wircspec_testing_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing pipeline for the WASP-74 transit on 15 July 2017\n",
    "#### The notebook also assumes that you have raw data stored in a directory called 20170715/ (which is the same name of the directory in which the data is stored on tycho, anyways). It will first compute darks and flats, then calibrate all the data (and store the calibrated files in a directory called calibrated/). Next, it will extract and store spectra. Stored spectra are retrieved and integrated across the spectral axis to produce white light curves, and the transit in the target white light curve is fit using the batman package. At the end of the day, we get a fitted light curve, and the final printed output is the unbinned RMS.\n",
    "\n",
    "#### The reason for saving calibrated data in another directory is so that one can test different calibration schemes without having to re-calibrate data (which takes a while).\n",
    "\n",
    "#### While this notebook is currently specific to WASP-74, I hope I have made the framework general enough to extend the testing pipeline to other transits in the near future.\n",
    "\n",
    "#### TODO: Parellelize calibration and extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This first block is the \"options block.\" Here, you will find all of the necessary imports as well as the majority of the important options and switches for testing, arranged roughly in order of most to least used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the test_name set below will set the directories into which\n",
    "#the calibration, image, and dump files are written\n",
    "test_name = 'flat_test_1/'\n",
    "raw_dir = '20170715/'\n",
    "\n",
    "#flags for whether you want to run different calibration steps or not\n",
    "make_darks_flats = False\n",
    "calibrate_all = False\n",
    "extract_all = True\n",
    "plot_white_light_curves = True\n",
    "fit_transit = True\n",
    "\n",
    "#different options for spectral extraction\n",
    "bp_correct_during_extraction = True      #correct bad pix only in cutouts; a bit faster overall\n",
    "bkg_sub = True                           #do you want background subtraction? probably\n",
    "extraction_method = 'optimal_extraction' #weightedSum was the other one, though no longer used\n",
    "measured_angle = 13.43                   #setting to None allows angle to vary as a free parameter in fit\n",
    "order = 1                                #what order traces are you extracting?\n",
    "fractional_fit_type = 'gaussian'         #'gaussian', 'cubic', 'spline' or None\n",
    "\n",
    "#different options for transit fitting\n",
    "sigma_val = 3                            #number of sigmas for sigma-clipping in transit fit\n",
    "prior_on = 1                             #optional: put prior on transit center time\n",
    "dt_prior = 0.0                           #guess on offset from predicted tranist time \n",
    "dt_err_prior = 0.05                      #error on transit time (make this tiny to fix transit time in fits)\n",
    "\n",
    "#starting and ending image numbers to be used for the darks, flats, and science images. also bkg img number\n",
    "master_dark_start = 1071\n",
    "master_dark_end = 1080\n",
    "dark_for_flat_start = 2\n",
    "dark_for_flat_end = 21\n",
    "master_flat_start = 52\n",
    "master_flat_end = 62\n",
    "science_start = 304\n",
    "science_end = 1070\n",
    "background = 297\n",
    "\n",
    "#the approximate positions of the traces on each image\n",
    "order1_pos = [[1397,631],[714,220],[1439,1251],\n",
    "                     [1973,1486]]                #approx radius is 28 pixels\n",
    "order2_pos = [[1313,963],[628,552],[1356,1576],[1890,1816],\n",
    "                     [1158,310]]                 #approx radius is 50 pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next block is the \"bookkeeping block.\" We'll run the necessary import statements, check if necessary directories exist, set up some more extraction options, and write a helper function to write filenames with the WIRC convention (wirc####.fits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import wirc_drp.wirc_object as wo\n",
    "from wirc_drp.utils import calibration\n",
    "from astropy.io import fits\n",
    "from pathlib import Path\n",
    "from scipy.signal import medfilt\n",
    "from scipy import optimize\n",
    "from scipy import linalg as lg\n",
    "import batman\n",
    "import math\n",
    "import warnings\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "def imgname(number, direc):\n",
    "    \"\"\"Given the image number and directory, constructs the image filename with the\n",
    "    correct number of 0s.\"\"\"\n",
    "    num = str(number)\n",
    "    num_zeros = 4 - len(num)\n",
    "    return direc + 'wirc' + '0'*num_zeros + num + '.fits'\n",
    "\n",
    "calib_dir = 'calibrated_' + test_name\n",
    "dump_dir = 'dump_files_' + test_name\n",
    "img_dir = 'image_files_' + test_name\n",
    "Path(calib_dir).mkdir(exist_ok = True)\n",
    "Path(dump_dir).mkdir(exist_ok = True)\n",
    "Path(img_dir).mkdir(exist_ok = True)\n",
    "\n",
    "\n",
    "#for convenience, these will be the final names for the dark, flat, bp, and hp maps. will also set background\n",
    "flat = raw_dir + \"wirc\" + '0'*(4 - len(str(master_flat_end))) + str(master_flat_end) + \"_master_flat.fits\"\n",
    "dark = raw_dir + \"wirc\" + '0'*(4 - len(str(master_dark_end))) + str(master_dark_end) + \"_master_dark.fits\"\n",
    "bp = raw_dir + \"wirc\" + '0'*(4 - len(str(master_flat_end))) + str(master_flat_end) + \"_bp_map.fits\"            \n",
    "hp = raw_dir + \"wirc\" + '0'*(4 - len(str(master_dark_end))) + str(master_dark_end) + \"_hp_map.fits\"            \n",
    "bkg = imgname(background, raw_dir)\n",
    "\n",
    "#setting more things for spectral extraction\n",
    "if order == 1:\n",
    "    order_in = np.array(order1_pos)\n",
    "    cutout_size = 40\n",
    "if order == 2:\n",
    "    order_in = np.array(order2_pos)\n",
    "    cutout_size = 60    \n",
    "\n",
    "n_sources = order_in.shape[0]\n",
    "n_images = science_end - science_start + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, we will make the master darks and flats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "if make_darks_flats:\n",
    "    #first, we construct the list of files used for making each dark/flat\n",
    "    master_dark_list = [imgname(i, raw_dir) for i in \\\n",
    "                        range(master_dark_start, master_dark_end + 1)]\n",
    "    dark_for_flat_list = [imgname(i, raw_dir) for i in \\\n",
    "                        range(dark_for_flat_start, dark_for_flat_end + 1)]\n",
    "    master_flat_list = [imgname(i, raw_dir) for i in \\\n",
    "                        range(master_flat_start, master_flat_end + 1)]\n",
    "\n",
    "    #then, we make the darks and flats. simple!\n",
    "    dark_for_flat, bp_name = calibration.masterDark(dark_for_flat_list)\n",
    "    science_dark, bp_name = calibration.masterDark(master_dark_list)\n",
    "    science_flat, bp_name = calibration.masterFlat(master_flat_list,\n",
    "                                            dark_for_flat, verbose = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we will calibrate all of our science data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "if calibrate_all:\n",
    "    for i in range(science_start, science_end + 1):\n",
    "        if bkg_sub:\n",
    "            raw_data = wo.wirc_data(raw_filename = imgname(i, raw_dir), flat_fn = flat, dark_fn = dark, \n",
    "                                    bp_fn = bp, hp_fn = hp, bkg_fn = bkg)\n",
    "        else:\n",
    "            raw_data = wo.wirc_data(raw_filename = imgname(i, raw_dir), flat_fn = flat, dark_fn = dark, \n",
    "                                    bp_fn = bp, hp_fn = hp)\n",
    "            \n",
    "        if bp_correct_during_extraction:\n",
    "            raw_data.calibrate(clean_bad_pix = False)\n",
    "        else:\n",
    "            raw_data.calibrate()\n",
    "            \n",
    "        raw_data.save_wirc_object(imgname(i, calib_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, we will extract and save all of our spectral data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting from calibrated_flat_test_1/wirc0304.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0305.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0306.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0307.fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shrey/Desktop/wirc/wirc_drp/utils/spec_utils.py:887: RuntimeWarning: invalid value encountered in sqrt\n",
      "  spectra_std.append(np.sqrt(spec_var))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting from calibrated_flat_test_1/wirc0463.fits\n",
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      "Extracting from calibrated_flat_test_1/wirc0617.fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting from calibrated_flat_test_1/wirc0618.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0619.fits\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting from calibrated_flat_test_1/wirc0773.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0774.fits\n",
