Notebooks/Interact with files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Feb 25 10:15:33 2025\n",
      "3.12.9 \n"
     ]
    }
   ],
   "source": [
    "# The following is to know when this notebook has been run and with which python version.\n",
    "import time, sys\n",
    "print(time.ctime())\n",
    "print(sys.version.split('|')[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C: How to read and write files (ASCII and FITS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part of the Python lecture given by Christophe Morisset at IA-UNAM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some informations are here: http://www.tutorialspoint.com/python/python_files_io.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a simple ascii file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# numpy is needed in some part of the lecture\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we will have to have some files on the hard drive to read them The following notebook cell will write a file in the same directory where the notebook has been started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data1.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data1.dat\n",
    "1   2.3  6   8 star\n",
    "2   3.5  7   9 galaxy\n",
    "3  -4.2  5   7 cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the goal is to read this file. The first way is to open the file, read it completely in a variable and close the file. Then we can play with the content of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "datafile = open('data1.dat', 'r') # Open the file to read it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data = datafile.readlines() # The variable data will receive the content of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "datafile.close() # Not need anymore of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(data)) # The data file is stored in the form of a list, each element of the list corresponding to a row of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1   2.3  6   8 star\\n', '2   3.5  7   9 galaxy\\n', '3  -4.2  5   7 cluster\\n']\n"
     ]
    }
   ],
   "source": [
    "print(data) # Each row is a string and terminates with \\n, symbol of END OF LINE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(data)) # number of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   2.3  6   8 star\n",
      " tralala trololo\n"
     ]
    }
   ],
   "source": [
    "print(data[0], 'tralala', 'trololo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   2.3  6   8 star\n",
      "\n",
      "2   3.5  7   9 galaxy\n",
      "\n",
      "3  -4.2  5   7 cluster\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for row in data:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   2.3  6   8 star\n",
      "2   3.5  7   9 galaxy\n",
      "3  -4.2  5   7 cluster\n"
     ]
    }
   ],
   "source": [
    "for row in data:\n",
    "    print(row, end='') # The end='' argument is to avoid the extra line between the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(data[0])) # Each element is a string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is easy to separate each field with the split command: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '2.3', '6', '8', 'star']\n",
      "['2', '3.5', '7', '9', 'galaxy']\n",
      "['3', '-4.2', '5', '7', 'cluster']\n"
     ]
    }
   ],
   "source": [
    "for row in data:\n",
    "    print(row.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 1 f =  2.30 type =       star\n",
      "N = 2 f =  3.50 type =     galaxy\n",
      "N = 3 f = -4.20 type =    cluster\n"
     ]
    }
   ],
   "source": [
    "# One can also transform the data if the type is known:\n",
    "for row in data:\n",
    "    this_data = row.split() # contains a list of strings\n",
    "    print(f'N = {int(this_data[0]):1d} f = {float(this_data[1]):5.2f} type = {this_data[4]:>10s}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "[2.3, 3.5, -4.2]\n",
      "['star', 'galaxy', 'cluster']\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# One can even fill a list with the data, by column:\n",
    "N = []\n",
    "f = []\n",
    "type_ = [] # take care, type is a python command, you can erase it if you use it...\n",
    "for row in data:\n",
    "    this_data = row.split()\n",
    "    N.append(int(this_data[0]))\n",
    "    f.append(float(this_data[1]))\n",
    "    type_.append(this_data[4])\n",
    "print(N)\n",
    "print(f)\n",
    "print(type_)\n",
    "N = np.array(N)\n",
    "print(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[ 2.3  3.5 -4.2]\n"
     ]
    }
   ],
   "source": [
    "# If the file number of rows is not too big, you can use list comprehension (and even send the result to a numpy array)\n",
    "N = np.array([int(row.split()[0]) for row in data])\n",
    "f = np.array([float(row.split()[1]) for row in data])\n",
    "# Each one of this command scans all the rows, don't use for huge files\n",
    "print(N)\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to treat special rows (headers, comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data2.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data2.dat\n",
    "# The following data are for test purpose\n",
    "N    f   x   y type\n",
    "1   2.3  6   8 star\n",
    "2   3.5  7   9 galaxy\n",
    "3  -4.2  5   7 cluster\n",
    "#4  -10.5  5  7 test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# The following data are for test purpose\n",
      "N    f   x   y type\n",
      "1   2.3  6   8 star\n",
      "2   3.5  7   9 galaxy\n",
      "3  -4.2  5   7 cluster\n",
      "#4  -10.5  5  7 test\n"
     ]
    }
   ],
   "source": [
    "!cat data2.dat # Just to check that the # comments are also in the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file has to be read row by row, to be sure that special cases are treated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# The following data are for test purpose\n",
      "N    f   x   y type\n",
      "['1   2.3  6   8 star\\n', '2   3.5  7   9 galaxy\\n', '3  -4.2  5   7 cluster\\n']\n"
     ]
    }
   ],
   "source": [
    "datafile = open('data2.dat', 'r') # Open the file to read it\n",
    "\n",
    "linea = datafile.readline() # this reads only one line\n",
    "first_comment = linea\n",
    "print(first_comment, end='')\n",
    "\n",
    "linea = datafile.readline() # this reads only one line\n",
    "header = linea\n",
    "print(header, end='')\n",
    "\n",
    "data = []\n",
    "while True: # loops until exit by break command\n",
    "    linea = datafile.readline()\n",
    "    if linea == '':\n",
    "        break\n",
    "    if linea[0] != '#' and linea[0] != '\\n': # comment lines are skipped\n",
    "        data.append(linea)\n",
    "datafile.close()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# The following data are for test purpose\n",
      "N    f   x   y type\n",
      "['1   2.3  6   8 star\\n', '2   3.5  7   9 galaxy\\n', '3  -4.2  5   7 cluster\\n']\n"
     ]
    }
   ],
   "source": [
    "datafile = open('data2.dat', 'r') # Open the file to read it\n",
    "linea = datafile.readline() # this reads only one line\n",
    "first_comment = linea\n",
    "print(first_comment, end='')\n",
    "linea = datafile.readline() # this reads only one line\n",
    "header = linea\n",
    "print(header, end='')\n",
    "data = []\n",
    "linea = datafile.readline()\n",
    "while linea != '': # ends at end of the file\n",
    "    if linea[0] != '#': # comment lines are skipped\n",
    "        data.append(linea)\n",
    "    linea = datafile.readline()\n",
    "datafile.close()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['N    f   x   y type\\n', '1   2.3  6   8 star\\n', '2   3.5  7   9 galaxy\\n', '3  -4.2  5   7 cluster\\n']\n"
     ]
    }
   ],
   "source": [
    "# very shorter way to deal with the file. No need to look for the end of the file.\n",
    "datafile = open('data2.dat', 'r') # Open the file to read it\n",
    "data = []\n",
    "for row in datafile: # This will read the file line by line, similar to datafile.readline()\n",
    "    if row[0] != '#': # comment lines are skipped\n",
    "        data.append(row)  \n",
    "datafile.close()\n",
    "print(data)\n",
    "# This way will include the header in the data... Not what we want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N    f   x   y type\n",
      "-----\n",
      "['1   2.3  6   8 star\\n', '2   3.5  7   9 galaxy\\n', '3  -4.2  5   7 cluster\\n']\n",
      "-----\n",
      "['# The following data are for test purpose\\n', '#4  -10.5  5  7 test\\n']\n"
     ]
    }
   ],
   "source": [
    "# very shorter way to deal with the file:\n",
    "# we know that the header is the first no-comment line in the file.\n",
    "datafile = open('data2.dat', 'r') # Open the file to read it\n",
    "data = []\n",
    "comments = [] # we can keep the comments for some usage\n",
    "header_not_read = True # We will turn it to True once the header is read\n",
    "for row in datafile: # This will read the file line by line, similar to datafile.readline()\n",
    "    if row[0] != '#': # comment lines are skipped\n",
    "        if header_not_read:\n",
    "            header = row\n",
    "            header_not_read = False # next time, data will be read\n",
    "        else:\n",
    "            data.append(row)\n",
    "    else:\n",
    "        comments.append(row)\n",
    "datafile.close()\n",
    "print(header, end='')\n",
    "print('-----')\n",
    "print(data)\n",
    "print('-----')\n",
    "print(comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N    f   x   y type\n",
      "\n",
      "[(1, 2.3, 6.0, 8.0, 'star'), (2, 3.5, 7.0, 9.0, 'galaxy'), (3, -4.2, 5.0, 7.0, 'cluster')]\n",
      "['# The following data are for test purpose\\n', '#4  -10.5  5  7 test\\n']\n"
     ]
    }
   ],
   "source": [
    "# Alternative way using \"with\". No need to close the file, done when the \"with\" block is terminated.\n",
    "data = []\n",
    "comments = []\n",
    "header_read = False\n",
    "def change_type(row_split):\n",
    "    # This function changes the type of the data read from the file from 5 strings into int, 3 floats and a string\n",
    "    # It also return the result in form of a tuple\n",
    "    return (int(row_split[0]), \n",
    "            float(row_split[1]), \n",
    "            float(row_split[2]), \n",
    "            float(row_split[3]), \n",
    "            row_split[4])\n",
    "with open('data2.dat', 'r') as datafile:\n",
    "    for row in datafile:\n",
    "        if row[0] != '#' and row[0] != '\\n': # comment lines are skipped\n",
    "            if not header_read:\n",
    "                header = row\n",
    "                header_read = True\n",
    "            else:\n",
    "                data.append(change_type(row.split()))\n",
    "        else:\n",
    "            comments.append(row)\n",
    "print(header)\n",
    "print(data)\n",
    "print(comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# We can define the result as a structured array\n",
    "# We use the header to define the field names.\n",
    "# data must be a list of tuples.\n",
    "a = np.array(data, dtype={'names':header.split(), \n",
    "                          'formats':['i2','f8', 'f8', 'f8', 'U10']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1,  2.3, 6., 8., 'star'), (2,  3.5, 7., 9., 'galaxy'),\n",
       "       (3, -4.2, 5., 7., 'cluster')],\n",
       "      dtype=[('N', '<i2'), ('f', '<f8'), ('x', '<f8'), ('y', '<f8'), ('type', '<U10')])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2.3, 6.0, 8.0, 'star')\n"
     ]
    }
   ],
   "source": [
    "print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2.3, 6.0, 8.0, 'star')\n"
     ]
    }
   ],
   "source": [
    "print(a[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# Easy access to the columns, by their name\n",
    "print(a['N'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['star' 'galaxy' 'cluster']\n"
     ]
    }
   ],
   "source": [
    "print(a['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.         11.40175425  8.60232527]\n"
     ]
    }
   ],
   "source": [
    "# Easy combine the values of columns\n",
    "print(np.sqrt(a['x']**2 + a['y']**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using numpy loadtxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Fast way for reading the file\n",
    "# One hace to tell to skip the 2 first rows\n",
    "# skiprows \n",
    "b = np.loadtxt('data2.dat', skiprows=2, dtype='i4, f, f, f, U10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1,  2.3, 6., 8., 'star') (2,  3.5, 7., 9., 'galaxy')\n",
      " (3, -4.2, 5., 7., 'cluster')]\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4'), ('f3', '<f4'), ('f4', '<U10')])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The names of the columns are f0, f1, f2, etc\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.3,  3.5, -4.2], dtype=float32)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['f1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using numpy genfromtxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Fast and versatile way to read the file\n",
    "# the names are taken from the file\n",
    "# The types are defined automatically when reading the columns\n",
    "c = np.genfromtxt('data2.dat', names=True, dtype=None, skip_header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1,  2.3, 6, 8, 'star') (2,  3.5, 7, 9, 'galaxy')\n",
      " (3, -4.2, 5, 7, 'cluster')]\n"
     ]
    }
   ],
   "source": [
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('N', '<i8'), ('f', '<f8'), ('x', '<i8'), ('y', '<i8'), ('type', '<U7')])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.3,  3.5, -4.2])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c['f']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a value of x is missing (not possible with space separator, so we use \",\" as separator):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data3.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data3.dat\n",
    "# The following data are for test purpose\n",
    "N,    f,   x,   y, type\n",
    "1,   2.3,  6,   8, star\n",
    "2,   3000.5,   ,  9, galaxy\n",
    "#3,  -4.2,  5,   7, cluster\n",
