2EEOYlCZiwGLVr12bdunVMnz6dhQsXEhoayqJFixg9ejRjxoyhcuXK5g7RdNzc1JtKF0fFOfaS6tw59Z4sGppKTZkrNlWoALnMWz937hwpT86RdRG8+uMFmPdewD22kp9MTYzLaQpjpveSzTiNMfNm0SbfKNqEZLNoIUqWu08Khu3evRvQv02HsCySgAmLU716dVauXMnUqVNZvHgxS5YsYenSpYwYMYLx48fzzDPPmDtEISzHuXNQt67+97KOcpw9qzcJO3fuHHX1nCPrKMnZs2dNsvjYWKeeTE3Muulybm2LWk6bRZtso2h4WpX1yZcskpPV0zYbN1YnrwZ+ySrVm0ULUQL9/PPPAERFRREVFZVjO1dX16IKSeRBEjBhsdzc3FiyZAkffPABy5YtY8WKFaxYsYJhw4YxceJEPDw8zB2iEOanGfnKvPdSaqq6SqeXl7rSp2bvpcyjZDqnSHlyCnWlJ317nwQGBmrbFbXGT6YmZt6IOSvNaE7j/E5jLGByk3Wz6ELZKPrJlyyiotSPnMiXLCFKlSlTpgDQsmVLnJycso1ug+WMwAs1ScCExatSpYq2OMeKFStYtmwZ4eHhDBo0iLFjx1K7dm2sreWvsijlsu69lI9F7JkrPVnSInjHJ1MTDdmI2TGHaYx5MlFyk1uMBd4o+smXLFq2BCenp2vnMq+tc3WFYv4lq1CnwwpRAtWrV4/IyEidY5Y2ui3TJHXJt1ZRbFSsWJEZM2Ywbtw4Vq1axaJFi1i9ejUKhYIqVarg5uZG1apVcXNzy/FnB9ksWQihT3FIburVgyxfsggPz31tXRaFuk7t/n31c3y8+jnrSCzorkvUw9Knwwoh8qc4TZMsik3BJQETxU6FChWYNGkSb775JjNmzKBZs2Y8ePCAK1eucPXqVf744w8OHDjAlStXSMu8UfKTz2qSsdyStcqVK1OmTBkz/YbCUmVkZBASEsL69eu5ffs2rVu3ZtWqVdSpU0dv+5s3bzJ69Gi+/fZbAHr16sXSpUtL342AAk7vy3rnVH0KE989LWByk1ehEEspElKo69TOnFE/Dx2ad9sKFfQetvTpsCIH9++r//tnLQBUvz6Y6AurKN400yQVCkW20TpQT9/u0aOHRUyTLIpNwSUBE8VW7dq12bJlS47vq1Qq7t69q03Mrly5ku3n3377jStXrnD79m2dz1pbW/PMM8/kOppWtWpVqlatiq2tbWH/qsJChIaGsnr1aiIiIvDw8CA4OJguXbpw+vRpbGxssrV/8803SU1NZe/evdy5c4dBgwYxYsQIvf/zKdEKOL3P0Dun6lOY5+6poYVCzFUkBIpgnVr37upnzZduzdrDzOsTIc+KnGC502HNyaJvAJ05A5m+sGoLAB0/rjs1WpRammmSN27coHPnzha9D2FRbApeKhIwYzstUTIoFAqcnZ1xdnbGO/P//PV48OAB//77b7YkTfMcHx/P1atXuXr1Kunp6TqfrVSpUo4JWuZjTk5OKBSKwvyVRSF6+PAhS5YsYeHChXTt2hWAmJgY3N3d2blzJ3369NFpf/jwYQ4cOEBCQoK20167di0vv/wy8+fPx93d3TSBpaaqn3Ob2qV5T9M22ylSnzTTfw7N8dQcPp+nAk7vy7rAHLC4ReZ5FQopcJEQEyq0dWouLjBkSPbjWdcninyx6BtA9eurk62s005N+IVVlAyZq7AWChOMxhbFpuClIgEzttMSpY+dnR01atSgRo0aubbLyMjgxo0bOY6o/fPPPxw5coQrV67w33//ZbtG5sSscuXK2NvbY2dnV+CHra2tTJksZCdOnCAlJYUOHTpojzk7O+Pj48PBgwezJWCHDh3Czc1N545Zu3btUCgU/Pjjj7z11lumCSwxUf2sZ2NdvW31/M8k8ck59G3Om7Vdvv5nVMDpffoWmKtPYTmLzFUqFYD2333W0SWNfBcJMYHiME2y0G8GFFMWewNIo1y5p0m2jFgKcyomo7ElPgEzttMSIjdlypTB1dUVV1dXmjRpkmvbe/fu5Tj9MTExkSNHjuDo6Mjjx4958OCBzuPx48dGx2ZjY2N04pbfBNDZ2ZmqVavm94+xWLp06RIAnp6eOsfd3d25cOGC3vZZ29rY2FC5cmW97fNNsy/etGlP7zanpUFSEri7g60tnD8P06er70rrPcUzT04xjfr165OWlkZSUhLu7u7Y2tpy/vx5pk+fjlcOnxfFY4G5JkZLniZ55sk6skK7GVBMWewNICEsTTEZjS3xCZixnZYQplK+fHmeffZZvZW6rly5wpo1axg+fDhubm7Z3n/8+DFpaWnZErOCPFJTU3nw4AE3b97Ms11GRkauv1vdunU5cOCA3thLqvtPKrxlXfNnZ2fHrVu39LbXtz7Qzs6OBw8e6L1GWlqaTuEYg0YiNCNgc+bk3TaHL/+aEbA5eZyjwMnDjRsQG/u06IbmOSBAPX3NoFPcIDY2NlsFP5OtH8hnjJppkg0bNiQxMZGzZ8+yd+9eOnbsSN26dWncuDF16tQxzRTJfMZo6KhR5v9f5ksBpgDl9G8jK0uYylmULPYGkLAohd4/FgfFZDS2xCdgxnZa+foCJISR3NzcmDVrVo7vW1tbY21tbbZqefpG5TSPS5cuERsba5a4zMn+SRnttLQ07c+g/sKo77+Tvb19tiqcubUHmD9/PiEhIcYFpil8oFDoX38D6iIIL7yQY+GD7k/OoVAoGKLnHNHR0bzwwgsFL/sdG6s7VTI8XP2Ijja4jHpsbKzO6IhmNCc6Oto0UxHzGaNmmuSWLVuYNGmS9vjevXvZu3cv0dHRplufls8YZ8+ejZ2dXeFXISvAFKDevXtja2tb+H8XixmLvQEkLEqh94/CZEp8AmZsp5WvL0BClDDW1taUL1+e8uXLZ3uvRYsW2i/spYnmJk5SUhK1a9fWHk9KSqJp06Z623/xxRc6xx4+fMjNmzepVq2a3mtMnjyZ8ePHa18nJydnu3mUjabwwf370KyZ+pi+qRe5jDy4uLgwZMgQ7t+/T7NmzfRWfjLJ3icBAeokISUFTp6EJk3UFfECAow4RQDR0dF6K2iZRAFjLPT4ChBjkVUhK8AUoCL7u1jMWOwNIGFRiqT/ESahUGlWDpdQn332GW+++Sb379/X6bTeeust0tLS+PLLL3Xa67sD5Onpyd27d826eFqI4iQ5ORknJ6cS9e8mLS0NV1dXPvzwQwYPHgzAnTt3cHd3Z+PGjdmmMx85coRWrVpx7tw5bcXV2NhYunbtyoULF/Dw8MjzmiXxz1GIwlYS/90cPXqUli1b8ueff+rcAGrdujVNmzbl448/1mm/cOFCVqxYwcWLF7XHHj58iL29Pdu3b6dXr17ZriHff4QoOEP7nxJfNi3zXevMkpKS9N6FtrW1xdHRUechhBC2traMHDmSSZMmsWvXLk6ePEnv3r3x9PSkZ8+epKenc/XqVe06m5YtW+Lr60ufPn04duwY+/fv591332XAgAEGJV9CCKHRtGlTHB0dOXDggPbYnTt3iI+Pp02bNtna+/n5cenSJf7880/tsf379wPw0ksv6b2GfP8RouiU+ATM2E5LCCFyMnv2bAYPHsyQIUPw9fXF2tqa2NhYbGxsuHjxIm5ubsTExADq9VQ7d+6kZs2atG/fnrfeeosuXboUbJ8lIUSpJDeAhChZSvwasMydVpUqVfDy8mLixInaTksIIQxlZWVFWFgYYWFh2d7z8vIi64xuV1dXduzYUVThCSFKsNmzZ/P48WOGDBlCamoqfn5+2htAiYmJ1KxZk4iICAYOHKi9AfTee+/Rvn177O3t6dWrFx9++KG5fw0hBKUgAYPcOy0hhBBCCEsnN4CEKDlKRQKWW6eVF02HJuVYhTCc5t9LCa/xU+ik/xHCeNL/mIb0P0IYz9D+p1QkYAWRkpICZN9HTAiRt5SUFJycnMwdRrEl/Y8Q+Sf9T8FI/yNE/uXV/5T4MvQFlZGRQVJSEhUqVEChUOTYTlOu9eLFi8WqclBxjRskdnMxJHaVSkVKSgru7u6UKVPia/0UGkP7H2NZ+t8/S48PJEZTKYwYpf8xjcLof0rr30lTkxgLrrDiM7T/kRGwPJQpUybHTVP1Ka6lW4tr3CCxm0tescud54Iztv8xlqX//bP0+EBiNBVTxyj9T8EVZv9TGv9OFgaJseAKIz5D+h+5NSSEEEIIIYQQRUQSMCGEEEIIIYQoIpKAmYitrS0zZ87E1tbW3KEYpbjGDRK7uRTn2IWapf83tPT4QGI0leIQozCd4vDfW2I0DUuP0dzxSREOIYQQQgghhCgiMgImhBBCCCGEEEVEEjAhhBBCCCGEKCKSgBVQRkYGM2fOxMPDg3LlyuHv78+ff/5p7rDyNGfOHNq1a6dz7MSJE7Rt2xYHBweqV6/O4sWLzRNcFrdu3eLdd9+lWrVqODo60rp1a3788Uft+5YaN8C1a9fo378/VapUoXz58nTr1o3ff/9d+74lx57Z2bNnKV++PJs2bdIeKy6xi6csvb+yxH6pOPQ/xamfkb6k9LLk/scS+x6Q/sfULKr/UYkCmTVrlsrV1VX1zTffqE6cOKHy9/dX1alTR5WWlmbu0HK0dOlSlUKhULVt21Z77MaNGyoXFxfVkCFDVL///rtq48aNKjs7O9XGjRvNF+gTnTt3VjVt2lR16NAh1ZkzZ1SjRo1S2dvbqxISEiw6bpVKpWrZsqWqVatWqmPHjql+//131RtvvKFyc3NT/ffffxYfu8bDhw9VLVq0UAGqiIgIlUpl2X9fRM4sub+y1H6pOPQ/xaWfkb6kdLPU/sdS+x6VSvofU7K0/kcSsAJIS0tTVahQQRUeHq49dvv2bZW9vb1q27ZtZoxMv0uXLqlefvlllYODg6p+/fo6nc28efNUHh4eqkePHmmPTZ48WVWvXj0zRPrUuXPnVIAqLi5OeywjI0NVp04d1fTp0y02bpVK/Q+7T58+qt9++0177MSJEypAdfToUYuOPbPJkyer2rdvr9NpFZfYxVOW2l9Zcr9UHPqf4tTPSF9Selli/2PJfY9KJf2PqVla/yNTEAvgxIkTpKSk0KFDB+0xZ2dnfHx8OHjwoBkj0y8+Pp6KFSty8uRJWrZsqfPeoUOH8PPzw9raWnusQ4cO/PHHH1y7dq2oQ9VycXHhm2++oXnz5tpjCoUClUrFrVu3LDZugMqVK7Nt2zYaNmwIwL///svixYupVq0aDRo0sOjYNQ4ePMiaNWuIjIzUOV4cYhe6LLW/suR+qTj0P8Wln5G+pHSzxP7HkvsekP7HlCyx/5EErAAuXboEgKenp85xd3d3Lly4YI6QcvXqq6+ydetWatWqle29S5cu6f09ALP+Ls7OznTt2lVnn4YdO3bw119/ERAQYLFxZzVs2DCqVq3Kjh072LBhAw4ODhYf+507d+jfvz8rVqzIFqelxy6ys9T+ypL7peLW/1hqPyN9ibDE/seS+x6Q/sdULLX/kQSsAO7fvw+QbRM3Ozs7Hjx4YI6Q8u3+/ft6fw/Aon6XuLg4Bg0axOuvv86rr75abOIeO3Ysv/zyC4GBgXTv3p34+HiLjz0oKIhWrVrRt2/fbO9Zeuwiu+LYX1na3zNL738stZ+RvkQUt/7HEv9eSv+TP5ba/1jn3UTkxN7eHoC0tDTtz6D+j+bg4GCusPLF3t6etLQ0nWOav3yW8rt8+eWX9O3blxdffJFt27YBxSNugAYNGgCwdu1aDh8+zMqVKy069s2bN3Po0CFOnTql931Ljl3oVxz7K0v6e1Yc+h9L7GekLxFQ/PofS/t7Kf1P/lhy/yMjYAWgGbZMSkrSOZ6UlES1atXMEVK+eXp66v09ADw8PMwRko6VK1fSs2dPunbtyrfffqvtwC057mvXrrFt2zbS09O1x8qUKUODBg24fPmyRce+ceNG/v33Xzw9PSlfvjzly5cH4N1336Vhw4YWHbvQrzj2V5by98yS+x9L72ekLxFQ/PofS/p7Kf1P/lly/yMJWAE0bdoUR0dHDhw4oD12584d4uPjadOmjfkCywc/Pz8OHTqk849o79691KtXD1dXVzNGBuHh4YwaNYqRI0cSExOjM1xsyXEnJSXRt29ffvjhB+2xR48eER8fT4MGDSw69ujoaBISEjhx4oT2ATB79my+/fZbi45d6Fcc+ytL+Htm6f2Ppfcz0pcIKH79j6X8vZT+p2Asuv8p9DqLJdyUKVNUlStXVn355ZeqX3/9VeXv76+qW7eu2fe1yItSqdQpufrvv/+qKlasqFIqlarTp0+rIiIiVHZ2dqpNmzaZL0iVSvXHH3+oypYtq+rRo4fqypUrOo87d+5YbNwqlbpcbEBAgKpevXqqgwcPqk6dOqXq06ePqmLFiqp//vnHomPXh0ylW4tb7ELN0vsrS+uXikP/Uxz7GelLSidL7n8sre9RqaT/KSyW0v9IAlZAjx8/VgUHB6uqVKmiKl++vKpr166q8+fPmzusPGXtbFQqlero0aOqF198UWVra6vy8vJSrVixwjzBZTJ37lwVoPehVCpVKpVlxq1x584dVVBQkMrNzU1lb2+v8vf319kvw5Jjzypzp6VSFa/YhZql91eW1i8Vl/6nuPUz0peUTpbc/1ha36NSSf9TWCyl/1E8CUYIIYQQQgghRCGTNWBCCCGEEEIIUUQkARNCCCGEEEKIIiIJmBBCCCGEEEIUEUnAhBBCCCGEEKKISAImhBBCCCGEEEVEEjAhhBBCCCGEKCKSgAkhhBBCCCFEEZEETAghhBBCCCGKiCRgwmgDBw5EoVDk+ti0aRMKhaJI45o1a5b2+rNmzcr3Oby8vAxqq/kdFQoFAwcOzNf1hBDGkf5HTfofIYqe9D9q0v8UnEKlUqnMHYQoXu7evUtqaqr2tZubG8uWLaN3797aY05OTty9e5eqVasWWVyzZs1iw4YNHDt2jPLly1O+fHmjz3Hv3j1SU1OpUqVKnm1TU1O5e/cuPXv2pG7dumzatCkfUQshjCH9j5r0P0IUPel/1KT/KThrcwcgih8nJyecnJyyHcva2djb2xdlWABYWVkVqNMzpuOyt7fH3t4eGxubfF9PCGEc6X/UpP8RouhJ/6Mm/U/ByRREUSiyDsErFAq2bdtGhw4dsLe3p06dOnz11Vd89dVX1KtXDwcHB7p06cKNGze0n0lISKBr166UL18eNzc3+vXrx9WrVw2O4cCBA1hbW7N//34aNGiAnZ0drVq14uzZs8ydO5dnnnmGSpUqMWrUKDQDwZmH4BMTE1EoFOzYsYOWLVtiZ2dHrVq12Lhxo2n+kIQQhUL6HyGEuUj/IwwhCZgoMqNHj+a9997jt99+o2HDhvTt25c5c+YQHR3N119/zc8//8yCBQsASEpKok2bNtSqVYtjx47x9ddfc/fuXV566SX+++8/g6+Znp7O+PHj2bhxI0eOHOHatWu0atWKhIQEfvjhB+bPn8/KlSv55ptvcjzH+PHjmTp1KidOnKBjx44MHTqU8+fPF/jPQwhRdKT/EUKYi/Q/IitJwESRUSqVvPHGG9SuXZvhw4dz79495s6dy/PPP0/79u3x9/fnt99+AyA8PBw3NzdWrlyJt7c3zZs355NPPuHq1avs2LHDqOuGhoby4osv8txzz/HGG2+QkpLC2rVrqV+/PsOHD+eZZ57RXlef999/n9dee4369euzePFiMjIyOHLkSIH+LIQQRUv6HyGEuUj/I7KSNWCiyNSrV0/7c7ly5QCoVauW9pidnR0PHjwAID4+noSEhGzzkR88eEBCQkKBrlu1alXt9bNeVx9vb2/tz5q53w8fPjQqBiGEeUn/I4QwF+l/RFaSgIkiU7Zs2WzHypTRPwibkZFBhw4dWLVqVbb3nJ2dC3TdnK6ZE1tb22zHpHioEMWL9D9CCHOR/kdkJVMQhUVq1KgRCQkJeHp6UqdOHerUqUOlSpUYO3Ysp06dMnd4QogSTPofIYS5SP9TOkgCJizSiBEjuHv3Lm+//TYnTpzg5MmTvP322xw9epSGDRuaOzwhRAkm/Y8Qwlyk/ykdJAETFqlmzZr88MMP/Pfff7Ru3Ro/Pz/KlCnDvn37cHV1NXd4QogSTPofIYS5SP9TOihUMplTlBCzZs1i06ZNJCYmFul127Vrh5eXl+wEL0QpJv2PEMJcpP8pfmQETJQo6enpXL16lXv37hX6tVJTU7l69apUBBJCANL/CCHMR/qf4kUSMFGiXLp0CTc3NxYvXlzo14qJicHNzY3Dhw8X+rWEEJZP+h8hhLlI/1O8yBREIYQQQgghhCgiMgImhBBCCCGEEEVEEjAhhBBCCCGEKCKSgAkhhBBCCCFEEZEETAghhBBCCCGKiCRgQgghhBBCCFFEJAETQgghhBBCiCIiCZgQQgghhBBCFBFJwIQQQgghhBCiiEgCJoQQQgghhBBF5P8BdIe4A5cu6wkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 875x250 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(2.5*3.5,2.5))\n",
