builder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "builder.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-torki/CSNN/blob/master/builder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuJmWP8e7LNY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ccbdba3-e2a6-4942-f37f-48529dd446e8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHdNxuez7oU3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e5a0b46-2433-4f5c-db4f-b2280d0838a3"
      },
      "source": [
        "cd /gdrive/My\\ Drive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL1xu7nrndYY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f552900-dbc6-48af-df2c-53eb30d44d33"
      },
      "source": [
        "!pip install healpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting healpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/dd/30/56144086dc132b5bf5b271c62d045460db6c6a5a2bb68d3f7bde678a6727/healpy-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (15.8MB)\n",
            "\u001b[K     |████████████████████████████████| 15.8MB 284kB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from healpy) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.6/dist-packages (from healpy) (1.18.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from healpy) (3.2.2)\n",
            "Requirement already satisfied: astropy in /usr/local/lib/python3.6/dist-packages (from healpy) (4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from healpy) (1.15.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->healpy) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->healpy) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->healpy) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->healpy) (2.8.1)\n",
            "Installing collected packages: healpy\n",
            "Successfully installed healpy-1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_wpn0EYnkvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50672a06-9d62-410d-be6d-16f8e2b858ed"
      },
      "source": [
        "!pip install git+https://github.com/vafaei-ar/ccgpack.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/vafaei-ar/ccgpack.git\n",
            "  Cloning https://github.com/vafaei-ar/ccgpack.git to /tmp/pip-req-build-r30qfdem\n",
            "  Running command git clone -q https://github.com/vafaei-ar/ccgpack.git /tmp/pip-req-build-r30qfdem\n",
            "Building wheels for collected packages: ccgpack\n",
            "  Building wheel for ccgpack (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ccgpack: filename=ccgpack-0.1.0-cp36-cp36m-linux_x86_64.whl size=898018 sha256=35615e28e032a0573491fdade19be414da13420e092741f40fcb9bac10c92feb\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1zfnn9x0/wheels/a8/3c/86/3720d002b7c606972520b7468f7b9daf3da3acc3052facac45\n",
            "Successfully built ccgpack\n",
            "Installing collected packages: ccgpack\n",
            "Successfully installed ccgpack-0.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEndLzvrop-T"
      },
      "source": [
        "import sys\n",
        "sys.path.append('/gdrive/My\\ Drive/git_reps/ccgpack')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jvaaplhg8DIy"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import shutil\n",
        "import argparse\n",
        "import numpy as np\n",
        "import pylab as plt\n",
        "# import healpy as hp\n",
        "# from ccgpack import ch_mkdir,download,sky2patch\n",
        "from glob import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xZ1ETHCnbgi"
      },
      "source": [
        "cmap = plt.cm.jet\n",
        "cmap.set_under('w')\n",
        "cmap.set_bad('gray',1.)\n",
        "\n",
        "parser = argparse.ArgumentParser(description='Short sample app')\n",
        "parser.add_argument('-r', action=\"store_true\", default=False)\n",
        "parser.add_argument('--nsim', action=\"store\", type=int, default=42)\n",
        "args = parser.parse_args([])\n",
        "replace = args.r\n",
        "n_gaussian = args.nsim\n",
        "\n",
        "nside = 2048\n",
        "lmax = 3*nside"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1nvMB3xpJbq"
      },
      "source": [
        "if nside==2048:\n",
        "    n_string=3\n",
        "    ex = 'gz'\n",
        "    import gzip\n",
        "    \n",
        "    def extract(in_file,out_file):\n",
        "        with gzip.open(in_file, 'rb') as f_in:\n",
        "            with open(out_file, 'wb') as f_out:\n",
        "                shutil.copyfileobj(f_in, f_out)\n",
        "elif nside==4096:\n",
        "    n_string=1\n",
        "    ex = 'xz'\n",
        "        \n",
        "    def extract(in_file,out_file):\n",
        "        os.system('unxz '+in_file)   \n",
        "else:\n",
        "    assert 0,'Nside has to be either 2048 or 4096!'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6ZkrjWVxhHW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "4e8312c5-2a3e-41cc-d3d1-596b230398c9"
      },
      "source": [
        "s = hp.read_map('./data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits',nest=1,verbose=0)\n",
        "s_p = sky2patch(s,32)\n",
        "np.save('./data/mask/mask_patches' , s_p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:352: UserWarning: If you are not specifying the input dtype and using the default np.float64 dtype of read_map(), please consider that it will change in a future version to None as to keep the same dtype of the input file: please explicitly set the dtype if it is important to you.\n",
            "  \"If you are not specifying the input dtype and using the default \"\n",
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:403: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3_QFsOODr-8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8b82eb06-aaf3-40e4-bdaf-ba1e2ff1cde7"
      },
      "source": [
        "s_p.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12288, 64, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6uXlbTjNlOxu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDA3Y5VnL9eD"
      },
      "source": [
        "patches = np.load('./data/mask/mask_patches.npy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWC-gCL3ks_J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43c079d7-552e-43e8-bf13-7126d494f538"
      },
      "source": [
        "patches.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12288, 64, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaQTc8Y1EzHI"
      },
      "source": [
        "output = './data/mask/64patches/'\n",
        "prefix = ''\n",
        "npatch = 12*32**2\n",
        "\n",
        "for i in range(int(len(patches))):\n",
        "    np.save(output+prefix+str(i),patches[i])\n",
        "#     plt.imshow(patches[i], cmap=cmap)\n",
        "#     plt.savefig(output+prefix+str(i)+'.jpg')\n",
        "#     plt.close()    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsXAdW68pV-v"
      },
      "source": [
        "n_string = 3\n",
        "\n",
        "ch_mkdir('./data/string/') \n",
        "\n",
        "for i in range(n_string): \n",
        "    strnum = str(i+1)\n",
        "    if nside==4096:\n",
        "        strnum = strnum+'b'\n",
        "    if not os.path.exists('./data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits.'+ex):\n",
        "        print('Downloading string: '+str(i))\n",
        "        download('http://cp3.irmp.ucl.ac.be/~ringeval/upload/data/'+str(nside)+'/map1n_allz_rtaapixlw_'+str(nside)+'_'+strnum+'.fits.'+ex,\n",
        "          './data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits.'+ex)\n",
        "\n",
        "    load_status = 0\n",
        "    if not os.path.exists('./data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits'):\n",
        "        print('Extracting string: '+str(i))\n",
        "#        with gzip.open('../data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits.'+ex, 'rb') as f_in:\n",
        "#            with open('../data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits', 'wb') as f_out:\n",
        "#                shutil.copyfileobj(f_in, f_out)\n",
        "        in_file = './data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits.'+ex\n",
        "        out_file = './data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits'\n",
        "        extract(in_file,out_file)\n",
        "        \n",
        "        ss = hp.read_map('./data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.fits',verbose=0,nest=1)    \n",
        "        load_status = 1        \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)    \n",
        "        plt.savefig('./data/string/map1n_allz_rtaapixlw_'+str(nside)+'_'+str(i+1)+'.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_HpzYlFphja",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8aa130a1-0868-44f7-ca53-4cabbf4d5cd6"
      },
      "source": [
        "n_gaussian = 2\n",
        "\n",
        "ch_mkdir('./data/ffp10/') \n",
        "                 \n",
        "for i in range(38,40):\n",
        "    mm = str(i).zfill(4)\n",
        "    \n",
        "    if not os.path.exists('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=febecop_ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',\n",
        "                './data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/ffp10/ffp10_lensed_scl_cmb_100_mc_0038.fits...\n",
            "Fetching ./data/ffp10/ffp10_lensed_scl_cmb_100_mc_0039.fits...\n",
            "-565288960% complete"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yk6AnRa7xwM3"
      },
      "source": [
        "n_train = 15\n",
        "n_test  = 22\n",
        "\n",
        "cmap = plt.cm.jet\n",
        "cmap.set_under('w')\n",
        "cmap.set_bad('gray',1.)\n",
        "nside = 2048\n",
        "\n",
        "def kelvin_check(m):\n",
        "    if m.std()>1e-2:\n",
        "        return 1e-6*m\n",
        "    return m"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDRCky_NdQ2Q"
      },
      "source": [
        "def kelvin_check(m):\n",
        "   if m.std()>1e-2:\n",
        "       return 1e-6*m\n",
        "   return m"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTIkag6f2b1H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f06de4fb-bb84-43a8-fa3a-28f768905e76"
      },
      "source": [
        "cmap = plt.cm.jet\n",
        "cmap.set_under('w')\n",
        "cmap.set_bad('gray',1.)\n",
        "nside = 2048\n",
        "\n",
        "\n",
        "s_files = sorted(glob('./data/string/*.fits'))\n",
        "print(s_files)\n",