    "4,  ,  5,  7, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "d = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1, \n",
    "                  delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1, 2.3000e+00,  6, 8, ' star'), (2, 3.0005e+03, -1, 9, ' galaxy'),\n",
       "       (4,        nan,  5, 7, ' test')],\n",
       "      dtype=[('N', '<i8'), ('f', '<f8'), ('x', '<i8'), ('y', '<i8'), ('type', '<U7')])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The missing value has been changed to -1\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Th emissing value can be set to whatever you want (but non a NaN here, as the typ eis integer, and NaN is a float...)\n",
    "d = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1, delimiter=',', \n",
    "                  filling_values=-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1,  2.3000e+00,    6, 8, ' star'),\n",
       "       (2,  3.0005e+03, -999, 9, ' galaxy'),\n",
       "       (4, -9.9900e+02,    5, 7, ' test')],\n",
       "      dtype=[('N', '<i8'), ('f', '<f8'), ('x', '<i8'), ('y', '<i8'), ('type', '<U7')])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# ons can select the columns to be store\n",
    "e = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1, \n",
    "                  delimiter=',', usecols=(0,1,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1,  2.3000e+00, ' star') (2,  3.0005e+03, ' galaxy')\n",
      " (4, -1.0500e+01, ' test')]\n"
     ]
    }
   ],
   "source": [
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one can select the columns to be store\n",
    "N, f, typ = np.genfromtxt('data3.dat', skip_header=2,\n",
    "                  delimiter=',',usecols=(0,1,4), unpack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 4.]\n",
      "[ 2.3000e+00  3.0005e+03 -1.0500e+01]\n"
     ]
    }
   ],
   "source": [
    "# The resulting array now contains only the given columns\n",
    "print(N)\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixed size ascii files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data4.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data4.dat\n",
    "#  Line      Iobs    lambda  rel_error Obs_code\n",
    "H  1  4861A 1.00000    4861. 0.08000  Anabel                               \n",
    "H  1  6563A 2.8667     6563. 0.19467  Anabel                               \n",
    "H  1  4340A 0.4933     4340. 0.03307  Anabel                               \n",
    "H  1  4102A 0.2907     4102. 0.02229  Anabel                               \n",
    "H  1  3970A 0.1800     3970. 0.01253  Anabel                               \n",
    "N  2  6584A 2.1681     6584. 0.08686  Anabel                               \n",
    "N  2 121.7m 0.0044621217000. 0.20000  Liu                                  \n",
    "O  1  6300A 0.0147     6300. 0.00325  Anabel                               \n",
    "TOTL  2326A 0.07900    2326. 0.20000  Adams                                \n",
    "C  2 157.6m 0.00856 1576000. 0.20000  Liu                                  \n",
    "O  1 63.17m 0.13647  631700. 0.10000  Liu                                  \n",
    "O  1 145.5m 0.00446 1455000. 0.200    Liu                                  \n",
    "TOTL  3727A 0.77609    3727. 0.200    Torres-Peimbert                      \n",
    "S II  4070A 0.06174    4070. 0.200    Torres-Peimbert                      \n",
    "S II  4078A 0.06174    4078. 0.200    Torres-Peimbert                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Here we cannot use SPACE as a separator, as some strings contains spaces.\n",
    "# \"delimiter\" is used to specify the size (in characters in the file) of each variables. \n",
    "# The types must be clearly defined too.\n",
    "obs  = np.genfromtxt('data4.dat', \n",
    "                     dtype=[\"U11\",\"float\",\"float\",\"float\",\"U2\"],\n",
    "                     delimiter=[11,9,8,10,2],\n",
    "                     names = True\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([('H  1  4861A', 1.      ,    4861., 0.08   , 'An'),\n",
       "       ('H  1  6563A', 2.8667  ,    6563., 0.19467, 'An'),\n",
       "       ('H  1  4340A', 0.4933  ,    4340., 0.03307, 'An'),\n",
       "       ('H  1  4102A', 0.2907  ,    4102., 0.02229, 'An'),\n",
       "       ('H  1  3970A', 0.18    ,    3970., 0.01253, 'An'),\n",
       "       ('N  2  6584A', 2.1681  ,    6584., 0.08686, 'An'),\n",
       "       ('N  2 121.7m', 0.004462, 1217000., 0.2    , 'Li'),\n",
       "       ('O  1  6300A', 0.0147  ,    6300., 0.00325, 'An'),\n",
       "       ('TOTL  2326A', 0.079   ,    2326., 0.2    , 'Ad'),\n",
       "       ('C  2 157.6m', 0.00856 , 1576000., 0.2    , 'Li'),\n",
       "       ('O  1 63.17m', 0.13647 ,  631700., 0.1    , 'Li'),\n",
       "       ('O  1 145.5m', 0.00446 , 1455000., 0.2    , 'Li'),\n",
       "       ('TOTL  3727A', 0.77609 ,    3727., 0.2    , 'To'),\n",
       "       ('S II  4070A', 0.06174 ,    4070., 0.2    , 'To'),\n",
       "       ('S II  4078A', 0.06174 ,    4078., 0.2    , 'To')],\n",
       "      dtype=[('Line', '<U11'), ('Iobs', '<f8'), ('lambda', '<f8'), ('rel_error', '<f8'), ('Ob', '<U2')])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs # The same delimiter (fixed sizes) is applied to the names. May not be what you want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Defining the names:\n",
    "obs2  = np.genfromtxt('data4.dat', skip_header=1,\n",
    "                     dtype=None,\n",
    "                     delimiter=[11,9,8,10,2],\n",
    "                     names = ['label', 'i_obs', 'lambda', 'e_obs', 'observer']\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([('H  1  4861A', 1.      ,    4861., 0.08   , 'An'),\n",
       "       ('H  1  6563A', 2.8667  ,    6563., 0.19467, 'An'),\n",
       "       ('H  1  4340A', 0.4933  ,    4340., 0.03307, 'An'),\n",
       "       ('H  1  4102A', 0.2907  ,    4102., 0.02229, 'An'),\n",
       "       ('H  1  3970A', 0.18    ,    3970., 0.01253, 'An'),\n",
       "       ('N  2  6584A', 2.1681  ,    6584., 0.08686, 'An'),\n",
       "       ('N  2 121.7m', 0.004462, 1217000., 0.2    , 'Li'),\n",
       "       ('O  1  6300A', 0.0147  ,    6300., 0.00325, 'An'),\n",
       "       ('TOTL  2326A', 0.079   ,    2326., 0.2    , 'Ad'),\n",
       "       ('C  2 157.6m', 0.00856 , 1576000., 0.2    , 'Li'),\n",
       "       ('O  1 63.17m', 0.13647 ,  631700., 0.1    , 'Li'),\n",
       "       ('O  1 145.5m', 0.00446 , 1455000., 0.2    , 'Li'),\n",
       "       ('TOTL  3727A', 0.77609 ,    3727., 0.2    , 'To'),\n",
       "       ('S II  4070A', 0.06174 ,    4070., 0.2    , 'To'),\n",
       "       ('S II  4078A', 0.06174 ,    4078., 0.2    , 'To')],\n",
       "      dtype=[('label', '<U11'), ('i_obs', '<f8'), ('lambda', '<f8'), ('e_obs', '<f8'), ('observer', '<U2')])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data5.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data5.dat\n",
    "#  Line      Iobs    lambda  relat_error Obs_code\n",
    "H  1  4861A 1.00000    4861. 0.08000 x Anabel                               \n",
    "H  1  6563A 2.8667     6563. 0.19467 x Anabel                               \n",
    "H  1  4340A 0.4933     4340. 0.03307 x Anabel                               \n",
    "H  1  4102A 0.2907     4102. 0.02229 x Anabel                               \n",
    "H  1  3970A 0.1800     3970. 0.01253 t Anabel                               \n",
    "N  2  6584A 2.1681           0.08686 x Anabel                               \n",
    "N  2 121.7m 0.00446 1217000. 0.20000 g Liu                                  \n",
    "O  1  6300A 0.0147     6300. 0.00325 t Anabel                               \n",
    "TOTL  2326A 0.07900    2326. 0.20000 g Adams                                \n",
    "C  2 157.6m 0.00856 1576000. 0.20000 t Liu                                  \n",
    "O  1 63.17m 0.13647  631700. 0.10000 g Liu                                  \n",
    "O  1 145.5m 0.00446 1455000. 0.200   g Liu                                  \n",
    "TOTL  3727A 0.77609    3727. 0.200   g Torres-Peimbert                      \n",
    "S II  4070A 0.06174    4070. 0.200   g Torres-Peimbert                      \n",
    "S II  4078A 0.06174    4078. 0.200   g Torres-Peimbert   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Here we want to skip one column:\n",
    "obs3  = np.genfromtxt('data5.dat', skip_header=1,\n",
    "                     dtype=None,\n",
    "                     delimiter=[11, 8, 9, 9, 2, 2],\n",
    "                     names = ['label', 'i_obs', 'lambda', 'e_obs', 'na', 'observer'],\n",
    "                     usecols = (0, 1, 2, 3, 5)\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([('H  1  4861A', 1.     ,    4861., 0.08   , 'An'),\n",
       "       ('H  1  6563A', 2.8667 ,    6563., 0.19467, 'An'),\n",
       "       ('H  1  4340A', 0.4933 ,    4340., 0.03307, 'An'),\n",
       "       ('H  1  4102A', 0.2907 ,    4102., 0.02229, 'An'),\n",
       "       ('H  1  3970A', 0.18   ,    3970., 0.01253, 'An'),\n",
       "       ('N  2  6584A', 2.1681 ,      nan, 0.08686, 'An'),\n",
       "       ('N  2 121.7m', 0.00446, 1217000., 0.2    , 'Li'),\n",
       "       ('O  1  6300A', 0.0147 ,    6300., 0.00325, 'An'),\n",
       "       ('TOTL  2326A', 0.079  ,    2326., 0.2    , 'Ad'),\n",
       "       ('C  2 157.6m', 0.00856, 1576000., 0.2    , 'Li'),\n",
       "       ('O  1 63.17m', 0.13647,  631700., 0.1    , 'Li'),\n",
       "       ('O  1 145.5m', 0.00446, 1455000., 0.2    , 'Li'),\n",
       "       ('TOTL  3727A', 0.77609,    3727., 0.2    , 'To'),\n",
       "       ('S II  4070A', 0.06174,    4070., 0.2    , 'To'),\n",
       "       ('S II  4078A', 0.06174,    4078., 0.2    , 'To')],\n",
       "      dtype=[('label', '<U11'), ('i_obs', '<f8'), ('lambda', '<f8'), ('e_obs', '<f8'), ('observer', '<U2')])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   4861.,    6563.,    4340.,    4102.,    3970.,      nan,\n",
       "       1217000.,    6300.,    2326., 1576000.,  631700., 1455000.,\n",
       "          3727.,    4070.,    4078.])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs3['lambda']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using masks on the structured array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True False False  True False False False False\n",
      " False False False]\n",
      "[('H  1  4861A', 1.    , 4861., 0.08   , 'An')\n",
      " ('H  1  6563A', 2.8667, 6563., 0.19467, 'An')\n",
      " ('H  1  4340A', 0.4933, 4340., 0.03307, 'An')\n",
      " ('H  1  4102A', 0.2907, 4102., 0.02229, 'An')\n",
      " ('H  1  3970A', 0.18  , 3970., 0.01253, 'An')\n",
      " ('O  1  6300A', 0.0147, 6300., 0.00325, 'An')]\n"
     ]
    }
   ],
   "source": [
    "mask_observer = (obs3['observer'] == 'An') & (np.isfinite(obs3['lambda']))\n",
    "print(mask_observer)\n",
    "print(obs3[mask_observer])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple \"write\" method from \"open\" class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "f = open('data10.dat', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.write('tralala')\n",
    "f.write('trololo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tralalatrololo"
     ]
    }
   ],
   "source": [
    "!cat 'data10.dat' # the writing method put everything together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tralalatrololo"
     ]
    }
   ],
   "source": [
    "!cat 'data10.dat' # the writing method put everything together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tralala\n",
      "trololo\n"
     ]
    }
   ],
   "source": [
    "f = open('data11.dat', 'w')\n",
    "f.write('tralala\\n') # \\n to indicate end of line\n",
    "f.write('trololo\\n')\n",
    "f.close()\n",
    "!cat 'data11.dat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tralala\n",
      "trololo\n",
      "trilili\n",
      "trululu\n",
      "trilili\n",
      "trululu\n",
      "trilili\n",
      "trululu\n",
      "trilili\n",
      "trululu\n"
     ]
    }
   ],
   "source": [
    "f = open('data11.dat', 'a') # Append to the edn of the file\n",
    "f.write('trilili\\n') # \\n to indicate end of line\n",
    "f.write('trululu\\n')\n",
    "f.close()\n",
    "!cat 'data11.dat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n"
     ]
    }
   ],
   "source": [
    "a = 'Smith'\n",
    "b = 3\n",
    "with open('data12.dat', 'w') as datafile:\n",
    "    datafile.write(f\"\"\"   Hola Sr. {a}\n",
    "This is a file # this does not close the \"with\" command\n",
    "with a lot of lines.\n",
    "It is easy to write it.\n",
    "The value of your data is {b}.\n",
    "\"\"\")\n",
    "\n",
    "print('end') # This one ends the indentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hola Sr. Smith\n",
      "This is a file # this does not close the \"with\" command\n",
      "with a lot of lines.\n",
      "It is easy to write it.\n",
      "The value of your data is 3.\n"
     ]
    }
   ],
   "source": [
    "!cat \"data12.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pickle (and cpickle) python specific format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Let's define some stuffs we want to keep in a file (data and variable names)\n",
    "a = 5\n",
    "b = 'Hola'\n",
    "c = np.array([1,2,3,4,5])\n",
    "def d(x):\n",
    "    \"\"\" Function mia\"\"\"\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pickle # The module we will use for this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "with open('Demo.pickle', 'wb') as datafile: # The file is open in binary mode\n",
    "    pickle.dump((a,b,c,d), datafile) # Writing the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "�\u0004��\u0000\u0000\u0000\u0000\u0000\u0000\u0000(K\u0005�\u0004Hola��\u0016numpy._core.multiarray��\f_reconstruct����\u0005numpy��\u0007ndarray���K\u0000��C\u0001b���R�(K\u0001K\u0005��h\u0004�\u0005dtype����\u0002i8�����R�(K\u0003�\u0001<�NNNJ����J����K\u0000t�b�C(\u0001\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0002\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0003\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0004\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0005\u0000\u0000\u0000\u0000\u0000\u0000\u0000�t�b__main__��\u0001d���t�."