    "\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "\n",
    "ax1= plt.subplot(gs[0,0])\n",
    "\n",
    "\n",
    "ax1.errorbar(t_l,num_obj_tc,yerr=num_obj_std_tc,label=\"number of objects S0 control\",color=\"red\",linewidth=1)\n",
    "ax1.errorbar(t_l,num_obj_t,yerr=num_obj_std_t,label=\"number of objects S0\",color=\"black\",linewidth=1)\n",
    "\n",
    "#ax1.set_aspect(1./ax1.get_data_ratio())\n",
    "\n",
    "ax1.set_xticks([0,10,20,30,40])\n",
    "plt.yticks(fontname = \"Arial\",fontsize=11) \n",
    "plt.xticks(fontname = \"Arial\",fontsize=11) \n",
    "ax1.set_ylabel(\"Number of droplet\", size=11, fontname=\"Arial\")\n",
    "ax1.set_xlabel(\"Time [min]\", size=11, fontname=\"Arial\")\n",
    "asp1=ax1.get_aspect()\n",
    "\n",
    "\n",
    "###############################################################\n",
    "\n",
    "\n",
    "ax2= plt.subplot(gs[0,1])\n",
    "\n",
    "vol_all_lc=[vol_t0c_all,vol_t10c_all,vol_t20c_all,vol_t30c_all,vol_t40c_all]\n",
    "\n",
    "vol_all_le=[vol_t0_all,vol_t10_all,vol_t20_all,vol_t30_all,vol_t40_all]\n",
    "\n",
    "#source: https://stackoverflow.com/questions/16592222/matplotlib-group-boxplots\n",
    "def setBoxColors(bp):\n",
    "    plt.setp(bp['boxes'][0], color='red')\n",
    "    plt.setp(bp['caps'][0], color='red')\n",
    "    plt.setp(bp['caps'][1], color='red')\n",
    "    plt.setp(bp['whiskers'][0], color='red')\n",
    "    plt.setp(bp['whiskers'][1], color='red')\n",
    "\n",
    "    plt.setp(bp['medians'][0], color='red')\n",
    "    #plt.setp(bp['fliers'][0], markeredgecolor='red')\n",
    "\n",
    "    plt.setp(bp['boxes'][1], color='black')\n",
    "    plt.setp(bp['caps'][2], color='black')\n",
    "    plt.setp(bp['caps'][3], color='black')\n",
    "    plt.setp(bp['whiskers'][2], color='black')\n",
    "    plt.setp(bp['whiskers'][3], color='black')\n",
    "\n",
    "    plt.setp(bp['medians'][1], color='black')\n",
    "    #plt.setp(bp['fliers'][1], markeredgecolor='black')\n",
    "\n",
    "#ax6.grid()\n",