        "\n",
        "def kelvin_check(m):\n",
        "   if m.std()>1e-2:\n",
        "       return 1e-6*m\n",
        "   return m\n",
        "ch_mkdir('./data/string_p/')\n",
        "npatch=12\n",
        "i = 1\n",
        "for s in s_files[1:]:\n",
        "\ts = hp.read_map(s,nest=0,verbose=0)\n",
        "\ts = kelvin_check(s)\n",
        "\ts = hp.smoothing(s,np.radians(5/60.),lmax=3*2048,verbose=0)\n",
        "\ts = hp.reorder(s , r2n=1)\n",
        "\ts_p = sky2patch(s,1)\n",
        "\n",
        "\tfor j in range(12):\n",
        "\t\tnp.save('./data/string_p/'+str(i*npatch+j),s_p[j])\n",
        "\t\tplt.imshow(s_p[j], cmap=cmap)\n",
        "\t\tplt.savefig('./data/string_p/'+str(i*npatch+j)+'.jpg')\n",
        "\t\tplt.close()\n",
        "\ti+=1 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxbibLJT7Oa2"
      },
      "source": [
        "from ccgpack import sky2patch\n",
        " \n",
        "def build_map(inmap,output,i,ntot,\n",
        "                ns=1,g=0,sml=5.,\n",
        "                lmax= 3*2048,prefix=''):    \n",
        "    ch_mkdir(dest)\n",
        "    dont = 1\n",
        "    npatch = 12*ns**2\n",
        "    for j in range(npatch):\n",
        "        if not os.path.exists(output+prefix+str(i*npatch+j)+'.npy'):\n",
        "            dont = 0\n",
        "    if dont:\n",
        "        return\n",
        "    \n",
        "    if sml!=0:    \n",
        "        s = hp.read_map(inmap,nest=0,verbose=0)\n",
        "        if g:\n",
        "            s = kelvin_check(s)\n",
        "        s = hp.smoothing(s,np.radians(sml/60.),lmax=lmax,verbose=0)\n",
        "        s = hp.reorder(s , r2n=1)\n",
        "    else:\n",
        "        s = hp.read_map(inmap,nest=1,verbose=0)\n",
        "        if g:\n",
        "            s = kelvin_check(s)\n",
        "    patches = sky2patch(s,ns)\n",
        "    for j in range(npatch):\n",
        "#         pop_percent(i*npatch+j,ntot*npatch)\n",
        "        np.save(output+prefix+str(i*npatch+j),patches[j])\n",
        "        plt.imshow(patches[j], cmap=cmap)\n",
        "        plt.savefig(output+prefix+str(i*npatch+j)+'.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIlaUo4t27CT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf14c912-fc60-4ae9-e4c8-e6cbb2408a3b"
      },
      "source": [
        "from ccgpack import sky2patch\n",
        "print('FFP10 gaussian maps:')\n",
        "dest = './data/ffp10_p/'\n",
        "ntot = 1\n",
        "for i in range(39,40):\n",
        "    inmap = './data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+str(i).zfill(4)+'.fits'\n",
        "    build_map(inmap,dest,i,ntot,g=1,sml=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FFP10 gaussian maps:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oekxcv_J2_sw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b953f65-3497-43cf-9296-aca5d21c4504"
      },
      "source": [
        "print('String maps:')\n",
        "dest = './data/string_p/'\n",
        "ntot = 3\n",
        "for i in range(3):\n",
        "    inmap = './data/string/map1n_allz_rtaapixlw_2048_'+str(i+1)+'.fits'\n",
        "    build_map(inmap,dest,i,ntot,sml=5,g=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "String maps:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqplJ7urtbwd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "efb421a2-a3b2-4e61-e916-05e46e4b91d2"
      },
      "source": [
        "obs_adds = {'COM_CMB_IQU-smica_2048_R3.00_hm1.fits':'http://pla.esac.esa.int/pla-sl/data-action?MAP.MAP_OID=13486'}\n",
        "ch_mkdir('./data/observations/hm1/') \n",
        "for name in obs_adds:\n",
        "\n",
        "    if not os.path.exists(name):   \n",
        "        download(obs_adds[name],'./data/observations/hm1/'+name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/observations/hm1/COM_CMB_IQU-smica_2048_R3.00_hm1.fits...\n",
            "-594788352% complete"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hI8GpTOsXpM"
      },
      "source": [
        "https://pla.esac.esa.int/pla-sl/data-action?CMB_MAP.METHOD=smica&MAP.LAST_VERSION=true&MAP.TYPE=CMB_MAP&MAP.LEGACY=true&MAP_PERIOD.NAME=Full&MAP.RING_HALF=0&CMB_MAP.KINETIC=false\n",
        "    \n",
        "obs_adds = {'smica_full.fits':''}\n",
        "ch_mkdir('./data/observations/') \n",
        "for name in obs_adds:\n",
        "\n",
        "    if not os.path.exists(name):   \n",
        "        download(obs_adds[name],'./data/observations/'+name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rDkNvcHBD62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4317e8bb-0856-4524-84ce-ccb65ce6f959"
      },
      "source": [
        "obs_adds = {'smica_dr3.fits':'http://pla.esac.esa.int/pla-sl/data-action?MAP.MAP_OID=13485'}\n",
        "ch_mkdir('./data/observations/') \n",
        "for name in obs_adds:\n",
        "\n",
        "    if not os.path.exists(name):   \n",
        "        download(obs_adds[name],'./data/observations/'+name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/observations/smica_dr3.fits...\n",
            "-1279614976% completeBuffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqKVjaDqukOx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "b868c1eb-9316-4206-93fe-e23650c333bc"
      },
      "source": [
        "print('OBSERVATIONS:') \n",
        "dest = './data/obs_p/'\n",
        "\n",
        "mlist = glob('./data/observations/smica_dr3.fits')\n",
        "for inmap in mlist:\n",
        "    prefix = inmap.split('/')[-1][:-5]+'_'\n",
        "    print('Observation: '+prefix)\n",
        "    build_map(inmap, output= dest ,i=0,ntot=1,ns=32,g=0,sml=0,prefix=prefix)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OBSERVATIONS:\n",
            "Observation: smica_dr3_\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:352: UserWarning: If you are not specifying the input dtype and using the default np.float64 dtype of read_map(), please consider that it will change in a future version to None as to keep the same dtype of the input file: please explicitly set the dtype if it is important to you.\n",
            "  \"If you are not specifying the input dtype and using the default \"\n",
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:403: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL3KIwAb0GZX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b26c4894-19ca-47bf-832d-1021564d9f78"
      },
      "source": [
        "a = np.load('./data/observations_p/smica_dr3_0.npy')\n",
        "a.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8foh-18rHz2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "1473fb9e-b691-4cf1-d8c5-e0dc29ab3e2f"
      },
      "source": [
        "print('OBSERVATIONS_100:') \n",
        "dest = './data/observations_p/'\n",
        "mlist = glob('./data/HFI_SkyMap_100_2048_R3.01_full.fits')\n",
        "for inmap in mlist:\n",
        "    prefix = inmap.split('/')[-1][:-5]\n",
        "    print('Observation: '+prefix)\n",
        "    build_map(inmap,dest,i=0,ntot=1,g=1,sml=0,prefix=prefix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OBSERVATIONS_100:\n",
            "Observation: HFI_SkyMap_100_2048_R3.01_full\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:372: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyeQoFenv67k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a16fb154-81da-4e1d-98b6-719f3f19d645"
      },
      "source": [
        "if not os.path.exists('./data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits'):\n",
        "    ch_mkdir('./data/mask/')           \n",
        "    download('http://pla.esac.esa.int/pla/aio/product-action?MAP.MAP_ID=COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits',\n",
        "             './data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits...\n",
            "Download complete, saved as ./data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83QScm1lR6Ju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9dd72914-01b5-434c-995d-6711a750dfcb"
      },
      "source": [
        "print('MASK:') \n",
        "dest = './data/mask/'\n",
        "inmap = './data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits'\n",
        "build_map(inmap,dest,i=0,ntot=1,sml=0,g=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MASK:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:372: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFNuC7bRWvU9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d46a6fdd-286a-4354-f1cd-02c5ea2cb949"
      },
      "source": [
        "print('MASK:') \n",
        "dest = './data/mask/256/'\n",
        "inmap = './data/mask/COM_Mask_CMB-common-Mask-Int_2048_R3.00.fits'\n",
        "build_map(inmap,dest,ns=8,i=0,ntot=1,sml=0,g=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MASK:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:372: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMx_zYC1a6gR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "4ed4c141-ca26-4bf2-e9d1-c08d5c74b50a"
      },
      "source": [
        "print('OBSERVATIONS:') \n",
        "dest = './data/observations_p/hm1/256/'\n",
        "mlist = glob('./data/observations/hm1/*.fits')\n",
        "for inmap in mlist:\n",
        "    prefix = inmap.split('/')[-1][:-5]\n",
        "    print('Observation: '+prefix)\n",
        "    build_map(inmap,dest,i=0,ns=8,ntot=1,g=1,sml=0,prefix=prefix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OBSERVATIONS:\n",
            "Observation: COM_CMB_IQU-smica_2048_R3.00_hm1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:372: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOFrqwpdnDbV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "a09492d2-5693-492b-e46d-b4307f4966cd"
      },
      "source": [
        "print('NOISE:') \n",
        "obs_adds = {'ffp10_noise_100_full_map_mc_00000.fits':'http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_00000.fits'}\n",
        "ch_mkdir('./data/observations/') \n",
        "for name in obs_adds:\n",
        "    if not os.path.exists(name):   \n",
        "        download(obs_adds[name],'./data/observations/'+name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOISE:\n",
            "Fetching ./data/observations/ffp10_noise_100_full_map_mc_00000.fits...\n",
            "-49061888% completeNOISE:\n",
            "Fetching ./data/observations/ffp10_noise_100_full_map_mc_00000.fits...\n",
            "-573456384% complete"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QO4F55GivpKB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "outputId": "81d05710-a866-4ead-e24b-2f7bf0fb8177"