     ]
    }
   ],
   "source": [
    "!cat \"Demo.pickle\" # This is a binary file, not readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "res = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 'Hola', array([1, 2, 3, 4, 5]), <function d at 0x11294f9c0>)\n"
     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "Hola\n",
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(res[0])\n",
    "print(res[1])\n",
    "print(res[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[3](5) # res[3] is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "a2,b2,c2,d2 = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function d in module __main__:\n",
      "\n",
      "d(x)\n",
      "    Function mia\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.1 μs ± 84.2 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit res = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import gzip\n",
    "with gzip.open('Demo.pklz','wb') as f:\n",
    "    pickle.dump((a,b,c,d), f) # Writing the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Hola [1 2 3 4 5] <function d at 0x11294f9c0>\n"
     ]
    }
   ],
   "source": [
    "with gzip.open('Demo.pklz','rb') as f:\n",
    "    a, b, c, d = pickle.load(f)\n",
    "print(a,b,c,d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FITS files"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "What is the FITS format?\n",
    "The FITS format is the most popular way to save and interchange astronomical data. The files are organized in units each of which contains a human readable header and a data. This structure is refereed as HDUs (Header/DATA Unit). \n",
    "\n",
    "A FITS file can contain one or more HDUs, the first of which is called \"primary\" and the rest are called \"extensions\". The primary HDU usually contains 1D spectrum, 2D image or 3D data cube, although any dimension from 0 to 999 are possible. The data are 1, 2 or 4 bytes integers or 4 or 8 bytes real numbers. \n",
    "\n",
    "The extensions can contain or arrays as in the primary HDU or ascii tables or binary tables. \n",
    "If a FITS file contains only tables, it primary HDU does not contain data, but only header. \n",
    "\n",
    "Both headers and data in a FITS file are organized in blocs of 2880 bytes. The header contain 80 bytes lines each of which consists of a keyword of 8 bytes followed in most of the cases by '= ' in the position 9 and 10 and then the value of the keyword. The rest of the line is a comment string beginning with '/'. Each header begins with the following lines\n",
    "\n",
    "SIMPLE  =                    T / file conforms to FITS standard\n",
    "BITPIX  =                   16 / number of bits per data pixel\n",
    "NAXIS   =                    2 / number of data axes\n",
    "NAXIS1  =                  440 / length of data axis 1\n",
    "NAXIS2  =                  300 / length of data axis 2\n",
    "\n",
    "which defines the format of the file as standard FITS, the data format and the dimensions of the stored data. \n",
    "\n",
    "One block of 2880 bytes contains 36 lines of 80 characters per line. The header can have several blocks of 36 lines. The last block is identified by the presence of the keyword 'END' The next 2880 bytes block contains the first part of the data. The empty lines after 'END' keyword are filled with blanks and the unused bytes from the end of the data to the end of the 2880 bytes block are filled with NULLs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0.0\n"
     ]
    }
   ],
   "source": [
    "import astropy\n",
    "print(astropy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.astropy.org/en/stable/io/fits/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use one FITS files from San Pedro Martir echelle spectrograph. The file can be downloaded from: https://github.com/Morisset/Python-lectures-Notebooks/raw/master/Notebooks/n10017o.fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "hdulist = fits.open('n10017o.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <class 'astropy.io.fits.hdu.hdulist.HDUList'>\n"
     ]
    }
   ],
   "source": [
    "# The result hdulist is a list of HDU objects. \n",
    "# In the case of a simple file, there is only one primary HDU so the list contains only one element\n",
    "print(len(hdulist), type(hdulist))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: n10017o.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      62   (2154, 2048)   int16 (rescales to uint16)   \n"
     ]
    }
   ],
   "source": [
    "# The information on what the file contains can be obtained by calling the info() method:\n",
    "hdulist.info()\n",
    "# The table said that there is only a primary HDU which contains 2154 X 2048 image with data stored in 2 bytes (16 bits) integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SIMPLE',\n",
       " 'BITPIX',\n",
       " 'NAXIS',\n",
       " 'NAXIS1',\n",
       " 'NAXIS2',\n",
       " 'EXTEND',\n",
       " 'COMMENT',\n",
       " 'COMMENT',\n",
       " 'BZERO',\n",
       " 'BSCALE',\n",
       " 'EXPTIME',\n",
       " 'DETECTOR',\n",
       " 'ORIGIN',\n",
       " 'OBSERVAT',\n",
       " 'TELESCOP',\n",
       " 'LATITUDE',\n",
       " 'LONGITUD',\n",
       " 'ALTITUD',\n",
       " 'SECONDAR',\n",
       " 'TIMEZONE',\n",
       " 'OBSERVER',\n",
       " 'OBJECT',\n",
       " 'INSTRUME',\n",
       " 'GAINMODE',\n",
       " 'FILTER',\n",
       " 'IMGTYPE',\n",
       " 'EQUINOX',\n",
       " 'ST',\n",
       " 'UT',\n",
       " 'JD',\n",
       " 'DATE-OBS',\n",
       " 'CCDSUM',\n",
       " 'RA',\n",
       " 'DEC',\n",
       " 'AH',\n",
       " 'AIRMASS',\n",
       " 'TMMIRROR',\n",
       " 'TSMIRROR',\n",
       " 'TAIR',\n",
       " 'XTEMP',\n",
       " 'HUMIDITY',\n",
       " 'ATMOSBAR',\n",
       " 'WIND',\n",
       " 'WDATE',\n",
       " 'DATE',\n",
       " 'NAMPS',\n",
       " 'CCDNAMPS',\n",
       " 'AMPNAME',\n",
       " 'CREATOR',\n",
       " 'VERSION',\n",
       " 'COMMENT',\n",
       " 'COMMENT',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY',\n",
       " 'HISTORY']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As described above, the HDU (header/data unit) contains header and data. The header is a dictionary. \n",
    "# To see what keywords were used in the header one can do:\n",
    "list(hdulist[0].header.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'107 Psc'"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and to get the value of a given keyword :\n",
    "hdulist[0].header['OBJECT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mType:\u001b[0m           Header\n",
      "\u001b[0;31mString form:\u001b[0m    SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =         <...>\n",
      "\u001b[0;31mLength:\u001b[0m         62\n",
      "\u001b[0;31mFile:\u001b[0m           ~/anaconda3/envs/Python_Lecture/lib/python3.12/site-packages/astropy/io/fits/header.py\n",
      "\u001b[0;31mDocstring:\u001b[0m     \n",
      "FITS header class.  This class exposes both a dict-like interface and a\n",
      "list-like interface to FITS headers.\n",
      "\n",
      "The header may be indexed by keyword and, like a dict, the associated value\n",
      "will be returned.  When the header contains cards with duplicate keywords,\n",
      "only the value of the first card with the given keyword will be returned.\n",
      "It is also possible to use a 2-tuple as the index in the form (keyword,\n",
      "n)--this returns the n-th value with that keyword, in the case where there\n",
      "are duplicate keywords.\n",
      "\n",
      "For example::\n",
      "\n",
      "    >>> header['NAXIS']\n",
      "    0\n",
      "    >>> header[('FOO', 1)]  # Return the value of the second FOO keyword\n",
      "    'foo'\n",
      "\n",
      "The header may also be indexed by card number::\n",
      "\n",
      "    >>> header[0]  # Return the value of the first card in the header\n",
      "    'T'\n",
      "\n",
      "Commentary keywords such as HISTORY and COMMENT are special cases: When\n",
      "indexing the Header object with either 'HISTORY' or 'COMMENT' a list of all\n",
      "the HISTORY/COMMENT values is returned::\n",
      "\n",
      "    >>> header['HISTORY']\n",
      "    This is the first history entry in this header.\n",
      "    This is the second history entry in this header.\n",
      "    ...\n",
      "\n",
      "See the Astropy documentation for more details on working with headers.\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Although FITS keywords must be exclusively upper case, retrieving an item\n",
      "in a `Header` object is case insensitive.\n",
      "\u001b[0;31mInit docstring:\u001b[0m\n",
      "Construct a `Header` from an iterable and/or text file.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "cards : list of `Card`, optional\n",
      "    The cards to initialize the header with. Also allowed are other\n",
      "    `Header` (or `dict`-like) objects.\n",
      "\n",
      "    .. versionchanged:: 1.2\n",
      "        Allowed ``cards`` to be a `dict`-like object.\n",
      "\n",
      "copy : bool, optional\n",
      "\n",
      "    If ``True`` copies the ``cards`` if they were another `Header`\n",
      "    instance.\n",
      "    Default is ``False``.\n",
      "\n",
      "    .. versionadded:: 1.3"
     ]
    }
   ],
   "source": [
    "hh = hdulist[0].header\n",
    "hh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                   16 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                 2154 / length of data axis 1                          \n",
       "NAXIS2  =                 2048 / length of data axis 2                          \n",
       "EXTEND  =                    T                                                  \n",
       "COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy  \n",
       "COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H   \n",
       "BZERO   =                32768 / BZERO                                          \n",
       "BSCALE  =                    1 / BSCALE                                         \n",
       "EXPTIME =                600.0 / Integration Time, sec.                         \n",
       "DETECTOR= 'e2vm2 E2V-4240'     / CCD Type                                       \n",
       "ORIGIN  = 'UNAM    '           / OAN SPM, IA-UNAM                               \n",
       "OBSERVAT= 'SPM     '           / Observatory                                    \n",
       "TELESCOP= '2.12m   '           / Telescope                                      \n",