    "pos1=np.asarray([0,10,20,30,40])\n",
    "pos2=pos1-2\n",
    "\n",
    "pos3=pos1+2\n",
    "\n",
    "pos1x=[0,0,10,10,20,20,30,30,40,40]\n",
    "#ax1.boxplot(vol_all_lc,positions=pos2,widths=5)\n",
    "\n",
    "#ax1.boxplot(vol_all_le,positions=pos3,widths=5)\n",
    "\n",
    "for i in range(len(vol_all_lc)-1):\n",
    "    j=i+1\n",
    "    bp=ax2.boxplot([vol_all_lc[i],vol_all_le[i]],positions=[pos2[i],pos3[i]],widths=2,flierprops={'markersize': 0.001}, showfliers=False)\n",
    "    setBoxColors(bp)\n",
    "    bp=ax2.boxplot([vol_all_lc[j],vol_all_le[j]],positions=[pos2[j],pos3[j]],widths=2,flierprops={'markersize': 0.001}, showfliers=False)\n",
    "    setBoxColors(bp)\n",
    "    \n",
    "\n",
    "#ax1.set_title(\"Time 0 to 40, Control\")\n",
    "#ax1.set_xlabel(\"Time\")\n",
    "#ax1.set_ylabel(\"Volume\")\n",
    "\n",
    "#ax2.set_ylim(0,80)\n",
    "#ax1.set_xlim(-10,50)\n",
    "\n",
    "ax2.set_xticks(pos1,labels=pos1)\n",
    "plt.yticks(fontname = \"Arial\",fontsize=11) \n",
    "plt.xticks(fontname = \"Arial\",fontsize=11) \n",
    "ax2.set_ylabel(\"Volume [µm³]\", size=11, fontname=\"Arial\")\n",
    "ax2.set_xlabel(\"Time [min]\", size=11, fontname=\"Arial\")\n",
    "###################################################\n",
    "\n",
    "ax3= plt.subplot(gs[0,2])\n",
    "\n",
    "#ax6.grid()\n",
    "pos1=np.asarray([0,10,20,30,40])\n",
    "pos2=pos1-2\n",
    "\n",
    "pos3=pos1+2\n",
    "\n",
    "pos1x=[0,0,10,10,20,20,30,30,40,40]\n",
    "\n",
    "\n",
    "for i in range(len(sph_all_lc2)-1):\n",
    "    j=i+1\n",
    "    bp=ax3.boxplot([sph_all_lc2[i],sph_all_le2[i]],positions=[pos2[i],pos3[i]],widths=2,flierprops={'markersize': 0.001}, showfliers=False)\n",
    "    setBoxColors(bp)\n",
    "    bp=ax3.boxplot([sph_all_lc2[j],sph_all_le2[j]],positions=[pos2[j],pos3[j]],widths=2,flierprops={'markersize': 0.001}, showfliers=False)\n",
    "    setBoxColors(bp)\n",
    "\n",
    "\n",
    "ax3.set_xticks(pos1,labels=pos1)\n",
    "plt.yticks(fontname = \"Arial\",fontsize=11) \n",
    "plt.xticks(fontname = \"Arial\",fontsize=11) \n",