      },
      "source": [
        "!wget -np -r http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_00000.fits"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-22 09:26:26--  http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_00000.fits\n",
            "Resolving pla.esac.esa.int (pla.esac.esa.int)... 193.147.153.153\n",
            "Connecting to pla.esac.esa.int (pla.esac.esa.int)|193.147.153.153|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 \n",
            "Length: unspecified [image/fits]\n",
            "Saving to: ‘pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_00000.fits’\n",
            "\n",
            "pla.esac.esa.int/pl     [ <=>                ] 576.01M  22.7MB/s    in 28s     \n",
            "\n",
            "2019-10-22 09:26:55 (20.2 MB/s) - ‘pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_00000.fits’ saved [603987840]\n",
            "\n",
            "FINISHED --2019-10-22 09:26:55--\n",
            "Total wall clock time: 29s\n",
            "Downloaded: 1 files, 576M in 28s (20.2 MB/s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6pnyHDXdCwG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9beb2bb7-9f69-4f13-9d55-4e80085c7f2c"
      },
      "source": [
        "n_gaussian = 1\n",
        "\n",
        "ch_mkdir('./data/ffp10/') \n",
        "                 \n",
        "for i in [800]:\n",
        "    mm = str(i).zfill(4)\n",
        "    \n",
        "    if not os.path.exists('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=febecop_ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',\n",
        "                './data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/ffp10/ffp10_lensed_scl_cmb_100_mc_0800.fits...\n",
            "Download complete, saved as ./data/ffp10/ffp10_lensed_scl_cmb_100_mc_0800.fits\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upghZ2RXi5F-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "68ad47e3-fc77-4dee-eae0-f5b22cfe3ddf"
      },
      "source": [
        "\n",
        "ch_mkdir('./data/ffp_noise/') \n",
        "                 \n",
        "for i in range(0,10):\n",
        "    mm = str(i).zfill(5)\n",
        "    \n",
        "    if not os.path.exists('./data/ffp_noise/ffp10_noise_100_full_map_mc_'+mm+'.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=ffp10_noise_100_full_map_mc_'+mm+'.fits',\n",
        "                 './data/ffp_noise/ffp10_noise_100_full_map_mc_'+mm+'.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/ffp_noise/ffp10_noise_100_full_map_mc_'+mm+'.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/ffp_noise/ffp10_noise_100_full_map_mc_'+mm+'.jpg')\n",
        "        plt.close()    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00000.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00000.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00001.fits...\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00002.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00002.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00003.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00003.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00004.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00004.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00005.fits...\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00006.fits...\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00007.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00007.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00008.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00008.fits\n",
            "\n",
            "Fetching ./data/ffp_noise/ffp10_noise_100_full_map_mc_00009.fits...\n",
            "Download complete, saved as ./data/ffp_noise/ffp10_noise_100_full_map_mc_00009.fits\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fo2cDgazEad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "57045fde-6bca-4d6b-db82-1f16714c6d42"
      },
      "source": [
        "ch_mkdir('./data/dx/') \n",
        "                 \n",
        "for i in range(45,50):\n",
        "    mm = str(i).zfill(5)\n",
        "    \n",
        "    if not os.path.exists('./data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=dx12_v3_smica_cmb_mc_'+mm+'_raw.fits',\n",
        "                 './data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/dx/dx12_v3_smica_cmb_mc_00045_raw.fits...\n",
            "Fetching ./data/dx/dx12_v3_smica_cmb_mc_00046_raw.fits...\n",
            "Download complete, saved as ./data/dx/dx12_v3_smica_cmb_mc_00046_raw.fits\n",
            "\n",
            "Fetching ./data/dx/dx12_v3_smica_cmb_mc_00047_raw.fits...\n",
            "Fetching ./data/dx/dx12_v3_smica_cmb_mc_00048_raw.fits...\n",
            "Download complete, saved as ./data/dx/dx12_v3_smica_cmb_mc_00048_raw.fits\n",
            "\n",
            "Fetching ./data/dx/dx12_v3_smica_cmb_mc_00049_raw.fits...\n",
            "-601997312% complete"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBAr9XnEDAhD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d057b97-2a58-4d10-fae6-e6951b11a753"
      },
      "source": [
        "print('dx12-v3:') \n",
        "dest = './data/dx_p/'\n",
        "# mlist = glob('./data/dx/*.fits')\n",
        "\n",
        "for i in range(45,50):\n",
        "    mm = str(i).zfill(5)\n",
        "\n",
        "#     prefix = inmap.split('/')[-1][:-5]\n",
        "#     print('Observation: '+prefix)\n",
        "    inmap = './data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.fits'\n",
        "    build_map(inmap,dest,i=i,ns=1,ntot=5,g=0,sml=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dx12-v3:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wl4xyDmA__nN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_hgvpR-Iw58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "ead1bf12-29cf-41ed-cc04-d1c710c191a9"
      },
      "source": [
        "ch_mkdir('./data/noise/') \n",
        "                 \n",
        "for i in range(42,44):\n",
        "    mm = str(i).zfill(5)\n",
        "    \n",
        "    if not os.path.exists('./data/noise/dx12_v3_smica_noise_mc_'+mm+'_raw.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=dx12_v3_smica_noise_mc_'+mm+'_raw.fits',\n",
        "                './data/noise/dx12_v3_smica_noise_mc_'+mm+'_raw.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/noise/dx12_v3_smica_noise_mc_'+mm+'_raw.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/noise/dx12_v3_smica_noise_mc_'+mm+'_raw.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/noise/dx12_v3_smica_noise_mc_00042_raw.fits...\n",
            "Download complete, saved as ./data/noise/dx12_v3_smica_noise_mc_00042_raw.fits\n",
            "\n",
            "Fetching ./data/noise/dx12_v3_smica_noise_mc_00043_raw.fits...\n",
            "Download complete, saved as ./data/noise/dx12_v3_smica_noise_mc_00043_raw.fits\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3-FlKG49nKS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac289b0e-52ee-4a01-e5b4-1161fd72a4cc"
      },
      "source": [
        "dv = hp.read_map('./data/dv/dx12_v3_smica_cmb_mc_00000_raw.fits',verbose=0,nest=1)\n",
        "dv.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00010937204207107985"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1cFR4Z7993L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29d316bd-66ed-4db4-ae37-c916cce33e07"
      },
      "source": [
        "n = hp.read_map('./data/noise/dx12_v3_smica_noise_mc_00000_raw.fits',verbose=0,nest=1)\n",
        "n.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3171996639176428e-05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBO8qiiZ2nkp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6db0dad8-5c20-45b5-9a10-96333fb626bd"
      },
      "source": [
        "print('total:') \n",
        "ch_mkdir('./data/dx_p/') \n",
        "dest = './data/dx_p/'\n",
        "\n",
        "for i in range(42,44):\n",
        "    \n",
        "    mm = str(i).zfill(5)\n",
        "    dv = hp.read_map('./data/dx/dx12_v3_smica_cmb_mc_'+mm+'_raw.fits',verbose=0,nest=1)\n",
        "    noise = hp.read_map('./data/noise/dx12_v3_smica_noise_mc_'+mm+'_raw.fits',verbose=0,nest=1)\n",
        "    s = dv + noise\n",
        "    patches = sky2patch(s,npatch=1)\n",
        "    for j in range(12):\n",
        "        np.save(dest+str(i*12+j),patches[j])\n",
        "        plt.imshow(patches[j], cmap=cmap)\n",
        "        plt.savefig(dest+str(i*12+j)+'.jpg')\n",
        "        plt.close()\n",
        "    print('total '+str(i)+' done!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total:\n",
            "total 42 done!\n",
            "total 43 done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmGz1GQpnGK5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "5d728225-5354-4115-9ac3-60ea5be29ca2"
      },
      "source": [
        "n_gaussian = 1\n",
        "\n",
        "ch_mkdir('./data/ffp10/') \n",
        "                 \n",
        "for i in [800]:\n",
        "    mm = str(i).zfill(4,10)\n",
        "    \n",
        "    if not os.path.exists('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits') or replace:\n",
        "        download('http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=febecop_ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',\n",
        "                './data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits')\n",
        "                \n",
        "        ss = hp.read_map('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.fits',verbose=0,nest=1)    \n",
        "        hp.mollview(ss, nest=1, cmap=cmap)              \n",
        "        plt.savefig('./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'+mm+'.jpg')\n",
        "        plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-e2dcf168f35f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m800\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/ffp10/ffp10_lensed_scl_cmb_100_mc_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.fits'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: zfill() takes exactly 1 argument (2 given)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnUctmZZmYnA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4500ee8-75fe-4b7e-fc86-f94885a1a1ce"
      },
      "source": [
        "from ccgpack import sky2patch\n",
        "print('FFP10 gaussian maps:')\n",
        "dest = './data/test/ffp/256/'\n",