       "LATITUDE= '+31:02:39'          / Latitude                                       \n",
       "LONGITUD= '115:27:49'          / Longitud                                       \n",
       "ALTITUD =                 2800 / altitud                                        \n",
       "SECONDAR=                   -1 / F/ Secondary type                              \n",
       "TIMEZONE=                    8 / Time Zone                                      \n",
       "OBSERVER= 'Leonid  '           / Observer's Name                                \n",
       "OBJECT  = '107 Psc '           / Object                                         \n",
       "INSTRUME= 'Echelle '           / Instrument                                     \n",
       "GAINMODE=                    1 / Gain factor in the CCD                         \n",
       "FILTER  = 'None    '           / Filter                                         \n",
       "IMGTYPE = 'object  '           / Image Type                                     \n",
       "EQUINOX =               2011.7 / Equinox                                        \n",
       "ST      = '01:25:41.3'         / Sideral Time                                   \n",
       "UT      = '10:34:27'           / Universal Time                                 \n",
       "JD      =            2455803.5 / Julian Date                                    \n",
       "DATE-OBS= '2011-08-30'         / Observation Date UTM                           \n",
       "CCDSUM  = '1 1     '           / Binning [ Cols:Rows ]                          \n",
       "RA      = ' 01:43:10.8'        / Right Ascension                                \n",
       "DEC     = ' 20''19''43.0'      / Declination                                    \n",
       "AH      = ' -00:17:29.1'       / Hour Angle                                     \n",
       "AIRMASS =                 1.02 / Airmass                                        \n",
       "TMMIRROR=                    0 / Primary Mirror Temperature (celsius degree)    \n",
       "TSMIRROR=                    0 / Secundary Mirror Temperature (celsius degree)  \n",
       "TAIR    =                    0 / Internal Telescope Air Temperature (celsius deg\n",
       "XTEMP   =                 14.7 / Exterior Temperature (celsius degree)          \n",
       "HUMIDITY=                 46.0 / % external Humidity                            \n",
       "ATMOSBAR=                731.9 / Atmosferic Presure in mb                       \n",
       "WIND    = 'S at 30.6 km/h'     / Wind Direction                                 \n",
       "WDATE   = '10:34:10, 08/30/11' / Weather Acquisition Date (Local time)          \n",
       "DATE    = '2011-08-30T10:34:29' / file creation date (YYYY-MM-DDThh:mm:ss UT)   \n",
       "NAMPS   =                    1 / Number of Amplifiers                           \n",
       "CCDNAMPS=                    1 / Number of amplifiers used                      \n",
       "AMPNAME = '1 Channel'          / Amplifier name                                 \n",
       "CREATOR = 'Python Oan ccds'    / Name of the software task that created the file\n",
       "VERSION = '4.12D   '           / Application Software Version                   \n",
       "COMMENT Visit our weather site http://www.astrossp.unam.mx/weather15            \n",
       "COMMENT for complete meteorological data of your observation night              \n",
       "HISTORY bin2fits V1.0                                                           \n",
       "HISTORY Programmer: Enrique Colorado [ colorado@astrosen.unam.mx ]              \n",
       "HISTORY Observatorio Astronomico Nacional -UNAM                                 \n",
       "HISTORY V1.00 By Arturo Nunez and Colorado >Ported to Python using pyfits       \n",
       "HISTORY V0.50 By E. Colorado >Added interior mirrors temperatures               \n",
       "HISTORY V0.49 By E. Colorado >Added BIASSEC parameter                           \n",
       "HISTORY V0.48 By E. Colorado >Aditional info for autofocus calculations         \n",
       "HISTORY V0.4 By E. Colorado >Now we include timezone, and remove lat. sign      \n",
       "HISTORY V0.3 By E. Colorado >Now we include weather data                        \n",
       "HISTORY V0.2 By E. Colorado >General OAN Working Release                        "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('SIMPLE', True, 'conforms to FITS standard')\n",
      "('BITPIX', 16, 'array data type')\n",
      "('NAXIS', 2, 'number of array dimensions')\n",
      "('NAXIS1', 2154, 'length of data axis 1')\n",
      "('NAXIS2', 2048, 'length of data axis 2')\n",
      "('EXTEND', True, '')\n",
      "('COMMENT', \"FITS (Flexible Image Transport System) format is defined in 'Astronomy\", '')\n",
      "('COMMENT', \"and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H\", '')\n",
      "('BZERO', 32768, 'BZERO')\n",
      "('BSCALE', 1, 'BSCALE')\n",
      "('EXPTIME', 600.0, 'Integration Time, sec.')\n",
      "('DETECTOR', 'e2vm2 E2V-4240', 'CCD Type')\n",
      "('ORIGIN', 'UNAM', 'OAN SPM, IA-UNAM')\n",
      "('OBSERVAT', 'SPM', 'Observatory')\n",
      "('TELESCOP', '2.12m', 'Telescope')\n",
      "('LATITUDE', '+31:02:39', 'Latitude')\n",
      "('LONGITUD', '115:27:49', 'Longitud')\n",
      "('ALTITUD', 2800, 'altitud')\n",
      "('SECONDAR', -1, 'F/ Secondary type')\n",
      "('TIMEZONE', 8, 'Time Zone')\n",
      "('OBSERVER', 'Leonid', \"Observer's Name\")\n",
      "('OBJECT', '107 Psc', 'Object')\n",
      "('INSTRUME', 'Echelle', 'Instrument')\n",
      "('GAINMODE', 1, 'Gain factor in the CCD')\n",
      "('FILTER', 'None', 'Filter')\n",
      "('IMGTYPE', 'object', 'Image Type')\n",
      "('EQUINOX', 2011.7, 'Equinox')\n",
      "('ST', '01:25:41.3', 'Sideral Time')\n",
      "('UT', '10:34:27', 'Universal Time')\n",
      "('JD', 2455803.5, 'Julian Date')\n",
      "('DATE-OBS', '2011-08-30', 'Observation Date UTM')\n",
      "('CCDSUM', '1 1', 'Binning [ Cols:Rows ]')\n",
      "('RA', ' 01:43:10.8', 'Right Ascension')\n",
      "('DEC', \" 20'19'43.0\", 'Declination')\n",
      "('AH', ' -00:17:29.1', 'Hour Angle')\n",
      "('AIRMASS', 1.02, 'Airmass')\n",
      "('TMMIRROR', 0, 'Primary Mirror Temperature (celsius degree)')\n",
      "('TSMIRROR', 0, 'Secundary Mirror Temperature (celsius degree)')\n",
      "('TAIR', 0, 'Internal Telescope Air Temperature (celsius deg')\n",
      "('XTEMP', 14.7, 'Exterior Temperature (celsius degree)')\n",
      "('HUMIDITY', 46.0, '% external Humidity')\n",
      "('ATMOSBAR', 731.9, 'Atmosferic Presure in mb')\n",
      "('WIND', 'S at 30.6 km/h', 'Wind Direction')\n",
      "('WDATE', '10:34:10, 08/30/11', 'Weather Acquisition Date (Local time)')\n",
      "('DATE', '2011-08-30T10:34:29', 'file creation date (YYYY-MM-DDThh:mm:ss UT)')\n",
      "('NAMPS', 1, 'Number of Amplifiers')\n",
      "('CCDNAMPS', 1, 'Number of amplifiers used')\n",
      "('AMPNAME', '1 Channel', 'Amplifier name')\n",
      "('CREATOR', 'Python Oan ccds', 'Name of the software task that created the file')\n",
      "('VERSION', '4.12D', 'Application Software Version')\n",
      "('COMMENT', 'Visit our weather site http://www.astrossp.unam.mx/weather15', '')\n",
      "('COMMENT', 'for complete meteorological data of your observation night', '')\n",
      "('HISTORY', 'bin2fits V1.0', '')\n",
      "('HISTORY', 'Programmer: Enrique Colorado [ colorado@astrosen.unam.mx ]', '')\n",
      "('HISTORY', 'Observatorio Astronomico Nacional -UNAM', '')\n",
      "('HISTORY', 'V1.00 By Arturo Nunez and Colorado >Ported to Python using pyfits', '')\n",
      "('HISTORY', 'V0.50 By E. Colorado >Added interior mirrors temperatures', '')\n",
      "('HISTORY', 'V0.49 By E. Colorado >Added BIASSEC parameter', '')\n",
      "('HISTORY', 'V0.48 By E. Colorado >Aditional info for autofocus calculations', '')\n",
      "('HISTORY', 'V0.4 By E. Colorado >Now we include timezone, and remove lat. sign', '')\n",
      "('HISTORY', 'V0.3 By E. Colorado >Now we include weather data', '')\n",
      "('HISTORY', 'V0.2 By E. Colorado >General OAN Working Release', '')\n"
     ]
    }
   ],
   "source": [
    "# The header can be printed as it appears in the file by\n",
    "print(hdulist[0].header.cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# The data in the file are accessible with\n",
    "data = hdulist[0].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2048, 2154)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and can be seen with [we need to import matplotlib.pyplot as plt before running this]:\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "f, ax = plt.subplots(figsize=(12,12))\n",
    "ax.imshow(data)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A column from the data can be plotted with \n",
    "plt.plot(data[:,1000])\n",
    "# where I am plotting the column number 1000. \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the same way a line from the data is plotted with: \n",
    "plt.plot(data[1000,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# For this example I'll use a spectrum obtain with the high dispersion camera on board of IUE. \n",
    "# The file is opened as usual:\n",
    "hdulist = fits.open('swp04345.mxhi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file is there: https://github.com/Morisset/Python-lectures-Notebooks/raw/master/Notebooks/swp04345.mxhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#but now hdulist has 2 elements (2 header/data units):\n",
    "len(hdulist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: swp04345.mxhi\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     421   ()      \n",
      "  1  MEHI          1 BinTableHDU     61   60R x 17C   [1B, 1I, 1D, 1I, 1D, 1E, 1E, 768E, 768E, 768E, 768I, 768E, 768E, 1I, 1I, 1E, 7E]   \n"
     ]
    }
   ],
   "source": [
    "# We can see that the primary header has dimension (), son does not contain any data. \n",
    "# The data are in the extension.\n",
    "hdulist.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('SIMPLE', True, 'Standard FITS Format')\n",
      "('BITPIX', 8, 'Binary data')\n",
      "('NAXIS', 0, 'Two-dimensional image')\n",
      "('EXTEND', True, 'Extensions are present')\n",
      "('TELESCOP', 'IUE', 'International Ultraviolet Explorer')\n",
      "('DATE', '16/10/96', 'Date file written')\n",
      "('ORIGIN', 'GSFC', 'Institution generating the file')\n",
      "('COMMENT', '*', '')\n",
      "('COMMENT', '* CORE DATA ITEMS - COMMON SET', '')\n",
      "('COMMENT', '*', '')\n",
      "('CAMERA', 'SWP', 'Camera')\n",
      "('IMAGE', 4345, 'Sequential image number')\n",
      "('DISPERSN', 'HIGH', 'Spectrograph dispersion mode')\n",