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      "Extracting from calibrated_flat_test_1/wirc0836.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0837.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0838.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0839.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0840.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0841.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0842.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0843.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0844.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0845.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0846.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0847.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0848.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0849.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0850.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0851.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0852.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0853.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0854.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0855.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0856.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0857.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0858.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0859.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0860.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0861.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0862.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0863.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0864.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0865.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0866.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0867.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0868.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0869.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0870.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0871.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0872.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0873.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0874.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0875.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0876.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0877.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0878.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0879.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0880.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0881.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0882.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0883.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0884.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0885.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0886.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0887.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0888.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0889.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0890.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0891.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0892.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0893.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0894.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0895.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0896.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0897.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0898.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0899.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0900.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0901.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0902.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0903.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0904.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0905.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0906.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0907.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0908.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0909.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0910.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0911.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0912.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0913.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0914.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0915.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0916.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0917.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0918.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0919.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0920.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0921.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0922.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0923.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0924.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0925.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0926.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0927.fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting from calibrated_flat_test_1/wirc0928.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0929.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0930.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0931.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0932.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0933.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0934.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0935.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0936.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0937.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0938.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0939.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0940.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0941.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0942.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0943.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0944.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0945.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0946.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0947.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0948.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0949.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0950.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0951.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0952.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0953.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0954.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0955.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0956.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0957.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0958.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0959.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0960.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0961.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0962.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0963.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0964.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0965.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0966.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0967.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0968.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0969.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0970.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0971.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0972.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0973.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0974.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0975.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0976.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0977.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0978.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0979.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0980.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0981.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0982.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0983.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0984.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0985.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0986.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0987.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0988.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0989.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0990.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0991.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0992.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0993.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0994.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0995.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0996.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0997.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0998.fits\n",
      "Extracting from calibrated_flat_test_1/wirc0999.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1000.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1001.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1002.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1003.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1004.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1005.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1006.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1007.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1008.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1009.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1010.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1011.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1012.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1013.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1014.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1015.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1016.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1017.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1018.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1019.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1020.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1021.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1022.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1023.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1024.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1025.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1026.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1027.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1028.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1029.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1030.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1031.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1032.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1033.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1034.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1035.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1036.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1037.