        "# ntot = 1\n",
        "inmap = './data/ffp10/ffp10_lensed_scl_cmb_100_mc_0800.fits'\n",
        "build_map(inmap,dest,i=0 ,ntot=1,ns=8 ,sml=0,g=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FFP10 gaussian maps:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjjXam8b7gBn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "f36c0fcf-cc92-4e9a-96db-ec8682c26d10"
      },
      "source": [
        "print('smica - simulated noise')\n",
        "noise = hp.read_map('./data/observations/noise.fits')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "smica - simulated noise\n",
            "NSIDE = 1024\n",
            "ORDERING = NESTED in fits file\n",
            "INDXSCHM = IMPLICIT\n",
            "Ordering converted to RING\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCD2fTQsCrsa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0865aab6-6d97-4e9e-b578-a24253b61327"
      },
      "source": [
        "obs_adds = {'sevem_f100.fits':'http://pla.esac.esa.int/pla-sl/data-action?MAP.MAP_OID=13455'}\n",
        "ch_mkdir('./data/observations/') \n",
        "for name in obs_adds:\n",
        "\n",
        "    if not os.path.exists(name):   \n",
        "        download(obs_adds[name],'./data/observations/'+name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fetching ./data/observations/sevem_f100.fits...\n",
            "Download complete, saved as ./data/observations/sevem_f100.fits\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC_5TbDuuL6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "8d11dc6d-6954-4753-c2ea-74475f91c415"
      },
      "source": [
        "print('OBSERVATIONS:') \n",
        "dest = './data/observations_p/f100/256/'\n",
        "mlist = glob('./data/observations/sevem_f100.fits')\n",
        "for inmap in mlist:\n",
        "    prefix = inmap.split('/')[-1][:-5]\n",
        "    print('Observation: '+prefix)\n",
        "    build_map(inmap,dest,i=0,ns=8,ntot=1,g=1,sml=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OBSERVATIONS:\n",
            "Observation: sevem_f100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py:372: UserWarning: No INDXSCHM keyword in header file : assume IMPLICIT\n",
            "  warnings.warn(\"No INDXSCHM keyword in header file : \" \"assume {}\".format(schm))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA1t8dVDFxKt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "cecac4d2-88b6-4888-ea36-594766a61d05"
      },
      "source": [
        "obs_100 = hp.read_map('./data/HFI_SkyMap_100_2048_R3.01_full.fits' , field=0 ,nest=1 , hdu=1 , verbose=1 )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-a1b548ffe11d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobs_100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/HFI_SkyMap_100_2048_R3.01_full.fits'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mhdu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py\u001b[0m in \u001b[0;36mread_map\u001b[0;34m(filename, field, dtype, nest, partial, hdu, h, verbose, memmap)\u001b[0m\n\u001b[1;32m    325\u001b[0m     \"\"\"\n\u001b[1;32m    326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m     \u001b[0mfits_hdu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_hdu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhdu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m     \u001b[0mnside\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfits_hdu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"NSIDE\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/healpy/fitsfunc.py\u001b[0m in \u001b[0;36m_get_hdu\u001b[0;34m(input_data, hdu, memmap)\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowed_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 626\u001b[0;31m         \u001b[0mhdulist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_get_hdu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdulist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhdu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     return HDUList.fromfile(name, mode, memmap, save_backup, cache,\n\u001b[0;32m--> 151\u001b[0;31m                             lazy_load_hdus, **kwargs)\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36mfromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m    388\u001b[0m         return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,\n\u001b[1;32m    389\u001b[0m                              \u001b[0msave_backup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_backup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m                              lazy_load_hdus=lazy_load_hdus, **kwargs)\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/hdu/hdulist.py\u001b[0m in \u001b[0;36m_readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, save_backup, cache, lazy_load_hdus, **kwargs)\u001b[0m\n\u001b[1;32m    991\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_File\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m                 \u001b[0;31m# instantiate a FITS file object (ffo)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 993\u001b[0;31m                 \u001b[0mfileobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    994\u001b[0m             \u001b[0;31m# The Astropy mode is determined by the _File initializer if the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    995\u001b[0m             \u001b[0;31m# supplied mode was None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/utils/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    486\u001b[0m                         \u001b[0;31m# one with the name of the new argument to the function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m                         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_name\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/file.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fileobj, mode, memmap, overwrite, cache)\u001b[0m\n\u001b[1;32m    173\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_fileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/file.py\u001b[0m in \u001b[0;36m_open_filename\u001b[0;34m(self, filename, mode, overwrite)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_read_compressed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfileobj_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIO_FITS_MODES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_on_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/astropy/io/fits/util.py\u001b[0m in \u001b[0;36mfileobj_open\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m    386\u001b[0m     \"\"\"\n\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/HFI_SkyMap_100_2048_R3.01_full.fits'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "947PlTNjsVKa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "996937eb-1de6-4acf-8544-d31a4b9a2758"
      },
      "source": [
        "hp.mollview(obs_100*1e6 , title='Obs-Freq100' , nest= 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rod4qTptH9syRByMmbTzs/YhG3egSJqRlDsBpG+tOWa5uU8o4PdTTRCcTlDDE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ZlhxOP/jpEVRxIf4UXsjwO+BWAagBzgQEE6Zl16FRtuBUJjgVAP8J3RwC1tAIqBV/YCM8oB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\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mlDCYlg/opIVbwd+1m6mE/mfZbPuDOI+Rw8py+NkjO+iTKWaFM1z371iQ3Thmzqn7/w1k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rpQyLK60SUqJmKsiOKVcTxTyhwzHOYF2T4m7aeZVvqUlA+GX2Oh2LkusHXWBLB2cnS2npIDJ3SP\nqph0NXk+MTfmjvp40dtvhsMwE8bylXrt2HnP56HOxnMBhSUO+Nk8zAz2cQAI88Adc2/Wds67q0UH\nPfr5d8wXAKJZ9Sh1UKmRYIYifc+MuZefx9pDllhpVarD20pfYqR8QR3YF2Qrrr0VvjcIzXqXeCXZ\nSF4ny0eDacrp4rSKk5v6HNGSpo9WtrcEMItZVGF7jKRo/qWK8ljQ2iBurTVFpYq3+eDeESyZ6hhV\nF+kubi1jwyEmh2sVZR/GqWGSKjrpZoUCie3+XADZSeMX0XPXTiIpktF48CgjygeHdK3sbujMngP7\nRoT1+DwfZYTDzCg+nQhG0OfCZzR8KWuaCuyqOl0xPtiEOthdnNZ1uARmj7B88dby+IHsrRfi+Z7D\nbeNY1vPygmxGOJrwTSMA/ygUgiMCp1E1oJYxRjjqVnrMq+sWtdv+Rb7+DUAWOLHVMqpiC1hhikp1\ntt5LAMMYtaxQ4K1uLqgt/YJsGhmimEXaOa9AvLqDlSixlZMOzJdemun3teJpU5DooMftAk7RxWUa\nFIDv6nC1cz7R2OoGrqsbYhf14A3RiB1DQSDo6kT+zRCNruy79WofxSzSwGV12NrBbJoyZgUQliSy\nZ2vbRA/Fw8wouT4KAe2eJUa+3+/BHQX/dbKwbwPfJICvNnpd7O8oIxonXJMp78ufqQsRu1V9wea4\ngBV2NgLwuKcH8G8hUE5S5eTNVfI0KglyGXykB26IRj1MXxmcUDLd2TDn26Uph6802thbV9IZo5Ya\nxvU5O7VlY5eCxMcX6uZVM+G8jDhmtGFcLK+UOXeEn6TKRSa5oM9vKSXiGPTnKBXoO2YuVLlIMQM0\nuVjpEI1JJ+GJkkMLKaqYFLAuCfyjNwJoPBa2x54Hrk/k+AQpAcuoc1TFZNIN6VXyzOchjQy5endU\nvI6SBxHwvSBbiag3jIJMQIf3zTf2djaUDPdy9XfddLL1ap9rdPXRin0tfkYN4xxmhjx+oIUUAzQR\n/dNO0UXWxroC5pjuae7umoqVGXMdOFIGb5rrtcWtoEgef32EUc+wOjc3w0j1hLk/o2XhI5ujjChJ\nNYTt3s/A/qTnsYZxMhT9iLdodt27iTaowiVFi8B7d7gPv8fjirUBy0qEVqDkcpgZjbJOmLpMH5kb\np1OiOBOteLI21n2R5SgykI68zGh9wge6D1NUSsDySNj6TYfC63YoPoIW0zKFeiYa1b1coETPbegm\nx+25eoZ9u3SNXO+6RjXuo4zQSh/ZWy9YI5c6RnlBNluv9jFJlUtp+KbdJVyJPdrh2Ddg8wLf3AkA\nLxQQ49TQTScPyVen4o7u/wBNLHGAcWr0eX8dno+LARzeCv599wIQiwri74fiqzKxAKPX3DuR2wIX\ncaLBdfOxLr3mnasoAVLFJKzo3I9RSyfdvkGavfUiKWYPBcu3Y0jl/Ps9xqlR4ZYOZ+Y10BHH9/Z1\nUtTFre9IWOcdFYbcTgo27uuZWyUPxnSec1491djwUBgJGtjXOoNxBN5Gr0bRR5RTMhS5vI3NK/aZ\nvdQorwQBxv/SpCWPH3Dz8xuh439O1ydDEePUsEIB06ZYFRXa05Sr+2VX9G9+Kc28Yhax2ypIIj1k\nlTyXBknRovtxU8XSWdM5aeAySxzgFF0M0MR/m4qLJQ7osz0WWP7HL1QM57x6qs/9r/P17wGyABhR\nxyWaoJYx6+7y0UTZBtHD3mu+6WBr21AVNndmzImZ1UzAPXV36hiFMY1+InG7i9NwRZV+F6ddsdl+\nqcNbypwexm/BTgUi+23T5tpnuD5I5ATte7alQ72Em0EPU4+dU8cia2M9UaAOHlFlzLrScNSMWaYQ\n+1Dz9XbOY1dFyq1nmGUKtWH3mSqvqPrt3lVxg+q/AqHzpLoe5aQV3I8kjuYLlLgkQ2xB99IGXwS+\n1nsKmnGscoouBbSDuv5RObifZlfm983G5bBhdyWArcawefWBgo+3838tgLJCAS2kWKGAvXWNB5oY\n0GggWON00yl1/ODjVcsY9nVQZk+HCu0PQbrhM7CP8a2XKSrhttrpMxxmljLdi9hPv2QAACAASURB\nVNuqjON154S5STHLgUx8LADO41pYGAk8kCEaVbF+FDp8Z3R+V8mjnmHGqOUwMwIKj5IRaORSRPX0\nSOTmdjCx/lMitttDB/Sa25S0kNIY/JNwLavlTxd5iCUs0MVpvy9RB62Zfhq4zCp5zFLGAiXMUuZn\nZ4xadQDeEwjfyzUHqFkb6+JLBiPpaIZ+NiTLOEpa4gB8pS232JGLAGyBEg/klUzpWqd1n/e/3FRH\n7AMl9jZ64bl5x/IyDcxS5orUsYvQRyut9DFBtTorX+m5tz+C1QI3zYHjzoZAnkaIj9jc3e/E+Gom\nsLVtVsnTGbuh699JN1y0kAAHaGLARStXKGCZQkqZE5geJnn+J3B+yjpZcFydu7MWCOBnVNy9INs7\n7S9/Zs4L843TNy3pYj3Xff4yLD5EjSpSAnHtnNezMZyMdeLm7N66OZcujvxP0UUN49QyRhmzWrQw\nxdRh6rF5vFiJ5t/RJzIqvrt+WjCCfvkzc2BFiZ5bLuj5GKWOw8yQpty3eucoZYkDdNDjv/cR4XHF\nSEbMgcksZc7Fq2Fcxdoneg6LWXS+Eif180pYEIB7ZB43S5nzMTpnDLOX2G81kotefWvkQqWKnhIW\nFCODP2khy9jqDrWMSe4iyGY8JcfN4J2Xds8c7E1QDc8lPPuC7B9ZXUWXBXugInBvXdOQCqY1inxN\nFiGKPZcxS86rp+TzkGomaGJAZ/s7sD8HiktjmI7YWSw7dCtXd3wRrJtON2nnmkb1E1Rruy9oZ9ml\nIB3xtl5rgRLfLuQLAd/I9Y3ejutk0cBlvf5JgelaxnhIPiNhxE2JJdf1TrK8wTuKN/9iX/9GIAuU\nlK/rgFpGo6jN3f2wHBTcL6AHeFnJj0pzVe0OegSsZhSwV8kTCPo2CJoFkbtiFiW/8C6uefSQfHWo\ngkaSXRWo2vdsS+TCQQTmUsFk+ZL4X/tfbmJ/VJv5MDOQtmQefQ5YVuKNCshNDPh83iY0z7a7qtDr\nGVa347cK8vaZNEUKWKGQZRfWHOGoAN9jJbKowp6m3OUsykm7hg1nDL5Q0uqnOeEy/NpcITyuIo9R\nSz3D+iwlekB4YokS83H9WYoWZikjn4dUMakEe1sPzs5G6Ep8KC5MrFBjB2mdLAGxg69tqb2hn9NK\nn3sf+r1s1DW1cwFYzYPZno+U9z3bwta2FUTSuApzNRO0kGKVvIT4G5wApqh0hfS4cdVCyknFpAPv\n6I2EmLz1ap8SyFvmqvS1jOksPjZX87bbaMyxYhKWXQKem/OMpqhU8nk7JKjP1QmcotI142Yp05j4\nXeCagAu3wvdfCNcrakzdUUKLI7g4snLbiq/M+RhR2LaEBfd+sxskJOB75tthpcx5EI0q/TGpR7/F\neoZZJ4uH5CuZh+2kSKqvYFrX4Z6emyiuuUoetYy54OfLn+nz5e6uufirzYuM20YvPDGvsBsZIo8f\nnEAd/RonqcLmdUb6aFVhdc18PD3DYRYpVrF2I2zzXjBfuignrfHIt+J9XqYBnoSz1ybgErX4SCfC\nqfapOiFZG+tUMC3w854AwvaWrtXfAzA/a+H6PdZ//zt0t6gK3/NYcgRcDH+eUqxw25tQEEXjXJ68\nNm5vwz1Po6xApF5wJcTKI7oOMTmfoktnJPiVzlEqjbBhXccXZLNIsYPy2GWJ5yuKgvKWANojCxuw\n1wzmzUdAUXyUm6EbP2pwQsByiEYVU4PhMz8JhU46EN/D2XlIPhmKFCOuBZ260K3M3V1zCQpGw3Uf\nM7etiUbdp+hSYXUm0E5SKmZ76IDHoXD6QM96CynftuVIIlsR4zlpgavoO5ihSABhRdeijV64acmy\n0wV9fzlpbG1bXbnH5nSG/S83KWGBFlIuMhrdTx6SL7DzQIV1GbMqJB9I4shO4Urx1UxgB/XsNDKk\nEeDqjufHuB3fSxs9dLC5u1+F6pfioro1jv1VI8bVHXX7Qod5jlI900fCJmImgPs0cCjxXo20lHwe\nqsN3PJz/I+acySjrExeB8vjhn4M7/p+//s1AFppHp2jRgxiMiHlfydiGUdK7Zt7+jrorHfS4K/gs\nZRo/zicii4sUQ5W5VQAjYV4cWvYd9EjZ+6tgyvxL8T3qGdZ7+MJ8ZBO36mL1GjlWB1hSNbNh2FVV\nAbGDwEEFi1b61G36Fmww8HR+hcYad0Pl8BvUMUvHauQc9o3AV+7umjYc55Wg7TbYOXwUxIx5APiL\nveZlllLitG+USCIXxK4i/sM7ArjD1GOrOzwkny8tkE3fMwGHdwLgqlaQzN56gWXEfYrqy6vkuVZL\nJIsvcQCWw3+jqm9DGB2EVfcMRfBcoDSCvofks7m737fEqpikkGXdnxIFFXuga9bIkPg/mXAtv9a4\nY4JqXd9f6X4Wsixg9Ib5KI575mPiyC2wQY1u+bUSYD3D9NChxHFcyW6AJgGPagF73g5g7FDoeL0t\nLt8kVXBGZ2yUOlK0qMtzPSSD4wr6UeDvIfmcoksJN/q2dShx8SiAnY/Cn38ReBGBrxhlK87Tzhq5\n9NHq23B2CteOaqOXBUrUPVvdwWqVsKLVSSSfRm5HmnKB7I6wafi5ft4KBa6xFgUPZzjMEI0Usqxn\n557AxvaW+fg2botGkUeq1WXd3N3PLGUa4w3iXoCxk7JOlm972QNcZXySKv9cHNMz7Zt2T7SYwLw6\n0RGUPSWHds6r2LkgsDlHKavkOWfmIfm0c94lHnhTVX70R7XfgtnfqWaCds7zzMxtpOKYn7d0zp+F\nLt9lGjSyM3M+Gm+aOuXfSl+oj1YmqaKMWXGuLuhZj4RiS2mM1U8zxSzSSp9iwHOdiQxFrFDgsjJR\n7DdqOJUxyyLFik2B5pC99UIgoEJJdZoKL0wWKdbvz+jsRY0kvhA4i1pwf4sFwfJrseegAPEyhXRx\nmsPM/Gg5qJ/mxIT7vXAtjpkv+VymIdGDe66/92nEsjrUixSre39Cyf0v4fnjiLkeXOQF9tOs+Bu0\n0f4W+LkrFMBYGPXfRuc2dFXtKtgfg47TG8lrxvdVwbQAeLCWGqNW3aSrYO0qyuP0YJFigd9HASCv\nbfvniSPuEhZ0LY4pZ0TerNkmdhD3VV2kmNzdNV8a6KMV7mipKk25W+nYt7hcD43mLgn2rcaihSxj\nl8LWfQnqomXUGS1kmfO0O+0gTbnbRjUypJg9Gp65t0NR+LmuKfcFlP/HlHNHqXMvRp7r85210B39\n1/z69wNZ8euRqQpZJU83K1TsPXQk6+pBhyZKA0xTAe8kXJGReLPfMFf/LWDFRyV0mqqfC0p2JSzw\nkHwlns/CWND2NMK7IVA0TL0enqAMb8NBjuLjYD3woSrcFlLY90Fx3h5RwAoTVJO1sU721gtX/S5h\nAfsS13/poEdg6QslaLO/yP5gJLRwL6IW8MXXkuJK2HRZ21ZCHRY3q4HLzn+JquxxZTlqYN01c8fz\nuH03RaXARFXgGFWbKvDPzSUA+EDJy3klX+haNtOvB3lQQXiKShGmOyzhNoyKqzH/OjH2QuiADeJk\n3Wb6xbW4EMBne2h724juRwrX2IrdGsZUkUrjRZWfj1NOqdIb4SiWSTRj/m4KSpVMaatrbZvztLO5\nu1/V70dBDfu+qSNwImzCfYmuyRf2I1PZdbJUGATT5y5Oi1g8IxDm2283QvdhVMB+/8tN91/s4jTP\nLFEPf0qOukk3zeUTVsnTzx/RPVsj1+2KGA3cq2EF+nFqpC11SQm0gmkeku8qzoeZUVfwKj46SdGi\n179nzktrZAj7FH3mIp2BNXJhRPfvPO0aAV7UmePNREw0dgqo0miwhnEmqBaQH1VSrWSKds5zmQa/\nd0/JgU+UvHJePaWHDi7TkNi/PFLxFHWKouTIMPXaNs6AHRdA6aYz2Sqr1Wixmgl1wjK4Sno5abgW\nlhZO6ZzVM6xO2ANV/ZZRTFglzzlDRxlh37Mt0pS7/hMfmSt+x+Q8RyncC2P1M/o5m7v7eUg+s5Rx\ngCWqmKSITGK51atOfA8d7Hu2pSIx2GF1cdpFdsuY1TkJThPRLqqCaewqAhNXzCkasaveSh+5u2vs\nf7npo/Wo2xY35Uap4ygjbG+Z25WVkw7MtQPqiC6b+xr2mWL0szCOjPIUjQzpHvwBuKZYR4cAWh2j\nKi6DaCxFuvflpPUMBHkergm473+5qcJjWa8dlxcsjfvi1jAOF4NH6ds4/6mDHoG7QE2IvqG2tk07\n5xX717bVjQrSFXELOo5tJ6nCLgqw1THqsiTMS8ojjx+0Qd4Imz9X9zjqsJ2iS3zabtwOa5h67Dv8\n77M21iUZErp8QzSqCAndrBIWnIcXrXwid85+pS5sKXM0068CZcacjkKv4nbcRm3nPPalCPFx87iX\nNrglwNtGrwr5YEg+FPlzF5NOVfS95Fj4+yBMvb2lz+4K+m+EDu6/9te/L8ja/LkezOhRZp+JmxRl\nEiIRNG6MxXX/SJLvpJt6hhOC3Qm14ltIJVtNJ5UodjZ0OKIdj5tLZ4O1hZbsdxoLmG26wvAsZbSQ\noptOofl0SL6rO4nW0j21TBu4DPOB1B3XpUMiiAq+HfSoqj6mioLBYPyZr2Rsa9uqqG0a+w1uwBy3\nTVYoEGh8LkLpCgXY93t00yky49q2qr9j5nZEkZDKiDotUWj1Mg3ukTdNBWcDYZFO3Y8oK1FERlXK\ndXMH+SizsEoePA4djCqNR7a3BELO2o+tN/ZyDT7RaGuCam+5R9V7K9H1n6CaAywpYRwP4KEC396b\npAo+SjgurJiLR+bzUABoRUHdSdjzel+ddGPpsP33nTpfIxx1v8C/BGDI7wScIkdomUKekuOioB30\nJLpjDWEcfQ6N57qVsPtoxb7fo5BlXzGPI8xW+rDfBoXvQdOiwO5+V8WeD9fxPO1u8Bp5Rn8J3ZFW\n+hz0nKedRoYUmOe1qdpCyrkcPFdSbKYf+xrvxg3Ee7MeOEDB/NaOoy5ciYB7KXMsUux8jB8Z7XaG\nKjWa/t5ThyNuZHHR1CG9afCePpfdTXgjfbS6MnQTA4xwlF7aeEoOOzkSeIx8mGomXAahmX6NM75G\nXbxhabexHEjdx8XFieAojsdO0cXm7n7X8GmjV697UwXGHKWJH92wxmpcU5za92wL7pvHpmdxPBjE\nOUc4CiU6j1NUCrQHTgw3zW1c4lbpJFX00Uo9w8xwWB2JlHzg2uilhnHFu3nFtgMs0c555igVeJgX\nyGdMQHj/SwlaTlCddI1CghynhmIW6abTtY/smIqarI11ujjt53yGww5YKpjmPO100EPWxrqeuc6g\nFXglxONj5j6Wrmb+K3WStl7to5EhztPuvpcVTDNMPRVM69qnVVTUMarkvqJYNUw9rfSx/+VmYi8T\nqCWxexs7x3F5IH6/pXQmonVQHKflvHrq4tbxOS1k2Xl4tYz59eQDjUdXyaOQZbdwOhtGv1HSoJ9m\nff8dUyy3R4pZt/HiyNpwmgBHEo/EYhaddzVJFTQaZne1lZ4WEX2KSpfyOcCSd6GjsLA9SPS+4qJE\nFZOUMesyK9Fb1vLFi5ylzMXCOaH7mKFIkifBSJoP9Odpyt2g/DIN6gBG+6xOE6i9pBzFJ4qfzfTD\nsf8fZP1rfF0Ps/2Z0MnJ4AT2WsbgURBFvK7xQVTkHuEoVKny6KPVeUNcC4Kev1bC4n19byfdPCXH\nVbjpDHP7YEbcwGWGqXc5hqhLtGIGlapY0pTDG+YPlRtND6o7sEwh1oZvF0auUBwhNtNPFZOMUqcg\n0aYWtR1CD5b9XRXet0rUacqTbt0ZBfL9Lzc9ANmgKp0FSmimn3WykkCxEsZMQTx1kiq3WsndXWOa\nCheZPMqI/s0hc9G6h+QzwtGkcjxpXtF20+nWJ230evXEB2GEctPgdhhF3tO13Hq1zx/kKPoZdb5S\ntLji+Cm6lJw+12ilkimBmAp9Vq0dT6ut/g1Yt75njVxfV+6gx4n5c5QKqBwJdkd3cT/EFlLuT8h1\n08+4GIRh74TzEwPuSX2GUubEc/k8mImnlUy0SXrWPe84IcVpHilgLXFA5+q2zk8758UPuyMAGjdu\n4+ZhJVNO0J+kSnIYOaY/G1QCjX6Da+SSxw900KN7agPu02ffIvXwYBQ+Rq26Cr9Tl7OByz8CvYeZ\n0fU/ZJjhZ21vXZ9hiEZfI4/jT+6Z22qkaGGCalfy7qRbhdL7SvjVTDivRUrXf6WPVo0dj4UgflPd\njmiAfpSRZLvpeOBRXVJHuZQ5AdrbGpOfoisxg/49WnZpE4CPsh1TVLK9pSQRPQDtgZ7jqA1mH4Zn\n89MkEY5w1As8y0iOZp0s/fn7YTR0KdzzIAUQF0UiNaGctIDZEXOh2CEaVdzcMnd8iH5yo9SxTpbb\nWtUxSooW8VuCavskVb4R20+z/vyLsH38joDtGLUuWhw1oqIMRz3DpCn389BNJ2XMumr7XwOQaiGl\nM3dHhVQBK1qamDffJHxKjuLwBS0AbL3aRwXT7H+56SP7vXVtdTZwmdzdNZYpdB/MR6bnwy7o2heR\nYZ0scnfXBCzDmHiOUkY4ylFGuGvqAIprdFbx6Zfib3bQo+f0GxVX5aSxzzSdaOe8CqWbQUrnrn5m\n9ACtYlK0lF8qzri2VqOexTlK3a/QbgtE2dcqjvtoVfEYlj2iDMMqefTT7I4PtratQjqjoriUOeXB\nD8G+32OYep2rJZxfOkqdzvuvBOTM9sKnL/YC0DJgGX2WNnqD1txZ7F3lV4+zz9U9K2SZSqY4TzsD\nNPGUHN3/d3RWOalcysEQjzpU6BWw4tehkGVStLjF20/k6z8AZKEOxTQVrJLnHaIMRdgv8UpnhsNC\n7pfw1X7eVOJsIeV8KD5XBd7EgJJ8o4BXPcM0MsT2ViJXsPVqH0M00kKKU3RRxyjddApMvS8w8o9f\nGHyU+LgVkVGX6oQC1ym69JAM4joiefxAO+c5awKB0ZG9kinpXwUuQAkLGs98/JrY6W09GClamKTK\nVe/5QpyKZvoVhG6rG7azoWA6TYWLlmZtrAvsBIFAxtStcC2jwOsYpQ5WFCwGaILjYdtnxhKC/MWg\nc1NpLogYlw5StKhSjhpPK7pPvbQJ5D42H6c8JF8crffMDZIHaPIAPE6NruMpnJtx1vRvLS0g1Uer\nAtq76kCeNcOsD3sXf1+RzxNBMO8r2a+RC1/o/UW7imUKYTRoV42qCuex3nfcfmVQCYTr6kJE4dVO\nuvUZ31HwWSPXzVZXKCBDkZPRmTFsAiXSr3S/UrQkSwfz6o7se7algPaVZDgWKGGZQmY4rHt1waDI\nXFA2AuEZDmO3dQ2iVpRNgE0EwdZGc02gBi6T8+qpeCrHxH1ZIxdumXtU8pHBFV2rJQ4wRi0VTNNJ\nt56NlMY2EWzaZ7icQDUT8DxJQHGtPoLMRYp9eYGDKhzsbuDHHTcnw09Q7ZtOcdMqWoPse7alAil0\n7V6QzRCNjFKnhHIJzL6hk26yt17QyJADlF7aHDjF513ecGC/UWK220qqw9RjJepG2N2w6XpOMWmU\nOp39BtM5OBJEdedxEdNJqgQeb4XzM6jna2cjyKKcVEejm06mqWCBEj1ztwxrxO1QpqnQqOk7dfsf\nku8bYnHzjZu6x7YEjIROSRrXOuOxutu2ts1TcmimX/fgLfMzxq/1XMbuD9U6B/xaoLuIDIyoKChj\n1rc/+bX5QhNvKeaeDSNEecK+xL7DDaPplAmxZRTHe+jwRYNHFjrDv9f4v41eJqniAEuUsCDgGBYd\neB7iySeKEYUsM0AT5aR5QTZdnPZliyYGsDReUMSt8jpGsW8CTSGj12im3z0ze+iggx5mKfNlA00g\nAn9ubdvHx/GZrmMUu6FOeRQUjhOX2CGPo+WtV/uwS3I3sVPq2NvaNnn8oFgcNgTz+EGvnzYfG8ZO\n1VNylFeeBLA4jG8NRy6XdatB8ZeQ+4rIkKbchZzXyMXaSdxDjiWafHQkuc7WtrlMg2LnWLjfleYL\nNNNUaGrx0/n6zwBZZwNhtJhFbS08V/XUQY/4QfPmHak6RjnKiHdvVmKnZzkRA+Ut8xY+xwMCD9yG\nUeqUSFaCJ+Ajc45E9E4rYQErkI7TWTOXPqhgWhVBPq5Y28gQ101JLVa7E1R769UyGocN0chhZihk\nmX3PtmhkiMs0SNByRGOaOUq9Fd7FaeecRO0vt0V5V1WOzaMgf9F8o4SLoRL/VuC1jV6BtCBLEEcn\nUTWeM4k6/ghH4RM9+FGokUcKGkM0qotyMXCBJiQCOU6Ny08MU+9cFA7q4WQlbMGNWsJtumlO0Bym\nnjFq2Xq1T8nugsCBfUmyyZUy5ylsbyVChGPUYraDfRhGvseViHrokDjiigDLWTPx4O4LFG3+3Hwb\nKHo28rnBHYGD/wkcsg56lDxSBrcCufOgOmxt9LoGEvMCrN10umExvw4r4W+D2VUfUxSRwS7hBPII\nwvL4gZxXT1nigH7O5xqVsWzJOv0Tgc4uTvOCbCX6tHlC4V4i4FjCgp+92Cnuo1W8rOGgUXbMnMQe\nNzDzeejSEpE4PkmV29mMUkcXp5miUmPH+2GE/qHGtePUuC7UIsU0MUAfrb51uPlznYH/DmTk1ztV\nWRvruj5/0vWVCe4etiReiv1SP+MpOSxS7Nw2u6suZyVTIjWv7mD2TNuqt/ENsCYG9Drfhc//gRJ6\nK33qfiyFxPknJbsqJl3nKS5kvCBbCfkbXFQ4ano104+VSFuviQGa6fcN2GjPRKPO1VFG2Hq1jzVy\naWTIx3R5/OBWOVG1PQpd2iV1Wbs47UDCu0cX1W23u7iArBkuCqsFm3nM/upjuofkw+2EL8YXiqFl\nzMKVZMmDTku6H481AlojlwJW1KW8FqYRnerwRJ7tKnnuIRq7dguUYGvbyc8P/L0OepjhMNNUUMYs\nB1jS6wzqvPTQwSLF0k+8HwqfomCr9TU+zouE8LhIUcICl2ngAEuUMicg/DFaavhYo+a45RdjbSSt\n2/d72INgxvxt8Mi9rQLIVneSjeU3gsPGVXWV7LaKjkWKKSftKv2vj/J4rElKERmsW9SBIjIqOm7g\ny1a9tPkWZBy/2yUVVKPUkaZccTDwwKxC77eTbhHpv9/D7B/wPMSv3lBUDgoc9dDh9/UoI4rRo4qF\nVIUYFnTDqpjEVnfI56Fy53GDN5QD44LYWQvE+J/O138GyHr9K4rT8dy8Hb/5c9NGSlSmfl9VOCUK\n8DMc1uz3uDZ0xqlRBfGOkmX0ovIV2we4NMIMh71tHgNjPcOJqeZ1gbUBmpTI3grbKEFXJPoFrlDA\nKnm00sd52gXIbgQe1efiN0RJBGsM8g6p0PK1swkx2eadhFjDuMYLvQrQOxsBKDxRUBujVjyY+6ok\nFFg1bjzKiEtQ8JEC0pDpGtQzDE+U2O1PIp0zo1Fd7u4a49S4YB4pgw8sEac7EqrbL8J/31GVM0y9\nixVOUSkeSkr3cDZ0TTjz2uu8Zy4LkaacrVf7qGNUSbBbQayVPiWQz823xLit0VoVk3DPXI/GauPY\n9Qr2rrZYz9NOihbX6OJxODuH9F4c0AST7OgzGOUsIveGR6FFnlZHcIUC+EpJZJh6AaEOc8udR2HT\nZoEStc0PBqPxtIj6xSxKGuD7PfppFoCd19kdoMn1ZxYocaFGHmn5gRkl6bhBt0IBvCmAvPVqH2XM\n8pQc9y6L27TRT81+pbFHXGAYoAn7TEGbQyGxHldwbWIgEY/9fo8+Wt3vMnvrBUM0qti4oiQSR20L\nlLC3HsD1sgqcSDTnps5wzqunPuZv4DJWK/25IjLe8T3KCPZAo3M7JsC49Wqfa+FFLayjjLCTE8Z4\njRr72bvh2fotEnDMEgizCj2HNixidD/NZG2sM0ATB1jiKTka0R/S89DEADwOBdaX6jhHk/pW+iCl\n+1PMokDdDQG43N01707ErcoOemhkyIuEHjpoISXie+h2dHGaOkbppNudHGoZ86WNRYrVxVsKwsSP\n9IytkYv9SqCKeS13tNLnG7ZR9qGctE8C9tbNvQjTlKswDH560Q7nkSV+llF9fZoK+ml2Angn3QLm\nwd9xmUIVf6EDQoOK5gxFAlm30RLJweQ1lynUM3RFXamzZuqk3VPBkaGIGQ47P7aMWR9TxhhuF3Dh\n3afkYIOh+Aogzr7BR6HZWy+w/8I3gnlkcC1snn+MG3c3MaBr+nmiddjAZew7SdjY93saEw6DNao4\nqGCaKSr93rbRK+B+KUxmrmi0ahk97ysUKMaX6F56TDsUlq4yOlO1jKm46xXwzd1dc1AYF7SqmcB+\ni3tPFrOozu5vEqHeGQ4LUL0d8untMIk4FmLXByFGPgmNiOcqDo4yQhEZahinlzZe/kwF7xylKqp/\nml//eSCrjV4F8idKdHHzhXsGbytJT1DNTo7W6P/xCyX8WTNfE452B4zqV4YiuCgxvWIWsQdqxRey\nzCh1ZChSRbJiXklNUUnu7poL/C1Q4iT6EY6qmvkWbaM91sHMUET21gsqmGbr1T6Bpl+qM/OQfOzP\n+Bp8DOD2sZJCASvaYrmNbxN10u2yAS2kHCROUgWDIYk9Eui4TAOddLsI4vZWIBl/rW6a3UUJrzGs\n2aYFSjhp2J+V8GdNY4wuTjtnaHtLY4LowRfb6GfttfHjY3MV8Ego5cRrgOag+do7RabgeELBnU8U\nRNfI5QXZ4ktdUiufgwFYBHmADEWQNu86RGL5UUZU0f4RdbP+rGA6QbWPhCep8i0svlIVOcNhddYu\nhuT/OPBY0gJhw9RTyxhupxPuM+nw3o+YO8qvmLktSdbGOnwhMHaUEbim8U0UGy1kmQqmGeGoQGR8\n7Tume3RFPzt2XoZoVCD/VODTPlS1Gje4lin08e2ImY+M7TtUsHyRdKXsO9y6o4HLOoPfoa5oh/m6\nfwErLqkQPQEbGdL4vUT3ooHLSlCd5jZAPXSQu7vmNk1xeWSJA7SQopIphqln37MtyphlgRJ1wv4E\nfBDGQIO6FrF7ZudEEWjgspv+RvPtTrrZ3N3vxtAzHPZqW75vYW096F3ZOjJFVgAAIABJREFUBJ6s\nClmmmEUKWRbguBi6nKFzGbdP5ygVr+c7YD4YGG+Ybx2SVsIrZU5CqZ1hFH/dXNOthnGXYRigiVnK\nWCOXXtqc+D5FJa30KdHfVYKMHaao0j9MvcDiRbzLf552trfCSOotc5C7vWXYYBDrXdGzNkE1VUx6\n945enZdaxlTgrO74z5yjVOc78GUzFNFCiqyNdVbJY29d12CaCuyuQF8UdeadcC+jGOUhc3FSL4of\n62cMU69n8KT5ZvgUlYoNwS91mgrsAe4/y03zwrmXNh+J1TDulmzc1LNpNoTZVcz+yhq5pGihiIy7\nhzQx4KDI/hw66PcU57s4zd56ssUZvW+jgPPWq30C918L2HDfxH2yq75J2M55bEnvrZhFFilmjFoV\nV