      "('APERTURE', 'LARGE', 'Aperture')\n",
      "('DISPTYPE', 'HIGH', 'Dispersion processing type')\n",
      "('READMODE', 'FULL', 'Read mode')\n",
      "('READGAIN', 'LOW', 'Read gain')\n",
      "('EXPOGAIN', 'MAXIMUM', 'Exposure gain')\n",
      "('UVC-VOLT', -5.0, 'UVC voltage')\n",
      "('ABNNOSTD', 'NO', 'Non-standard image acquisition')\n",
      "('ABNBADSC', 'NO', 'LWP bad scans')\n",
      "('ABNHTRWU', 'NO', 'LWR heater warmup')\n",
      "('ABNREAD', 'NO', 'Read at other than 20 KB')\n",
      "('ABNUVC', 'NO', 'Non-standard UVC voltage')\n",
      "('ABNHISTR', 'NO', 'History replay')\n",
      "('ABNOTHER', 'NO', 'Other abnormality')\n",
      "('THDAREAD', 8.8, 'THDA at read of image')\n",
      "('EQUINOX', 1950.0, 'Epoch of coordinates')\n",
      "('STATION', 'GSFC', 'Observing station')\n",
      "('ORBEPOCH', '22/02/79', 'Orbital elements epoch')\n",
      "('ORBSAXIS', 42170.5, 'Semi-major axis in kilometers')\n",
      "('ORBECCEN', 0.2381567, 'Eccentricity')\n",
      "('ORBINCLI', 28.379, 'Inclination in degrees')\n",
      "('ORBASCEN', 199.759, 'Ascending node in degrees')\n",
      "('ORBPERIG', 265.437, 'Argument of perigee in degrees')\n",
      "('ORBANOMA', 337.433, 'Mean anomaly in degrees')\n",
      "('POSANGLE', 30.32, 'Pos angle of the large aperture (deg)')\n",
      "('LAMP', 'NONE', 'Lamp')\n",
      "('PGM-ID', 'LABDS', 'Program identification')\n",
      "('ABNMINFR', 'NO', 'Bad/missing minor frames')\n",
      "('CC-PERCN', 82.7, 'Cross-correlation % successful')\n",
      "('CC-WINDW', 29, 'Cross-correlation window size')\n",
      "('CC-TEMPL', 23, 'Cross-correlation template size')\n",
      "('CC-MEDN', 0.431, 'Median cross-correlation coefficient')\n",
      "('CC-STDEV', 0.145, 'St dev of cross-corr coefficients')\n",
      "('SHFTMEAN', 0.287, 'Mean shift between image and ITF')\n",
      "('SHFTMAX', 1.425, 'Maximum shift between image and ITF')\n",
      "('ITF', 'SWP85R92A', 'ITF identification')\n",
      "('TILTCORR', 'NO', 'Tilt correction flag')\n",
      "('MEANRAT', 0.987, 'SI vs LI mean')\n",
      "('STDEVRAT', 0.9, 'SI vs LI standard deviation')\n",
      "('COMMENT', 'BY RA:  EXP 1 APER L MAX DN = 165', '')\n",
      "('COMMENT', 'BY RA:     0 MISSING MINOR FRAMES NOTED ON SCRIPT', '')\n",
      "('COMMENT', 'BY RA:  EXP 1 TRACKED ON FES', '')\n",
      "('COMMENT', 'BY RA:  S   PREP USED', '')\n",
      "('COMMENT', '*', '')\n",
      "('COMMENT', '* CORE DATA ITEMS - LARGE APERTURE SET', '')\n",
      "('COMMENT', '*', '')\n",
      "('LDATEOBS', '23/02/79', 'Observing date')\n",
      "('LTIMEOBS', '02:49:03', 'Observing time')\n",
      "('LJD-OBS', 2443927.6174, 'Julian Date start of obs.')\n",
      "('LEXPTRMD', 'NO-TRAIL', 'Trail mode')\n",
      "('LEXPMULT', 'NO', 'Multiple exposure mode')\n",
      "('LEXPSEGM', 'NO', 'Segmented exposure code')\n",
      "('LEXPTIME', 10799.793, 'Integration time in seconds')\n",
      "('LTHDASTR', 8.2, 'THDA at start of exposure')\n",
      "('LTHDAEND', 8.8, 'THDA at end of exposure')\n",
      "('LRA', 14.425, 'Homogeneous R.A. in degrees')\n",
      "('LDEC', -72.4333, 'Homogeneous Dec. in degrees')\n",
      "('LLAPSTAT', 'OPEN', 'Large aperture status')\n",
      "('LFES2MD', 'SO', 'FES(2) mode')\n",
      "('LFES2CN', 363, 'FES(2) counts on target')\n",
      "('LTARGET', 'HD 5980', 'Object as given by Guest Observer')\n",
      "('LTARGRA', 14.4417, 'R.A. in degrees (given by GO)')\n",
      "('LTARGDEC', -72.4347, 'Dec. in degrees (given by GO)')\n",
      "('LOBJECT', 'HD  5980', 'Homogeneous Object ID')\n",
      "('LIUECLAS', 11, 'Object class')\n",
      "('LFOCUS', -1.5, 'Focus')\n",
      "('LFPM', 0.08, 'Flux particle monitor')\n",
      "('LGSTAR2X', 307, 'X coordinate of guide star in FES 2')\n",
      "('LGSTAR2Y', -835, 'Y coordinate of guide star in FES 2')\n",
      "('LJD-MID', 2443927.67989, 'Julian Date middle of obs.')\n",
      "('LHELCORR', -0.00228, 'Heliocentric corr to midpoint (days)')\n",
      "('LDATABKG', 67, 'Estimated mean background level (DNs)')\n",
      "('LDATACNT', 150, 'Estimated maximum continuum level (DNs)')\n",
      "('LCNTRAPR', 290.74, 'Predicted center line of spectrum')\n",
      "('LXTRMODE', 'POINT', 'Extraction mode')\n",
      "('LXTRCNTR', 290.7, 'Center line of extracted spectrum')\n",
      "('LRADVELO', 2.67, 'Heliocentric velocity correction in km/s')\n",
      "('COMMENT', '*', '')\n",
      "('COMMENT', '* THE IUE VICAR HEADER', '')\n",
      "('COMMENT', '*', '')\n",
      "('COMMENT', 'IUE-VICAR HEADER START', '')\n",
      "('', '                        0001000107680768   1 1 013004345            1  C', '')\n",
      "('', '      *    *        *   *   *10,800*      *   *  * * * * * *     *  2  C', '')\n",
      "('', 'SWP4345,HD5980,180M,LGAP,HI,DAY54,1979,LABDS,SITKO                  3  C', '')\n",
      "('', '                                                                    4  C', '')\n",
      "('', '                        10799 SECS                                  5  C', '')\n",
      "('', '                                                                    6  C', '')\n",
      "('', '                                                                    7  C', '')\n",
      "('', '                                                                    8  C', '')\n",
      "('', '                                                                    9  C', '')\n",
      "('', '79 54055056* 8   * 215 *OPSTST23*133019 CAMINIT                  * 10  C', '')\n",
      "('', '                                *133019   1178 3830 4337 1149    * 11  C', '')\n",
      "('', '                                *                                * 12  C', '')\n",
      "('', '                                *                                * 13  C', '')\n",
      "('', '                                *                                * 14  C', '')\n",
      "('', '200834 XPREP 2                  *202631 SPREP 2                  * 15  C', '')\n",
      "('', '015359 EXPOBCM 2 12 0  MAXG NOL *                                * 16  C', '')\n",
      "('', '020605 EXPOSURE END TIME        *                                * 17  C', '')\n",
      "('', '                                *                                * 18  C', '')\n",
      "('', '025850 READPREP 2       NORMAL  *025923 SCAN 2  RDLO SS 1 G3 58  * 19  C', '')\n",
      "('', '025925 X 56 Y 72 G1 99 HR 106   *165808 SPREP 3                  * 20  C', '')\n",
      "('', '024907 EXPOBCM 3 180 0 MAXG NOL *                                * 21  C', '')\n",
      "('', '054910 EXPOSURE END TIME        *                                * 22  C', '')\n",
      "('', '                                *                                * 23  C', '')\n",
      "('', '054953 REREAD 3 3       NORMAL  *055055 SCAN 3  RDLO SS 1 G3 44  * 24  C', '')\n",
      "('', '055056 X 60 Y 76 G1 82 HR 105   *                                * 25  C', '')\n",
      "('', '                                *020838 MODE SWH                 * 26  C', '')\n",
      "('', '023438 APERTURE OP              *                                * 27  C', '')\n",
      "('', '                                *                                * 28  C', '')\n",
      "('', '                                *                                * 29  C', '')\n",
      "('', '                                *024716TARGET IN SWLA            * 30  C', '')\n",
      "('', '015155TARGET FROM SWLA          *                                * 31  C', '')\n",
      "('', '                                *                                * 32  C', '')\n",
      "('', '                                                                   33  C', '')\n",
      "('', '                                                                   34  C', '')\n",
      "('', '                                                                   35  C', '')\n",
      "('', 'LABDS* *  *SAVAGE          *   *   * *        * *0* *              36  C', '')\n",
      "('', '005745*-722603*   *OB* *11.8*+0.04*         *    *       *   *     37  C', '')\n",
      "('', '                                                                   38  C', '')\n",
      "('', '                                                                   39  C', '')\n",
      "('', '                                                                   40  C', '')\n",
      "('', '                                                                   41  C', '')\n",
      "('', '                                                                   42  C', '')\n",
      "('', '                                                                   43  C', '')\n",
      "('', '                                                                   44  C', '')\n",
      "('', '                                                                   45  C', '')\n",
      "('', '                                                                   46  C', '')\n",
      "('', '                                                                   47  C', '')\n",
      "('', '                                                                   48  C', '')\n",
      "('', '                                                                   49  C', '')\n",