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1038.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1039.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1040.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1041.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1042.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1043.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1044.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1045.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1046.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1047.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1048.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1049.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1050.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1051.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1052.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1053.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1054.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1055.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1056.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1057.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1058.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1059.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1060.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1061.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1062.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1063.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1064.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1065.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1066.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1067.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1068.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1069.fits\n",
      "Extracting from calibrated_flat_test_1/wirc1070.fits\n"
     ]
    }
   ],
   "source": [
    "if extract_all:    \n",
    "    #first, we initialize all of the relevant data storage arrays\n",
    "    wl_arr = np.zeros([cutout_size*2+1, n_sources, n_images])\n",
    "    spec_arr = np.zeros([cutout_size*2+1, n_sources, n_images])\n",
    "    spec_err_arr = np.zeros([cutout_size*2+1, n_sources, n_images])\n",
    "    angle_arr = np.zeros([n_sources, n_images])\n",
    "    bjd_arr = np.zeros(n_images)\n",
    "    \n",
    "    #now, extract from all the images\n",
    "    for j in range(n_images):\n",
    "        n_img = j + science_start\n",
    "        name = imgname(n_img, calib_dir)\n",
    "        print('Extracting from ' + name)\n",
    "        calibrated_data = wo.wirc_data(wirc_object_filename = name,verbose = False)\n",
    "        #we can save the times array first\n",
    "        bjd_arr[j] = calibrated_data.bjd\n",
    "        for i in range(n_sources):\n",
    "            #adding a source to the wirc object\n",
    "            calibrated_data.source_list.append(wo.wircspec_source([order_in[i,1],order_in[i,0]],\n",
    "                                                    'slitless', calibrated_data.n_sources+1))\n",
    "            calibrated_data.n_sources += 1\n",
    "\n",
    "            #get cutout for the source\n",
    "            if bp_correct_during_extraction:\n",
    "                calibrated_data.source_list[i].get_cutouts(calibrated_data.full_image,\n",
    "                            calibrated_data.filter_name, image_DQ = calibrated_data.DQ_image,\n",
    "                            cutout_size = cutout_size, flip = True)\n",
    "            else:\n",
    "                calibrated_data.source_list[i].get_cutouts(calibrated_data.full_image,\n",
    "                            calibrated_data.filter_name, cutout_size = cutout_size, flip = True)\n",
    "\n",
    "            #extract 1D spectrum from the source\n",
    "            calibrated_data.source_list[i].extract_spectra(sub_background = True, bkg_poly_order = 0, plot = False,\n",
    "                            method = extraction_method, poly_order = 1, verbose = False, trace_angle = measured_angle,\n",
    "                            fractional_fit_type = fractional_fit_type, spatial_sigma = 9)\n",
    "            \n",
    "            #write the relevant data into our data storage arrays\n",
    "            wl_arr[:,i,j]=calibrated_data.source_list[i].trace_spectra[0,0,:]\n",
    "            spec_arr[:,i,j]=calibrated_data.source_list[i].trace_spectra[0,1,:]\n",
    "            spec_err_arr[:,i,j]=calibrated_data.source_list[i].trace_spectra[0,2,:]\n",
    "            angle_arr[i, j]=calibrated_data.source_list[i].spectra_angles[0]\n",
    "    \n",
    "    #save times, wl, spec_arr, and spec_err arrays\n",
    "    pickle.dump(bjd_arr, open(dump_dir + 'bjd.p', 'wb'))\n",
    "    pickle.dump(wl_arr, open(dump_dir + 'wl_arr_order_' + str(order) + '.p','wb'))\n",
    "    pickle.dump(spec_arr, open(dump_dir + 'spec_arr_order_' + str(order) + '.p', 'wb'))\n",
    "    pickle.dump(spec_err_arr, open(dump_dir + 'spec_err_arr_order_' + str(order) + '.p', 'wb'))\n",
    "    pickle.dump(angle_arr, open(dump_dir + 'angle_arr_order_' + str(order) + '.p', 'wb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we will prepare the transit data for lightcurve fitting. If requested, the white light curves will also be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kB5uwUb2l6Zgep/L5+8ykIYTIF0JsFkJUCyGqhBDfTpTfIYToEkLsTzzOPGqb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\na/8LA9QfPQgRgQwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1328503c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1azFlOcia2IRl2WrCZ/2G++59AZcni9fcezmreT8Woeevl93En6t0WGIhGhO9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\nFpQuQwJPZfooXXYZi6r0idd05ybFYsBYamedcDCZsxN2pImbXNR2qXT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WaAn6bqUnrYci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\nURRlkVGNX1EUZZFRjV9RFGWR+Q8tntKv4ie+YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a8a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yCmSonIQMFels9M47q3m0ayMZ7WvRZyTYlfd53KEjOKODFCT6mOAcxGu0oO+JEakzEvfo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\nJBht2ujKb2VlsIof7v073jx5HzWFKzmSfpjDYyx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PXsuH1/JhSFIf66Qr2Um36aTJXc+K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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ed92be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cBWchyEkWDDVw0TEtGaoyVCQQc1UMq2UaKcURjzEz3EwwaaY+90o0goFUOUm2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CnATDdMv8HDiYxzNAfHeDV7BVlagJXhXkr8Q9gjggzfEsZEOY5oDBH1ELd4iWVo3PSeaqqrq7FY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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12eff6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yxo033EVi4qd4Z1q9Bd2968gSK3FW1uJ3+SnoeBGtmE5d1ueRtomMGlqY3beN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\nplu4YUzhe8kvYIp5+cLTB/jKMwfUfpAJavL4AEbH9jE2VkVW5q3I8vg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MnnNyeSefzdPT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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f815198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1T0GdPZ/hiu9zzJPLggXfo7S09F+qb6J3lPd/v45ebx5mdxe9lqNMv+I6eja+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fdd2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ncmnrTJ7RVPF+TJD++HzUmkgeUjbyjnkty5q3c2eng35TDuZZf6W05G2ORe1j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\no5xN+SQtKeYMmGiIJtEzhbTWtONIJRGWAGPBg6iqFUvPBvL8QzRqaTK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Ff8FsX0l1/z6G\nIz34am4mv+h1ioO/o8RtsLexj5U/f5vGnvH5saKqOCSR4dpejq09xte0PvpDUb5hXsUXjq+je72D\nqil30WsZxBM5QOmEqzjk6Gdi/q+IDDrZ/M5ippYcwlGdz5+86SzMT+MHVxcnujmKMuZWrlzJ56OD\nbC+eR3vWEjrb1tPp3M/RootY9NYr2B1D3HzgPqxhF8fSu9jr24hmv4Tyrs1owxOZuuNBJk/Zyc2T\nfsvytDCNvQFW/PxtNlS3JbppY04VhyQR6Rnm6B+ruUf00BU2+GHkOa7etZ2WAzlUzH6IVq8DPfgO\ni7M/R58lwr6pb5Bhb6H7z9l0XGijrOswv7EspXiihyfunK8+r0EZlzRN4/bPfZYVbcd5dtGVzDBK\nqPS+wnlejX0l/8Dcjc+RofXz+apvYcJCddZB+l0naItY2NW1Fj1QQMFbDzHFW8s1BY/wzbzjBCNR\nvvrsbn74WtW4ukmfKg5JINIf5MjT+/nScCuD0TDP9jzC/M0HqZUXs33e1+jUDmMENlGW81mEbmN9\n7i7mZK/F2JrG6zMXcLmxhd/0foXsTBfPfKkUt03dklsZv1wuF/90xQrckSB/mLmCK465afb9Hl9W\nCu/O/Tqz3nqGCdEI11bfwaAjxI68tei22bQGrezqWAPRQjK3/iNFXb2UFPyCR2Y/h0sb4tkd9Vz8\nH2+yobo90U0cE0LKc/PDLubPny8rKioSHeNvFmoaYNdTlXwr0EJOpIVfHv013XVZVBYto9/Uhoy2\nETXpzJ10FQWikDeKNzLV90dErZtXDy9ncfFWnqx6kL70TF755hJyUuyJbpKiJIX9VVV8taaehbUH\nWNa0ir65bjJqv8zRrgHm1K6jtvhL1KRtY1PRGgpbfCytuxXNcJMTrWZ+5hIGwm1gfYa+pTWcEB7W\nHr+GNQ1lGJhYarXykM1Ftt2CsGpobjPea6aie62JbvZHEkLsllLO/1ivVcUhcQJVXTz6/HpeiGg8\nMLyKS/bt5YDzMupShjGijYSsdux5uVwklzEhksaWwrfIyH4By6s2VmuLKV2ylZd33UtfbiFP3V3K\nRFUYFOX/2VZewYMN7dy5ew0Te/dyqMTD5LqbqO0NU3ZkG8eLvsA23x/Yn7uPyd0esvqKmdt6FZl6\nkDlOL1bNQddwOcFpf2aouJOAP4OXaq9hS8cFmIWJr09I4yaXk2j9ALbCVDLuTO65PlUckpzhD1Oz\nahffL9/JclnDDe+9xdH+Mqp9MxkOlSNMGrb8QhaLpaRHXNR7ezgx9SVkZwPta10cuyKD0om72Ljl\nqwRnLeHxu+apOQZFOYPXt27jR+093Fn5OhcP7+GFTBe+3ms5PGTmoiNVNE65mjWFj9Gc1oIp7GLi\nACyou4icwFIKLANMc2YTMUL0D+/GnH4EzXeELkeUFxpuYU1fAUVZbn6Un8WkHR2kf3Em9ulpiW7y\nGanikITCkTC7j1ewfs06LPUNzBjuYl5TNfXdM6nOK2PA1IBudJGTOY8Zrrl4DCfNKW28l12ObOrG\n984hns+8hDnXbMN2VLKj4VYmLSnle7fMQQiR6OYpSlL784ZN/HQoxPzjh7mv50U2Rp1YtQvpDeRS\nVPseLb6FNKSsps1Ww9HsIYLmECkDLhY2LGdW/1xm2gwyrenopth8niENwloFbTm7+G7rZ2mOuJlj\nNnOn1c51370QkyU5D9bOyeIghLgceBzQgN9JKR/5qNcnQ3GIBCOYTALTyaN2KTFOtNO+6Y/sWb+Z\ng34fuklnOu1kddURHAxjBAQhk4dhazpNmXkM6G1YiFCQVsY0dwk2rBz1tLHRozN5Zz32gY1smzKH\nofPNLDX+SsOeRWy1LeGHty1i+YLchLZfUc4VUkreeWcHzx2o4qhD8u3KVzBLC+UpubgH5+Cq62Ew\nrQSBGYlkwNbM8UmrOeLoQw8OMbV7Gfld2WQGDdymMOm6mXz3LExCJ6q9TUVqB7/qXkw7dkyamUUF\nE5g50YPXYUFKSarDwsL8NHypjk+lLZ/0gPCcKw5CCA04AqwEmoBy4FYpZfWZfubTKA5SSlr7h7Hq\nJlIcFkwCiEqE/uEXcRlRg8GOHhrL1/Pc2ncoSenHKiSdRjadRg490fOwaF2kmuqxGN2Y+3S8w3bs\nuhO3NQWzsNAZbKIj0EDQ8GMx2ZiScSHTnbOxYGavs4OXM90UHdyErh/GOctGGsfo7NAZrPPxtuVS\nFuVP5gf3X4FmUmcLijJaoVCIiooKnty7ivTOfm46Uc0hcwqDprnUB7MwDw3iDui0pueQZUTJCgzT\nbjfYn7OXsO0YYb2N3uEMiusKKem1U+jyke8pQQCG0U3A7KcDM0EjSkhIpJCYBJilhg0zNqljQRDQ\nh9no6WHR/FKKp0wlNd2J1anTVt9BzfZK8ubkcaD7MOv3V6KHuwkwiAh3EY6G8PaaeOxnqz9RgTgX\ni8Mi4J+llJfFlx8GkFL+25l+5pMUh1AwyEOPfAdDN+M0DTOpt4OJbT3YouDR0vFYJ2HRMgmLAY64\nQ7ztS6Xd7cI30MJEfytIP4f7i8iz6SwOHMUfnEaQMJIGQpFOhoaDaOFCMq2TOM+eS4bNhyZ0IHYa\nGkFiEbGzjHbRhRsnDmlnp7eXVycEyYrUUBjpwGg/QvuAoGUwny5LPsLkIstm5Qffvo7s7PRPtpEV\nRXlfOBymsrKS9XvWYX2vnNkD0J4JUbPOYNTOccNDqy2EfSCTqaKPElGPGHDRFMmjNtXAlVJFg6mf\ncHspF7a6mWL34tRTcGhuHLoHu+7EFP/bP5MoBj2in3q9hXpXDc0pEfrc5xHScjgu+jD8rdjD7XiD\nvcxryqBgYAbSkUZYN7BPMXH9PXePut3nYnG4EbhcSnl3fPkOYKGU8hunve4e4B6ASZMmzauvrx/V\n7/n9E09TU11BSDfjt2di4MAZHMQSlYSlDUNz4HfaGLSGyO3vp7DhOPbhCG0pWTSnZNHr9JB/op+J\nQw5yzDoTbWnYTFYgVsE1kxmLKXYpW3+ol3pzK3WFfiKOLvTeCNFOL1GrgxTdQU7IywlNUuMcJqe+\nkWp/HfucWUzImcU1pQtZXHgevjSHmk9QlLOsq6uLF1evoeXIVnIibjI1M1b60cQgYYZoDFtpiXiJ\nREzohokeuwuntYvZkSj1uoualEbw51PQbsM7pEN0ACPSgkYYs8mO1ZyDrvswtEykKY2IEKRoVrJ0\nwXlmcGkfzE8Y0iBkBNFNOrr48PcrBWWEHzf/hGef2zTqtv7dFodTfZIzh3vvKuNG5+1EjTAhw0/I\nCBCKDhM2goRlkLARREpwm1PwWDLwmNOxay4MGSUqo0gMUiwT8FoyYjfI89cTCHUSFSbCNichq4em\ntH56M4+RGu6EkAtrexkzNDcTfF6s6SnoHg9NoUZ2lG8haAly/V1fx+lNR9cELquuioGiJKGoEcUk\nTERCQSrf3cmmbVsJtDVh6+tleIKDLvMkXKKPZscATVoa7qjELutI6feT1S3wDJkQCISUeP1BdEMw\nbHURNFuxWVJwmZ04NBsO3YNNcxE2ggSjfoajfoKGn1A0SFhqRKSViLRww0/vISdvyqjbMZri8NHn\nPWOnGTh1dtUXX/ep+sKXf0rG6gBWk+P94Z6P4jeGGJID6FKgSxOa1AiF+3jnxDHeK5zBzJsuZnKW\nG7cB+mCAYEsPCydn4cpLw2Q+85vPU5jMrCuXfppNUxTlLNJMsaN7s9XG/KUXM3/pxe9/z4hG6e/u\nxulyYbbbCUaj1LYeY+j4QQ4cWss+336OiTC2QStp3RqRdhtEBRF9gLDWj6G14rcIghaImgUeoaFF\nPBBJxRJOwRp2IIwwmnECU8SPIEp2bt5Zb3OynDnoxCakLyVWFMqB26SUB8/0M3/rhLQMRwl29hGo\nLMdqsyDMLsI9g4QGArjKFmLJ9mJyqNtQKIryKetrIDzQStVAGzu6jnLwRB2T06awMv8SpqcXoxsa\nulVHBofo7T5M30AzPm8+FmcG2LxI3Tp+rlYCEEJcCTxG7FLWp6SU//pRr0+GS1kVRVHOJefisBJS\nyrXA2kTnUBRFUdRdWRVFUZQPoYqDoiiKMoIqDoqiKMoIqjgoiqIoI6jioCiKooygioOiKIoygioO\niqIoyghJ8ya40RJCdAKju/PeBzKArk8xzqdF5RodlWt0VK7R+XvMlSelzPw4Lzxni8PfQghR8XHf\nJTiWVK7RUblGR+UanfGeSw0rKYqiKCOo4qAoiqKMMF6Lw/8kOsAZqFyjo3KNjso1OuM617icc1AU\nRVE+2ng9c1AURVE+wrgqDkKIy4UQh4UQR4UQ30twlqeEEB1CiKpT1qUJITYIIWrjX1PHOFOuEOJN\nIUS1EOKgEOL+JMllE0K8K4SojOf6cXz9FCHErnh//kkIYRnLXKfk04QQe4UQq5MsV50Q4oAQYp8Q\noiK+LqF9Gc+QIoR4UQhxSAhRI4RYlOhcQoii+HY6+TghhHgg0bni2R6M7/dVQojn438PZ30fGzfF\nQQihAf8FXAEUA7cKIYoTGOkZ4PLT1n0P2CSlLAA2xZfHUgR4SEpZDJQB98W3UaJzBYHlUsoS4ALg\nciFEGfAz4BdSymlAL/CVMc510v1AzSnLyZIL4BIp5QWnXPqY6L4EeBx4XUo5HSghtu0SmktKeTi+\nnS4A5gF+4JVE5xJC5ADfAuZLKWcR+zC0WxiLfUxKOS4ewCLgjVOWHwYeTnCmyUDVKcuHgez482zg\ncILzvQasTKZcgAPYAywk9kYg/cP6dwzz+Ij901gOrAZEMuSK/+46IOO0dQntS8ALHCc+35ksuU7L\n8hlgezLkAnKARiCN2IezrQYuG4t9bNycOfDBRj6pKb4umWRJKVvjz9uArEQFEUJMBuYAu0iCXPGh\nm31AB7ABOAb0SSkj8Zckqj8fA74LGPHl9CTJBSCB9UKI3UKIe+LrEt2XU4BO4On4UNzvhBDOJMh1\nqluA5+OiBqn3AAACSklEQVTPE5pLStkMPAo0AK1AP7CbMdjHxlNxOKfI2CFBQi4lE0K4gJeAB6SU\nJ5Ihl5QyKmOn/D6gFJg+1hlOJ4S4GuiQUu5OdJYzWCKlnEtsKPU+IcRFp34zQX2pA3OB/5ZSzgGG\nOG2oJsH7vgW4FvjL6d9LRK74HMd1xIrqRMDJyOHos2I8FYdmIPeUZV98XTJpF0JkA8S/dox1ACGE\nmVhh+IOU8uVkyXWSlLIPeJPYqXSKEOLk56Anoj8XA9cKIeqAF4gNLT2eBLmA9486kVJ2EBs/LyXx\nfdkENEkpd8WXXyRWLBKd66QrgD1Syvb4cqJzrQCOSyk7pZRh4GVi+91Z38fGU3EoBwris/wWYqeO\nqxKc6XSrgLviz+8iNuY/ZoQQAngSqJFS/jyJcmUKIVLiz+3E5kFqiBWJGxOVS0r5sJTSJ6WcTGx/\n2iylvD3RuQCEEE4hhPvkc2Lj6FUkuC+llG1AoxCiKL7qUqA60blOcSsfDClB4nM1AGVCCEf87/Pk\n9jr7+1iiJn0S8QCuBI4QG6/+foKzPE9sDDFM7GjqK8TGqzcBtcBGIG2MMy0hdtq8H9gXf1yZBLlm\nA3vjuaqAH8bX5wPvAkeJDQNYE9ifFwOrkyVXPENl/HHw5P6e6L6MZ7gAqIj356tAapLkcgLdgPeU\ndcmQ68fAofi+/7+AdSz2MfUOaUVRFGWE8TSspCiKonxMqjgoiqIoI6jioCiKooygioOiKIoygioO\niqIoygiqOCiKoigjqOKgKIqijKCKg6IoijLC/wFHJ3QJtVG/QAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fd21e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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69hWOVAeo2tRMwLOTsL+Jylwn3wr8lJloeHD2BCt877LgyE08k/AkIyON/Ojd\nAAfTinlk2hp+5bOTPmsT9tgj1KxLQ1RdilOTw5saLxeoqnEqkmkPxSMURrPt6lmT9ImeGaTgl0i+\nIi5fgDv+8BKZVh8x7gzSTf9Jm1nPwMi1RIfCaAQfQ+jpFEwMYyTd58Cf6qU5KOOOvgBLI3LYwlZ6\nNXKOaTpQe9PI0sfy/PR5ZFbU8LD1dSK6LbTKFjIUb8YZ0YYoC5AbtZ/usJVyjRuxcS2rAtNZoA+g\nEVS0d/ydyKZd2K9RYC+W0Vh3FQtLVzFnedEXqlHsPoTj8XsZ3GpFZtCT+u4mqivcVHzYTsD1AaFA\nG8fyDHzPezuiSs4LC3dxz+FUFA4zq/MfYVm5wC27xng3ZxFvFZ7Lz8M1RKx4nfouC8Ft6agMN7NN\nF8atdHCbagvrE66lbXiIrf9xBZEa5Sn+xM4e/07wS3fgkkj+DY++fphc2zjRHguhxPvo1aRSpT6P\nvJJNyIr2cyS/jy1T22koepWxvEfQKZ5nYe3H/Hl8gsXGbN4VN9OiDVMdcQS8U1iqsPLq1NmkVTXy\nX8cP0jbxAyqib6AnzY3D3IxD181g/EYeNwzwATpGOu7hCrGIZQYZMjHEsePPExqrYvCHMXhnefH7\nb+F73//BFw59ACG1jIhH95PyveUE7S56r76Y0gWRTD8vDaX+AmTyBKY2O3hLsY/EAGg7lrFxxla0\ngpm/9F/IlrkONpQk862WPSxqO8LTsgzU/TPJT5pgOGoYO8eYEVJxHB2HgznYXG7CXi0fNg6cwk9K\n8r+Rgl8iOUkdg3Zc9duI8tvpzHiM/tACaiLzKHD66TyUTG+LFquziaBsCwFZD171BAeL7MxIW4hS\nTOYl5xuMOI7T7vSity7jGrGSJ/JXE1Pbwa9b99CkuhpBaGAobg9ewzA9idvZFneQ/nEZhVULof87\n/MyfxGKtBrt/mP0dz9GX7GXwxmSUKX0Y9N/hssvuQqM5BWvdCwLaW58gZe0i/MN2eq6+kLLzEkjO\ntaAyXIpMZiaq8wANooPruwK8prqBw+kbSLcv5jLHMtadP0B5Vga3176HecDKxoZSZAo/hnlOZK69\nRHm9ZIZkHAhnc+P4x2THmlgnBf/XRgp+ieQkPf/sP0jGgDW6gUOOCxBMJiKcWmwDoxyLymFAUUDe\ncDHX1y/ne/vmcdeO2dxcOZ8PfJ38VbaH92afzzuJ15KmVvJg+GleSLyJuNYW/ti+lUb5VWD+GN2C\nHUzJ+Yjq+M10uTxcvCcLS/9yLg84eToYR4E6ih5nIzsGX6FnWiKqGQIJiUcxm1cxb959/31x1ikh\nCOi/9yyJN5Th6bbRv+YCVtziLWezAAAgAElEQVSYgz7ShFp/OQpRQ69tM3FhGWXHtbyenYpV2c+t\nA8swhZN54iIbHQlJ3F+5jr3uWPx9pZRFuThSMIQndJRSQU2fTEVFOJlhVRix8SieUPjUtV/yL0nB\nL5GchI176hDsvYS0m9kglnKRzofRnUyv38fBqOlcFjiGKRTibXEBTyvn8FziTJ7OLeI5SwoHFOnY\nx2NZtOMgf3Q+TdaEk9/E3sd/Df2aX3btoZZrEOI2klO2AdtEF086VATa5jO7o4xLbZ3c2zFGnvk2\njAoNh0Y2sc/2Ps6CZAoKq0lLqyY66nymlfz2qylcEIi4/28krJ6Bq2UE292Xc9FdxSiUkWh1V2B1\ndDAcHOXGriAHxIW8U3oQQkb+1l0GGg8bV+iRCyI31XzI043nIlN6yc+CIc0+UhwBkoMydoXzuGxg\nD5qQmR091q+mDsn/Q7qASyL5HF6fnwOb3qdAVs+RQCLXqHW8p6/ANZGKzr2Q2/yD2H3fphgoFny4\nFXJGhATwe7C4R0ix92DRJWDOjqUeNW0RPp4Ze4C6sQuoCF8Hqe8iZOznD+1FdI6fw7mhbr7j3IDm\ncBB//l1EJBRjC0xwYPBNRuXj6IqiKZ65G4UCcnN+Q1LSlad2pP8/CQLmh9YRHL+EkY+OE/fyf7D0\n2w+y7W9hlJpFtNr2My/2ElbVjfJ68eXkJ+5lYf9yfjlwkPuT2pg6L4Nlu+vZ3ltKfeu5LEg9wOPp\nIZIa65mpmM77hKkNGwmlxPFaXR8Xplu+ulokgBT8EsnnevLF9eRO9HPMNJWZqnie174Bnd9iRiCd\nEr+CEVQM+2qwePvRBkfRyuQkywREAcKE0c5R0RvfScXANFa69jNz3My79j9iVxmx5b9IlbGVhu4L\nmT+h4zn5b9G1e+nsm4t8/m1EqPU0OI5RP7oFMUZLzhwf0UkHgDTmlL2AXp/x9XSCIBD9+/V42hcw\ntG47aVOLKFywmNp9IVp9b1AUGGNNv5bNeQpeKExC5tlJvvUHrFT+iVdmt5F1PIE7j/2D+yzf48G0\nvUxPUjHWvI80ezFpOoFyeQ5ZXhsdHaMEwyUopIu5vlLSVI9E8r+oOtYIxw7hNvspQcHbpq2Ye2/i\nYnc2RX4FTaEKxPgt3JrzClkzh2mdsxj3okSSVo6RMV9HXM5STPYlXHxcz0pbLUdta9ll/y7eiCZ2\nTH2Mt3Q9NHTfxOV2L88E/0BrVRb7lHdjnnsnaqWSPUPvcmxsM5Z5Y5RcXklkQh9G42qWLvno6wv9\nEwSlisQX30dlktP78J+YM3UYlUpJhPJy6hyHyJJpuLp8mDpZCXumedgUsZv80Wso7L+AloVBzD4H\nV9Zv5+3a65lv8tKcPIDC20KBSotDkJNpbSF11E25deJrretsJAW/RPIvuFwudj/5JPkRfZj9M+jW\n+wgMTmWBJ5Z6XSvdli08lPgc93hex3HAyPzqnfxy/H4s8h42iFewT5iGRbeBTPEx3htfyj7Hzcic\nVkJJz7EudzsN/iiC7XdxT+AgN4/t5rmulXiSlzAjcR6e4AQfuF7Al3mYwtXHSSyWkZ72AEuXVDB7\n1q+RySZnUTN5TAJJT/2VcFBg6J7bmb9MT1ChoEkVgTvoYJU9xKzuNt5TXo66pJ0ubQfZQTP7InXs\nnV/GhZ3l2Jr0tHksGLMCOEPlpLhF9CGoDqUxkZTOunrp7J6vmnQBl0TyGUI+H3+/7hYsCSF6FVcS\nrbXzlG4rZX03MRB/gF+EnidWmKB+NIOG3isYjp+HPODENFpNkqoWa9o0ag1ZOOwm0txqZOEhgsp3\n6U02sTOuFvvIUoJj8/kN61GMpKFTGEkz5GFURnJYV41t+l9J04YxmxczJesmzObZX+08/r/Jvu4p\n+v7rL0RO0/F+wiMEPXKiNQdZoFnE855W3j4/HbnazwMTv6WpZhHBgIH8iL+T8L6aUAAeXXUltxWv\nY8t+NUW2Gzkck8I+vCSkCDij9By7ZvE3qt7TgbQ6p0TyJQQGBth73Y2o86bgEVaQpvGyxfg6mX33\n0h7VwePep3B26tjZPZvelNXY4zOJ92wm4I5hxFLAiJiGZ8KKYcKJYBxhKHoCBWGGYjTs1bfj67qZ\naE80Pw/YiBeuIMoSiSiKNCtbqU9ZjylmgAW5PyU76zLkct1kd8dnilhzJ56j1dg+2MeFxe/wVv+F\ndImZzAg5uVGdxpR9Qe4/J4Y3Td/i3LhyXMPZlMssJK3M45J1G5lSP0h/fhRjOf149xwmjQz2I5DQ\n14/CraLG7mKayTDZZZ6xPneqRxCEFwVBGBYEoe5f7F8iCMKEIAhHT/z87FP7zhcEoVkQhFZBEH5y\nKhsukXwVrDXHOHTxt+gvXIhFvhiHcQCb5lnaRm4kDh9XCX9nYq+OnrYCjufeyYQpFm32H7DN6WF4\nQQvj6RXYkrrwxnuwJ/YhmocxxLVQm7GdPSL42u5mpT2CnzqDFKizkIkBjo1u4hfZP6YyZz3B+OtY\nc+kW8nLWfGND/58sDz+GXKtA3P4x8VoHBl8crwU+YNjbzZKQhrf3Oun0zqMnJ4Kg0knkyIUskX9I\na3IqF3QeZMPxlZwXpWbI2EKcdYDcoIymcCxWSxqvSRdzfaVOZsT/N+AJ4JX/5Zi9oihe9OkNgiDI\ngSeBc4FeoEIQhA2iKDZ8wbZKJF8pf28vO370E3xld2BRBNhrbGaGsJPn/SuY54qhfEo9a/c30iBb\nSv306xGjdhEwdRCVZmU01IRGI5ChE8FlxDkeATIf+xQi5d1FhHtvISoUy6VuK/G+TkosCxl2trHV\n/xfc882ca/oul61ajVJ5+qxVI9PribzxRkaffp4Lyx7jr6ofYwyt4APnXylxTWd6zHn8fZ+T3xVe\nS2HeQ/RXJrKNfLIKlEz7uBahS4knK4bu7GGSqyrJjb6IJiHMkMPNtuHxyS7vjPa5wS+K4p4TN1v/\nd80GWkVRbAcQBOFN4BJACn7JN05oYoINt9+BP+Maxs29tCjcuLWV/Nq+hvNtaWyb0cUzFc/SNjCd\nAwun0Zr8W5J9OfijOnjP7mBMrgMvMA4RAR2e4FRCQ/G43CXow0qW+iFj/Aj6UBtzE6/F7R1ke/oz\nhEMruHzm1RQXF092F3whUTfdhu3lV3FWDDNtZhWt48vpteTT3lVLjejl4riLeaBO5MWFZSSnlzPU\ndAkLIx5gRBXDqq4DvN++ksVTXmC8phGL71wiVAK6AQcxKgcd7iIydKdg+QnJ/+dUndUzVxCEGkEQ\nNguCUHhiWxLQ86ljek9s+0yCIKwVBKFSEITKkZGRU9QsieTzhf1+3rv1VoLJa2hIaOWIoYcNGh+V\nw3dxiS0Na/4R7m7ezUhNNJXF36bKUkdwNJNeVw0jx5Xk1M9FO3w/ip4HCR9/iP7Wn2LrvBCPZyZz\nFAZuHneQOvwWikAtS2IvI4iHihkvkZS4lltvvve0DX0AeUQEkdfdgKNXy+LAiwSVo2Q5VtCT4MHs\nauYxZwOIENV5PimZNUREDPKhYgFDubEs6KuhfzQBYzCV1oxxIiZameWTMygaGFXF8krz4GSXd8Y6\nFcF/BEgTRbEE+Avw3hd5EVEUnxVFsVQUxVKLRbpyT/L1CIfDbPjOLYQsq6lOaKBCP8JO53zSh85h\nZVDDobn96CcspFVUU53/XerTN5LZNo44GEPn2DXskd/L7rgY3NGP44t5GSG6luQYkVmRRr7jVDCr\n7yjBiXVEIXJOzCUo1Bo6pv2DRUue5Nprb+ZM+FuPuvEGBJUaa3MEl0c/jyAK6NSrcGiCZFt3cVjw\nc2GfQJV/Oekz1jMxtoqspCG0AT/n9VfwYee5WKY4CQQaSVSqkIkiY6NutvTZJru0M9aXPqtHFEX7\np37fJAjCU4IgxAB9QMqnDk0+sU0i+UYIB4O8+92bwHApB2OP0KDw0jp6Hhd5w4xkRfB0nozvVoT4\n1uHHKE+4k57YYXT94xzQXkC/JhFB5yQ2+l3WduUyY+B+AuEQzlAAR8iGM2xHHWjFoo8mMXotWqUJ\nUQhim3uY5Rc9h0wmn+zyTxlFVBSRV1+N7dVXycqrJFnTgmxiKgcz9zG9wcW+8SrmmeYi9FyFPnsz\nyQXbONqbS1T8MCt7DvN+2kKWi9mMq1oxed1kKxV02PwoxwYY9wcwq06f7z1OF196xC8IQrxw4oRb\nQRBmn3hNK1ABZAuCkCEIggq4GtjwZd9PIjkV3IMDvLzmGuSGS9gef5gGmZzmicUs04fYtCINV2Ib\nO3b/hGv3/pwa2VVYY1IYDFXi8GQyqE5kkXmIv4y7eKPz26yQLUaBEr1MS5YumZmmTBZHpjIn9hxS\nDTn4E9uxz6pCsxaKV91/RoX+P0XdfDOCXI61L4eLLH8AQpSMrKEj3k3S2F7qZH4u6RXYY7uYuCl7\n6VflE8rVkDY6QLGjlV19C/FnTaB0NjPTL8OPHKdbzfPHeie7tDPS5474BUF4A1gCxAiC0As8BCgB\nRFF8BrgCuEMQhCDgAa4WP7kqLCgIwl3AFkAOvCiKYv1XUoVEchJEUcQ2fpiDH1cwsmWM6OyL+If2\nAL2hGI67SyiNcXIsz8Cb9XczbbiRtgMJVCTcw0hMETXGt0nvcvF28kL+7BuneCwLlNDvbMPbXQ7R\njTBdYDhuCJEosOWjj8ki+5yVpEffhSCc2RfJK+NiMV3+LSbeeYeYlDHmJO+nvHcRonEmMrGBvePl\n3BGxiODYpQSNH5GcfoxInw2fUsmq7kP8yriG+QVagg1NGJTT0YQDOG0+Pu6x8cPSr3dpirPByZzV\nc83n7H+CT073/Kx9m4BNX6xpEsmpdaD+JXpe3kZU8NuMZ1fxoaeDiUASx7155KY4aciO4+NDt2Ho\nc1FXlU1N7h04DIk0xL5Pdmsv5dEX8LDXyzR9Kn2OZgI1b2KPVOO9wocppR+Fwkx83NUkxF9GRETJ\nWXflafSttzG+/h1GeoqYUfgMR+RzyB87n9bYWuJGy2mPms+3+hRs1S9mVuI2yruXEFcwyszGBjS5\nXgbcxWQJLWg9E+SgoXEiwGjIh8MbwCjdkvGUOrOHIRLJCS7PMFV/tZPPNbwQv40P7TEEFHKO+/NI\nzA3QnJfO3yu/j7bZSXNVIeXT7mMw3s/R+C0kdbZiU2dzOUnM0sUz7O5EqHmTxoUpCHcOYErpIjPz\nByxccJC83J9jMk0760IfQJWcRMza25ioHsHRJeey4g8BMAsrUIVFDo5XEe8VGXNfjihCVKyHpGQ7\nWr+PiwcPUj4wG1nhGDJfE8V+GQFkCGNe/nak53PeWfLvkoJfcsYTQyFeu/dJSvSx3KaroX18Fgn6\nZo55ijAUq+hMS+Wtw/cQVz1C5eAydi65jIHEeg7G7iW+txZVKI4c7XnM1xlxBGw46l6ndrWZjOX7\nMBg1zJzxBhnp3520hdO+SWLuuANNYSGDR2Iwd7xBnNGNxV9Cf0yQoG03/UqRi/u1tFmLiEk7QneU\njO64BBb01THmM2OPjycQaMYkV6ASwT7hZ3vX6GSXdcaRgl9yRhP9ftbf8gAFUQXcqxhA50/iiogX\nOeCZSdRUJdY4C68fvpfE8kH+YVzDwfk5DJmPsytxK1md/cT4pmM2XsNCnZxQOEBP48v0XRsmvbCa\n6OhFzCnbiNl8UutinRUEpZLE3/2WcBD6K6JYnvwWIiKCZgaaUJiqiVrS3SLHXTeiUPixR1lwZ5rJ\nGuwm3m+lfnwmGkMvKu8oeX45gtXPoBKG7N7JLu2MIgW/5IwVdrnYessDxCbN4PvK42SF4Eemh3nT\neyFZU5T0Jybx7OGfIhyT82rhjfSmammNPsaOxG3MbDQTF7gJs+E85un9qGVKmtpfof8CBXGZraSk\n3MK0kudRKiMnu8xvHHVWFrH3/RBXnwIqtqOWO0l1L6Ev2oPVupUxBcwbjWZ8wkJySjMGyyAyUWTZ\naA3Vw8Uocr2I/mZK/DL8opyA3cPfK6TpnlNJCn7JGSnscrHvOz9Dkz6L+zRHUfvTuNHwNK97b2dK\ngpHa9Bye3PkzBvrS+Ch7KU1yI1sS9nPU2M78mutJ8X2fYk0E5xhl6GQajva8RVeZnNSCGiyWFWRP\n+clZOY9/siKvW4N+ThlDR83MVm9GLioYN6eiDQZp8nYxyyZywHE1Gt0EramRjERGUjzaRlBU0BaR\nTSDYgFmUoxBF7HYfuzul6Z5TSQp+yRkn7Haz+/bfI88o4z7dAfSefO4zPM17wetQRZk5kD6VF16+\nn1ZXHm8Y5vF3hZZjaW/icyWy9OgDlPmKWK7xUKSPZMjdzpahv3G8QEZe6REiIgopLHj0jD8988sS\nZDISHnkEQaVGX7kbkRAFE5cyFOmle+QjABIcM/F69cQljONK05HX3UYmVg4PzSYyuR+lf5hcvxzZ\nsBebQUbzoGOSqzpzSH+9kjOK3+5g5/3vEJk2k+8b9hDvLOQq7WZekxUjmiy0CDG88vgPOZJQxkZN\nLsOW3ahjt2Jqv40rW1azLBBkSYQGvUJD+dgmNsWFsOUmU1xWiUYTSUnxs9/45ZK/KZTx8VjuvpvA\nMCRRjzqkpz1WD95xWmUTLB8SOGabgylykLECD6pgkPmjTXQ5UvCkaxADHUzzyfGF5Yz5QnxQ2T3Z\nJZ0xpOCXnBGCYZGXXtvGkV/twmK0sFZRQ44jj1mqJnboRlFEzqHXreHBD15kT+kSthjDTKS9SMAX\nS179HaweTaMEG6URidi8PfwpuQNXcgkqxXGml5ajUnkpLv4ranXcZJd6WjFdcRWCSk7G0MfIkFE0\nvhqHNkjL+F5i/CID/tUEg0rEbBUOnZ5sWxtyIcTRcDEhdTMxohyZKOIYd9LUI92L91SRgl9y2mtx\nuHnqR39mdm0IUHOT2ElRSE+WYph2/W7GY26nJj6d7x7+kG0LCtkSX0lAW4Gq7VZuPl7AMn8EuXRR\nYk6n29/G+kwzlw3H4IjcQumsTeg0YxRN/TMRxqmTXeppR24wYFp5HuqWDmSCC13IRGdsGJe1HqtS\n5LxhNb22HCwx/fTlRJLf0coM2QSHBksxxHWiCHnJCchRDHkY1AtYnb7JLumMIAW/5LQVCod56HeP\ncuDnb3KhvJjGUJC14ghzlHZiFcOMaLdTk/Jr+uKjef7Nh9heEouir4bC6hySu+7hhrEEImQWCsV6\n8qLyafM2oZRHctGwn8GCZ8kv2IPZlMPsWRuIiTlnsss9bUXecCuEINu7B01Ig1yWD0C9t4XicZFj\ngfORy4O4Z4QweNxMH+vEGTAwaolBDHYx3SfHE1JgRWR/q/Ql76kg3XNXctoJhML85tE/ENXvZK4n\njYLILF4N9fCmQsO5yi4C2l6qNcO0ZT5CpNLOqx/cx7op56LvaaRVeyk5ilxmeGUYJ6rJiIQM82za\nndXEagoYsdQxUfAycVo7SYm3kZNzHzKZtFzAl6EpKECTm05M2yFkBSvJcs9j2NxAYHQ3YlwuBdZi\n/BYN8rQAPqWS2LHjKEzFtMsyyZa1ERfKRRD9jI87qa4b5uJp//K2HpKTJI34JacFURTpGHHw22df\n4693rmF69SArZTKyo9K4VzjMDlWIC1T1dEY0sUtbRP3UH5Mb7uTDg7fznn4lgbE6hlUXsjCUR5yn\nG13/k2TG6Mgwz6bZWUFMdAyds/9IoPQvyOVhsjKfIi/vJ1LonyKRN65FPTyCkR7kYT0DZjVK9xh1\nag/zrCI9E/lERQ3RkpNKTkcbOQoH9WN5GKPrkIuQGZChGnXQPuLgkzUgJV+GFPySbzS3280Hm7fy\nu+fu5OBLl2M+NELaRB4ZU1LYFqPiblk5iSqRYnUzm9XQYF7DyLS5LLcd4O/H7uENz+X0h2qROfPJ\nFdMI2N8g6N5AYfpFpBuncizUgHrm/2HvvsPjuur8j7/P3OmSZjRNvVvVtlzlEju243ST7kBIIQlJ\nIBCSpSywP5b9QVjaskCApSSkh5IQUklvtuPeuySrWrJ6naLR9HLP7w97f4+XTXHAQXZ8X88zj+ee\nO1c630fSZ67PPXNPI4PL7kaX3UU0soplZ6+jvPyiqS79I8W2ahU6i4nS8c1kpqzkJRaQ1kna/Jtx\nJCHsvxpFSZNYkMQT8DF3YpyhcB7kpiE9wuyEQjBhIpSpcHgsNNXlnPa0oR7NKcnr9bJ1+3ae6+rD\nmdtG7sE5RENLyc4w4cmf5EfOtWT5plNvUDli7GafXIC1uoiAO4fPDj7Bv3U8xCORq2my9FHRaSXm\nuAgl+Cr6tI8VuavJtpYxoh9Fv+x+YuYgXu90ZtX/X+rqFk516R9JOrMZ++rVxJ55EdzX4E7W0+/e\nRK5vHyOOC5jpLSaRMGMsD5EWgkJfK1ir6M0oJkt3hMJ0DpIUfahs6hinMidrqks6rWln/JpTTkt7\nO1999s/8mzBRzCEq1lXhmnRQEzmE3TPBr6x7KQ1UMF+3n8HMHRw0fYxkw3RGnS5+0nc3/9z0J34d\nvoOD1iTTuqJEsi7DGWvDKMdYWXgtNmsJrfp+euf/FIwhkokvc+UVT2uh/yFz3HAjxniYwsQ+TGkr\nIbMNc1JyKDVMZUgy5JtNtnOMlppyygc6cSkRWkM1GLKaMUsdjrQgGAqzu0Vbk/vv9b7BL4R4RAgx\nKoRoepf9NwghDgohGoUQW4UQs4/bd+RY+34hxO6T2XHNR1MkEuHbu7exvmgWN25+nvLmaTh9kpmy\nmcZZ2bwi+5gZs7DI8CSbZDVdzn/BN78YmwjwXNNnKd7q5le6O2g1H6SsbYSseC05ZGCK7+acguuw\nGj28bmhifMYj2OyjVJR/l1Wr7sJkMk116R95popyzHXTyTmyHb00Uhk6j5gxzWBgKwYJkcBlKEqa\n8Fl6yoYGWBz10+KrxuM6glSjLIjr0HljjHnDJFLqVJdzWjuRM/7HgIvfY383sEJKWQ98D3jgr/av\nlFLOkVJqtzDUvK9/fm0tvskUd7z9HFmhCmyjMXLLJE9Wqwwko5So46jWN3kt8RN2LVhNZ3kWy8ff\n5N63/4sDLV9mQ4ERX+wtprepzBjWoWSvwBx9m5UF12HW23nNuI946RZyc7upKP8KlZXXTnXJZxTH\np2/BPtSGUU5gFiX0ulIIXwcTiqQkWEI8biGj7OiUzUpfJ/G0iXGXmwmll9KUQiRlIJ2pZ2+vf4or\nOb29b/BLKTcC77rcvZRyq5Tyv38K2zm6qLpG84Hdv30faxUrK7t2Eqec/N4+xmdk80drEHtKIVNp\nosPsJTT2U9YuLiIem2Dupg18Y8dm9qUuI1p8L/H+Tkp6rVT4LYyW3IxRHuKcvMvQ6a28YtxDr6uX\nmRU7ycu9krKyO6e65DOOfdWF6E0GSgObMaoWjDIXRYXW9CDVkyojvnrszjG6SwsoHm1HEWk61Wmk\njW1kqTqEhF49bO7Q5vP/PU72GP9twGvHbUvgTSHEHiHE7Sf5e2k+QvYMjfPTsUm++NqTGMJl2P0H\neKx+Fo1iglIimCxvMRZeyrThz/LSMjOJJi/L923lG4G9/MUyj10FDxJqdUDEQ344l+HSWzCYg6xw\nzSet6HnZuJPJghYuqH8Du30+dXU/1O6uOQWE0Yhl/jm4OnYi0FEevohARpKxyb1kpiEcvBhFSTO4\n0kF1z2HmqJM0jteRm92ITkJxGsYicXa0j051Kae1kxb8QoiVHA3+/3Nc89lSynnAKuBOIcTy9zj+\ndiHEbiHE7rEx7eLNmcQXT3Dzjib+9an7SBuKCei9POE5l2rdAPNlIybTLuID/0xePJ+nzk5Ssr+R\neyZ/zrxkgB+XJHil5HksbfXssS5l+qQbX/F1ZOu7WW6vIE6St11vULLgeRZXr8flXMSs+vvQ6bQx\n/ali//gNWCPjZCc7UAxuRrMEUV8HCQG5oWnE41Zs5aMY0ikWj/cwGC5AyU0j0yPMSCgo3hjBcIJA\nJDHVpZy2TkrwCyFmAQ8BV0gpvf/dLqUcOPbvKPA88K7TJqSUD0gpG6SUDR6P52R0S3MaSEvJFa9u\n5xt/vJeUaRqlk8PsyajlZsMGPiWfocdgJdH/RfQZRxiqbOHXzd/hz+r3eVY/i3tLhmmPVRPp+CY7\nzBdwlTdBIO8ySnXNLLXPwq8GOFh3P9UNz5PjNFJffy9zZj+K0eia6rLPaJnLZqM4HRQPbEYvTWSk\nS1HVJC1KgOpJGBuZicM1xEiegyJfKwBHzMVEdF0Up3SkUwKyjGw97H2f76R5N3938AshSoDngBul\nlO3HtWcIIbL++zlwIfCOM4M0ZyYpJXc+9Cx3PvxLFKWc2kCMN4vncp2ykat0a3ggeSPG4dVk2vZx\nlfUP/Of4LwmnPFxrXc1Oo43o4HXoArNZ4d3Cl8YiBJ0LmWFoZ459Pn1qD4PLvo+rpJWS4i+wZPGb\n5Hgu0oZ3TgFKphHT9FU4ew8iiFOYOJukotIfacSVkISCF6DTqXRdkE9ZXxsuEedQoA6Tu5lMqYCE\nUaGyqUMbGfhbve8HuIQQfwLOAdxCiH7gbsAAIKX8LfBtwAXce+yPKnVsBk8u8PyxNj3whJTy9Q+h\nBs1pKBFP8MA3f8ANm9cwlruYnHQGh6uqqNZtY7Z6iK9Fv8u8UA4J506uMPwRf8rFPepnedM9Rr9q\nJDU+l4tjW6kdP4zVeB1R0wgLLOMUWGbRQiPJc+/FaDKwaMFz2l01T0GZ511OZNsTFE7uoDdrKQPZ\nEp2vEfKW4YpVkUiYsU7zkR0KsiowyjO6Gq70vEzQKylNq3RHkmzVbtj2N3vf4JdSXvc++z8DfOYd\n2ruA2f/7CM2Zzts7xPY772JJz2HaZn2cgoQZXUUlvcYdrJYbuDv+TZZPOhkq3sQd0cfp8RXzpmM5\n61ydeIWRxOh5LEhuY/5oknjG9ViSu1jgWohVn8VW4y4yz34Is9nB4oY/YrWWT3W5mndgqc0nVVhK\n/uFt9M9Zjk6UoEQH6DCnKIsa6BquxV3YRCjLSu34IWL2ixi3OTGkvcxM2OjxxyHbzNBElHy7ZarL\nOe1on9zV/EP17zjA4SantawAACAASURBVKuvpHC8lc4Vq5mWsmCvqOM10z4ul6/x9cTXOHcixqDj\nFT67/Qla907jrezlrHEfwGeMkhhazZL4Hi4cqyBqKqWM/SzPPQ+pN/C8cxO25feTYS1gycJntNA/\nhRnL7VhLLyMz0ItRHcGTmg9AZ6KDgqgkML4CRUnRdlExeaMtKKh0JCtJmLooSukgDarVwP7ewBRX\ncnrSgl/zDzOwbT9jd9xCziwfBxddR53fjqV8Oi8Yd/IJ+QJfS9/FBV4DY3IHNz//Bu2JmWxdsIRt\njhYmjGF0PTdwbaiPJeFFpNR2FmaameNayRG9l7XVjzOj4XdkZdZy1sKnMJvzp7pczXvQZ5uwTGtA\nzVIo8O4ngwqC1hTjgT3oAFd8NsmkEV1dhPzRARYl/TR56zDmN5GpKiAlPqmyr08L/r+FFvyaf4jB\njfsYueMWjMvg5Yx/YnHQiVpUxXOGbXxCPseXuZXlvkzWO8f4xNa3OTxnGTsWzaMlo4MBq4/S9tv5\n/ISOgmQNxsBTnOWeQ15GJVuzmwgs+Q/qyrdSVHQDixqewGh0TnW5mhNgLs/GVz4T+2A7QuiYtNjR\n+fsZNkFBWsE7VoHTPUDCrOf8oS76JovQ28fQoaNMjTMZSrKn910/W6p5D1rwaz50fWt2sPvH38a/\nqpDGxGe4JJFFX04RrxnXcYHhCT6n3Eq9P4cXCnV8e90jHJ62gj3VpQybO2k3+5nTcieXhp1Yw2FM\nY79iXv4KnOYCGktewLXwp7gdWcyb+wS1Nf+OXp8x1eVqTpCp3I4oWE12sBMhU5ioQq8K9ht9FEUl\nkd6lGAwJupbnke89OiFwwJiLVMPUJYyIiQR9gSjJtHbfng9KC37Nh2rzk8/z5OtPY62fRUHo0yyx\nz+RPdiNBy28IZK/hc/FvURrM582SLL6+4XEiejf7ZpeQMrTSno4yr/NOlsaycY4fIB76AzMLLyPP\nUk532TNYal6kpPg2Fi96FYdj0VSXqvmAjOU2Ss15JHIU7KEu3Ol60jrJuH83BgnW2GLSaT2xeZK8\noU5cIkxbtArV0kZR0oiQoDcotA5NTnUppx0t+DUfmq1/eZumxsNcrltBrbyYhM7AD3UbyPfczX+4\nDDw/+i0Wxm3sLHbwsT1bqRrtZf25KzCJw7QHzMwY+QzzEhZyB9fhT7/GzILLKc6ooSPvLVLVrzC9\n7odUVX0TRdFmdZyO9G4L+kwjA4UVOMbbMONmzKYS9zaTFJBnMOAdL8KZO4hOprhqvIfm8VpEXhNZ\nqgJASgf7+rQbtn1QWvBrPhQbX9rCzl3buSgxG0s0xdq+v/An207qPI/xE2UlE32fY5WqZ0Ouleq2\nHm469CJvXnwpSV2YjglJRewaalIGirufZly3g9q8S6nImk1H9k7UWY9TUvxZCgqumeoyNX8HIQSm\ncjvenJU4/O0gBHFDPpmRBIcyVdxJCPYtwGiMMbDIxfyhw0RSGYSyoijocKopIqrKPm1mzwemBb/m\npHv1mXVs2bGGVYnZkE7wM/12xipVptme4b7IbRgGr+IKVfKi20xR3zjf2fUg6885H7/JSCq8G3v6\nGgrTgmntD+DLGmFR2a1U2+fTkXGIdMN9uN3nU1n59akuU3MSmMps5BhmYEt1o6gxstK1APQnu3Ek\nwOhbgarqmFhkxO5rQ6DSRy5SpqhJQiia0G7R/DfQgl9zUv35dy+x68BGLo7PQS8M3GPdR6U9SoZ5\nKz+Z+CbTvHNZheRpp4Hc4SA/2PZL9syfw4DDjT66F1/801SkjBT3PE2swM3y8jtwmPLYkrGD5Fn/\nRZatjpkzfo4QylSXqjkJjOV2aoSe8Rw72RMduNM1hE0pgt696IASUyZ+Xz62wmGyAiPMkcO0BGsQ\nmS2UJ00QTtMbiuELazds+yC04NecNJvW7KS5aw8rozOxKlbuN21jmm4MnxjgQf+/sTqQR4U9wZN2\ngc0b5ac7fkx7XS2d+WX4wl7CE3OZkc4j278DV0kNDUWfJCnSvF3wNNmLHsNitjN79oMoinWqS9Wc\nJIa8DMxmPYfya3F42zHqshi1m0j7uokoYAe8gzMwmyOMzHVy6cggR4IlJPIO4U4rIAU2i4H92jj/\nB6IFv+ak8HuDvL71DRYFp2EzWPmjcRsuMUGfTHLIfzufCmXQWB/iBdVIRjDKQ3u+R3dpNY2ltewP\n2ykYG6VQOQtTrJ8Su4EyxwLGZD9NC/6Dkvo3cDhqmDf3ccymvKkuVXMSCZ3AVGqjL+9cHP62Y41l\nGFKSQ5Yo1jRk9FyEqgomFumpGT6CRMewCUxSwayCXhHaOP8HpAW/5u8mpeQ/7n2A6kkXdrOF5427\nMIgoqughHriSapORR5Ylaeo1Ux/v5pmD36LbVc2OytlsjBdyju8l0rbrsKai5Kb2Uu5cxLixhfEL\n7ibTPkpZ2b+xoOFpMjIqprpUzYfAWG6nShZiMQ5jSE3iSs5ERTIaOoQtBflWF8GJHAxlQUzedjKU\nKEdS+UiZpiAtSSG14P+AtODX/N1+8Ovf4YlJ6slnjbGZsBS4dDvZEbwVJV/w6Gw96oEw/xl7jN9s\n/Skt5hmsm76QbSkXF8QeZsx5C/kJPVmBZ6jPv5SIMoJv+T34fCXMnvUXplXcihDar+pHlanMxgwU\nenLycPjbcav5jNlTTPoOApAHBMZKycicIOFMsCzZT0ugBqO9jfIUhGJR9vcFSKtyags5jWh/TZq/\ny5/XbIbhXj4WLON1SzspVGoNr/NE+AssBl4q1HFZ0xrWBP6FuWsOsLH0Al6uX8oeBNPND5LSf57K\nSSf68d8zu+gSFEUwfNZP6e6tZ+mSx8jLq5nqEjUfMmNRFvmKjo05c3H42tEJKz6rEyZGCeglaUVA\n79HF+/yLzKwaGCeYsOFzdlGQMpCOCCYTKTpHQ1NcyelDC37N32xj92EOv/Um5/vz2Gz3k9RFKDO8\nxq9jn+GqsIG3Fo3yf9sf5d/bHqZnTzkvn3cLb5XNpNk0ht31OKW+L1IacmEYe4SqnPm4TAUMz36Q\nw+O5zG34DkVFRVNdouYfQOh1mIpt9OYtwhE8uuKWNVkFQLsyhjktKU9MJxK1kapNUjrSDcARox6H\nqgMp0Fv17NOmdZ4wLfg1f5P2oUF2/OIhakISb7aLIf0gQr+bX6Y/wceDVsYXb6RhXzvLtzWyjjtY\ne8HH2Jlhpcu5BZ37bS46/GVyoy4Mow9gs+dRY1+Av3gNbckQRtddzJulLZ5yJjGV2ZmVsoMthDnu\npTBZQ9SYxhtsJDMNOUYdfm8RGTlekpFOiswjdMaLMAEGCVkmRRvn/wBOKPiFEI8IIUaFEO+4dKI4\n6pdCiE4hxEEhxLzj9t0shOg49rj5ZHVcM3VGRkd540e/IiM9RFnGEjYbDuEz9vC8vp4rJzIpmPkK\ngT3Z3LBhHevKv0pbnY7t5hD9xU+QrfPxqebPYYm5MIz+FmOWgyW5lxOzddOYtZvDwU9y/aXnT3WJ\nmn8wY5mNeqmnNb8Mh6+dDJ2DgWw9UW8HACYgMTgDnU4lMFvHytAAXcEyZFY/BWmJECnt1g0fwIme\n8T8GXPwe+1cBVccetwP3AQghnBxdqnERRxdav1sI4fhbO6uZeoGhUZ79yc8xxbu40HQNfzA34TMN\nscWkZ8VoNXUlu+nfY+P2Da+xtf4Ohgp62JzdQX/J05w7bOeq1muRMRf60d8iMmyszL+WtMXH/uJn\n2NZzIV//zLXaurhnIFOpjVoUXs5ejMPfisBMUldAMj7BqCHJpEFQNrqCVMpAdBYsH/CjSoWxzEEq\nkoJQNErHaIhgLDnVpZwWTij4pZQbgfe68fUVwO/lUduBbCFEPnAR8JaU0iel9ANv8d5vIJpTRDqd\n5nf7m/njgUM0Dg6TSCY5vHUXT97/U5zeRhZYr+dH1sPELD3szhijof8i5rhbMW7t5ZJdjexY9DmG\n8nrY4t7FiOMA3zxQQlX3J4knXQjvA6jWTC4svAlMUTpm/JYtXQu58fpryTQbp7p0zRTQWfTY8jLp\nd1fjDLeAVMmL1aEiOZI6QlZSkmvMZGyiGENpkJyhI+hEml6DpDCtkIzqUSUc0BZmOSHvu+buCSoE\n+o7b7j/W9m7t/4sQ4naO/m+BkpKSk9Qtzd/qrrdeZ83kSwg1zVmvR5nV5sA9K0ltexPx3C9xj7WX\nLEsHO20dNLTfxVz7fuwdOynuhB1LbmPUeYRttm4SquSRtXqadVcTNGUSCz2M0WzhsqJbEXqVnrk/\nY3PHAuafdw0Lp+VOddmaKWQsszF7zMxkroHsyS6StkKGbBJXsAVLVhUWIDJWQr6ri6BrgFLzID1p\nJzPSOpA6MAgO9k+wrMoz1aWc8k6Zi7tSygeklA1SygaPR/vBTaWHN2+nr+cl7n+2kP/zdBmzxpZj\nykuxaN1WWvO+wKMZIzjMvexxHuCstn9ihuUQGZNbKN2XZPuSWxh29bDJEmI0o5//WjNEM59nwqQQ\njv4Bq8HOZUW3oygKA/N+xoHxMm664V/41NnVU122ZoqZym3MSRvZW1yHc7wJAy68FjsRfw8AQT3Y\nBhciJQTn6Vg+2UVvuBC9SKOX4LAZaeyfmOIqTg8nK/gHgOLjtouOtb1bu+YUtaWpmXuGXufaTZXs\nLPEwXj3Osq7fcPaucR6Y/k02WP3kGofY4djNopavUWo6hMG8nfo1XnYvvI1hZw/rFDMB1xa+s83C\ngex/wm+KEA0/TbllOpcVfAadTmFw9q8ZEILbbvg1VYWuqS5bcwowltmZgcJLrqW4vY0A2JKVxNMR\nevVBogpUJWoIhHKQNTGWDIyTknrGbaPkpSWCMI0DWvCfiJMV/C8CNx2b3bMYmJBSDgFvABcKIRzH\nLupeeKxNcwrq7unhS61b+c6bkp4iQcORRygZbCdScxc/XPBJxgyj5OrH2enczZyOL1Oibwd3I4uf\n6+XgvNsY9IyyRs1D5D3N5fsX4hefZEI/AtG1LPVcxhLPpQT0IToXfA9/dg+rLvw9Vqt2wzXNUXq7\nidJsC6PCgWL1Y06MU5CqJmpU6Y214I5LHDqF4WAFFmcQx+QAApVBywQVSUEkmmAgEGU8FJ/qUk55\nJzqd80/ANqBGCNEvhLhNCPF5IcTnj73kVaAL6AQeBL4AIKX0Ad8Ddh17fPdYm+YU4/V6+dKGTdy2\nezudjlzqRp8kNeN6avO+xvd1egr1HdgJcdCxg5quO6hT+5gs6WDFn9ppr72e3vwYG3Fiz3uEiuar\nKIotZpIeMtU2Li6+lZKMOno82xhd+RXStgkWLXyEzEz3VJetOcWYy7OZJgw0VlbjGmvGongYyjIx\n4WtFQaAHjAPTAJioDlBm6aMXE0VphXjUDKCd9Z+AE7q4K6W87n32S+DOd9n3CPDIB++a5h8lmUzy\ngxdfpzzyCsHkcuYFHsJUdBMT8TK+pW9hlm6C/YqkRrxBXt8XmZMYo23OMNfd38JgycUcKclmjz6K\nJf93ZLVewdJoDZHUIcyynRUFn0KnF3TOuoe0pwmT+RLOWvQf2qLomndkLLMxf5+Z16uWcWfbegYK\nV4AoJBzuIaCkUHV6KrzziMWfR85UOautlaeVlZyviqMXeAU09k+wsiZnqks5pZ0yF3c1U+eJ19+i\nP2MHZZ3zOSv6OzpLV9Cb9vC0rpe4fpK2rA6mJxVC/q8xPxFm7ZI0H394J768FRyumM4hS5BU3lM4\nuhayLDGDRKwRXbqJFfnXoeih76y7CWYOUjHtIZYt/aUW+pp3ZSqzUY+eQZ2brNRhdGqcvEQtKiod\nahfmNBQqTvonp2EsCjJ/fISkamRSn0InweaQ2hn/CThZ0zk1p6n9TU08HOnm1l1GZojneNJTwiWx\nRTwqR5kZKsWWzAQWADDm8PHHBdn88JfPM1Z0KcN5JXTbWgl6XsM4nsNZoWWokX3I9EHOK/o0Fr2J\n3oXfpyvi4Zor/oTVrI3na96b3mOl3mIiOgndRfk4fa2orjJ6bJA90YzVVg0IAsEKFHcjLv3RT+sO\nZfrJTTiI6sLazJ4ToJ3xn8ECgQDf2ryDOzc3Mcuwld+ZzdyQvJxHdd3URgowpDLYYonT62mlf/Fb\nPLY0j7vvf47RoqsYys+l39nIqOsNIknBBYOrUWPdpJP7WFZ4AzbFxsC8n9MbFyxd9qAW+poTInSC\nrDI7bp2Vl2efg8vbBMJOwOwh7DtCSkjiAooGjq7NECkOU2AdpF+fYlpKEI2oDAdjjAZjU1zJqU0L\n/jNUOp3mPx/6I7dt3si07M08kUpzs7Ka3+q6yEnbyY47iLu387Xqr9HdEOUV7xJ+ce+fGCu4HL9b\npc/ZRItnDePGST5x6EZSqTDJ6HqW5F+DW+9msP5+BnU+IhnfoqpAG2/VnDhTmZ1FSStdhRU4Q80A\nZCerSMkEnbpxYgpUR6sJh7NRy+LM1bXQk7KTnxIkokdPMLThnvemBf8Z6tkf/5JzNqynqGIrf0gZ\nWJVxKX+Uo8TNcSr8Naj2PRRMe5ubU99j6VMT/OvOHvpLVhPKHqLD08jmwjdJGXzcvPPTRMkgHn6B\nhrwrKDAVM1L7JEOZHezouYHbLlkx1aVqTjPGchvz0WOYiDPuMpEV6qVELcWfkWI41IQ9BTlYGA6V\nYMwNMt/bS1w1IhFIqQdUDmrDPe9JC/4z0I6nnsf91jocc5q5X2Sw0HYFb6ZjHMkeZc7ASkKGAJOV\ne/HuWMw3doyhZC1jLCeTlPMt9hbuZlPBWjzJENdv/yyThhISk39ipvt8Kqw1eCteZdCznV1Nq7jr\nUzdoN1zTfGDGwkxm6A2kJ9JsKJ2Pa7wJIfIYtGUw6T26Lq8OiE1WoShpiji6AIvXGEdIcDrjNGln\n/O9JC/4zTO/hXgL3PYpl6RF+ZdIzN+MSmqMJ9rkGWHDkCiSSwvpf49lTjs44m/78ML6cTYy6O3mt\n8AD7PbtZMJlm9Z47mTRUEg8+RlX2AmZkzWeicBMDJW/SfPBcahZcSkmOfarL1ZyGhKIjs8yOTZhZ\nM38ZrkAjCB0mtZJIcoIhfZSQHioGawFI5EbJsYwxaI7hSkukEuPgwARHZ5lr3okW/GeQyYkwm7/6\ndeJLIvzMqjLXfDHDEYXNtkEW9F6CI2WirPrH7DtyCWFDLUFHM5HMEQ46m3mp+HUGMwb45KBCQ+Nd\nRHVFJCcepDSjmnmOFUzm7KGn8hkO7FvJuHkWN16wYKrL1ZzGTOV2ZsWzsZol0YwAxsQEFfFKwuYU\nvdF2LGmojk0jHLIjiyNMz2yjTxqpTOqIRSRjk3FGgtoneN+NFvxniGQixYP/ejeR6Vaesg4yX7mI\ncMhIb9Yuzh1dSUHEhSPvAf4ir2LuYAbB7D6iBHkufwtt2Z3oVR3/1m4iq+efSEkXqYmHKbTVsciz\nirCjhSM1v2fv/nMZylzMd79wnTbEo/m7mMptLMBI5kSYPTkz8IwdJIMi+pyCoLcZvQQrOryTJZhy\nJpgfaiMijdikIB7JAuBgv3aL5nejBf8ZIJ1Wuef795HMVtll2EmFcSWLIofItj1F6dhVeIJFmDIe\nZ3fxDK5tHGbAoxDWxViT2wymCZb6U3xhZz6D/rtQkhbSwd9R617BEtcqItntdE+/n40HLyZSeiU/\n+8JqMkyGqS5Zc5ozFmdRp9Oj88fZWNaAx7sPKUykddPwRwcI69KkAHWyFkVJU5I+Os4fERJVGtDp\nktrMnvegBf9HnKpK7r/nz6RT/Yyqr2PJXsjicC9dthaGR79MTqASRXmBybkWPrY3Sbt1HhHLAFs9\n+5GGAb6+KQNT1zIS6WswxlOooadZUHA1M20L8OVvomvWL3mmcTX5DTfxg+uWole0XynN308YFDJL\nbGSoCoNVJUhDL/pUmJJYHTF9kq50P0kdVA/POXqAPYXT7GPUmESR4MwOazN73oP2V/oRJqXk0d++\nQtC7n1TkJSJ5VUyf0BHM2Evb2D9TEKhDijfpXahj2v5ZdMdmE8lqZbetH2QPy7dcRixmZ2bwXJKJ\nw+hia1hefBMl5goGK55lsOYP/OHATVx04U188YJabXhHc1KZyu1Ux51YzCqb8mbhHm8kN1VKvydB\nwN+MRYWyWAGRSTsUhKh1tNOvQG4KpP7ozB7tAu8704L/I0pKyWMPvclI9w6soVeYLMulKFBBvmUN\nT3E+pf560som9s6xUtm8gOhYnGBWiK7MAIPOnZx9YAmXHN6Dqr+ZUPItnMooFxTfRrbeRVfdQwTK\nX+O51lv56vW38YmG4vfvkEbzAZnK7SyQWbiDAfa76vGMHUAVVsKmYnyhLgAEguBEKRZ3gAa1kQh6\n8tM6ohGBN5xgcEL7BO870YL/I+rRx99mpGk7heH1jFTaMU0sYY7pdb7vcrP0yLWoYpD1NdnkdNWR\n17eP0YwqRm0dNOZuZF7jNBYNdNJWcSWR2PPU2apYkXsNRqGnddY9JAq3sbH/s/zwM19kfqlzqkvV\nfEQZS7OYjh5jIEIoz45O6URJx8mPzyIsQwyIIFEd6CbrUZQ0xYnJowcKiEWOTiVu1C7wviMt+D+C\nHnxyHSPbd2AX3bw8LYx18iLmGNbz/TwdSw/fjCll5C81cdIDRVzS9Rxd2RcSsG1nW04T+kiCWeNJ\nhtyXIhIbOCf3SmZmn0XQ4Gd/w7fQ5zbTHbmTf7/5q7gzTVNdquYjTGfSYy3KIiMmGa4sYEN+HU5v\nMyWxCgZdMcYm2zCqMGNkPgB6q8RtGcenU1FVE4peG+d/Nye6EMvFQog2IUSnEOIb77D/50KI/cce\n7UKIwHH70sfte/Fkdl7zP6lS8tVXNjG+cQ+KOcGerF3cFLgeh2jm3lw/nvF5lPln8OL0IXxDbr7d\neT+N2dcxkb2b7e5BfJmHmXNoMfbUx0A9yIWFt+CxFNPh2cjAiq9jzPISN3+D2y//MopOG8/XfPhM\n5XZKUtmYFUmjfRae8f1IbAQys/FNdqAAzoST6KQdkROhztnBkEHFqoJLu8D7rt43+IUQCvAbYBUw\nHbhOCDH9+NdIKb8ipZwjpZwD/Ap47rjd0f/eJ6W8/CT2XXOctlCE5Wt2Ylq3D5lhRU38mXPMl7FN\nN8kmz0GicTvTe67n9ZkxOnxOHu38GQcyb8DnaWNfdg997m0s3tPAwkApft0hFnsuwKiYaK7/Oerc\nR+gNVGAve5ZLln5mqkvVnEFM5Xbmp1wU+0cwmyV62YpOTeJMzsYbGyAm0qhA1F+K1eljpqmVGDqc\naQG6OPv7AqRV7QLvXzuRM/6FQKeUsktKmQCeBK54j9dfB/zpZHRO8/5UKflZ9xDn7Wxl/oYDZJNJ\nbvj3+MsXcjieS8K+gWaDiYKur9A400Jj1MgTLT+hlY8xWDhGm/0wbblbmTNsZ+5EMQEL1GTk4zEX\nMTz9MaS7jWfbP8Wis55kSU3dVJerOcMcXZjFTLZvgkheFhsKynH42yiPTMeXEacvNUhcB9aJ2ShK\nmtKYFwCrFESiOkLxFC1DwSmu4tRzIsFfCPQdt91/rO1/EUKUAuXAuuOazUKI3UKI7UKIK//mnmre\n0cO9I/z4yAg3vbqVknAmBbFH2VTppmp8GSFzJ2ud4xR0fopoTT779Qq/PfBz/BMz6KzR02PrZH/u\nNmoCZvK7VqEa67Cl25npOBufey9e915+2fRdvv7xf6G+KHuqS9WcgXRWA5a8DHQJhYHiEvY4FuIZ\n249OOvFlK/gDLVhUqB1biJSgNwk85nGiOkk07ARUdnZry3z/tZN9cfda4BkpZfq4tlIpZQNwPfAL\nIcS0dzpQCHH7sTeI3WNjYye5Wx9N/dE43+8c4MvPrMWTyKck/SteKFe5zncd3YYj7HZvxzVwFrr8\nuRxwGPjBlv/C3mXkwOzpDGQdZnvudgpDJkobr6GYucjQKyzOuYyEIYS3/iEe77iRh267hHK3tlSi\nZuoYy+3kp51kqklcMoEh1QxSxSCqGI0cndZpSmURC7gweCaZ7mpjRFFJpy14HGF2HdGC/6+dSPAP\nAMdP1C461vZOruWvhnmklAPH/u0C1gNz3+lAKeUDUsoGKWWDx+M5gW6d2aSU3LGrmdVr12MR1UwT\n/8mTBRG+FPo8G5Qe9mYfYlx1khm5moPTsviXt+5j9oEhdi64nEFHC1vytmKLWpm9+xNU6GYhJ19k\ntmsxmYZsRuf8mt1D9Vy97HoKsy1TXarmDGcqtzM/6abIP4RLCbGlMIfsiU6KYw1MqAHGRYSEkKS9\n1WTYfcywdJAQArsKDvvR4Nc+yPU/nUjw7wKqhBDlQggjR8P9f83OEULUAg5g23FtDiGE6dhzN7AU\nOHQyOn6me7TlMAciSaq8buoM3+VJZ4S7EnfxsjrEEdMYffYjlHV+noH6LK5d/ywXbt/Lzoab6fM0\nsSl3K0oyi5U7LqHUOAsiG8m1ZDItazZjxW8wZhqnU3cnV819xxE9jeYfylRmZyZWMieC9BcVsNmz\nBM/4QczpQrzZaQYjnQgEeePLEAJK5NFxfosUxFNRxkMJusfDU1zFqeV9g19KmQLuAt4AWoCnpJTN\nQojvCiGOn6VzLfCk/J9vrXXAbiHEAeBt4EdSSi34/079/gDf7/fy1WeeJifzBZ6xhPm44dO8lowQ\n1ftozdlG0eFbcOV6CE8E+Ozm5zlY+ym6Co+wIX8zalrPVZtXUGicj4gdItPgZZH7EiatfXirnuXh\nts/wvSvna7dg0JwSFJsRY46VNFaGc0uxqUZMiSYA4hY3gYl2DBIKgrWk4kYM1iQekxcV8E0c/R3W\nhnv+J/2JvEhK+Srw6l+1ffuvtr/zDsdtBer/jv5p/ko6nebzm3bjCCfAXMVw7AXm5n+cbYEsDPpe\n3srdQeb4WSyITOf3c/Q8/Of/5HDWKpqqUqwrWE9apLl14zzMprMQiUGkvpHlruuJGUIML/wRW7vP\n5ZbzLiHHZp7qUjWa/89c66Rwq5OhVJwZkz1szzfjiYzgMs5kLLaDJCp6BMnRQqy5g9Q5W9gWO5t0\nKBdHZoKd3X4+dOBQYwAAIABJREFUuaBkqss4ZWif3D3N3Lt+M7uz3Nz10psUKw/QWTmfbn8dFv0A\nb3n2IaO5nNN3CetmK1yy7TVMI/nsnV3HmpI3SOgjfH2zHbtYgZRJJsxrON91DaouzcDi7zMedtFv\n/jRXztGGeDSnFkutkwUpN4UTfeissCG/AZevGWdsNhPGGP1ynJgOLL55GI1x5tvaSQrQqRbycsa0\nM/6/ogX/aSQYDPJwKM05O9bgz81hi0VS4r0Sk/4w653NRIXgws5bGSswMuYLceuBLRyYcQ0bS14n\nYgzwg71xlMmLiFjyGLU8wxWZ16MIPV0NPyWuj/KL1i/wg6tmaUM8mlOOsdRGrcmGPhyjvaSKtL4I\na6QJoTMxYTfhm2jBogoqvCuQEvL0R4M+UxWEk2F6fRFGgtoN2/6bFvynkd9t2orPnMnyzgD2xB+o\nzryJPt0RDtqO4DWPc27rnTj1Bt4okvxk94PsK7qDvfm7GbId5rYjYaJHzsHnWsCE8jxXG1djUiy0\n1T0Gjm5+feAWvn3lMm2IR3NKEooOS42DNFa8jiLmeXvYlzuOko5h1JUyFjoMgCFlI+F3YM4KkWP0\nIZCMBfSA1ObzH0cL/tOE1+vlmckk17/yFCFHP4cKZ3IglkHU0kenvY2zWr5CdcLMi3NCrNi7lUl5\nKUecYfbkbaUomCJnTy1j+VeSVDdzrr4Bm8FFe/5aDKXbeKNjFQumX8hFM/KmukyN5l2Z61wUJ924\nEnEqJ3rZWFiHw99GfnQh/tQoXhEjgUQZrSQjy0eDax8TOohOFmG1+rThnuNowX+aeHHDJnrsblzS\nAokmcsOfIFvfww5nMzPbvsTcqI1dtYPEuye5YkSl21HAX/L3oZp8XL7RQdh1C0LtpUZvpchayaCl\nHd2sp+gereNQ4hN8Y1XtVJeo0bwnc7WDhaqH/GAfoy43Q9bpZE42oejy8WdJeqKHUYBc/0qEgPmu\nFlQBQhpxO/3aGf9xtOA/DQwPD/NcIMon1ryMNGzH5LyeAaWbfY527KPnsWwyhz7PMHtSWfys9RXa\nHGfxlHMAg3s9N25ScKpXEzPosStHqLctYgIv/kW/IBzN5jdtt/Hr6+dhNihTXaZG856UDAO1pXkk\n00k6SuuoDaRp9bQDkDK78Pn2oSBwTdaSShixGSexkcIsIZpK0DYyyUQkOcVVnBq04D8NvLluHS15\nxRRE4ihGJy0JG9I0SJ/Zy4qhxUwa4ryea+Wxg/dwIOuTPJU5gTn/OcpG0kzvncWYux6L+jZL7OcR\nUyN0Lf4ROiXNPftu51uXLaDCkznVJWo0J8Rc6yQtrUzYimgYaWNjiZPMUD+e+Ax88UEmdElUBOpI\nAZk2P9MzjhAX4A9kIWWa3T3aWT9owX/K6+vr441AhJtefg53cj9R5Wpc+j42u/dRe/hmclSFt6uG\n+G3Tj2mPfpwX7RB1NBKz9HHD2zkMFF+DPvEmyxwXApKWWb/BkjXOIwdvZEHNIlbPK5rqEjWaE2ap\nc1KWclM4OYlNRunImoltohkL84ga0nSqg0gga3wBRmOMhvztqAJS0UIM1n52auP8gBb8p7y1a9fS\nZ/fgSEEqezkjSg+H7O2kI9NYPFlIU1Eb/3zkSSKHa3m5qJxuezfW/Gf43Es5pOyrias9zM6sxaxk\n0Fr6AtaCZt5sv4gM94X8cPXMqS5Po/lA9LlWFmcU4ooMcqi8ktywm15bM0KnZ8Juxj+2Gz2CQv8K\nAAozh7CqYJQ6HLYgu7RxfkAL/lPa6OgoO7wTXPfqS8yIdrHTVIzBNESH7TCLj1xNzJwmL9VC4e4Q\nL9Zexk7XITJLHubsQ5lUBivxZhfj0fVQnFHDSGYLhrpXODQwm2HTTdx7wzxMem1cX3N6EUJQPaOY\nkJJgMKeOOWN+Nk/zoU+GsCcr8Ye6iIg0hlQmcb8DiyVMnRIkJSCaEDQOTBBLpt//G33EacF/Cmtp\naUGNphAZVvbnryZb38Nm9x48A1dRl7SwtnKEW3a9wquVt/Ba/kEsRY9TP5HkYzuy6Cq7gnTsFRqc\n5xIXYQKLfsGov5Cdsa/wwI0N2sVczWnLXOtEkokxncmssQ6asuuwT7SSrS4ioaRpE6OkkJgHZpCV\n5WNO4UZUAeFgEUkZYW+Pf6pLmHJa8J/CdjU2c/7OHcyIKXSYxum0txFSTSwabeClGSl+svc+dslV\nPF7TibHwGebHYlz2cjmjRdcST+1hXtYczDor3bPuJZ4y8db4/+GBm5dooa85rZmn2alScykPjDDq\ncmGKFRMwHAK9A58dfOP70CMoHju67lOFuxWzCiSzMVh72HJ4fGoLOAVowX+KGh8fJ9nWSTo3h6eL\n5mA299Ga3cr0rtvwFehZ0r8e8/4o9y3OQea9xoJomovenIawnYffYsDDGOVZ9Qw69iNym9k0eC2/\nvuUiLEYt9DWnN2FQOLe0EgUvnSW1VPitbK1sRqhpMtMl+IOtJISKJekm5nOSlTFBNXEAsqxxNnd6\np7iCqacF/ynqUFMT5X2DJA11mA1H2ObZhWPwMip1OWzPDnHL9md5afY1DOVtwJHUc9tuC+bEXEZd\nNSSTrzLfczFxEWNy7n10jdXypY9/BavxhG7GqtGc8vJnFeJX0oStFSwY6WVvvpPswCGc6nKSJGhX\nfKSRmI7UkpExwfzC9UeHe8J2GvsDZ/x8fi34T1E9r7yG213JBleS7uxmQikbi0eW8ZdZBh7Z/DOG\nwhU8XeNDZxrlX3omGB5ayEDhSqKxJ5ibtYRMXRaHax8hiWDGnHso0FbS0nyEWGqd2KSTgkACT8xH\nOlpJULcH9LmEsnSM+BtREJT6rkJKwbS8AxglJMMFSL2XbV1n9ln/CQW/EOJiIUSbEKJTCPGNd9j/\naSHEmBBi/7HHZ47bd7MQouPY4+aT2fmPKp/Ph6NziHU51WRajtBq72Rxx+001Vu4efdbZBwc5IEV\nl5J0b6AhlMTbuJzhoqtJxJ6lxlRFddYc+uzNKMU7Ceg/y5Ia7XYMmo8WxW5ika0Ud2yM1tJqnJE8\nNtYcRElFMclyJnwHSCOxqB6i4zk4svxUptMgjZgyetnSeWaP879v8AshFOA3wCpgOnCdEGL6O7z0\nz1LKOcceDx071gncDSwCFgJ3CyEcJ633H1HtL7xEVunHiBp62eHZTXH39VgdTvrTQa7e9mcOVK5k\nd/5BhBJlwabZRN3XkI5toEjJZK7zPMYVH8F5v2IsPI1PrvziVJej0XwoFs2qJGIIMZJTRf2ISlOx\nDqdvPzbOJqnGadcHSaJiHpiD2RxmQeHbqAJ0MlML/hN4zUKgU0rZJaVMAE8CV5zg178IeEtK6ZNS\n+oG3gIv/tq6eOcxb/DxuH2bM3koiUkxDaB4vzjbx6Ks/IjZp5oFZM9A7t3PDfgs2ziGZ7idb+Fjk\nuZQIMTrqf4XQp7hw+W/Q6bSLuZqPpswZOYQVyIzn0DDWSSJaR8CwF6F3E7coHAkewICOsvFLUVUd\n1UV7MKsQnSyky+tnMBCd6hKmzIkEfyHQd9x2/7G2v3a1EOKgEOIZIUTxBzxWc8zwS7vYke8iWzdK\nU1YH53fcxBuLrNzx5jNYO8Z4euV1xHJfwz0pqWqfTcBWgFndwdk5V5GWKbZPewxnTjc2zz/htFdN\ndTkazYfGUJhJpZJPsS9I0GLBOlnBuro2jPEAiq6c0OhOQoqKKW0lMl6IM9PLzCTItBVh7j+jz/pP\n1sXdl4AyKeUsjp7V/+6DfgEhxO1CiN1CiN1jY2MnqVunl1QgxsTmEQ7o+jjkOERF7zX0V9pJBUe4\nYvNLHK6azdqcGJOZfdz+ShGjeZciYm+wwnM1ioSN+a9QVLmDofgyFs+6a6rL0Wg+VEInOLeqBkXn\np7V8BvU+M4eKFVze3Vh1S1Blmh0mLwgdGYNnYzTFWFjyOghQMGvB/z4GgOLjtouOtf1/UkqvlDJ+\nbPMhYP6JHnvc13hAStkgpWzweDwn0vePHP8zLfw8cy8m4xhDxgmqE/NYX6vnoSd+SFSXxW/qL8Cc\n/zyf3GBDMa8grh5mvn0eZp2Vrdm7yJ/xKoOTxVx7wX3a8omaM0LJ7GIChiixzArO724kGati3Lwb\nnd6NatTT69uFDkHZ2HLSaYWi4r3kpgSpmIeNHWNIKae6hClxIsG/C6gSQpQLIYzAtcCLx79ACJF/\n3OblQMux528AFwohHMcu6l54rE3zV2KHA6zt34k9JdnjPMjijttYOy+Df33yQUzDQR49/0YSrrVk\njQjO7sxlyFNDrjJMUUYN3YZBsub8mVjazNw5D6DXa1M3NWcGU5UDg2KgdMRI0GQmc7KCtTMGyAgN\nIPRVWIcaabek0KsGwqNlOLO8zEsA0oAvPkL7SGiqS5gS7xv8UsoUcBdHA7sFeEpK2SyE+K4Q4vJj\nL/uiEKJZCHEA+CLw6WPH+oDvcfTNYxfw3WNtmuNIVeJ/sZ03dEN4rQPoY7kkPWWYgr2ct3MzW5Zc\nxHqrQtB2kC+/aqGz/BqIv0WD8zwiapixufdhNEXoTH+bmaXVU12ORvMPozMpLPRUkB0LsX7+UuYN\n6Wkp1uH07cJsWA4C9qUPoxd6XP0XYjAkqZ32MooEgcLmM3S454TG+KWUr0opq6WU06SUPzjW9m0p\n5YvHnv+rlHKGlHK2lHKllLL1uGMfkfL/tXff8XFWd77HP2eeaZqiOupdsqpl2bKFu43BBdMhYQ2m\nZiGFXTZlc3fZZHezu8kle9NuyuZmsyEE0ggkQABjGxfciHGV3G1Zvfc6KqPpZ//QsFF8IdhgeWTr\nvF+vec08Z57xfM/o8W+eOU+Ts0K356amG1c317Fufu2sICUY4EzMKRa3PsiOUiPf+9W36E7K4jdJ\n84hN/jWf3WxlOPZGxnTtzI+ci1Fn5kTGJmJiO3i98X6+eOvHwt0VRbniViwowmN0IkzprG2swu/O\nottWiRARCIuVYOMOXDpJWv9cvF4zyRmVzPLpkH47e6t7wh0/LNSRu2EW9ATo3lrHeTlKvb2BrK7V\nHCuOZuOOlzAOuPnl4ltxRp2gqHacNGchLcllxIt2Mm3FtJhaiS54izM9RXzuti+qcX1lRrIWxzNi\n8FLSZOBsQiaRo9nsnOMkeqgWnW4FOq+LStMAemHE11ZObGQ/S8UQoONQQye+QDDcXbjiVOEPs5F9\nrXzTX0WqGKXH3Eqqdy1NMW4e3r2Z3dffzDs6G7HmXTx4KIWqvA343Zspd6zFExyne85PCEiNVvl5\nCpIjw90VRQkLfayZRL2VaJePvQtXsLBFcD4NEnr2oNeKMBgFHb3voBM6sltuRwhJ2uzfYQmCLxjk\nRMvMO02zKvxh5B9yc2hfAzqcNFvaKGzbyI5yM9997t9pS0jjzegC4pJe4J9ej+Js3icZ9+5kYdRi\nInQWjqfsJDqug9/X38nX77kx3F1RlLCan5KMx9xDotvOmpYmAp5U6lPOYnV14TfOg94qWgxeIl2R\njA0kk5hUzxwPIE28VNEW7vhXnCr8YdS7tZEf0kyCbgSnoQNnQgml1YdJbu1jy+LVtFjbePBQD61J\nj+IS9eQZ7GTaimkzdGEr2kzTYCar5n0Gkzq/vjLDJc9OZMg0TGmjnrdy5xM9ksVvlgjS23Zi1C9F\nJyRnfVVo+gjimm4nwjzGCscJkJI3Tr3nHubXNFX4w8TT4OQ/T7UwS+um09BJct9DHM4TPPnSTzm8\ndDHvyARWjr1MwtgGnBYTkYEa5sWtxhNw0Tz7aXRagC3tn+SBJdnh7oqihF10YRLJwobFO8bBeYtY\n1igZtAuCUYcxeV1gzsTVshuXTpLSVoDfZ8SRv4vEgI5xX5B9M2wjryr8YSADQap/X81RrZ1onZsx\nXS81+Rl86Xc/ojUhkwOOYuIjXmXDkXK64ufg92xmaeLdBIN+DqZsJz6xia316/j3v7gt3F1RlGkh\nzhFHkkHgjuimqF/Pyo4epCeOXyyOJL11F3r99fgDXk6JDjRTDKJ1CXFxrawJDgPwr5vOhrkHV5Yq\n/GEwerCTX/YNMEffyVCwGy3wl7iDvZRW1XF4fjlndCP8824nNZkfxzX+Mstjb8asWWk2dRNbuJOe\n0QS81ocoVBt0FQUAnU6HmBWBy+SiuFnwYvFqEgZzOZXsItK4D6O0oDNG0tf1JghBZs316HRBcnN3\nYgpKmvpdNPTOnIO5VOG/wgLDXhp3NHJG68QqfAzrxzgxJ5qnnv2/VCws5xiJfP3UC9QlP4rHV0Gp\npZDkiGy8fhftBc9jNLr4+bmH+O6GBR/8Zooyg6TlZ5Ks2TDKQc7lF/FoRR8yqOelhTbS2vehGZYz\nNtpPjX4EczAZ71AqjowTlHgFIPnn186EuwtXjCr8V5hzawPP+8Yo0rpw+Qbpj36IlQf24rJFUReX\nwfqe3+D33I4zwkBUsI3Z0Uvp1LmpSNhHUmo1+1pWsLbsRszqMoqK8ieysrJwmvy4zb3M6wG9cBDj\nzOetTA+Rchd6XSY6zUD34A40fQSJZ2/Bah3i+pgzQJAD9f3Uz5C1flX4ryBPg5P2E91UiG6idB76\nIgx0Jnp5ZOdrnCgrxT3cxoKzcTSnX4/L/SrzE27CqZd4xSD24u04x6PY1HgXn1utTsugKBdKSEgA\nvZOgwU1hG/zb9fdy7/EA6AK8M1+Q2nUYYVhIV189PZqb2N5ZBP16EvL2UuDVA5Lv7qgJdzeuCFX4\nrxAZCDL4eh0vGn0U67vw+Eapy7yZz/76Wc7MLeV8MJbPHD7A2YJHcLm3URRRgsOQiNHnozH3BSyW\nYZ47dx+fWF6EUa/+bIpyISEEuTlZ6COiQNdNrN1I/ogR22gGL87SY/fuxGAsQQgd3WN70VsSsZ2/\nCUdiHasttUCQLac7qe0eCXdXppyqIFfIWEU3fd2j/MHfRZzORX1MDBnt50gb7udUeiGfPvMrzqU/\nikt2Yvd1UxK3kj59kLNRB0nMOE1lRxnVQyU8tjIn3F1RlGkrKyuLkw4TXnMfq87DV1Y8yPrqSAL6\ncVrmjJDaVYnOWEpN1xncwk/SuTlIqSO9aBs5voly+P23asPci6mnCv8VIH1BRna38FtbgCKtG7d0\n05B5HY///tccXVROZvthnIH19Edn4PZsZ3bSGhB6NP8YYvYbePxmXqy5m4cWZxJpNoS7O4oybWVn\nZ2NzDxIhXDicLvwJVlY3ezB5YvhxfjR231b0xrn4ZYB+7yH0UflE1CwjPqmWtZYaQLLldCdnO5zh\n7sqUUoX/Chg90smg08OusS6StREqU5JZfXAf7VmZtAVMLKjT05SxniH366RGZJNjzAcpOZv+C+yR\n/bxw/mMM+aJ5dIVa21eUPycuLo6cgJcBiw1fRBc3nZV8d9kjLG1OxWV20lU2TlrXSXSGfE51n0AK\nQUrFbGRQR2bhTtL8Ep2A72yvDndXppQq/FMs6A0wsqeVn1tdFOp7GNYEvY4ibj+wi8Oz5vDgiT9w\nNu8TuLyHsXuHmJtwE2M6aNcdJzb/OPW9szjZO4fb56aQGq0usKIof44QgvysTHbl5+CO6MAxNE5L\nahQfO+dG7zfz/dRMDOY3MRjnMuwbYzhwBIOjHHv1DcQn1nKT9TxBCXuqeznSeO1eOkQV/ik2drCT\njlEPu1xOMrQh3snJ5r5tmzhTXMINNbs4l/IZ/DgJug+QlbKEGGnHFPDTN+93SCl4vuZjjAYsfEqt\n7SvKRcnKyiLK4yJhbJQRawtrz8IbZRso7crGbWumpiiKlN7zCC2Vw13v4NN8JJ2/HhHUyM3fRVLA\nh07AN96sumYvzXhRhV8IsV4IUS2EqBNCfOk9nv+iEOKcEOKUEGKXECJz0nMBIcSJ0G3Tha+9lgU9\nfkb2tfJj8yjztA46bVb8moOlVSdwyQDu4C2MRiTQ4d9GtMnBPONCfAKq7E8T6ehme91NDHuTWJob\nR0lqVLi7oyhXhezsbJKd/ZyJi0NauogdHuFQtoOPnwKdNPBsgoNg7DaMxlKGvD48vt+gt6UTf/J+\n4hPrWG+tJijhWMsQu89fm+fw+cDCL4TQgB8BNwPFwEYhRPEFsx0HyqWUpcDLwLcmPTcupZwXut3B\nDDK6v4Nql4dTnhHS9E7ezpvN4689z/5ZZcxrCtKTUE5j8BBx4wMUp95EQAhGPecR152gz5lEVU8J\ngz49f71qVri7oihXjZiYGPKlj91zFxE9MsigtZWV5/TU5t7C3I58iKxiV04hjqFGhC6OfZ2deLR6\nYruXoPfEkJe3m6SgG4CvbT5HIHjtrfVfzBr/QqBOStkgpfQCLwJ3Tp5BSrlHSukKTR4C0i5vzKtP\n0OVj5A9t/MDo5Dp9CzWOBJL6XUT1j7Dm/ElqZm1gNNhMwthhopOLyAmkoQUlzcXPoWl+fld9Bx2+\nNJbPcrA8zxHu7ijKVUMIQVF6GkLo8AaCGM09RLoG2T4rgY2nzegDJvY6vIyn7cFsWsaYX0/30POg\nGUg59jiJSfXcFXMMTUia+128evzaO1//xRT+VKB10nRbqO39PAa8OWnaLISoEEIcEkLc9X4vEkJ8\nOjRfRW9v70XEmt5G3m7n6LibPv8oDs1FZWYBj7/yc5KGe6nJfpCADgbduzEYTCy13EQAaPU/TURO\nN8eaFmEbj8MZkPzD+sJwd0VRrjrZ2dlk9HXw2uK1mMb7cFp6WXQe3l7wAOVtRehtNfw2aSm20eNo\nxhIO9pkIiDewjuVj7SqnaPZ2ipioQ09trsLrv7Yuz3hZN+4KIR4EyoFvT2rOlFKWA/cD3xdC5L7X\na6WUT0spy6WU5fHx8Zcz1hXn63HhfLuV7xmdXKe1UOtIoLixGduQi7HohTijZnEuWEm8Z5D0zJVY\ng0YGve2Mrj7OyGg0VT3FVHpTua00mTlpamxfUS5VVlYWszsaaY5PIWZwDKOhE4N/iLPWUe5oysbo\nt9Ac18hg8RGiAlkgIni74zQB2UvKqYeJNLm5J28z8RGCoXEf39t5bZ3K4WIKfzuQPmk6LdT2J4QQ\na4B/Au6QUnrebZdStofuG4C9QNlHyDvtSSkZfLWOXZofLTCMXfNwLDOfB159GYfXQGPWrXTSSObI\nEfwpDpb45hOU0Fr0H2hGL6/V3I5xOBe3kPzduoJwd0dRrkrR0dFkWMwU+cbYNXcJBAcIWkZYXhXB\n6wvXsrR5DnpLM88Yb2fI/gqGiFX0ekw43T9Dh4Xkii+SlnGStaYjaDr4r7frqbuGTuVwMYX/KJAn\nhMgWQhiB+4A/2TtHCFEG/ISJot8zqT1GCGEKPXYAy4Bzlyv8dOSq7GawcYj/kIOU61upjYtn+bET\n5PV3UlXyMH7hQrq2ozOZuNG2AQmcNf0aS243FU1LyHTD29jZuDCDLIc13N1RlKtWYWEh2TWnqcgr\nJa2nC6kbwhvRjr67iWXO5Zi9kQTiD1CfFY/N1YrOkM2uboEUv8c2kkd0w+0snr2JG1KDSAkPP3vk\nmtnQ+4GFX0rpB/4G2A5UAb+TUp4VQnxNCPHuXjrfBmzASxfstlkEVAghTgJ7gG9IKa/Zwh8Y9eLc\n2sh/Wl0kBQcx6/wcyyzk4bdeoS1tHWOmZM4FD5Dg9bM4/T4SfTZaZSfa0v0MDTs4OZBHzchihEHH\nZ1erPXkU5aNYsmQJ6cMDxGg62hLy8GldRGpWZnXF8sbsdFY0l6GZO9khCxmbvQOrroyg1HO45xiB\nwFkS6+/C4U1kheUXzEuLpMPp5qnN10b5uqgxfinlVillvpQyV0r59VDbv0gpN4Uer5FSJl6426aU\n8oCUco6Ucm7o/mdT15Xwc25t5NS4m+2j4yzQt1Ifm8hjb76MxWWhJX0NDTSQN3yeBRl3keFPpDPg\np2vOD9H0Pp6r2kDZQCQHCfCpFdkk2M3h7o6iXNUiIyOZX1ZGXuN5NpXfSHxfPUOGOiIjhyg400R+\n8Daixh0YE7fyC+5h0LwDfcQKWsai6R5/hmBgiOTjnyMjsYH1lkrMeh0/P9DEmfahcHftI1NH7l4m\n7rohho9186+6PhbrmxA6yfnkNG47uo/qoo24cRPp2syipDvI0mXTqAlqYp4hKqWDXfWrKdZa2G0u\nIc5m5FPqDJyKclksW7aMvK4WnLYoNH8yTkMrQieIkDEcThjmltrb0Ok8jDmO0j17DIvXh2Yo4A+9\nSXjk99F7TaScfIKUuOf5lxsSkcAjzx696od8VOG/DIIuH0Ov1fG0vg+CfrIMTlqis/ivF75KR+Qy\nnLYcOgNbuMG+lixzAcdsghrjayReV0HPUBInBvIwdVxHtdvDP6wvxK7OwKkol0VMTAzlc0rI72ph\n05I1lNbW02k6T7HZSPnpEUR8OcubV6G31vGmN5eBoi1YtHnodA7ebI/Ay2+xOvPJ6FmOv/m7rC2M\np3/My5Mvnwx31z4SVfg/osCYj95nTlPbN8hLPsFKfS0enUaJ9wjGBh11uXfR7T/B9boUcuyl7IuD\nNm0HmUvfYNxl49snP8Madx1bohKYmx7NPQtm/LFvinJZLV++nOK2OqpSsulKms+o+wjdxh4CkVYC\nzXtZ6lpJ8lA+WvwetkSUU538IqaImwlIC7vb63H7zxHXcCeZMe3cZj1PpFnPK8faebmy9YPffJpS\nhf8jCIx66XvmNONdo3yFDoq1bixaEFu0k3te30FN7j14GCPLX01pzPUct3vo0PaTd90LeMasfKXi\n71kRc4jq0ZsZ8Pj46h2z0elEuLulKNcUh8PB0txs0ocHeH3BchZ0R1BrrWS+3oHDU0ytp4p76xdg\n8Nnpj6ogkOfnfNwmDLY7GPZHcLr/FYIBLylVn8Avfsozd81C0wmefPkUlc1X5xk8VeH/kAKjXnp/\nepqR7jE+b2ijFzulhk7SzB089OortMt5dDvm4vW+ybL4O+g1eqiIP8m80mdxj9j4+qHPMTv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e7b0da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% data trimmed by outlier rejection algorithm: 0.5215123859191656\n",
      "npoints after trimming: 763\n"
     ]
    },
    {
     "data": {
      "image/png": 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MznIebGUz46WZrMmuYvZjya4kxw9KoekY4d3aEIaxjbItmxkTru+a6KJu4mLq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\n/3uCa/8Md9+tNDnQ6O7ctxMFVudCVnPraLSHF1X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OCn4mMneMMcaYvRQbzFCqS9gwJpsR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\nSRoLMsYYY5Lm/wN9i8A573wPYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ab4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if fit_transit:\n",
    "    #load necessary data\n",
    "    spec_arr = pickle.load(open('dump_files_' + test_name + 'spec_arr_order_' + str(order) + '.p', 'rb'))\n",
    "    bjd_arr = pickle.load(open('dump_files_' + test_name + 'bjd.p', 'rb'))\n",
    "\n",
    "    #rewrite times in terms of transit center time\n",
    "    t0 = 2456506.8918       #data from Hellier et al. 2015\n",
    "    period = 2.137750\n",
    "    nt = np.floor((bjd_arr[0] - t0)/period) + 1\n",
    "    tc = t0 + period*nt\n",
    "    time = bjd_arr - tc\n",
    "\n",
    "    #summing along spectral axis to get white light curves\n",
    "    white_light = np.zeros([n_sources,n_images])\n",
    "    \n",
    "    for i in range(n_sources):\n",
    "        lims = [5,75]\n",
    "        plt.plot(spec_arr[:,i,:])\n",
    "        plt.show()\n",
    "        plt.plot(spec_arr[lims[0]:lims[1],i,:]/np.median(spec_arr[lims[0]:lims[1],i,:]))\n",
    "        plt.show()\n",
    "        white_light[i,:] = np.sum(spec_arr[lims[0]:lims[1],i,:],axis=0)\n",
    "        white_light[i,:] = white_light[i,:]/np.median(white_light[i,:])\n",
    "    \n",
    "    if plot_white_light_curves:\n",
    "        for i in range(n_sources):\n",
    "            plt.scatter(time, white_light[i,:], s=20, marker='.')\n",
    "        plt.axis([-0.12, 0.15, 0.85, 1.07])\n",
    "        plt.xlabel('Time From Eclipse Center [d]')\n",
    "        plt.ylabel('White Light Flux')\n",
    "        plt.title('White Light Curves for All Stars')\n",
    "        plt.savefig(img_dir+'white_light_curve')\n",
    "    \n",
    "    #getting ready to do outlier rejection via sigma clipping\n",
    "    flux = white_light[0,:]                             #target star\n",
    "    img_arr = np.transpose(white_light[1:n_sources,:])  #comparison stars\n",
    "    avg_comparison = np.mean(white_light[1:n_sources,:],axis=0) #average comparison star\n",
    "    norm_flux = white_light[0,:]/avg_comparison         #mean-normalized target star\n",
    "    old_size = norm_flux.size\n",
    "\n",
    "\n",
    "    #using a moving median sigma clipping algorithm to reject outliers\n",
    "    for i in range(3):\n",
    "        fsize = 20                                        #size of the filter\n",
    "        mflux = medfilt(norm_flux, fsize*2+1)             #median filter (pads edges with zeros)\n",
    "        mflux[:fsize] = np.median(norm_flux[:2*fsize])    #filling padded region with zeros\n",
    "        mflux[-fsize:] = np.median(norm_flux[-2*fsize:])  #filling padded region with zeros\n",
    "        sdev = np.std(norm_flux - mflux)                  #difference flux by moving median\n",
    "        ind = np.where(np.absolute(norm_flux - mflux) <=  \\\n",
    "                       sigma_val*sdev)                    #indices of outliers to sigma_clip\n",
    "        norm_flux = norm_flux[ind]                        #discard outliers\n",
    "        time = time[ind]                                  #discard outliers\n",
    "        flux = flux[ind]                                  #discard outliers\n",
    "        img_arr = img_arr[ind,:][0]                       #discard outliers\n",
    "\n",
    "    print('% data trimmed by outlier rejection algorithm:', 100*(old_size - flux.size)/old_size)\n",
    "    print('npoints after trimming:', flux.size)\n",
    "\n",
    "    #re-doing median normalization\n",
    "    flux /= np.median(flux)                               \n",