1+FpaA7eu58tPmdpD7itYg/37qBKwH83A+xNcgf2fd72GAAVmmNQKNwadyob6afJQ5IWPZ5+PdR\nv+2gnoEOepJi/HPdS3f7qA6x6mLgTw6GGH/iJwk34D8RZAFOUueOuSJ0hiJStDBvGpeskscqeayT\npYcu+PjxOx2caSoULB8p6HTS7b5KltIDEXlBa+TqsFxQsihmUaDrd+YWF7xjTjQdp8bn4d4xSFlC\nkjS8isx59RRb0gNTzQR2VWTOlz8Lhr92HbNZt16YpEqVybw4DNlbLxigSQF1Rg+jpUPgPKHqYo1c\n7JjAWRxRRCf3ds6Txw8+d2c+yEFcDECmQcF3mUK4mczW7XaQkAiKvoUsi8h5IfAYPlXAs2P42nkD\nl8naWKectIDyzQCill/bDJ3RvYlK5k/JEVBeNve343a4NrfB2nS9XCbhjOleDSbj5BrGvRVfyxiL\nFCuoPEhsbuw3+swZihx0cc18+y5u2ayS56TQctLsf7lJFZPuffk3M9fbiuKe62SJm7ISli7mg5zG\n5+ILut/mXXVlahiHLwQUdjZCRzED9ApQ72woaG3u7of5sPVzDo1jNswlSaLqfyS9W4mCej3DSaLp\nUBeCg0q4dlfE1yommaZC1ylbXR+XjTgpflGacj/zfbQKnIeRWhwx2pf6d320+udboIQeOqhhnPO0\nq0t2LzEktj/qGbEJ8XBO0aVu07uoaKlKhCA76HEbpOgA0E2nb5J10k0ZsxxlRB2vCyRA5IHeWz3D\nKiTSYLfFa3pBNnZbukiVTAlM3DN3ipig2pWyX5CtUdDqjm8oNjKELeHLGnYX58bYhJ73GsYVG56H\nAvCYfka0z3IO4Rk9Y6/zBCO/kff03xUKXKQ0e+uFj5Py+MFlPSJgaSEl0NKo4id764XO6x1zn8Iu\nTvsih13VM9xMv2+tcstck8mXj56b894yFLkm2GFmKGaRBUrUCX2k53lvPXSyls2pF/SKq/XX2Km6\nqXMaOT/bW+ZClvHfxm0+xcgd99KMfCZLhUJydQdb0v2NchMaHZ9VV/Mz3HOyljHx7z4FliUTkccP\nvrV8gCWdiecJCLUUzmWz2yp0eCyQU8uYC+lGaQkeB1HVWl3fyO07T7vizyEVVNVMMEx9Ukj9PoDF\nt8y5kQdYopYx165aphA+0aiulT5RU1Z3sN8mU5o1ct05wy4qvjBivhAU5WdStGgr9/s9Jqh2OZmH\n5MNg0uWcpkIxYSbpuNnqjsDWT/frPxNkxa9xauilLdk+OmnuZk9nItYYHeR3cgKiPqGqgJPhYf7I\nvO0fK5wRjqqaqhQSf/kzdSPuhu8vYEXBLnB17DtVxDmvnrqGSO7umoJPXGMPo8VuOjWumFBLeY5S\n7Ps98UBmAsjJCp5WS/qe2C35e1jZ7qXNybruPj9v0BEq1iBuuP/lpgsaNjKEfauRRvbWC07R5T5k\nh5lRlyaQljvo8QDrtjzvm2sU2bCA4iRVArGNIUl/rQ0su4REE68qaHg38LmuWXSDL2FBFdf9AA6C\ntlIn3cxS5lsxjOp+1jAuMPoAVd/z+rlxS6ibTniuhB27A24zcRDsQqjwo0jqRzoP3XTSSXdiMrui\nJPKUHFrpc+HHIRpJ0eI2Kw/JF7j4VmPdZvo1Hgvm1nvrAkuRVB6JrTmvnsKozmR7EFrglmFfaoQc\ntaJe97OLxPIJqjWy7NS5HaeGfc+2KGXOt7HsXb0fDilxVTKlzxWWEFbJg5IA4CZU5XbQ4xIg3AzX\nJwA7ngcAF0b18T2eix26R0rYRWSwG3jH1M7hCt0ZipQ8OhWY4xblAE0sUEI9w/TRqjH2UrCGuq9n\nMQJ1+xOY/UOj7ws6X7G7Ws0EzJt3Vfe/3MQu4QDP7O9En0O7oSJC9iUr4XWDHlsmAL27GlE2oW3M\nBUr4u6mbtbm7nwxFLrAbbaa8M3ZJo6lTdImbUqV7cJkGGFEBEY16Y+eqnfN6Bq+o8OulTcVOLS5b\nEzcKo2fqMoUCwodQ1zuMnOxTFVF1jCo23jf3yqxgmlLmWCWPKSqZo5QJqt24d45S+MBczf8h+TAf\nRmVXcd+86B1LpTlXlit6hiP/bW/dXME8mqBHY2gbVNfwkSVxjZsGtwL4GMQ5c/YAL6y5p+u3QIkv\nJwxTr/PdYO45aoNIh+tOYvhNWqPubjrFd71vLgnRQsrBYzlpuumkiQH//EM0SkQzjGCb6Sd3d03/\n7pp5Qb8SOnNr5MI9vXYrffBW6IQ+l4xHDePYOcXjZvqxu5rU7OXqe4rIJIXvhwJHB1hyGaMSFnTv\nL+KOC1NUirh/SgVkHJPG+GttGv27/Mvz8Hxf0fNdTpp6hiWofTd4V94Lk5o7ujaxk8d1c9HRyzTA\n4xAjAtiuYvKfig/+X/r6zwZZoEBSwEqyAfNR8PgKlScfCJVPUclfwjbLDIfdNPqsmbopb5g/+PUM\nU8G0b179H/beP6TvM0v/PrgTVhjBRTaLFIvTsEIFwRKCM6GCgwwyFgenOJUKggGDrUSo4CA1mMZi\nIxEifL4YJNhKBIsEicFgu7YSmQ8oKCYShzgYJAaTMZhIhAifQRHx9fxx3ff5ZJ7nYdlnfz+7eUOY\nTqKfH+/3fd/nOudc57oWzNSe/FGB/p4JoM1w2hWbj73cFSh5dKRM7ZmC1CY57JDpRrt2Q22JHlrZ\nIpsypuilhWb6KGSJ7P0tz4bNDlQNmJMadZISrwBtk0U1ozwPXIaounyCFbVsrisIt9HNOrk+icYz\nEbPjJJODr0dH5LLu0gDlTKrilanMf5ss1+7hdiDB3zevGLXS42PQcdpsg+O00uP2PlmpbbUgH+t+\n2acKkiR0D0dMr3fDzEnsXoK+olbdNllOnmbOnK9RQpITrLBDphuJZzxPYRsHqmDYkQ7rsyg7vRpE\nWcvS4O0EK1QyzhB17B4eY5sslfh/FJBIUqJhgBlliJGsa+/hLgFm17Hv0OcL4+1HOfr5bbLgmqb/\nMp6nKCFJHUM+uj1FGVmpbRHtg8l0nNKKAqRRuTwK9OYcbjpxPLZrmQt6VhsH3sKcokz3OVixMKbA\nNkKNWqvfBg7PdXNT8ShIOUBDWvIjcu0qwmsGXsYiRZxkVgd3kGCY5SR8FDzcVs3lJOoZpIIJclnX\nM7qJ7wV7dERWapsmEuSzygbHqWTchYLtU1TV+l0guz9Um9C+UUUzBqd1chVkA98mTjaaTbtcQwMD\ncM3cENh+kk5semnRWnkPT1R6aYHz2m+7h8cYpZpi5tkkh6zUtoDzY92/SsaxO4EMHVrfdi1Mc4bp\nSj+zLinJkBdqnwO2yHGJmnyvi+5GuZkWejnJrKp598CuqYJSxZg+w8YBAzSwSr7zSLNS2+I9JQVm\nWuhVchDa2S30wtXXtM7OBZX/XnMVet6y9HThWVEt1kwJyw6ZSsoem1cNR6l2kdvIEU1SAu363W2y\nlGCFacycw03sXpjgnNN5GQN5bLG10e1t6VhVrmGERvo5zhMB5l8GHmMwLrYbYCu4w0e01bEqgblp\nSrWPxnTeVzHGAA0cvNJ+PM4ThqhjjTzWySW1n+HV8qzUtld27HN8ajP6hXJLe7GEpNZKRSgA1Glf\nZO9vwV2BUFvGrYK2yMZW8EGN3cNjtNPFOrkqCpyxdDL2vRLGCHDtV/pePDAY1bpL7Wek+VWJwLe6\npnPBJ0fndDZUMUY7XVpLZ4BV7ecWesllnb/8VK8ZB3tmOA1joePwP+N6A7JAB7SrKN9+zR/rC3Pj\n35gd9tlr02S3tPAoU8tkggps44BKxn3iLbYdjnYCsHianjbspMMXezzMJinHbioA7KV02LbQSzN9\nRANpW8HlD4apJbWf4eJx3bSxRh6LFLllSFR3bqQfe6hJji7aGaReh9losKO5JGAZOVrRHT1BEztk\nKpO7ZH5gxIlGgZCLzkGJwct+qWqI/Z3aHhE82R0U5G+rtD9OJVw1nzisYMJbBfms0k4XnXQwQQVJ\nSvQ57qrcPEytqiPBbJk1ZUKWDIdDuQIQ5ea+eZOUC/iUKxCMUk0+q65OXcWYT61lpbZ9WsmugN1J\nE4JjteAvP1UbeC+l+xh9D7fIhutaT630wC1zHlQ/jc7/sN/iXmwznKaUaRYp0tTQjLJX5yw80zpK\n0ARnLJ1hbhxgv1bGWcq086EWKYJz5oTqApbV6gl8iX4a0wd8uZ5H5CKNUg0F4t500U4dQ7TTRQ+t\n/w+D62lK6aRDAO/nAgW8MCgwlw+J/KIh6iBhbm7dSg+MCXQ1MAD3dS8SNJHaz6CGEQGU91XZO80M\n9jOcJBsnw+Ik3hhVzHBaU1FhJLyHVlWkvhGXJCqUF7LkorhR+sIS6QqXlSpQd9MmEHdTLR37uzQl\nwHW0Egq+CZrgirniekyERql2QvxJZuELcx2wegbVcrmuPThIvdt1xWEP2ziQbMh5jfjHiUduKzA3\nI7mRApbJOdx08eQ4WBOnaF3V/qwFRTmR5BM0uTxANaMCFe+hZ3k98JZ+ogrd656PjAWXgjLttXEq\nVS0dCb8T/O94rPV/mhk9z9C2HKBBZ+poeuhjkHrfBxEM7B4ec/HSaUo1nfyREqg46DJKNVQYtqCJ\nuna64Lw5V6uHVlg1r165Fddrn81MCWP2/hZWJZAcwUM9g865tVPoz+807FDKtM79L5RI99Po4HOT\nHO2HsTBtHKRVjnbS+oC8J3pGOZPi/Y7pXFuwoAn4trnVUSnT2kfXw+e/r73WQyv2A24cvUYevCvQ\nGT1Xo/YjX4SJv9VwNr4jUNdFO2NU6TM/C23Vdj3bbtq8exLtsGY5qQ7QGe371H4GzOiztNBLH80U\nsKxO0KgkUqKqfDSUHqJO4Ozr0HL+n3O9AVl/dT1QJhXNVKN8wcUQeKJQIbcUoPhaQbDP9OePpkBR\nzShlTPGVpas9vbTAgnmZnfKwGEdDhv+WWoZxosy+UxZbxhQFLAtg2TRmB+LQbBxgl5WFR1FVzps0\nVn4tfkAVY9Qw4lY7nXTocA+trgjIeFcbKfJMdg+PMUINOYebChgL6cP5K1PQzXie4tjLXdrpcl6b\ntYC1KhNuIiHbhsiPuQd8EgBVOHwbGFDl7JIpsx0RgKhjyKcrIx8pmqLGzNns/wjI2UG61fNCmV6c\nZuK8suODV4bloYPiszTnKRpEt9PlUzQRwHFJFTvrUvtmknJvOyVoSk82fSlA0E0b9lUQGg3G2PMU\nU8SiDuRSBde9lDSKvGVyN4w8PwsclVDZi9NOixTJ8y6ony9SpIz9PR3YSxSS8TxFGVPMcpIkJWTv\nbwUrjj+J/3ZN372QJezREWVM+aQeT6UibzfQYfujJvP2UjpUY1uklGn2UsGEtTeo98fJ2jat/chx\niROx0SZohBoF4zIFiTiAMBhaPEUs+sDDEHXwkd5nmFp6aaGRfp+qmqBCz/2qqkwNDGhvjgZO1lm1\noZ0w/Z1avKvk657cUcC3b8XXGqYWLoVprPfwCli0hrH3pOw+TqVe66p+v4EBjvPEVe/rGfT9VseQ\nzoi2tI3JFtmMUUXG85Srg0drnSQl8LWqQrG6uU6uTxxHGYUBGqAtEM7ntJ6inZNt7mF3xNWZptST\ns6zUtssNVDPKJjmqWNxWAtdIPwev0j6ssYJbwDJ2RxXwCSrgsdZLAcvaY58FDTK7gU3ovpUzyTq5\n9NLiQ0KuWfZ1WjyWx+IJbZOlas198wk77urMjDp+RSx6NbuJhH7uRdCci1XRu4FfOKIzJMpUtNMl\nLtDHqkaNUQV3La0h90JrNvLHOCWpnJzDzfS06B10tibTAMNWkFzFD1p/dhNvJ5Yx5dIGUY5ikHrn\ngfJeelq8nkGXSeFFADkz4fOFNj0fGNTr7zKep+jggvbS+wJEixTpeTww+FD3IUmJy0SUMo0Na1Cn\nkX4Oss0Nr+nV8xmknuM8cW9dHpgPL0SgtkU2JMNzqtNn4i2dVZOUq3p5S/Et6rLFamgRizTSz0Uz\nrpuAZZTeiLErulD8D7zegKz/+1XHkIDHTAA/bwtQXYwVrI8Erv5sOtRjK8v//QNTEHyqNs+CKVt7\nHEjZfGhQFoLTBzogpinVJFmdMo7UfoYWX9KcRG13xHspZZomEk5kzTncVMaxJpXjHloZCZN5kbOU\n8TzFFtmqfoUAWMYU1qqqzCTlblewwgmf+LLNPXecf92guIBlWDX/7wYGXC/JOnG+WYImt4qI3omj\nVHubiOshgx9WG+TglSbnItkySYkC5w/pbDIKytpEAG+/F8dmiDpl9U8VQGK2Git4zfQ5HyW+ficd\n4rBtHHDwSs9zijJKmeYEK+SyTgcXmOE0FUyIS7aC24C006XWW9K8wmP3JPLZRremcW7gvnZ2RfwJ\nbukZFDOv6tvttHl4NLaOY8xRT2aSclXsngVwPhamXMPUIi8EsLkaqgetqiRGPlAMlJGLwo8GFWl1\ncfsF+v0gglvJuNbr93rOuazDea0xxsI6f6xAeyO2zIPa/15K63uFE+LcPDMPtH00CyzcEmi0zT3d\nh5FwgK/p+0S19SnKyHy1o2DxS4GcXloExG/g1YMkJT680EQCxoIGjz0n43lKlchvtDYjF8ZOKeN/\n+TfmAWSdXKoZJZd18lijknGfarVfoRagHTh47uCCm+JWMSadu3vA97o3HVxID6hcxk26N8lRqzNT\noDCXdTcstznc8L2IRcaoopUe1+GL6uHcErcz89WOWn1J3AIpjzUGaKCIRY7zhFXy0y3YZ/q+m+SQ\n+WpH1foHArkNDGCnlFjENc2qgnkdQ9iGKAjcN/e9i+AwkpejmfReyrwSGQnQdQy5ZyILmt4+ytHZ\nE3XhVjjhtma9tHCSWXpoZZEird339HPcVzK0SQ6b5DBKtZ6BqRLHnAaLInetlGlsc49qRpmijF5a\nxMH8Mn22R3J8C73kscYW2X5ejVKtSte3YMMSA+3ggtqeC4E+8Ez78tjLXXgv8BLfF/iJothRkmKR\nIhdjnqTczZAfm2JMBxd8sq+HVgG873BiehxSOMmseL1fka4Gf6G9mseazqkvzCvhkafYRnd6wOTR\nkd7/sarBRSz64Eckou8eHoMZJamjVHOQHURv5wxG0xZVu4fHsE+V2OSx5jzlJhLUMUSCJndGGKRe\ngHLhfyScgDcg65+/mukj89WO28VctpBtXBFASFKiDfaZMvbLZlCk1uHFMHE1Sbln0tTLguPglQLr\nJjkKsiPmWe9FCwfImg7p6PBuV4KpbBcCP3UGP5orENt3+vdIUr0Y+DKxz99EgrXAXdhLBbB3Sa/R\nROKvtG/sW9yuhjHDEul+finT+k62pp+b0YE9SrXIuL/HJw5jxh997WJra4PjnGSWvZTaJTEo2kM8\nC6tmVGA1ECptQq8bhTGjAW4rPeSzyu7hsTTomNF3izYM/KjDIOdwk3oGSVJCO11636QCglulvKdW\nSCFLcF/ZVQUT7gFnD9NmuJxTi2GbLFgIbYA1c3L7EHXePokHaBTxPHil+7tMAYykJRAiT41TmsZp\np0t/975hrap+5RxuyvQ7jEbXMwhtaYPs08wIVATyahzn30tZWofnO/zA50fDbuDE5W7axI8ZfW2N\nXg2Hfvy7UK3rpMMPz0nK6eCCQNNtU7Z7VVVezhv8g353gAasXm27YWq9khjdEaKnXBTJbCKhFt69\nsPavqR1kn4sLFi2xomSKLaBpz9+Gds7nqAr1W1wKIkGT/07kgPXT6Jl6NEy3RKhWXBVItq8CEb4+\nVLWu6jVOsEIBywrk36KK3uO020EBy/BJOjEbppbs/S0Xk6xnEFsIldCnIeg8DYKVARz20+gt2zGq\nfNpumtIgJ3ERs3ssUuSuCZmvdrAFgck4UblOLjctDPJES5MRBfsqxjjJrMtlHO2YZFHKxYnso1mf\n8YU5z6+DC87p2yZLAf+exCujw8bRjvbxn02dgkKW1M6PBuFj6WGcUaq5bQJgseVmnwdPzPO6p8sU\n6Pw6YyL2hwGZWBXvo5kkJVQxxjzFdNLBEoVUMEE1oxrumNNejgnBMLX+uTq4wAAN6WrYxgHVjAoM\nPdaePsFK2uFiRl6dzfTRTZvOwrDGD14pydgiG64JsJxmBt4PxP9gBL/Bcezz0EkI7Ubn8p3TM1ol\nnwEaqGHEz0CvUv9Klfe9lO51HK7JOdzUXjgThkGi5+Qn0le0BSnVH3u5i3Xhlc1V8tkhkwRNKjx8\nppgWgWoRiz5pHAHnGFXwNFQar4T1lVDyHCVGjnbMuYL/C643IOufu6Yp9Ukxfp6uVl2PIKo+bVbM\ndXPLB66pJ38xELEpT5doaxhRa+Z6+NmnAbS9F/hfFQJs/FwtOdccecfglnkbrYEBbZ7vQvujEzeS\njuCFNm3crNS2Av6qAv0Mp+HHIHp6B7c04baysSjGWcGEyPg/qNxuV1F7YHNPGV0XapnWm5OtIyG3\nijEnl0Yl+WjQvcIJSkjqM11R64O2dAYYs6htsjQ1dCoEnIdgecqyK5jwCl4x89gvdVDYNVUBtsgW\nST+pthBJPQfb0Ii2JfQ6qf0MBmhIT2KVgnXiPJ4kJWoVLaiaVsEE8xRz3QSUV8kn89WOtxs7uED2\n/pYLjcYJxRpGRBw/p6CyRCGb5Lj1EV+YqqR3VU5/+TfBPuOUQF8cvuih1c1q81l1/S++UEWgknEd\nqHlIsmBNwC7ncJMeWumlhez9LYHvZ+bVn+M8oZh5b8e208UShRznidbL9+mDt5MOXxtFLLpZ9hKF\nTFChQPSFAPrr8ihRs4oXyrLt83DoXzEoC5/90RE2ge+TegYpZ9JdCk4yKxCVRECnBTenzWdVba4V\nJRyjVGtK9Ya+XyP9av3cD1poZxR4Jil31e4ClkntZ6S5hl0B3L0X2kgtCICt6H3j5G00OI4tyf6g\n732CFexO0KT62zC8cQPs7wWo4jRe1LeLXpvZ+1vYgviT2ncvMbuJzQn0uafn7fAMf4XW/leSuShk\nSZ/TjjD7A9OU0ka3zrNngRLxts6AdXJpo5tSpl0+ISqBL1EIdy3NqQtyCcyZa5RFA20+MXdtmKfY\nK+fc19nF22EK8D2Bs2UKBJzmwjTgr3UWzVPsQs+r5Htld5kCaDOXl7lu+vzHXu660O4fTWAkWk8d\nvArnQ3B6qGScdrqc3xkV6xlLa3zFNlYX7RQzTx5rWksPw1ly27wyPEq1dy+8W3AtfJ/64NzxtTnV\nYYoyKpjgaCdMpifM226ddLhzQfT6bCLBFGVOFh+ijkrGVS26CpSJR1nGFM30uRMCX0Yz6SPsY1xS\nhqcCr3YFP6eO84RClpQM3hZ9oZF+SpmGf1CyPmIGq+ZCsXZP9zIS2y0L58v20gK/Mde447ret5s2\npihjmywB7P+51avXrzcg619yHecJfKJJkYumA+NGbP/dFphai6DqM4M1BcqLpuyGepWt+UILPI7h\nPw79aa6bK1UvU+AK8THjPsls2r6lPWS3eQISkaAaeQSr5NNPI8PUkpXapp0uypl0U81c1mmkX4d3\nSwAmV8UFWyOPciZdPI8vg4XODR0WU5Tp8LiDm3c20u82Ewma4INglPwQz4xLSGKXBWrilFM3bZQz\nSTmT4g8UqfIUQd0aeeweHmOaUlL7Gfqcn4JVCfzG94+6UDanMeTIO2qny/3+8lhjnVwPorUMU8eQ\nE5N5x9x0u5s2H0Looh37TkGrkw7ng8QpuzhBWc4k1iW7HXt05Grd0YIjGry6Plpos+3+rYDIKNV+\n/6cogwfBJ64rtERXFECiPyT3FaxS+xkKYFfNzZdb6MU6Bcx6aaGCCQ+srAaZjEdHqu4sBAmCBXwK\nyjYO4Ly53ETG85QqhF+rcrtGnp7RnQByPsdbLFzXZ7lu+tkiFr2q0k2bDtVPzE15eRFIrh+ZRviD\npY8tqwJwmhm1YK4oKA/QQB5rtNLjjgG9tMjs9l0F0QEaVGUcE6jeITPtRdqbtgiK2m/WFQBdQve6\nnEkX+JynGPs06LbZS4mW3lBVKON5Sq3ThbS8ROSotdCr+xFMlO3zYJP0YZAeCVIdvbQIUASOkT06\nUiXZBOrsG8h4nlI1qlMJQORz2bv6XJFbad+ESttDVex8UOYyWCJto8LVdNWZMlEBhqhzX9RowL5M\nASPUuLp8F+2UMSX+3BXdx3Vy2SZL3yFo1a2SD28JCDXS72btvBOsjs6FxPKaqZr+kcEHqgyVMeUC\nnbt/KyA4SbnA3dc6V8uZVIvqI3O9uhlOU8y8QMKpQL84pzV8mhls44B8Vsl8tUMx89rHwUyd2wZ1\neob3wqT1OJWYiZMVuaiWUGs0tZ8h/uiaQTJUA380N5Y+zhNOMuvtOK8CnTPnQFUy7i1jzuq1Clmi\nlR6tubNgPyjujFEFz8ynliPfzU4BiTCJuIIPPhSwDGd0P+P5cezlLhNUUM+gzrR7obJ7Wa33YWrh\n7bTwajtd9NFMH80+2dtMH9n7W+SzygynaaGXLtoZoMF1Du3RkYuhxhYxST3DTjoYMe3j1H7Gf21A\n/8+93oCs/y/XQXbIxG6FQ+JZ2GxnlcnwtgLFRVPr8GJoz10MAfF6aP3tpcLPjoVycYWmEGPmPkg9\nC5aeCNtLaQOscMKVxWc5yUAg1vNYmVUcf7VvQun8awW0Yub1mc+7jD2oAAAgAElEQVSbu7/30awA\nNCdA00IvthwAZbBUyN7fgjkFzQja7D0d2tGpvpxJBYVhBbns/S2qGSUrtU0rPQJALQIK0epikxwB\nlWAknaTErXiKWMQui+8wSrUqUmu6b9aZ9omzJD6NYsnQupnDD9ucw0337BqknnxW1ebZ3FM2OqoA\nb7/HxQAb6dch9FDtmUjwN3us91xWhSLy1qLBawu9ChZPQ5AJ5XO7rMDXTZsqNrdes5W4Za40T70O\n+jhlEytadjPdBpmgQplh8GPcJMfX4jC1aSmRoHVGhe6v/SBx0OeBL3jTtO4o0M/ZHVzAdpMcpiml\ni3a2yWKZAvpp1Pt+ompsKz3YoyNOMus6azaBawztpQQgo0fnGFUeMDkVWtNnggzEl+brjdXwmS+F\n8e2vA5/lnKpO41S6594QddicKreD1DNEnSpIQTutknEPKHxpDpbthj6nPOD+Ses26HBFj8stsrVm\nc/F1nc8qvbTQwICLgFYyjn2nysY2WQqy7eaJSDWjTmKvZZicw02SlHgrupF+3bOCIJexECpln+s7\nxOBOUnv/JLNqfXYFy6wfAjhc0JlRQpI+mummjQ4uUM6k1nmwU4kWVNtk6ewJk6F5rIkO8cycYxY1\nlOIkXBMJ90tlRusqTvxG/8pahslj7a/G9otY1Puc0bOP/DBumRO/KxlXJeiFudQJZwNg/1LAJCrT\nj1OZNrUvN094uaqzOI81AZpnYUrx7bSVUJwQnqRcoOKDwIsNAr/dtPm09awFnb7AgbU7AryxEh/v\nzwg1rHDCp1Q76fApzDzW3Oevlxat2XvAM3PR19R+hs7E+4Hb1JamFmTvb8GX5lIfJ5l14eqjHdEn\n4qDSaWawOVxQtJZhdUuCoPAYVTrfk1rLRzt6fhEcN5FghtMucB0T4irG4JK5o0IBy9pTc+mpbEsq\n6R2lmlGqXc7CMnVW1jLs/rQU/a+DDPAGZP0rrjlTy+G2NkIPraomBPX4vgCsKhnnqwC0vjKVrFP7\nGcrcw2QGCfPR5WhOepCtw5pPgsP8oyMFuc9Uso9WHK5X9ELvFw//OobEzbmi7CYrtc08xX4AR3AR\nS+hbZCuzORXajpt78L0yOz+AzwpA8InAYgcXsKsCJ2vksZcK4KieNH/mqrJ3+wa1+q4IaC1RqIN+\nJAT6Z/qeUQmbOnOyNVfCQTsmkBUtiuyrkLGX6jCIZrV2We/bR7MTba1THAO7pspNFJocpN7bO7ms\nu4H3bCCd2ilcE62DCwp+E8owI9ekjW6O84RWekhSorbu9+LXDNCgz2MH2C8FtLppUyAZ0f082glg\nO2lwRYEhWvVEYm49g1iufn+FE+6vyFgAa2+bCyIyIiC9RbaqO8M6iKPhs7e1E+aeg3ZWzzFBk8sL\nFDNPLcOqTN5X9TMS4usZ1LDBd5I2GKVa6+O+Eo2oEzRMrVcM/vLTYE/1lkB2NAV++Tfm1dVV8lXN\nCc+LCoN3FOSjrUlWaluDEF1ouusf0Z9WPVc7o+c/RB1FLGqdjQWg/C3Yz4LQpx0J0LSqkttCLyuc\nYJV8ypgi89WO7s0vBCDGqGKScpKUkKDJx+DN1qSZNmqeHNmNwF88FWRWRgzmtP9PMuucs6hyPUo1\nZUyJ+D4B9jHeLqQ3gPDz0UrlIuVMKpkK3qusaQ/Zu9rDuaxjDwXAOulwvmikB5Qxpftfbg76nI/z\n6Ah7dJQOsKs6lzrpgO+1XmPVtomEeEe5YEVgf6dKj7cpH2r//NEMfiOQbSuqnMahgOhzx7Ogo/W9\nQOok5fBRml4xRZmbkG+TJdD2UUgm3g6Jxe2QQHwtUHnRTNqFX4oPVMhSWp5nQS2wApYdXPbSQhvd\nHO1o/9QxpIpSPWrFPnzN3eHXqhRzy9KWSGe1lmKiEs9kVrXGoy5WrK5Hm6Q2urXezwbO42qaG/cn\nE+0kWuxkvtqhlR53EbCfqUU8TiXtdEmIdMcw+ycHposUuXxHKdPYtWDrE9qxm+QoUbqpCqu9i0tU\n8K75pPs0pUxRxg6ZHktGqWaNPJ+o7qZNCX3ghw5RJ5DfG87x/53XG5D1r7kumkCV3dACb6VHRsBn\nw+YPXC3qQ7CoD8ApqPpG/lI8aOymXqeCCQXiurStQGwl7h4e8zHkOG01SjX9NLJIUToA2h8VUEo1\nOThGlWvg5LHmPotR7d6qQsZ+EzeiHaBB2d4DHfKnmaGZPg9G0Vk9TgBF/0YrxUeueawMyGzNNcHs\nhoJi5CewIF7CJOWqWJ1TpSROK1qmyO5xbJ/y9ORRJApbphSqI1enkCUFnjM6QOxTRIwOkhesqZUZ\n26iRGGynVOk4eKXPtEkOjfRTw4jrTtUyTDHzzFPMCDUu4hm1mrbIxr4NLdi5tFJ4bDvwLBw4YWqv\nkw591ivmE4f2HQqMyTA9eE1Vym7a9LNvpceguRrA1Zeqer7evrFPcW2rApZZpkAt7p8rCHE2vF8A\njmVMYcOqzMTP6tY+I+lAPU2pt7rGqIIzIfDdNv4QW+bv6Dn10KpKRqhmRC6ZbRzQRIKDV6HlGcjK\n+azCGYGvg2ztnYNX5m3MKHh67OWuuFe/FnC3Hwi64jJmzt7fop0ud3OISc8JVmR/srlHPYP6+d+q\nvWaX8VZ1LuvK0u1PZDxP0UmHSMkL2qdRnLeKMZ/27aMZFrRuypjCfo+Ps3NF3y1WeEpIYstq0Rew\nLO7cgn6mnkHa6BZAvf/aPfsKN4Pe4Di8G3hB9QLc9pXuRXRXsI0DemnROnmm/TlFmagHoQptSb3v\nIkXYggBlJHLnHG7SQq9TAbpod13AJhIM0KCqxS911piBtaRBevb+loDIJYGdP5s8TBvphzHz6jkv\ntKZqGfYWJZeCFto/4hZC22TBh2EScSzQNYIyOOf1GiMxiXhbf8ao0vf9XolFLy3aW1+bg8vYgmyh\n1/1E18jDHh1pvSyo2rlNFttkuRNAEwmdYW+LvjFAQ9rfkxrdr++VQLinabu5tdcYVVBvzpWLk82R\nt8UZ7bdNcnyasZRpqhjTug1WP120s0V2es0mdN4de7lLkhJa6JUA8VMlQjaslnoEy0UsMstJdzfY\n4DjlTGq9/4BzaXlf+9E296hk3MH3BscVp4LnJl+Han5S59wADUrU//deb0DWv+m6H4LKbXObnVjV\n2kuFIFoQfqYsPXU4G1pGB6/0szwNoOyUDqQ/hp/L3t/iNDNqsZQZ/xQqYq30uBAl9xVo7R9VOapk\n3Cf0dg+PqRQfgvAsJ32KJUGTeCbXcO9Fa1EAStDk9hHFzGMtQYbgdyLdktD3XKZA37He4ANza418\nVt3ctJ5BF3B0f8SrylxT+xmqZgWbkKMdZbt2BeYpZpEiahihnkEB0y9em6Zb1T2tY0jeeLbmrYs2\nul3yYog6ZWofC0h6MP69+C72DT51x4wOhkiItV+J2xJ9Be1ayPaKwO7ofseKVhvd4q/ZRZHOu8SP\niyP3pUzrfet1L/pphPsBLJ0z53RsksMEFdQwQhMJtRff0fqK/nit9OheBp22LbKhXL8bLU92D48p\nq3ygQF3LsNZNkEeYoEJgMLTR7LLWgc2hYHBFgTaKha6SjyVxj7nI0eG6AGw9g5ok+iBtR8UDVRFq\nGdbn/SCtGURvWPvBNLqbNk3nJjWYcZRjrs5fziT5rLpuWw0jTsK+beYDCU5QXg4q7j/oOUV+VQu9\nait+hdrepopclHSYpFzr9Rd4QhCn/dbJ9Qqx/Q5VNpNq0RSxqDZkMkh0PFCysEkOjJi3+EtIkqBJ\n1Z7NPdcs2yHTJRLyWINn6fMj6mA10wfl5u3ASsaxJM4tXKZAr9mlKpNPsD1WSzYrtU01ozTS7w4H\n9msFabf4+shUaTxvLmZsSbx6ZhNqNy1TwAbHOXhlabmXrrQ+2Q6ZDFLvIrbesn10RB1Duqcj5pww\n+073LWo2NZHQ97kjULvBcfG36gJd47opeUqYV7TWyFOL8EftqWhp1EIvvbSoIvgbcwumTXLgip5T\nHBLix7SYLe+LgztNqQv7xpZinM5lLnAkZ8RZmqcY+wVubL5IkVfO7Y6SAW6bV/KjwOkwtfChnvco\n1c6tisbbo1SronYGrc3WwBO8HBK5UrSfKsz12G7ba5qOD0Q9aCKhc2fEXMYhOgDYHXFa7RvcTi1O\nQscKbSP9PiQTE4t4vrHwmvXQtdckjd5cb0DWv/WKZHTqtYl5JwSQ983HrCPHZo08xql0VWJOabJu\nh0y1L36vgFDGFHyiLMiu6SDnSgByCyLTTlHm2RBnzLVp7B9VkYoq0g0MUMNIoA1LxTxOxShbv60D\n4CyudxNL89wKQpW2i/0M90+MQosTVDhplkuWrlh9p+DlG/eyNmbG8xT2EP5sujeznGSVfAapV/Ad\nMR0ij46Y5SQ5h5u00U3G85QLeU5QoYBTJ1BCr4CXdQoA9dOIbe7RSQcDNLh6f+Q1WFdoLT06ws6q\nDF7AMosUqXrwgQ7o6AlXQlIVo/eDHIfddLmMRvqxFQWXOFFlj44w+5NPfka9J/uFeE9RJ4ZzAiTD\n1ML9AJTuKxMkaS4uyjl9zxlOM0o1TSTIZZ0mEmS+2qGGEXerjwJ//TTCqNpDkbQcVf5b6VHboFOH\n/gg1smzJEqHbvsVNi6M2TxT7jIH/eXh+yxRI5bs3WN9cMrUxgzEv32ut1jCitfq1uU7aRVNywZyC\nYg0jbsB+M1SKeSBQtkkONqG11EczbXRLfy7Ie5xkllKmGaSeUaqVCNwSSMx4nsI6cWuWaUo5zhPa\n6XJPOPsKtb0epgc6rF4cnD6aGafSq7kR4JxgRXpWn4L9Gh+qKGCZbtqcQNzBBTcaZ0Tfq4MLAnNf\n4W3ZnMNN/5xRl6uXFphTy3SMKppIcOzlLoUs0UTCVb9PMov9DNdAG6HGB1hsQq3mVnro4AL5rDJE\nnQJrPQ5smDH3Hp3hNKn9DOoYctup6F0Y/62VHhYpopF+H1KIhvEznObPgS8aRUFHqNHa6NVzXSXf\nld3jOqPXRMW4as6v7KNZe+SsuVh0dG5gIYiQBnmDTXLgN8HXcRn4UeAuWtKMUKPz4Mewz1YN3jJ3\nFoi0hQhCo4h0BOf0hmT4gZJhH3YK/nu5rNNMn/ZLEPblnKq6loXL8ESyeZxAjRXxTXIcbDfSD1/o\n2UcLpKiv1UoPx17uivcZuE/LFJCV2iaXdXUorilRqGXYZYTsG+DLIKlSL2ma2CZdpkD3MaEhrYzn\nKaYppY9m+mmkiEUqmBB37LZkfew70jSPtTB1+rbuyVHOG3gQrjcg69/rOsoJpesgqncjEI35TBnb\nJjk6EIJw5CY5DMa2z1nzttVJZtNG0Z8oQzp4lSbQ85GysROsuEceMzpwLpqyvXuhItBFuwty2qMj\nGhjwiSESIcNZVhtimFoF36rg0D6q7L2PZgX/wF0oIQlPgz/WMA4c4wE1RJ3kAT4zNz/up1EB/LKy\n4hpGxNNqFbC7aKGUf1ZBt5pRahl2GYgGBjQZt7knHsWIDq4ZTjNo4nsNUaeWn625WGvmqx1WOKEs\nell8sCgd0EIv9lu1N04zwywnyWONIep0HxLKSCOQia0FWwkZZWnwG5wjLXOwICDWSo8fTAmasAVV\n5gpYdoNkLoUDezUEh+9DgAlVzaOd0C48J9AyTakCyy39fgMDCq5nget6jTa6XQyQp/q7fhp92GGK\nMlgIJPS76RH8KENwmhnqGHJT1x5amaeYWU4qSAY9rz/GtuMXAp98GBTew6j2AA363HPm+mhUpDP4\nox0d5lzVQZ+kBK4oYP4hDILEaagdMrHNPSYpT3Mg74Z7eF8V0YNX5oMBRzv63NFIPU63HueJAk0w\nUu+lRQnEoyMfOknQpO/Tbg4+o9K4be5hduStdPu9nnsUppW1VKiQ/YQ0J++h9lZU+T7aMW/rFLGY\n5nd9qla32WXsXVyMtppRGhhgnmKqGKOIRa3he+LNxOrlCVbggVp50YOzjCmmKFPF+Gs9ozzW1Ho1\nMJtllpP000hWapspyvQcbwtQRS/LKGTaSD/WgoO8GU5j3+hzFLGoBKzXVBX7uUAwt/WcU/sZ7JDJ\nQJjg4/20LVUHFwRAr+vMKmSJnMNNjnbS04XRT2/38JgSzmd65n00qwr1Qn9a6VFl5TdBVuZ9JTIz\nnNb+e6xOQiP9HHu5K45sQl0CrgsAx1Z3BI55rFHIEvMUc9GUbPiEYr3+f6x2x4SOGfOJ62g8n/lq\nR92LMwbv6jNk72+JchEtZs4rZhQzj83h3FIX7mwzcSK/0Xp1I+URSTnMcpJhapXcbhy4ansUOI33\nooIJKDMGzXwifZ5iGulXi7XC3AqunkFsQlzU3cNjWKcSsVqG1dYP1fJoU/bm+qvrDcj6d7/GdPjz\n1msl03e0obbIlpbMjHmGHM02o5lnD62ugxKNjNfJVVl6VCKM9GpRx5aN/U699Da6sd/CiGlaZYdM\nVXvCobdGnjLIT3R4VTKuQPlW+Mzng7r7R/ocDQywlzI3GY6q1lmpbSfaNtIvIJFEgfV7Tb6cZNa1\nZYaoUyZXKt2fTjq8SmBJtdbiNOE4leI2XZb4YOT7VDDBAA1skkMhSwoq38v3i9F0NsnZ9OSSbHeO\nqGeQ3cNjOnwDP6OfRlgNh9ll8R1i1pagyTXHGulng+MulDhBBfMUq1z/gzhCz02He+arHVUkwoFk\nc2D3gvHweZNQLaWqbM4EYHlLz7KaUW8hbpLDMLVuK9JPIzmHm9AWAFiOnlfG8xS5rJNzuOm8tBKS\nOsTDQR7bgl20w0xoUT8IZtpzCsqddDBOpUQtl3FfwTa64ZIqhXwikLRKvk+DdtHukg6TlMMl82Bc\nxZh+77wSAN7Xmt/guA7qIPYZeUexQrWXEsjqo5kZTstF4I4AzRbZbhHTTpcT9rmqNd5Hs2sjdXAB\n5lRxK2JRbam/Q4R5G1GgCu21DY4LcG0cwIhep4hF6hhyeZA4gbZ7eIyjnVBlvgP2K4HxXNb1s6YK\nzAynWaJQn6/IMPsLZhedlG9zWvejVAu438B1vqgPZsQfg32rNu5pZtJt8vtKorbJcvPfzFc72LcB\nqF3RCR4tY1rpYS+loLlIEba5xxbZOpvuh4SpBXdyiAbcPAuAuF6AP67LWOGtYMK5h5H830i//n5F\nba3o8ddNm9b+1+bagZzXfuXdsF5nAri9pmcwGUReGBHw3SbLK7/cDb9zVW29RYqcZzpCDUlKvIoV\n23v26Eh7J7Tf+2nUefBjoAt8Yc5jotycNxv3eyP9SlR6zTXkyplUZavA4EqoqrfqmfG1QOMMp2FO\nrbjMVztqY9tLilj0acl2ujRg0qvvGj3+bE7djQYGyGeVXNYZos7N1+3XuKp7FLrNeJ5S8rWqdZSg\nKa1tuLnn3yN+xpzDTVXB/x6nGvCFuaQPv0kner20OJl/kxx47zWtyDfX/9v1BmT9h1zn1Z/up1GB\nalSLlN8o2IxQQxMJ1slVMIiE6DUdVDFgR4mAmKmzqkyF+wbnBDxS+xlp8JbU33NX2dDu3wbOwFVV\ngaIlgwft4FV20wQAIiGSByJX85E5UIvBeoIKpil14FXFmJsxu3TE+xoKaKfLWzN2D6woHDKBtGk3\nxGuxzhBwr+rwHaHGq3lReJLv09lwD61p8vM9VMkL6unbZLHCCbL3t3RYrCmjXCdXP3s/TEjdVCUq\njj2b/cV5B6VMY/X6TtG/rIBlyURcA6tS0B+ijlzWvcVrJr5ULy0sU6BD7Xf4KD8vlIHHcWkemxtY\nn2RWzzVhLpA6RhWr5DNOpTLEDwXa/2QCxpYQZyX6vPFAAWOeYgWmMQWKaBQceVAFLLvGWZxKjZl5\nGVOuFdRKT1p3qj2shbkgM9FmMKKW0BB12DVZemxw3AciFinyyVnKBEQnKRdwOoNLhDCiYFnJuE/h\n2j0B7yjhUM0ofzAFo7i3Imn5TybwELV87FvgbXMbmmhX1EQieGnu6ud+qVZfOZOUMs0W2dQzSDHz\nAtGt4u3VMYR9pVbecZ7QR7NAVql+f4oy92Q0u0cPrd7azmdV7enfqaoVVbhjxbqNbu2dYNkTqweW\n1P+334tHGN0AYtW4lR5WOKGAGiaJj/Mk8AafY38nTa04OXfwSglJNaMCMmd13zOep2ih1/lGtAex\n4nadNQevzCcYs/e3VEV7dEQ/jdQzSB/NLq0RxWpt40D35mFop3aitTlirvfXSYcDtg4uUM0oQ9Qx\nQg01jLBMAQM0MG3Bmuy29rgTrc+bOKzXVV1N7WcwRpX+LTgRjFCjfRJ8KjmXFin11vzdAJACIInt\ntRVO6LwIieM0pWySww6ZpPYzWCXfJ7mj92IFE7TT5TZlMcmsY0j3YQJsQWek/U7gaS9lXi23CbX+\n4qSeJdXSj/e8gwv698D9zTnc1NqxP7sQ7NGOpaePf6MqlX0VKA1z2t/x80UPTNfjWtMkPO8LWEeK\nShlTOsODJI3dRInT9TAVeuoNFPhnrjcg6z/8OmvwXhjFfmriEj1Ou6/zYzorpSBkZx+EBd4egvCz\n0APf3KOPZp9gsV/CJjkkKRFp+G7QjFoAPtPGLyFJD63e8ummjWFq6aaNRvrTml1PTVWtclPLI4zS\nD1OrzNSmBQavaIM3MMCxl7uMU0kx86yTyxhVynRuh+x4cw+zP7NEIfMUq9LWFaa4JnBh1HEq1V4Z\nVgslmpnaQ+BpkCT4PbJ/sT9qGuxnCnreHngcqlIbB+Iz3DcHO3UMYR8rmDKjoH2CFW+LDFOrLHtO\nvJRon5HHmssgTFGG3VR1rYdW1smlgQHXRNo9PCbC+1UFZh6rHZrPqg7CjQPWyZVo37dIN6xIGXsk\nBVs9aWL406D184HpAH03HJ6rCjoXzXTQnTO3CznBiu7bBAL07QJvqf0MVQ9uhYnBq+YeZ0sUYi24\nlg8vBA5OMgtP9ZxPM+PrjmuWrj48e22Q45m4bFmpbTcrjqA8l3V6aBWv5Yy53tciRQp2L/Q6tQxr\nPT5TRbWIRR95j0TcmGysks8YVWpvBWXwYy93faKuhV5Vi95TVTW2zk+wQj6rLqJbwDKN9NNEghFq\nOM0Mx3ni3KqDV5rcY9XcA9OSqlRmpbYpYJl5islKbZPHmv99PqviwmzuUcgSU5QJ6N7XM5G8x0VJ\nfPxMFU87o4DaQq8SgSAfUMq0QMY/KJDaZSUAPmwxgbiMX/FXgwab5LjtDqtKomY47e2hXNY5eGWu\ncr9GnnwBF8y9SaMwaTN9nuBFeyrb3KOFXufsTVDBAA0sUUgzfayS7+K/B9kGX4a2cuDqkQwirNdw\njaYETV55HaBBROre8HvnJWgaNdkOsoOY7a20G8EKJyhnkpPMOtXAVnQm2sNgb3bV0pzA0J4fok7t\n+fOh3f22wYyqXTeDDEUUFm2jW+fxxwIodllE9CUKfequmT64H6YeqaGTDiYp9wGEfhrpodWHYDbJ\nYZJy8lkNozm56hbYgRKCm6IcdNEuALaADynVMsxpZhiknpzDTaeF/OWn5jpctgKWq/1Jm2LDFtnS\nz7tt4lbeFcAuZZrMVzuwpme6wgnsZ1pzvGv++d5c/6LrDcj6z7gaGBAp+DfauHxmMCrwUMiSm9l2\n05ZWx/5A/z5IPbaMm5aSVLDs4IK3dIpYVKUsaU64jOKlvFBWE0URlylQtjMS2lVfinQcfefsW+CB\nuS9WG91632Do6lyERKiYPFa5nlFl2bOcFKcnTOrYQ1zJfJha5inWVFu9ytxRwDTz1Q5bZHPs5a5A\nytc61PZSAhe9tDBAg6omv0MB1P6kNuRDkdmLmffpwWUK9JlPhUN5QZUU2tQy66YNvhT47aXF1abt\nOzQMYGt+2NgvELCzAw84vFAlsYYRlyVop8vNgr2SVa/dNE+xMsCruvexIrNIkQDcMtjnAqCddHC0\nowM6aqJdtEAIfz/IG7ylNRSNZlvodfL1FtksUUg5k654fezlrjsH7KUCoF4QmBmmNm3HcdUcbDFq\n6Xbzl+Yj82NUOWjYItstpLbJwn7AW6vRs3OIOgX6IlN7pj0Al8cCiVFQd4QaF+QsJYD6x9ovm+RQ\nzqQqv4kg4HlTQC62G61Tbakh6iSOe1VrI5d1TjMjY/EkboFTxKJPmQ5S79ybdrrcWiaXdXggYJ7x\nPOVVoXxWFeh+HeQvPgWz/+P+nnaZtK3KZwIDUdSzlR6qGFMFdUIE+xOsYFXBS/GW1lYRi9i3eo1N\ncuAzVVvqGWSIOrpod4sYn5pdActUtZXrAVS/0Bq3zyVMm72/pXt+xtxPs44hgfkvDc4LtNYwwjC1\nGu+/A7yjsyJKxvCF9lKUyRiggQommOWkm0GzoNejPS1SG7XHemilkw5995+Fik9SzyNJCZWM++Qz\nCwL3U5Sp+vRRmFwcNfha+ymel5vkCEx9r39rpk9nUqjI3TNzgv8q+fBC5+kShWlV97ZwVq8FEPKj\n/ttOhZZsJy4pwmM935uBr8WIuWjwLCexe+F3qvDBnSUK9T71+k5bZGv6cCHwZL8V4C5jCvsYnevX\nw965ivPUsve30vvmmiqfHVzQ53+q52XfpRP0VnrEl3s/JHEP9HyeW5jcDAlM5MbZQ51J1Onfnlto\nxb+5/qXXG5D1n3XlHG5iK2oHcCZkUl+LG8X5wEW4pU0afcS2yIYCbdZKxt1n6macaHxsXjnimsjg\nY1Sp2hQEEE8zo8PmQ1UOMp6nPOiahYrKjAKd6zQlQ7Z9L7QBvwoVllvmU372uapOPDDXB+JHc37Q\ncZ74OPIOma4bU8Cygx37Ru9v74UM63u1tkpICjB+kNYXymdVh0irVucQddiySu5xgm6MKrpod5Xh\nGCza6WKABmoZZpsstx3irMDoX35qThRO0KSq2o3QzlwIAo+nQhsmKSLzIkWautw4wO7p+ZJU0IpV\nAR+xvhLaFaG1MWsCLtG4uZxJlih0L7yjnQAu2hXE4ueNHma7fxvWz1oaJNYx5MrO22TBqPhIO2RK\n5yaYZE9SzjzF7JCpwzIIiLIWJhyD0O4ShTqEy8Kk0GiQX5SxKYYAACAASURBVLgaPBU3DvSdf9S6\n+sPrGXG7fubYy10Bq7cDuHpqaSuVBSUFSxQqGPcKVFcy7r6gnXTAF0ElP671pPZKPqucZoYpyrzq\nEU28h6hTcPtGgbuYeee32MYB9jAEvW/AhkMQeRHaJ/fUzokteruDE+83OE49g2ySwxB1SkquiaRu\nv8LJyHUMYVeDX9yq9nPkTsbJMts48GGF6L9XzDx2TettiUKfFrOk9kf2/hbN9KnK9UPYm5t7Dmpa\n6MU298hn1Qnc9h6+b4693OU0M+QcbgpQv61nl5Xadu5XKz200KtW1xwuoDpGlZ7viLmocgUTqq5e\nVRXSEgKJhSylq2+fmThQgbM0RhVLFFJC0ocb6hl0v1P7XKC1j2YaGFD1+XsF+Oh7d7ST9gIco8p9\nYUnovZKUpPmwn5lr0lUzCmfER4zDD7GlywPtsyUKOfZylw2Ou/H1RVNSYo+OnFNq9hfWyNM9qBdg\n8RblGdO5GNt1GwecZkYDP/+gz757eIxZTjJFmaqld3RW5LNKEYte0Y/t9ThFHe2+4j0sY4pSpmlg\ngA4ukJXaZodMJRR/HyqcnbgXYys98EC0lMjLO8hOOzXMU8wmOT5UEWWKomj2PMX/xZH0/3fXG5D1\nX3FdtJAlheydeh1e6+TCnDbAxZBV8IWqB9HMs4mEzIB3Qqk8jM+m9jNE4F1B5qA/pP3a9lLSSYlA\nhNGQobUJFKltIcImM8qkeBYEPL8Csz/rEPskBP8Rc8+7zFc72BXchy3qeh3nibLn6Kn2HV6Nii2N\nrNS2u9R306YS/B3xS6xV48cTVEBS5OgRagTs2vXz0e+LdwUwO+lIk2vXBHga6edox7xqED3zYqUt\njklzRQBgjTy6aSNJCSdYoY4h9/CK03yRe7bCCWxFgbyaUQWzjQMRUj8W/22KMmY5qcrBOQGQMqY0\n1FAmoF3NKE0knP/zPAAo5gR8NjjOBBXpZ3cpcC8+MdcVoiJkuiNpXt8q+d6O66WFQpY8y3fgeCe0\nUu6gDDdhTnrmse5N1HeKEhe068AvZVqVrjAKzyem6uFt02uVh5bgbQXBOBUbVbe7aIe7Anmt9FDN\nqFo1YbLpT2YuvjnLSX3OFVVjSkiyRfZfqbBbqwDRNlkCZoHjGC1o4sSqfafW2jiVqu6NKRiXkFTA\ne1fPeIQaVaR+h4vRWouARBvdqkA+OpJumoG9q/Y917QfS5lWdS+IVeayLh7YTbB/DImXHXgFYoIK\nH3IpYBnOqUpkRaq+jVFFH82qHF/TM4iVMX6uCs0iRdpvGwdqZXUFG57h0JIMiYk9VNWPBwGgtIdn\nd0XPY4bTcFfAN7bv81l1+ZFhagXKf641GEWOIy3iOE/cozS2H12E+EGwWbmv37VhVYTa6RIoP28+\nFUyd6c91/X2UfGBB+/VebJt/LW2nVfJpo5tZTjqBm1sG70py4WhHIGuRIniWHrrgtpI6kuZCvbGN\nHakEyxSIivBAe6qYeZrp0365pfU/TiXVjGqI5GrgU90EHrwmPzOns8k103LRGioiTSe4oTU3TK3b\nBc1TrOTUnnOcJw6Ke2lxnbBY1S5nknVymaScCSr8vPLBmHMCgRfDAAy3w738eRh+el/3lAdhb7+5\n/rXXG5D1X3oVaaH7dEZoAbGmgyN6981wGkumJ3RcG+ajYFezLE7D7uExHfJfqxIQLQ0OXmkj7aUE\n6uK0XPQBjGPKNqwsmhEFaz4UGOGxDo9Iwj7JrDLzLGXu9pVaXXypTRvJlpYLtKn9NEk5o1Trs8S2\naeB3MCdeT2o/w/0Qx6hKTzJm4aRd+06E6D+H9tQk5XAmONrXmau+75AJY3rNTjpooZdc1lkn10X+\nRqihlxaXmIiu84wanDH3mIsWGVE53xbAfgitoAByt8mCLyWO2kmHt9xipSKfVTY47mTw+L98puyw\nn8aQIaPMPRyCTSRgzZzfwpeWNoq+ah7Y7FtVALir1+ulxQMl5wNgDsKr05QyTqWbuE5Sjj06EhD8\nWpyvbbIYo0pVpqvmvpxJSpinmFqGyd7f0ns8FRjgQ9P7fR8GNG6Z86r2UqpcljPpWmG2uacpx88E\n7jq4oPV3S+v/KEdBP7Z17FOwhPZBBRP+fFbJF28lS4MHU5TpO98TuLcFVXEaGND//1YJSIImtdTD\nM1wl3wN5JePYb/WMY8V3iULnAdrHQabh0RGVjHPs5S5r5GkNBW6fnRJ4iErusUV47OWuK9W7FMg3\n+GRmFDElqfXcwIDrGVmnAnFWalvk8jCxW8QiK5xghRMuW9FKD3wWfDR/UEv9NDMCKS8EPuwXoTV7\nKQCMd5RoRJP62N6OpOujHXMtL57qDDrBCvatQGMtw04e30sJjNUxxCD1rmjeRTtFLOo7LIfpxGAa\nPUJNenKwPZwTZw3a0vIKo1QLGIQzpJOOtPbaqVDxTIQKaJGqVklKQn2oiC2y6eCC9svjYO8UBJYj\ntYC7Ov9ilayIRbW4w/kwQIN+JgCRdXKZ5aSfWa30YMPiau2ldC700uJg0bXKvgkA61NVVe2swPog\n9apGXUECwcnwnkErcY08Aci7ej4t9FLPIMde7lLDiJK4NZ2JL/9G+2iD42n5kzJVsOYp1v0tNyhQ\nRZKn5kr9b65/8/UGZP2XX6fMx9ij3APJwH0qD8H2trL6ZQrc462AZQX2cwJUdQz5RjrOEx0Wtw3e\n10aKI+XcV5YYXd1p1+ThJjmqSj06wpYFntbJZZxK/d6ZcAB+H1qJ18DOiAAcdaj6afR2Wi3DWJL0\n1Nt9AbZOOrDLapsOU6tqUJjW47YCOUWaGttLBQHQnwALOpjsByk5s5rmrG2RrQP3x7RbfCcdCvS3\nTYd5C26GHP3FYlUj8sDyWVXlLEz5kdRh1ki/Z9pevbMjahjxgYISkjTTRwlJ57Pls6rqwI/mE0lJ\nSuimjRlOs0iRMuYwDdrBBfEyvlcF6QQrcDWI/NXrID/OE7L3t1wjh9v2V4a9ZUxRwDLTlDpvp59G\n3bsgTni0I1J+AwO00KvqQgCk0W2A21qDOYebLn3RRTu7h8fIfLXDNKWqLn2uVsQk5Zp6DDpeyxTA\nWT2zFU5AuTkxPXL4KphwzTASAjn1DNJDq9bvJfMWOs/EL2mkHzuDS2jEtnYj/X8lQNtOF3YN6bE9\nBLN7WFcgKp/Bhx0mKXej62b6qGScHlp9bXJbvx/9K6M8wBKFXmmMnqL5rGqdnRGwK2eSbtrIeJ7y\nVk2SErbJoohFr5jalSCQm0TPs0Lfu4mEOJvhOzTTxzZZVDPKcZ6EdYiqY4m0w0ABy5Qy7ZO+Xvl5\nHAD5oyO3geJD81bZwSsB5GguPUytJvuuaC9EiYTlILsan5tXU9eUVESgtEaeWsbBg3SdXNrp0p7/\nXNPHPNZ5FG1rYqX8D7HaH42jHwtsr3CCYy932SGTJCVqeYeK0x9DGzCaYa+R5wMJBSxrmnXN4Av9\n+0G2wFsdQ5LHeKH/zym1Ju0ermA/Qo2Lc0YLHe7qfOUzfT9+EyYKb+iMqGCCcSrdjmyFE4xTyTZZ\nqtx3BYAVuHjHeSLQZUcCXa3qAvDA3Gos6uxFeZnMVzs++TxEHeNUYvfEg+UzxQbmlETwc3M6BPXa\nb38yPf9Iz+CK7jUv3oT3f6frDcj6b3O9Jdsd3gvE41vKTkaoUdAPG62VHp+ASdCkgzFmvY+OFDDv\nBnCWVCYzy0ls40AZ7T1l8a4/80ABNU4TRfHKUqYZppZlCshjjQRNboIbg/wEFQJWGweYXVQwnlPZ\neYPjqhwE9fU6huimzdWjGTO4rkA+QYXagCMKznupoJL8rYJitJ+IatunmeHYy11uWFpVmkuWzoSD\n+e0iRVy0kNF+EUDaPWWG0S5iggoWKaKCCUapdj5KJB9XMMEEFVQzSjHzqqgE0dgiFlVd+LWARgu9\nIvvfUMVomyxV/K6F1u2ZtG4aVzTxE/XTVjjhujq2jItL8jSQfa8pQFYzShGLNNPHKNXkHG6yQ6ZP\n3eUcbnKCFVeJbqbPg91JZkUGbwmtok/UwuikQ7ykL9MCqZOUa728qyqOfReCeRWYXXRj7WUK/F5w\nNx1s/mQiycahDkvqnvTQyiTl/NnC8EEwlo6JwOuTTTzTM7UVAfg18mDO3DdwlGrnz0WJAeoNPlKb\n2D7H25VdtNPAALUMu2VRrDiWMi3O1oTEaW1FxukuX/CO1mVU2h6nEq6mK2/2VSC7f4z7FsbKYmo/\nwyuc9h0+DGLvqhLWRreLFa+SzwANjFPpYqqRrJyV2maEGv/vKFBcxCKN9NNNm7eQeCrbmWgYHLlM\nixR5C6+DC2qV3gryMO+bm4FHU+Yo7MqozhHnGrWHKuOCAnMVY6qSRB7d1ZDYBOubQepVrftY4LOd\nLucLjlDjJG7qXvO8vGKqnkbQeSatw8VbAgLzFDNGld77S1VoNziu6sz1ABoCiGM1EMpDu7efRk4y\nq/NiTklKIUtKrm6bOHbLIphvkQ3t4RyJQOy8QPAQdZCItIuvnEjfSYf7HMaq9IiFgQ27iNmsBhTq\nBY4qmHBNPftB7exV8tkkR2eh3dYU6RXgugCsXZUzxTIFool8b25mbj/o+fOZiQ/3TgCDL9Icqz9Y\n+r/XyVWH5O03Yf3f+XoDsv67XbuHxxgwU4b5NGzojwS4hqmFhDYSl3TIzFNMMfNuLNtOl3Ov/vLT\ntCgjbTqErFT8lF5a9PrRW/GDUH34wFQCvxTK7iNpoBbHkUtIkpXadlI7nwW39p+hw+MbHRxNJJyQ\nbZ8qyC5ToEOv3lzJuIVeCllyqw3WzPkGsXqXxxr2K8lKZL7a4ThPXKzVvgvyDPfDBOb58J0SQcfl\nS3M19GgZkaCJaUppYIAh6qTN9J5aOMXMc5JZdg+PMU0pf/lpuHcbBwISFeYTXsPUql0ygfvGVTHG\nwStl/tn7WzQwwDC1+oxlBkUCojWMUMcQU5SlLYA+CjpepcFb8WOcu2SdCuaLFGli9QPdpy7a3aty\nhRPYd5rcbKaPJhKk9jO8HRrBySjVcN982pNPDG4F5esw2TlKtbdG7YqqOWvkCTSdDV5mgRjeRrd7\npR1kB5D/oYIR5/QcYpvaycHBJ88rtkEywq4iuY7Q6uujmdPM0E4XixSpVXXdfPrLHqo6GU29T7Ci\nINmi9ssINQ5gIlk+Eoe3yFb19huBol5asMt49S8CiINXCqrZ+1tegYxDDRnPUwrgy4FTcw/sqiqu\nixTRRrdXIGOFI1YUoxp7JePks+oSE7ZxQDdtVDNKLutkpbYpIUkfzVrTYzL7NXtMLus6GwI300rB\nfoIrtnfSIXCdALM/+ch/NHNfolCyG18FcL8Q7J5CYkKvcdmknXfTwvkTvEOZC/ylM+FZjplLYuyQ\nCedVJTvOEz23usAnXFDAj9IvzGg/7B4eU1UpqbW9RKH/jDsCfJ82Wd5LiR/aTpcqnldNQPuWNKCO\ndgTiVzghasIXYa0F/bYaRgR6g3J9LcPMcJo18vRsgk3UJOWSm9g4UDIZRFw76XBttinKyGWdQpbI\nfLXDNlnaW3d1P4uZ17pc0FoxO0iDKnsp0HUvVNwfElT5r6trELT6GDMfsIiVdctT5SxJifbW14GO\nUCdQHFu/CZocHMeJ4JiE8M6bcP4fdL0BWf8dr9thA0TjYftWwZt2HWSp/QzM/iKQFIIMl8w9C1P7\nGez+rQ4a7ge+zPsKoD4pcsZc1yUKBN6LU4uj6UxnmQL93dPAOwqSEJvkKFvtVHXjBCuqKiRUKYpA\nz66CXcaJ6TYsAMCPAi41jLBEoVprj5VtRnLpJjmaePyViL82HALaVZXSo5fWIkWcYEWg6y0FLFYt\n7etYYfCWgFHk+URD4SrGXFuGdlUAiljEboaW1FzwgixXeb2DCx4gahhxn78ETT4FGgnznXTofqzg\nfJwRalgjj3VyFUzGgh5Rr7l6fuSv2T+q4rRDptvH1DCi9/9IYOie6T7VMkwHFxwYVjBBH82skq/n\neip4GyZQIArcrhOs6KANQoZRvDISbg9emQcvu6MKzCTlaQ7Jt6rqccp8IjK1nwEfBp7cWVNF6qy5\n2W0LvfBAVbM4yUSvuQF1nN6zzT26aaONbucmRusPLoVqyvvmLZ3Y6qtlGLsaiOutuKUUL9IGy+10\nYb8XubiGEU3q3QiVrx+1xmMrOQ5qxKGUIhbppEP35ZxARC8t7tJQziTWhVup2Df44EcDA0xS7kbq\n41S6lIl9JTmTOoZYI4+//FRgooYRb3vNcBo+UevMlgXmGun3fcytoHP3O+B8kG5IBvBnf8S+UbWu\nlmHa6Fbr6LqI6tGcnHPa9xVMQG84L+4GsnqoynFX5wQjSuAuWpg+LRMYjeCfz/T3pUxDfXqyLvKs\neGHwttZmKz1uxTVOJUUsMkaVezSyYPBz3e8GBtjguPb8/TC8EmzIummjhCQFLGvvFohbNUQdVOiz\nDlML11X9XCVf8gVB0HmJQhiRMOdgaJ230Ou6WlxSJ6GFXp15D1ES9FtNmrbQq+/7lqapS0imAdY3\nSsZKmVYSYKoMWxaq9NlzSdTYkf7+U+BsOhY00+eT3PUMqop1BvdOZcbc0ipJCXymSrD7LL6nc6PP\nDHrNY82b6z/segOy/rtf05TCb0IZ/qz5BAuj2jBLFGrjvydwsUyBsr1PFIT6aKaQJcap9FHsGGTi\nOLJzG9YCIAmVnx0ydRjFjXtOWWMp09iKKmKcS8s2JGjC5lSmjlMvshM5oJcWF3K0X6EgtnHgRP4k\nJXA3tCe+tPShF+QtyplknEo3So0iidEaxewProZdxVhanfr3QR4iSCaUMk32/hYFLDNCDbuHx+ih\n1X3x1siT6vGIYaUi0XNOQGaUamX6T9ViGaaWFnoFCB6GUn8Lab2in+BTTLYCZv8Xe28cUmd2dn1f\n+LwyQgWL1CLF4jNSYQQhgwxOZQSLFKmDgx3sSAXBgMGpjDCCg9QhmRjsyAgRzodBgolEOOEQDjGc\n4KQmEukBBcVE4kMsJ0gMJjUkkQgKZ4iI+Pv+WHvv0/7z8H7f+z5tpz0bhpmJRs993/vee+11rWut\nucDI+MiVIp6qRHHLdVhuyZyyhvnQBec1QY1Mhyim/aPc4M91giWitATjxm5GQ0QTi9LQedDl4zAO\n91Tm8aL8/aPc0BJOTIDae0uxrPngSzlmr7GENpT0QY6Y0jaBphaiukeX9OwOC3RtXNRGTUTg10fz\n+LLRGuXamG5ZEEwP06vS29YhlhTgmaFec+YtAUeeC1gsUIVdVwm4iKfaqL5wjMHPtKLlvExjFwV4\ny5xKppdhaahOiw315qd2X89xnI4MW5VUZ6N3wI7QxR9M1+Idym1KP3+IPonnv8U9e5WGInTBHT0D\nX1Jfp0wg9rzmWw8jvKCAMTppY5Im4gJuCYLRaCkpbI3gGp+kOkQV+Uy/IPxfRO/SeQvapz6GMPuO\nUlKsUsE4HUHrNUJPEHivUa57/L6FOJf9o1y4KTBdz4wOC3fcM34u0Gtrumc+c7GVKV7/h1IovvuB\nvuePrukhTpNYl3f0LGM06/PecfPlcwcKbunr3/3AsB4ZA3sdGZ86xu6Rmg1amQrRWDyzEFjOBQEQ\nThmUC2RsUaSD5BUHVCotlCMbmYaorj1vb5d5akLYufWq9DlGJ92M0suwAMspARkx+3+ggYSY8LsO\nUH1DJjPzsua12KwnrpR4Vs/OabvsEQKjX7mDo7Nn4YLuxZ8cK577eh9ryiRMvKCAGM16R1PaD7Yp\n5IlrbMmOv8vIgqzvywhM0ykXPh2xoG9oIKGvndRm531bvKlfJ2PSZznGaosidXHd+quOlXsWTmYL\nVIWOxxcUwAeZPD0WM5l5li+GicVM98wg/dhJMRIsq7vMToEVZxakClYZo5NxOtRif9mFUlcK4Pmw\n1vRBjnQQcZXKfGSMBw+ccuWl9gzjwA23GF2xoOl5QQHUuwX0mTb/FqI6qV/SJmn3nRD3U91b//l7\nGJGAPq4FnufuZ/dZKC9Yu7ra7K6u0wOuWuawrUNaiFLMJt2MkqCBTsakL7phQSjvy6sxmsWq9MAO\n+RQcvNDieUq/w2fGebNGb//AE7cAx7WJ7B/lakO5os1qnTJdn/tvS+g0vUa5NtzbOmVXsQA/sdAJ\nZrf1jAfpZ5I2sZP3HfCZItO2/7GA0WkG5Dj/xCCZCXpuJiYN1lf6jDvkk6Sa/PQObUwG7x/eMfip\nDgz56R3sW80N3zlVwoZYi+dqyfc5h0mqBerXwGol6j/cs2Dp4SNzvEjdC4kLj7YFRH4rADNCj0Ba\nvgNp/yl9ltcPrVMG5yyUZkkZZrJL8PFKHYxjy8oObGWK3Nf76ja8TdBPlTv5uD0+DvmUzcTIT+8w\nwBlWqKSTMWnlrjirg4vaQFuIis39kQ465awxSndwn/fBy3PUwpeZzlfftGAXcADgbCZCKCGAN0+N\n5vinFhjXJNXk7e2GsOMuIuwf5WreOeH3H53kgHuugeMRQeTtDVa5qXnChv4OT/SsOSfQ4BMUuKRD\nzgTtgfVj3lTSShk81NfWKdO7/VODEQsmub7xhOd6F856ht5nFMacvuojB1Y+1jxZoTIT+2SvKeKp\nSuK/0r0Zog9SDkT+1unr7gs8r1EOT7Q2jtOhMt6gK6l/A8rMPCtmNaG5oT9332OjLnPyLGZ/kUef\nOxBwU8/uuPBvkxrqmcF+6bz4Hunf1SQpJUXBwQs9nysCk9yx0FCRHX/XkQVZ38uRcoDrJy7nbN2V\ntN7P6Kt8913QKd10QuOP3Ymx0mkjvOdSnys3/sQBjA0LBo/z1ISIDn+636AEPnOsydf6vT7zy3v6\n+DxDSxBofB8q6zUyjLg4nF4kgnaRPHzgFuOfugXiK4PzFiKFZqgPi4r/fFFa2KaQaRqpYT5oGLgi\n8TXtbpG9op/lnd53yA/6o1UqVCK4oHtiVxF4uq0T8jw1jNEppunxMTYlQFnEU5XQrrqT6SDwm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y2ZWh8aAHeGBh45+mUeW2d8U42PYbbXiPj+lkjJyXaVh0TvERx97c0v0fpRvuWbAfiNJCP4Mh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\n",
            "text/plain": [
              "<Figure size 612x388.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWMyEAxqsknF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "218cc1be-3e97-406b-974d-30261dabf4b2"
      },
      "source": [
        "obs_100.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.3081324619291115e+26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMLpTAZstn7p"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}