      "('', '                                                                   50  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 51  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 51  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 52  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 52  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 53  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 53  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 54  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 54  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 55  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 55  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 56  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 56  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 57  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 57  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 58  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 58  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 59  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 59  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 60  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 60  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 61  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 61  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 62  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 62  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 63  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 63  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 64  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 64  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 65  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 65  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 66  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 66  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 67  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 67  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 68  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 68  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 69  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 69  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 70  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 70  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 71  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 71  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 72  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 72  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 73  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 73  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 74  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 74  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000000000 75  C', '')\n",
      "('', '000000000000000000000000000000000000000000000000000000000000004040 75  C', '')\n",
      "('', '0231F4FACC000000A6737B00C50E003AC80D31444B2E6827253D4540321E314B62 76  C', '')\n",
      "('', '27003D87F306060B6E70005E8085320D2C2A13282C1A3211151200A5ACAB000000 76  C', '')\n",
      "('', 'AC0400FF000436B9A5C100AF52B312341E2B33352C16182D2E1A1F181C202D2B34 77  C', '')\n",
      "('', '70717A247D247C227B1525A01E9320941F951E96219301B8E10000E02CE0F90C40 77  C', '')\n",
      "('', '7C007C0083007A7B7B7B7C7C3A8A0082453C3D314A7A93944EC989987D7D824300 78  C', '')\n",
      "('', '003A30333539302F2E3234322B2B000006C1F65682820001008282404040404040 78  C', '')\n",
      "('', 'BBAB06C2F255838100010080803B3535333A343030353735323286B784B941B76D 79  C', '')\n",
      "('', 'B96BB72DB978B778B931B77AB97BB744B940404040404040404040404040404040 79  C', '')\n",
      "('', 'D0000080000000000000FF40404040404040404040404040404040404040404040 80  C', '')\n",
      "('', '404040404040404040404040404040404040404040404040404040404040404040 80  C', '')\n",
      "('', '0FFBFFFF000000000203FE025046436BFB30000000000000D00000000000000080 81  C', '')\n",
      "('', '00000000000000FF000000880000000C0000000C00000000000000000000004040 81  C', '')\n",
      "('', '4F25600000000000FF000000FF0000000000000000000000000000000000000000 82  C', '')\n",
      "('', '0000007F000000FF00000000000000FF000000FF00000000000000000000004040 82  C', '')\n",
      "('', '                                                                   83  C', '')\n",
      "('', '                                                                   84  C', '')\n",
      "('', '                                                                   85  C', '')\n",
      "('', '03060000001C00000021879BE02CDFE1000004000016909E4ABAAB06C1F2558381 86  C', '')\n",
      "('', '00010080807BC978C931C9000061414151000040404000400F0D0E404040404040 86  C', '')\n",
      "('', '030D0000001C0000002186A3E02CE0E1000004000016909E4ABAAB06C1F2558381 87  C', '')\n",
      "('', '00010080807AC977C931C9000061414151000040404000401F0D0E404040404040 87  C', '')\n",
      "('', '030E0000001C00FFFFEF86A8E02CDFE1000004000016809E4ABAAB06C1F2558381 88  C', '')\n",
      "('', '00010080807BC978C931C900006141514200004040400040290D0E404040404040 88  C', '')\n",
      "('', '01360000001E00FFFFF68A9DE02CE0E1000004000016809E52000006C1F2558381 89  C', '')\n",
      "('', '0001007F80797F777A317900006140414100004040400040080B0C404040404040 89  C', '')\n",
      "('', '020500000019000000338AA0E02CE0E1000004000016809E52000006C2F2558381 90  C', '')\n",
      "('', '0001007F807A7F787A317900006140414100004040400040050B0C404040404040 90  C', '')\n",
      "('', '023B00FFFFFE00FFFFF986A5E02DE0E1000004000010809E4ABAAB06C1F2558381 91  C', '')\n",
      "('', '000100807F7AC978CA31C900006141414A00004040400040190D0E404040404040 91  C', '')\n",
      "('', '023B00FFFFF40000000C86A7E02DE0E1000004000010809E4ABBAB06C1F2558381 92  C', '')\n",
      "('', '000100807F7AC978CA31C900006141514A00004040400040260D0E404040404040 92  C', '')\n",
      "('', '02110000002300FFFFED89A5E02CE0E1000004000006909E4C3B3535333A343030 93  C', '')\n",
      "('', '35373532327AC97BCA44C9000061514140000040404000401D0D0E404040404040 93  C', '')\n",
      "('', '021A0000002300FFFFED89ACE02CE0E1000004000006909E4C3B3435333A34302F 94  C', '')\n",
      "('', '35363532327AC97BC944C900006151414000004040400040160D0E404040404040 94  C', '')\n",
      "('', '021B0000002300FFFFED88A8DF2DE0E1000004000004809E4C3B3837353C353232 95  C', '')\n",
      "('', '36383735357AC97BC944C9000071424140000040404000401F0D0E404040404040 95  C', '')\n",
      "('', '02310000002300FFFFED869CE02CE0E1000004000002809E4A3B3535333A343030 96  C', '')\n",
      "('', '35373532327AB97BB744B9000061414140000040404000400C0B0C404040404040 96  C', '')\n",
      "('', '053100FFFFF40000000C87A3E02CE0E1000006000016809E4A3A3335333A342F2F 97  C', '')\n",
      "('', '35373531327AC77AC748C700006140414000004040400040040D0E404040404040 97  C', '')\n",
      "('', '053300FFFFF40000000C87A9E02DE0E2000005000016809E4A3C3736343C363232 98  C', '')\n",
      "('', '36383735347AC97AC948C90000614A414000004040400040060D0E404040404040 98  C', '')\n",
      "('', '053300FFFFF40000000C87A1E02CE0E100000E000016809E4A3C3837353C373332 99  C', '')\n",
      "('', '37383735357ACA7AC948C90000714A414000004040400040250D0E404040404040 99  C', '')\n",
      "('', '404040404040404040404040404040404040404040404040404040404040404040100  C', '')\n",
      "('', '404040404040404040404040404040404040404040404040404040404040404040100  C', '')\n",
      "('', '****** SCHEME NAME: F3HLAC. ****                                       C', '')\n",
      "('', \"*ARCHIVE   12:00Z MAR 18,'80                                          HL\", '')\n",
      "('COMMENT', 'IUE-VICAR HEADER END', '')\n",
      "('HISTORY', 'IUE-LOG STARTED                             16-OCT-1996 00:18:27', '')\n",
      "('HISTORY', 'PROCESSING SYSTEM: NEWSIPS VERSION 3.1_A', '')\n",
      "('HISTORY', 'OPEN VMS VERSION', '')\n",
      "('HISTORY', 'SWP04345', '')\n",
      "('HISTORY', 'PROCESSED AT GODDARD SPACE FLIGHT CENTER', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START RAW_SCREEN                            16-OCT-1996 00:18:37', '')\n",
      "('HISTORY', '  39 BRIGHT SPOTS DETECTED', '')\n",
      "('HISTORY', '   0 MISSING MINOR FRAMES DETECTED', '')\n",
      "('HISTORY', 'LARGE APERTURE SPECTRUM WILL BE EXTRACTED AS', '')\n",
      "('HISTORY', '          POINT SOURCE', '')\n",
      "('HISTORY', 'LARGE APERTURE CONTINUUM DN LEVEL = 150', '')\n",
      "('HISTORY', 'BACKGROUND DN LEVEL =  67', '')\n",
      "('HISTORY', 'ORDER REGISTRATION', '')\n",
      "('HISTORY', '  GLOBAL OFFSET  0.78 PIXELS RELATIVE TO FIDUCIAL: SWP 13589', '')\n",
      "('HISTORY', '  RELATIVE ORDER LOCATIONS DETERMINED FROM EMPIRICAL POSITIONS', '')\n",
      "('HISTORY', 'END   RAW_SCREEN                            16-OCT-1996 00:19:03', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START TTDC                                  16-OCT-1996 00:19:07', '')\n",
      "('HISTORY', 'TEMPERATURE USED FOR CORRECTING DISPERSION CONSTANTS =  8.80', '')\n",
      "('HISTORY', 'DATE OF OBSERVATION USED FOR CORRECTING', '')\n",
      "('HISTORY', '          DISPERSION CONSTANTS = 23/ 2/79  02:49:03', '')\n",
      "('HISTORY', 'ORDER  66 ZERO-POINT CORRECTION =   0.115 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  67 ZERO-POINT CORRECTION =   0.119 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  68 ZERO-POINT CORRECTION =   0.115 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  69 ZERO-POINT CORRECTION =   0.115 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  70 ZERO-POINT CORRECTION =   0.110 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  71 ZERO-POINT CORRECTION =   0.110 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  72 ZERO-POINT CORRECTION =   0.110 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  73 ZERO-POINT CORRECTION =   0.111 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  74 ZERO-POINT CORRECTION =   0.112 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  75 ZERO-POINT CORRECTION =   0.109 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  76 ZERO-POINT CORRECTION =   0.108 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  77 ZERO-POINT CORRECTION =   0.105 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  78 ZERO-POINT CORRECTION =   0.103 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  79 ZERO-POINT CORRECTION =   0.106 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  80 ZERO-POINT CORRECTION =   0.103 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  81 ZERO-POINT CORRECTION =   0.101 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  82 ZERO-POINT CORRECTION =   0.101 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  83 ZERO-POINT CORRECTION =   0.101 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  84 ZERO-POINT CORRECTION =   0.101 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  85 ZERO-POINT CORRECTION =   0.099 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  86 ZERO-POINT CORRECTION =   0.100 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  87 ZERO-POINT CORRECTION =   0.091 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  88 ZERO-POINT CORRECTION =   0.096 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  89 ZERO-POINT CORRECTION =   0.095 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  90 ZERO-POINT CORRECTION =   0.096 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  91 ZERO-POINT CORRECTION =   0.094 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  92 ZERO-POINT CORRECTION =   0.092 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  93 ZERO-POINT CORRECTION =   0.089 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  94 ZERO-POINT CORRECTION =   0.093 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  95 ZERO-POINT CORRECTION =   0.096 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  96 ZERO-POINT CORRECTION =   0.089 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  