    "    for i in range(img_arr.shape[1]): \n",
    "        img_arr[:,i] /= np.median(img_arr[:,i])\n",
    "    #preliminary matrix math (Moore-Penrose pseudoinverse) for OLS fitting\n",
    "    model1 = lg.inv(np.transpose(img_arr).dot(img_arr))\n",
    "    model2 = model1.dot(np.transpose(img_arr))\n",
    "    img_linreg = model2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the next block we set up our batman TransitModel object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "if fit_transit:\n",
    "    #initial guess for transit fit (Hellier et al. 2015)\n",
    "    IN = 79.81  \n",
    "    RP_RS = np.sqrt(0.00961)\n",
    "    b = 0.860\n",
    "    a_RS = b/np.cos(IN*np.pi/180)\n",
    "\n",
    "    #create TransitParams object for batman\n",
    "    trparams = batman.TransitParams()\n",
    "    trparams.t0 = 0.                    #time of inferior conjunction\n",
    "    trparams.per = period               #orbital period\n",
    "    trparams.rp = RP_RS                 #planet-star radius ratio\n",
    "    trparams.a = a_RS                   #semi-major axis (in units of stellar radii)\n",
    "    trparams.inc = IN                   #orbital inclination (in degrees)\n",
    "    trparams.ecc = 0.                   #orbital eccentricity\n",
    "    trparams.w = 90.                    #longitude of periastron (degrees)\n",
    "    trparams.u = [0.0897,0.3588]        #limb-darkening coefficients\n",
    "    #note for limb-darkening: 5990 K, log(g)=4.39, z=0.39. Coeff are for 6000 K, 4.5\n",
    "    trparams.limb_dark = \"quadratic\"    #limb-darkening function\n",
    "\n",
    "    init_error = np.std(flux[10:20])    #rough estimate of new RMS\n",
    "    \n",
    "    param0 = np.array([RP_RS, dt_prior, 0, np.log10(init_error)])\n",
    "    var_names = ['RP_RS', 'dt', 'lin time', 'log(sigma)']\n",
    "    m = batman.TransitModel(trparams, time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "if fit_transit:\n",
    "    def eclipse_model_linreg(A, model, times, fluxes, compars, compar_linreg): \n",
    "        trparams.rp = A[0]                         #planet-star radius ratio\n",
    "        trparams.t0 = A[1]                         #center of transit time\n",
    "        \n",
    "        transit_fit = model.light_curve(trparams)  #making transit model\n",
    "        \n",
    "        wfunct = A[2]*times                        #lin(time) detrending off normalized fluxes\n",
    "        fluxes_norm = fluxes/transit_fit - wfunct\n",
    "        \n",
    "        coeff = compar_linreg.dot(fluxes_norm)\n",
    "        dfunct = np.zeros(times.size)\n",
    "        for i in range(compars.shape[1]):\n",
    "            dfunct += coeff[i]*compars[:,i]\n",
    "            \n",
    "        return transit_fit*(wfunct + dfunct)\n",
    "\n",
    "    def eclipse_neg_logl(A, model, times, fluxes, compars, compar_linreg,\n",
    "                         prior_on, dt_prior, dt_err_prior):\n",
    "        '''Calculate log likelihood for intrapixel sensitivity variations\n",
    "        + transit light curve'''\n",
    "        fit = eclipse_model_linreg(A, model, times, fluxes, compars, compar_linreg)\n",
    "        sigma = 10**A[-1]\n",
    "        cq = np.sum((fit - fluxes)**2)/sigma**2\n",
    "        \n",
    "        if prior_on == 1: \n",
    "            #penalizing likelihood for being far from dt_prior\n",
    "            cq = cq + ((A[1]-dt_prior)/dt_err_prior)**2\n",
    "            \n",
    "        logL = -0.5*cq - 0.5*times.size*np.log(2*np.pi*sigma**2)\n",
    "        \n",
    "        if A[0] < 0: #radius ratio must be positive\n",
    "            logL = -1.E8\n",
    "            \n",
    "        if prior_on == 1:\n",
    "            logL = logL - np.log(np.ma.sqrt(2.*np.pi)*dt_err_prior)\n",
    "            \n",
    "        return -logL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial guess for RP_RS: 0.0980306074652\n",
      "RP_RS 0.0917262206371\n",
      "dt 0.000729388564527\n",
      "lin time -0.0123177345567\n",
      "log(sigma) -2.75934538333\n",
      "RMS: 1740.43836205 ppm\n"
     ]
    },
    {
     "data": {
      "image/png": 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uzHfTRb3kMyn8yHTEf6bh//fBREKzoFsWLdJZTk086j9zWxNuhcVvAfeUmDq2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\nMIzY/H8e52FfTglC2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112793208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if fit_transit:\n",
    "    #fitting the model to data. minimizing negative log likelihood == MLE.\n",
    "    param = optimize.minimize(eclipse_neg_logl, param0, args=(m, time, flux, img_arr, img_linreg, prior_on, \n",
    "                                                    dt_prior, dt_err_prior), method = 'Powell', options = dict(\n",
    "                                                    maxiter = 100000, maxfev = 100000, xtol = 1e-8))\n",
    "    #storing the best fit\n",
    "    fit = eclipse_model_linreg(param.x, m, time, flux, img_arr, img_linreg)\n",
    "\n",
    "    #printing the best fit parameters\n",
    "    print('initial guess for RP_RS:', RP_RS)\n",
    "    for i in range(param0.size):\n",
    "        print(var_names[i], param.x[i])\n",
    "    \n",
    "    #storing the best fit transit data\n",
    "    trparams.rp = param.x[0] #planet-star radius ratio\n",
    "    trparams.t = param.x[1]\n",
    "    transit_fit = m.light_curve(trparams)\n",
    "\n",
    "    #plotting the fitted target white light curve\n",
    "    plt.scatter(time, flux, s = 20, c = 'black', marker = '.')\n",
    "    plt.plot(time, fit, color = 'red')\n",
    "    plt.axis([-0.12, 0.15, 0.85, 1.07])\n",
    "    plt.xlabel('Time From Eclipse Center [d]')\n",
    "    plt.ylabel('Relative Flux')\n",
    "    plt.savefig(img_dir + 'white_light_fit')\n",
    "    plt.close()\n",
    "    \n",
    "    #plotting the transit fit\n",
    "    plt.scatter(time, flux * transit_fit / fit, s = 20, c = 'black', marker = '.')\n",
    "    plt.plot(time, transit_fit, color = 'red')\n",
    "    plt.axis([-0.12, 0.15, 0.97, 1.03])\n",
    "    plt.xlabel('Time From Eclipse Center [d]')\n",
    "    plt.ylabel('Residuals')\n",
    "    plt.savefig(img_dir + 'transit_fit')\n",
    "\n",
    "    #what's our final RMS?\n",
    "    rms = np.std(flux - fit)*1e6\n",
    "    print('RMS: ' + str(rms) + ' ppm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d2dcc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_rootn( resid,rms_phot ):\n",
    "    #makes root-N plot for a given set of residuals\n",
    "    nmax=math.floor(resid.size/10) #max bin size for 10 bins\n",
    "    nbin=np.array(range(nmax))+1\n",
    "    rms=np.zeros(nmax)\n",
    "    rms[0]=np.std(resid)\n",
    "    if rms[0] < rms_phot: \n",
    "        print('WARNING: RMS LOWER THAN PHOTON NOISE LIMIT',min_rms_phot,rms[0],rms[0]/min_rms_phot)\n",
    "        print('Setting photon noise limit = unbinned RMS')\n",
    "        min_rms_phot=rms[0]\n",
    "    for i in range(1,nmax):\n",
    "        npoints=math.floor(resid.size/nbin[i])\n",
    "        rr=resid[0:npoints*nbin[i]]\n",
    "        binresid=rebin_factor(rr,nbin[i])\n",
    "        rms[i]=np.std(binresid)\n",
    "    \n",
    "    #white_noise=rms[0]/np.sqrt(nbin) #pure red noise measurement\n",
    "    white_noise=rms_phot/np.sqrt(nbin) #diff vs photon noise\n",
    "    results=np.zeros([nbin.size,3])\n",
    "    results[:,0]=nbin\n",
    "    results[:,1]=rms\n",
    "    results[:,2]=white_noise\n",
    "\n",
    "    return results\n",
    "\n",
    "#define a function to rebin array by an integer factor\n",
    "def rebin_factor( a,binsize ):\n",
    "    '''Rebin a vector to a new shape.  \n",
    "    newshape must be a factor of a.shape'''\n",
    "    newsize=np.int(a.size/binsize)\n",
    "    newshape=np.zeros(newsize)\n",
    "    assert len(a.shape) == len(newshape.shape)\n",
    "    assert not np.sometrue(np.mod(a.shape,newshape.shape ))\n",
    "    \n",
    "    #binsize=np.int(a.size/newshape.size)\n",
    "    for i in range(newshape.size): \n",
    "        newshape[i]=np.sum(a[i*binsize:(i+1)*binsize])/binsize\n",
    "    \n",
    "    return newshape\n",
    "\n",
    "#define a function to rebin array using a fixed time interval\n",
    "def rebin( ti,fi,ei,err_out,binstep,print_opt ):\n",
    "    '''Calculates error-weighted average of an input vector.  Set err_out=1 to return vector of errors.'''\n",
    "    ind=np.where(ti != 0)\n",
    "    #print('ind',ind.shape)\n",
    "    ti=np.array(ti[ind])\n",
    "    fi=np.array(fi[ind])\n",
    "    ei=np.array(ei[ind])\n",
    "    tstart=np.ma.minimum(ti)\n",
    "    nbins=math.floor((np.ma.maximum(ti)-np.ma.minimum(ti))/binstep)\n",
    "    if print_opt==1:\n",
    "        npoints=ti.size\n",
    "        print('number of bins in plot',nbins,'points per bin',np.floor(npoints/nbins))\n",
    "    binflux=np.zeros(nbins)\n",
    "    for i in range(nbins):\n",
    "        ind=np.array(np.where((ti >= i*binstep+tstart) & (ti < (i+1)*binstep+tstart)))\n",
    "        if ind.size >= 3:\n",
    "            if err_out==0:\n",
    "                binflux[i]=np.sum(fi[ind]/ei[ind]**2)/np.sum(1/ei[ind]**2)\n",
    "            else:\n",
    "                binflux[i]=np.sqrt(1/np.sum(1/ei[ind]**2))\n",
    "        #print(i,binflux[i])\n",
    "    ind=np.where(binflux != 0)\n",
    "    binflux=np.array(binflux[ind])\n",
    "    \n",
    "    return binflux\n",
    "\n",
    "#make root-n plot\n",
    "fig,axL = plt.subplots(nrows=1,ncols=1,figsize=(4,3.5),sharex='col',sharey='row') #sharex=True\n",
    "fig = plt.gcf()\n",
    "axL = fig.get_axes()\n",
    "ri = flux - fit\n",
    "rms_phot = 0.000237\n",
    "#rms_phot=np.std(ri)\n",
    "results=plot_rootn(ri,rms_phot)\n",
    "nbin=results[:,0]\n",
    "rms=results[:,1]*1.E6\n",
    "white_noise=results[:,2]*1.E6\n",
    "#figure out nbins = 30 minutes\n",
    "dt_min=(time-time[0])*24*60\n",
    "# print('RMS for '+str(dt_min[66])+' min bin (ppm)',rms[66]*1.E6)\n",
    "#tmp=np.minimum(np.arange(npoints),np.absolute(dt_min-30),where=True)\n",
    "#print('minimum',tmp)\n",
    "plt.sca(axL[0])\n",
    "plt.scatter(nbin,rms,s=10,c='black',marker='.')\n",
    "plt.plot(nbin,white_noise,color='red')\n",
    "#plt.axis([0,100,1.E-3,1.E-1])\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('Bin Size')\n",
    "plt.ylabel('Relative RMS (ppm)')\n",
    "plt.draw()"
   ]
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