    "ax3.set_ylabel(\"Sphericity \\n (>0.125 µm³)\", size=11, fontname=\"Arial\")\n",
    "ax3.set_xlabel(\"Time [min]\", size=11, fontname=\"Arial\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig(\"files_effective_droplet_1/figures/S0_number_vol_sph_v8.pdf\",format=\"pdf\")\n",
    "#plt.savefig(\"Daten_Timelapse_Droplets/plots_s0_v1/S0_number_vol_sph_v8.pdf\",format=\"pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "663fe0c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median vol. amphiphile 0.008946767578125\n",
      "median vol. amphiphile 0.036143224609375\n",
      "median vol. amphiphile 0.016230766601562502\n",
      "median vol. amphiphile 0.008157220703125\n",
      "median vol. amphiphile 0.007256107421875001\n",
      "median vol. control 0.00926001171875\n",
      "median vol. control 0.048535677734375005\n",
      "median vol. control 0.07834536328125\n",
      "median vol. control 0.09488079199218752\n",
      "median vol. control 0.06971183984375\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(vol_all_le)):\n",
    "    print(\"median vol. amphiphile\",np.median(vol_all_le[i]))\n",
    "    \n",
    "    \n",
    "for i in range(len(vol_all_lc)):\n",
    "    print(\"median vol. control\",np.median(vol_all_lc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8744e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median vol. amphiphile 0.396\n",
      "median vol. amphiphile 0.536\n",
      "median vol. amphiphile 0.38449999999999995\n",
      "median vol. amphiphile 0.315\n",
      "median vol. amphiphile 0.47250000000000003\n",
      "median vol. control 0.441\n",
      "median vol. control 0.5485\n",
      "median vol. control 0.55\n",
      "median vol. control 0.563\n",
      "median vol. control 0.553\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(sph_all_le2)):\n",
    "    print(\"median vol. amphiphile\",np.median(sph_all_le2[i]))\n",
    "    \n",
    "    \n",
    "for i in range(len(sph_all_lc2)):\n",
    "    print(\"median vol. control\",np.median(sph_all_lc2[i]))"
   ]
  }
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