97 ZERO-POINT CORRECTION =   0.089 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  98 ZERO-POINT CORRECTION =   0.087 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER  99 ZERO-POINT CORRECTION =   0.083 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 100 ZERO-POINT CORRECTION =   0.087 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 101 ZERO-POINT CORRECTION =   0.083 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 102 ZERO-POINT CORRECTION =   0.076 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 103 ZERO-POINT CORRECTION =   0.074 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 104 ZERO-POINT CORRECTION =   0.072 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 105 ZERO-POINT CORRECTION =   0.071 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 106 ZERO-POINT CORRECTION =   0.069 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 107 ZERO-POINT CORRECTION =   0.067 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 108 ZERO-POINT CORRECTION =   0.065 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 109 ZERO-POINT CORRECTION =   0.063 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 110 ZERO-POINT CORRECTION =   0.061 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 111 ZERO-POINT CORRECTION =   0.059 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 112 ZERO-POINT CORRECTION =   0.057 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 113 ZERO-POINT CORRECTION =   0.055 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 114 ZERO-POINT CORRECTION =   0.052 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 115 ZERO-POINT CORRECTION =   0.050 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 116 ZERO-POINT CORRECTION =   0.048 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 117 ZERO-POINT CORRECTION =   0.045 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 118 ZERO-POINT CORRECTION =   0.043 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 119 ZERO-POINT CORRECTION =   0.040 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 120 ZERO-POINT CORRECTION =   0.038 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 121 ZERO-POINT CORRECTION =   0.035 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 122 ZERO-POINT CORRECTION =   0.032 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 123 ZERO-POINT CORRECTION =   0.030 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 124 ZERO-POINT CORRECTION =   0.027 ANGSTROMS', '')\n",
      "('HISTORY', 'ORDER 125 ZERO-POINT CORRECTION =   0.024 ANGSTROMS', '')\n",
      "('HISTORY', '', '')\n",
      "('HISTORY', 'SPACECRAFT VELOCITY:', '')\n",
      "('HISTORY', 'X= -2.86     Y= -2.42     Z=  0.71', '')\n",
      "('HISTORY', 'EARTH VELOCITY:', '')\n",
      "('HISTORY', 'X=-13.59     Y=-24.64     Z=-10.69', '')\n",
      "('HISTORY', 'NET CORRECTION VECTOR TO HELIOCENTRIC VELOCITY:', '')\n",
      "('HISTORY', 'X=-16.46     Y=-27.06     Z= -9.98', '')\n",
      "('HISTORY', 'HELIOCENTRIC VELOCITY CORRECTION:  +2.67  KM/S', '')\n",
      "('HISTORY', 'END   TTDC                                  16-OCT-1996 00:19:10', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START CROSS-CORR                            16-OCT-1996 00:19:14', '')\n",
      "('HISTORY', 'WINDOW SIZE USED:    29 X  29 PIXELS', '')\n",
      "('HISTORY', 'TEMPLATE SIZE USED:  23 X  23 PIXELS', '')\n",
      "('HISTORY', 'ITF USED:  SWP85R92A', '')\n",
      "('HISTORY', '  82.7 PERCENT SUCCESSFUL CORRELATIONS (422 OUT OF 510)', '')\n",
      "('HISTORY', 'MEDIAN CORRELATION COEFFICIENT: 0.431', '')\n",
      "('HISTORY', 'STANDARD DEVIATION OF CORRELATION COEFFICIENT: 0.145', '')\n",
      "('HISTORY', 'MEAN SHIFT IN PIXELS: 0.287', '')\n",
      "('HISTORY', 'MAXIMUM SHIFT IN PIXELS: 1.425', '')\n",
      "('HISTORY', 'NUMBER OF SUCCESSFUL SHIFTS FILTERED AS UNRELIABLE IN', '')\n",
      "('HISTORY', '    POST-FILTER ROUTINE:  11', '')\n",
      "('HISTORY', 'END   CROSS-CORR                            16-OCT-1996 00:19:59', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START PHOTOM                                16-OCT-1996 00:20:08', '')\n",
      "('HISTORY', 'ITF USED: SWP85R92A', '')\n",
      "('HISTORY', 'MEAN TEMPERATURE OF ITF:  9.3 C', '')\n",
      "('HISTORY', 'ITF UVC=-5.0 KV; UVFLOOD WAVELENGTH = 2536 A; ITF SEC =-6.1 KV', '')\n",
      "('HISTORY', 'ITF CONSTRUCTION: RAW SPACE, FOURIER FILTERED; JAN92', '')\n",
      "('HISTORY', 'END   PHOTOM                                16-OCT-1996 00:21:43', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START GEOM                                  16-OCT-1996 00:21:45', '')\n",
      "('HISTORY', ' EARLY EPOCH ORDER SPATIAL DEVIATION CORRECTION APPLIED', '')\n",
      "('HISTORY', ' DE-SPLAYING ANGLE OF -0.29E-04 RADIANS', '')\n",
      "('HISTORY', ' PREDICTED CENTER LINE OF ORDER 100 - LINE 290.74', '')\n",
      "('HISTORY', 'END   GEOM                                  16-OCT-1996 00:30:51', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START COSMIC_RAY                            16-OCT-1996 00:31:00', '')\n",
      "('HISTORY', ' MEAN FN VALUE OF INTERORDER BACKGROUND =   46.907', '')\n",
      "('HISTORY', ' 11968 PIXELS GREATER THAN 1.500   SIGMA FLAGGED IN', '')\n",
      "('HISTORY', ' COSMIC_RAY IMAGE', '')\n",
      "('HISTORY', 'END   COSMIC_RAY                            16-OCT-1996 00:31:13', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START BCKGRD                                16-OCT-1996 00:31:15', '')\n",
      "('HISTORY', ' INTERORDER POINTS IDENTIFIED FOR POINT    SOURCE', '')\n",
      "('HISTORY', ' GLOBAL BACKGROUND DETERMINATION SUCCESSFUL', '')\n",
      "('HISTORY', '    NORMAL GRID INTERPOLATION', '')\n",
      "('HISTORY', 'END   BCKGRD                                16-OCT-1996 00:32:11', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START EXTRACT                               16-OCT-1996 00:32:14', '')\n",
      "('HISTORY', 'BOXCAR EXTRACTION', '')\n",
      "('HISTORY', 'NOISE MODEL USED: SWP VERSION 1.0', '')\n",
      "('HISTORY', '', '')\n",
      "('HISTORY', '**********************LARGE APERTURE DATA***********************', '')\n",
      "('HISTORY', '', '')\n",
      "('HISTORY', 'MEAN SLIT HEIGHT FOR LARGE APERTURE POINT SOURCE USED FOR EACH ORDER', '')\n",
      "('HISTORY', ' ORDER 100 FOUND AT LINE 290.68', '')\n",
      "('HISTORY', '*** WARNING: ORDER 113 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 114 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 119 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 120 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 121 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 122 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 123 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 124 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '*** WARNING: ORDER 125 EXPLICIT CENTROID DETERMINATION INVALID.', '')\n",
      "('HISTORY', '             FIDUCIAL CENTROID USED.', '')\n",
      "('HISTORY', '', '')\n",
      "('HISTORY', ' SWP RIPPLE CORRECTION VERSION 2.0 APPLIED.', '')\n",
      "('HISTORY', ' ABSOLUTE FLUX CALIBRATION DERIVED FROM LOW DISPERSION FLUX', '')\n",
      "('HISTORY', ' CALIBRATION.', '')\n",
      "('HISTORY', ' ABSOLUTE FLUX CALIBRATION SWP VERSION 1.2 APPLIED USING:', '')\n",
      "('HISTORY', '      MODE = LARGE APERTURE POINT SOURCE', '')\n",
      "('HISTORY', '      CALIBRATION EPOCH = 1985.00', '')\n",
      "('HISTORY', '      CAMERA RISE TIME = 0.130 SECONDS', '')\n",
      "('HISTORY', '      EFFECTIVE EXPOSURE TIME = 10799.793 SECONDS', '')\n",
      "('HISTORY', ' TEMPERATURE-DEPENDENT SENSITIVITY CORRECTION APPLIED USING:', '')\n",
      "('HISTORY', '      THDA OF IMAGE =  8.80', '')\n",
      "('HISTORY', '      REFERENCE THDA =  9.40', '')\n",
      "('HISTORY', '      TEMPERATURE COEFFICIENT = -0.0046', '')\n",
      "('HISTORY', '      TEMPERATURE CORRECTION FACTOR =   0.997', '')\n",
      "('HISTORY', ' SENSITIVITY DEGRADATION CORRECTION SWP VERSION 1.0 APPLIED USING:', '')\n",
      "('HISTORY', '      MODE = LARGE APERTURE POINT SOURCE', '')\n",
      "('HISTORY', '      CALIBRATION EPOCH = 1985.00', '')\n",
      "('HISTORY', '      OBSERVATION DATE = 1979.148', '')\n",
      "('HISTORY', 'END   EXTRACT                               16-OCT-1996 00:32:36', '')\n",
      "('HISTORY', '****************************************************************', '')\n",
      "('HISTORY', 'START FITSCOPY                              16-OCT-1996 00:32:39', '')\n"
     ]
    }
   ],
   "source": [
    "# The first header contains the minimal infirmation:\n",
    "print(hdulist[0].header.cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('XTENSION', 'BINTABLE', 'Binary table extension')\n",
      "('BITPIX', 8, 'Binary data')\n",
      "('NAXIS', 2, 'Two-dimensional table array')\n",
      "('NAXIS1', 16961, 'Width of row in bytes')\n",
      "('NAXIS2', 60, 'Number of orders')\n"
     ]
    }
   ],
   "source": [
    "# The number of axis is 0 which means there is no data block in the primary HDU. \n",
    "# The header of the second HDU begins with the keyword XTENSION and with the specification of the data\n",
    "print(hdulist[1].header.cards[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# To progress further we need to know what is in the table. \n",
    "# As usual, the columns have names and type of the stored data. \n",
    "# These information can be obtained using the column attribute of hdulist:\n",
    "cols = hdulist[1].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method ColDefs.info of ColDefs(\n",
       "    name = 'ORDER'; format = '1B'\n",
       "    name = 'NPOINTS'; format = '1I'\n",
       "    name = 'WAVELENGTH'; format = '1D'; unit = 'ANGSTROM'\n",
       "    name = 'STARTPIX'; format = '1I'; unit = 'PIXEL'\n",
       "    name = 'DELTAW'; format = '1D'; unit = 'ANGSTROM'\n",
       "    name = 'SLIT HEIGHT'; format = '1E'; unit = 'PIXEL'\n",
       "    name = 'LINE_FOUND'; format = '1E'; unit = 'PIXEL'\n",
       "    name = 'NET'; format = '768E'; unit = 'FN'\n",
       "    name = 'BACKGROUND'; format = '768E'; unit = 'FN'\n",
       "    name = 'NOISE'; format = '768E'; unit = 'FN'\n",
       "    name = 'QUALITY'; format = '768I'\n",
       "    name = 'RIPPLE'; format = '768E'; unit = 'FN'\n",
       "    name = 'ABS_CAL'; format = '768E'; unit = 'ERG/CM2/S/A'\n",
       "    name = 'START-BKG'; format = '1I'; unit = 'PIXEL'\n",
       "    name = 'END-BKG'; format = '1I'; unit = 'PIXEL'\n",
       "    name = 'SCALE_BKG'; format = '1E'\n",
       "    name = 'COEFF'; format = '7E'\n",
       ")>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the cols.info returns the names of the columns and the information of their format and units.\n",
    "cols.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGsCAYAAADQat0+AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAaoZJREFUeJzt3Xl8E3X+P/DXpGmSlraBAr1sgYLcN4jcgnIreCuuWo8FFBEVWV3FVRfd/Yr4U0RE8UIRZQFdQHFFsMgt5W6576scLYVC7zbXzO+P0CTT3G3SpOnr+XjkAZl8ZuYz02TmPZ9TkCRJAhEREVEIUQQ6A0RERES+xgCHiIiIQg4DHCIiIgo5DHCIiIgo5DDAISIiopDDAIeIiIhCDgMcIiIiCjkMcIiIiCjkMMAhIiKikMMAh4iIiEJOvQ9wNm3ahDFjxiApKQmCIOCnn34K+P4kScL06dORlJSEiIgIDB48GAcPHvRrvoiIiEJJvQ9wSktL0bVrV8ydOzdo9vfee+9h1qxZmDt3Lnbu3ImEhAQMGzYMxcXFtZJHIiKiuk7gZJtWgiBgxYoVuPvuuy3L9Ho9Xn/9dSxatAgFBQXo1KkTZs6cicGDB/tlf5IkISkpCVOmTMErr7wCANDpdIiPj8fMmTPx9NNP13i/REREoa7el+C48+STT+LPP//EkiVLsG/fPjzwwAMYOXIkjh8/7pf9nT59Grm5uRg+fLhlmVqtxqBBg7B161a/7JOIiCjUMMBx4eTJk1i8eDF+/PFHDBw4EK1atcJLL72EAQMG4JtvvvHLPnNzcwEA8fHxsuXx8fGWz4iIiMg1Bjgu7NmzB5IkoU2bNoiKirK8Nm7ciJMnTwIAzpw5A0EQXL4mT57s9b4FQZC9lyTJbhkRERE5pgx0BoKZKIoICwvD7t27ERYWJvssKioKAHDDDTfg8OHDLrfTqFEjj/eZkJAAwFySk5iYaFmel5dnV6pDREREjjHAcaF79+4wmUzIy8vDwIEDHaYJDw9Hu3btfLbP1NRUJCQkID09Hd27dwdgbui8ceNGzJw502f7ISIiCmX1PsApKSnBiRMnLO9Pnz6NrKwsxMbGok2bNnjkkUfw2GOP4YMPPkD37t1x5coVrFu3Dp07d8btt9/u0/01a9YMgiBgypQpeOedd9C6dWu0bt0a77zzDiIjI/Hwww/75JiJiIhCXb3vJr5hwwbceuutdssff/xxLFiwAAaDAf/+97+xcOFCXLhwAY0bN0bfvn3x1ltvoXPnzj7fH2Bub/PWW2/h888/x7Vr19C7d2988skn6NSpk9f7IyIiqo/qfYBDREREoYe9qIiIiCjkMMAhIiKikFMvGxmLooiLFy8iOjqaY8sQERHVEZIkobi4GElJSVAoXJfR1MsA5+LFi0hJSQl0NoiIiKgazp07h+TkZJdp6mWAEx0dDcB8gmJiYgKcGyIiIvJEUVERUlJSLPdxV+plgFNZLRUTE8MAh4iIqI7xpHkJGxkTERFRyGGAQ0RERCGHAQ4RERGFHAY4REREFHIY4BAREVHIYYBDREREIYcBDhEREYUcBjhEREQUcvwa4GzatAljxoxBUlISBEHATz/95DL98uXLMWzYMDRt2hQxMTHo27cv1qxZI0uzYMECCIJg96qoqPDjkRAREVFd4tcAp7S0FF27dsXcuXM9Sr9p0yYMGzYMq1atwu7du3HrrbdizJgxyMzMlKWLiYlBTk6O7KXRaPxxCERERFQH+XWqhlGjRmHUqFEep589e7bs/TvvvIOff/4Zv/zyC7p3725ZLggCEhISfJVNIiIiCjFB3QZHFEUUFxcjNjZWtrykpATNmzdHcnIyRo8ebVfCU5VOp0NRUZHsRURERKErqAOcDz74AKWlpXjwwQcty9q1a4cFCxZg5cqVWLx4MTQaDfr374/jx4873c6MGTOg1Wotr5SUlNrIPhHVE5/v/Rz/2vavQGeDiGwIkiRJtbIjQcCKFStw9913e5R+8eLFGD9+PH7++WcMHTrUaTpRFNGjRw/ccsstmDNnjsM0Op0OOp3O8r5yuvXCwkLOJk5ENdb5284AgG9GfIObEm4KcG6IQldRURG0Wq1H92+/tsGprqVLl2LcuHH48ccfXQY3AKBQKNCrVy+XJThqtRpqtdrX2SQikskuzmaAQxQkgq6KavHixXjiiSfwn//8B3fccYfb9JIkISsrC4mJibWQOyIiIqoL/FqCU1JSghMnTljenz59GllZWYiNjUWzZs0wbdo0XLhwAQsXLgRgDm4ee+wxfPTRR+jTpw9yc3MBABEREdBqtQCAt956C3369EHr1q1RVFSEOXPmICsrC5988ok/D4WIiIjqEL+W4OzatQvdu3e3dPGeOnUqunfvjjfffBMAkJOTg+zsbEv6zz//HEajEc8++ywSExMtrxdeeMGSpqCgAE899RTat2+P4cOH48KFC9i0aRNuvvlmfx4KERER1SG11sg4mHjTSImIyJ3KRsZv9XsL97a+N8C5IQpd3ty/g64NDhEREVFNMcAhIiKikMMAh4iIiEIOAxwiIiIKOQxwiIiIKOQwwCEiIqKQwwCHiIiIQg4DHCIiIgo5DHCIiHykHo6bShS0GOAQERFRyGGAQ0RERCGHAQ4RkY8IghDoLBDRdQxwiIiIKOQwwCEiIqKQwwCHiIiIQg4DHCIiIgo5DHCIiIgo5DDAISIiopDDAIeIiIhCDgMcIiIiCjkMcIiIiCjkMMAhIvIRTrZJFDwY4BAREVHIYYBDREREIYcBDhEREYUcBjhEREQUchjgEBH5iCAIgc4CEV3HAIeIiIhCDgMcIiIiCjkMcIiIiCjkMMAhIiKikMMAh4iIiEIOAxwiIiIKOQxwiIiIKOT4NcDZtGkTxowZg6SkJAiCgJ9++sntOhs3bkTPnj2h0WjQsmVLfPbZZ3Zpli1bhg4dOkCtVqNDhw5YsWKFH3JPREREdZVfA5zS0lJ07doVc+fO9Sj96dOncfvtt2PgwIHIzMzEa6+9hueffx7Lli2zpMnIyMDYsWORlpaGvXv3Ii0tDQ8++CC2b9/ur8MgIvIIZxMnCh6CVEu/SEEQsGLFCtx9991O07zyyitYuXIlDh8+bFk2ceJE7N27FxkZGQCAsWPHoqioCL/99pslzciRI9GoUSMsXrzYo7wUFRVBq9WisLAQMTEx1TsgIqLrOn/bGQAwve903NfmvgDnhih0eXP/Dqo2OBkZGRg+fLhs2YgRI7Br1y4YDAaXabZu3ep0uzqdDkVFRbIXERERha6gCnByc3MRHx8vWxYfHw+j0YgrV664TJObm+t0uzNmzIBWq7W8UlJSfJ95IiIiChpBFeAA9pPVVdag2S53lMbVJHfTpk1DYWGh5XXu3Dkf5piIiIiCjTLQGbCVkJBgVxKTl5cHpVKJxo0bu0xTtVTHllqthlqt9n2GiYiIKCgFVQlO3759kZ6eLlv2+++/46abbkJ4eLjLNP369au1fBIREVFw82sJTklJCU6cOGF5f/r0aWRlZSE2NhbNmjXDtGnTcOHCBSxcuBCAucfU3LlzMXXqVEyYMAEZGRmYP3++rHfUCy+8gFtuuQUzZ87EXXfdhZ9//hlr167Fli1b/HkoRERuuaoqJ6La5dcSnF27dqF79+7o3r07AGDq1Kno3r073nzzTQBATk4OsrOzLelTU1OxatUqbNiwAd26dcO//vUvzJkzB/fdZ+122a9fPyxZsgTffPMNunTpggULFmDp0qXo3bu3Pw+FiIiI6pBaGwcnmHAcHCLypcpxcN7q9xbubX1vgHNDFLrq7Dg4RERERL7AAIeIiIhCDgMcIiIiCjkMcIiIfKQeNmkkCloMcIiIiCjkMMAhIiKikMMAh4iIiEIOAxwiIiIKOQxwiIiIKOQwwCEiIqKQwwCHiIiIQg4DHCIiIgo5DHCIiIgo5DDAISLyEUEQAp0FIrqOAQ4RERGFHAY45DOn7robxwffClEUA50VIiKq5xjgkM/ojh6FMTcXJelrA50VIiKq5xjgkM9JJmOgs0AUEJxNnCh4MMAhIiKikMMAh4iIiEIOAxwiIiIKOQxwyPc4FggREQUYAxwiIiIKOQxwiIiIKOQwwCEiIqKQwwCHfE5Q8GtFRESBxTsRERERhRwGOERERBRyGOAQERFRyGGAQz7BGcSJAIFjQBEFDQY45Ae8yFP9xMk2iYIHAxzyPQUDHCIiCiwGOERERBRyGOCQb7ANDhERBREGOORzgsCvFRERBVat3Ik+/fRTpKamQqPRoGfPnti8ebPTtE888QQEQbB7dezY0ZJmwYIFDtNUVFTUxuEQERFRkPN7gLN06VJMmTIF//jHP5CZmYmBAwdi1KhRyM7Odpj+o48+Qk5OjuV17tw5xMbG4oEHHpCli4mJkaXLycmBRqPx9+EQERFRHeD3AGfWrFkYN24cxo8fj/bt22P27NlISUnBvHnzHKbXarVISEiwvHbt2oVr167hySeflKUTBEGWLiEhwd+HQkRERHWEXwMcvV6P3bt3Y/jw4bLlw4cPx9atWz3axvz58zF06FA0b95ctrykpATNmzdHcnIyRo8ejczMTKfb0Ol0KCoqkr3Ix2wbGbObOBERBZhfA5wrV67AZDIhPj5etjw+Ph65ublu18/JycFvv/2G8ePHy5a3a9cOCxYswMqVK7F48WJoNBr0798fx48fd7idGTNmQKvVWl4pKSnVPygiIiIKerXSyLjq8OWSJHk0pPmCBQvQsGFD3H333bLlffr0waOPPoquXbti4MCB+OGHH9CmTRt8/PHHDrczbdo0FBYWWl7nzp2r9rEQERFR8FP6c+NNmjRBWFiYXWlNXl6eXalOVZIk4euvv0ZaWhpUKpXLtAqFAr169XJagqNWq6FWq73LPBEREdVZfi3BUalU6NmzJ9LT02XL09PT0a9fP5frbty4ESdOnMC4cePc7keSJGRlZSExMbFG+aUasG2Dw3FwiIgowPxaggMAU6dORVpaGm666Sb07dsXX3zxBbKzszFx4kQA5uqjCxcuYOHChbL15s+fj969e6NTp05223zrrbfQp08ftG7dGkVFRZgzZw6ysrLwySef+Ptw6q+iHCAqHlAweCEiouDn9wBn7NixyM/Px9tvv42cnBx06tQJq1atsvSKysnJsRsTp7CwEMuWLcNHH33kcJsFBQV46qmnkJubC61Wi+7du2PTpk24+eab/X049dOx34H/PABENgX+fiLQuSEiInLL7wEOAEyaNAmTJk1y+NmCBQvslmm1WpSVlTnd3ocffogPP/zQV9kjd9b9y/xv2eXA5oOIiMhDrG8g3+A4OEREFEQY4JDvsZExEREFGO9EREREFHIY4BAREVHIYYBDREREIYcBDvmGJFn/zzbGREQUYAxwiIhqQLTtQUhEQYMBDhEREYUcBjhEREQUchjgkE+INm1wBM5XRUREAcY7EREREYUcBjhERD4iSmxwTBQsGOAQERFRyGGAQz4ncC4qIiIKMN6JyDc4FggRFAzuiYIGf43kewoOZUxERIHFAIeIiIhCDgMcIiIiCjkMcMgDHlQ52bbB4UB/REQUYLwTkW+wkTEREQURBjhEREQUchjgEBHVgAiWXhIFIwY45HsCu4kTEVFgMcAh37CZTZyIiCjQGOAQERFRyGGAQ0TkI5xNnCh4MMAhD3hZ/cT5eIiIKMB4JyLf4Dg4REQURBjgEBERUchhgENEREQhhwEO+Zyg4Dg4VD8p2P6MKGjw10g+IYk2DZE50B8REQUYAxwiIiIKOQxwiIiIKOQwwCEiIqKQUysBzqefforU1FRoNBr07NkTmzdvdpp2w4YNEATB7nXkyBFZumXLlqFDhw5Qq9Xo0KEDVqxY4e/DIBck2xFcFYybqf4QOQYUUVDy+51o6dKlmDJlCv7xj38gMzMTAwcOxKhRo5Cdne1yvaNHjyInJ8fyat26teWzjIwMjB07Fmlpadi7dy/S0tLw4IMPYvv27f4+nJBj9NXFmZNtEhFREPF7gDNr1iyMGzcO48ePR/v27TF79mykpKRg3rx5LteLi4tDQkKC5RUWFmb5bPbs2Rg2bBimTZuGdu3aYdq0aRgyZAhmz57t56MJLSsuXUXyxn14Yv8pNynZK4qIiOoWvwY4er0eu3fvxvDhw2XLhw8fjq1bt7pct3v37khMTMSQIUOwfv162WcZGRl22xwxYoTTbep0OhQVFcleBEw9cg4AsPoKzweRL3CyTaLg4dcA58qVKzCZTIiPj5ctj4+PR25ursN1EhMT8cUXX2DZsmVYvnw52rZtiyFDhmDTpk2WNLm5uV5tc8aMGdBqtZZXSkpKDY+MXBE42BkREQWYsjZ2IlQZ+E2SJLtlldq2bYu2bdta3vft2xfnzp3D+++/j1tuuaVa25w2bRqmTp1qeV9UVMQgx9fY0JKIiIKIXx+1mzRpgrCwMLuSlby8PLsSGFf69OmD48ePW94nJCR4tU21Wo2YmBjZi4iIiEKXXwMclUqFnj17Ij09XbY8PT0d/fr183g7mZmZSExMtLzv27ev3TZ///13r7YZCspLinD+0P5AZ4OIiCjo+L2KaurUqUhLS8NNN92Evn374osvvkB2djYmTpwIwFx9dOHCBSxcuBCAuYdUixYt0LFjR+j1enz//fdYtmwZli1bZtnmCy+8gFtuuQUzZ87EXXfdhZ9//hlr167Fli1b/H04QeWzp9IgmkwYPvEFdL51WKCzY8XJNomIKMD8HuCMHTsW+fn5ePvtt5GTk4NOnTph1apVaN68OQAgJydHNiaOXq/HSy+9hAsXLiAiIgIdO3bEr7/+ittvv92Spl+/fliyZAlef/11vPHGG2jVqhWWLl2K3r17+/twgopoMgEA9qavCq4Ah4iIKMBqpZHxpEmTMGnSJIefLViwQPb+73//O/7+97+73eb999+P+++/3xfZI1+wbWTMXlRERBRgvBMREfmIgsE9UdDgrzEUcJoEIiIiGQY45HtOxiMiIiKqLQxwyDc40B8REQURBjhERDUggsE9UTBigBMCpCBogxMMeSAiIqrEAId8TuBAf1RPcTZxouDBAIc84EHpDEtwiIgoiDDAISIiopDDAIeIiIhCDgMc8oCXbWoU/FoREVFg8U5EREREIYcBTigIgga+Egf6IyKiIMIAh3yPVVR1ToWxAr+d/g1G0RjorBAR+YQy0BkgosC7f+X9OFt8Fj3je2LByAWBzg4RUY3xUZt8j5Nt1jlni88CAHZf2h3gnNRtCoGXVKJgwV9jCAiCJjhBkgkiIiIzBjhEQerUqY+wdeut0OkuBzorRER1DgMc8gtRFLFpyTGc3subc3WdPjMH5RXZ2L//2UBnhYiozmGAExKCq3pIUCiw89cz2L/hPFbN2x/o7NR5esOVQGeBXBBhHSKBk20SBQ8GOOQXF48XBDoLRERUjzHAId+wGehP0ukCmBEiIiIGOOQrNgFOUXp6ADNCRETEAIf8hEPh+BC74BMReY0BDhEREYUcBjjkE5JoU8rAEgciIgowBjhEREQUchjghIJgKzBhAxwiIgowBjj1mAAPAxEGLEREVMcwwAlhutocj0ZiGxwiIgoeDHBC1DfffIMZM2Zg586dgc4KuVBRUYF3330XCxYsCHRWiIhCCgOcECA5aIRz9uxZAMDvv/9e29khL6Snp6OiogJnzpwJdFbIBxQCL6lEwYK/RvI9UWKzHQ8ZDIZAZ4GIKCQxwAkBhXmXPE5bUXERRUUH/JgbonrGZgJxziZOFDxqJcD59NNPkZqaCo1Gg549e2Lz5s1O0y5fvhzDhg1D06ZNERMTg759+2LNmjWyNAsWLIAgCHaviooKfx9KUDJUlHuc9s+tA7Fz110oLT3l+Q48aTQsu7CzkbEvOaqCJCIi1/we4CxduhRTpkzBP/7xD2RmZmLgwIEYNWoUsrOzHabftGkThg0bhlWrVmH37t249dZbMWbMGGRmZsrSxcTEICcnR/bSaDT+PpyQceXKH4HOAhERkd/4PcCZNWsWxo0bh/Hjx6N9+/aYPXs2UlJSMG/ePIfpZ8+ejb///e/o1asXWrdujXfeeQetW7fGL7/8IksnCAISEhJkL6q+U2X1s/Qr1BzKKcS9n/6J7afyA50VIqKA8muAo9frsXv3bgwfPly2fPjw4di6datH2xBFEcXFxYiNjZUtLykpQfPmzZGcnIzRo0fblfDY0ul0KCoqkr3qC4PBgKysLPsPqvT2eGivF1VW7oisUvGU4OPW2GM/24Y92QX4y5fbfLpdIqK6xq8BzpUrV2AymRAfHy9bHh8fj9zcXI+28cEHH6C0tBQPPvigZVm7du2wYMECrFy5EosXL4ZGo0H//v1x/Phxh9uYMWMGtFqt5ZWSklL9g6qDfvrpJ7tlAsJk76/ojTXbiWyyTRHwdJRk8qlinfnvyBiTiOq7WmlkXPUpVZIkj55cFy9ejOnTp2Pp0qWIi4uzLO/Tpw8effRRdO3aFQMHDsQPP/yANm3a4OOPP3a4nWnTpqGwsNDyOnfuXM0OKBT4uORA8qD3SPnRq9BfLPHpfgHAKIoYsO0wJh866/NtBwOPp9QgIiILpT833qRJE4SFhdmV1uTl5dmV6lS1dOlSjBs3Dj/++COGDh3qMq1CoUCvXr2cluCo1Wqo1WrvMl8HFaafRfH6c2g4pqXbtL6uGrHfgfytPqcU+d8cBAAkvzvQp7uaeToHJ8p1OFGuw9wOzX267WDAXlRERN7zawmOSqVCz549kZ6eLluenp6Ofv36OV1v8eLFeOKJJ/Cf//wHd9xxh9v9SJKErKwsJCYm1jjPdVnxH9mAKKHg55Nu0woe/ulf2/wa/ilc8yofkiTZ9RSvOOHdNrxxuabVay5c/e47HOvbD6Xb2KaFiKgu8WsJDgBMnToVaWlpuOmmm9C3b1988cUXyM7OxsSJEwGYq48uXLiAhQsXAjAHN4899hg++ugj9OnTx1L6ExERAa1WCwB466230KdPH7Ru3RpFRUWYM2cOsrKy8Mknn/j7cOqdX079AmiAh8PD0baao+7uW38eLfz+TfOPS//3DgDg3MRn0C7LeUP26vJ7SRoRUT3l99vO2LFjkZ+fj7fffhs5OTno1KkTVq1ahebNzVUJOTk5sjFxPv/8cxiNRjz77LN49tlnLcsff/xxy4SEBQUFeOqpp5CbmwutVovu3btj06ZNuPnmm/19OEFBf6kUigbhNduIl3PmFIUpAFfxjVilDY7NfXvz0mOIvu0G1DDHTtVGGxWpNmdm95eSy8DBFUDPJwClKtC5qTFRFLF5S08oFGoMHMASNiKSq5Xn6kmTJmHSpEkOP6s6i/KGDRvcbu/DDz/Ehx9+6IOc1T36S6XI+3BPjbdT2w1X8y+VgSMVBdjHPQBdEXAiHXjkx0DnpsYKCrbBaDQP+WA0FkOpjA5wjogomHAuqjqmPOuyj7bk3Z++ps1c60tFzLHtf2LTom8CnQ3HdNfHfzq5LrD58ANRNAU6C0QUZOpoywiqMS/bfngV4Ej2s4kLitAKcdYuOISiK+W496WesuW/zJoBAGgYn4AuQ0e53U5g2uCE1t+CiMgRluDUNT66N13OW+1VercBjrsJOW1u5Ac3rMWxbVu82r8rgYidjm7LRc6JQhzdluPw8/NHDvl1/xcr9NBXbfdEREQWDHDqqbLyM16l96oEx80wuqvnzcYvH74L0ei/7t21pbRI71E6scyIglWnYCyo+ZxfGdeK0SPjEDpsOVDjbfnbpkXf4MOH78LhLRv9vKfABXtiAPdNRM4xwKm3vK2iqlkxiaOamOrcGHS6yzh2/B3o9Ves265JxmpJ3rwslGy6gEsf1byB+KwzlwAAJabgv7HuXLkMosmE3z75wM97Co5zwUEZiYIH2+DUNTVosyHWpEqjxlGEb8KQHTvHQK+/jLy8XzGg/58+2WZtMF4uBwBI5fLGsNVpg1PjW2gA2v1I7qowg3z7RFT3sASnrqnRvan6AY67NSXZZJv+u9no9eZeZDqdZ5O1Borkw149BkO+fNs+23LdJtiM5eTJXGhEVL8wwKlXqn8T8O6mKqFqJOaw0MAmO2KF0SftU2zNXXccXaavwdYTV9wnriZnhSEnd+/02T5MJvl5Ka/lxsX58+fj5OjRMObnu0/spdK9eSjJuOjz7XpKFEX8diAHJRW+aQ/GKiqi4MEAp56qqDgve290U+pS48u2i2+aKIq4OD0Due/urFaQ46ya5/3fj6GowogJC3d5vc2aMlVzWgtP7Ckq89u2Hcn7f+9Df+Ikzj872efbvrb4KAp+Pgl9Tk1nma9e0Pfyf/fhme/3YMB7LsYG+qgb8O94QFfTPBJRbWKAU9fUoIrKtg2OJImybelrWq1UjSqCyvxc/dbapbpsr68GMrTSGT3LW5G+yOf7rq7y8guBzoIdQ16ew+Wblx7DtpWnarbti6Ver3PylO1o5tX7/v5vn7mbf0GZi4D02mnAWAFsnVutfRBRYDDAqWNq1jy0Jm1w3OzZJkCSRAcD/blYveKozUzj1bhPVbjpTSTbt8kIrJ8B5Mq7WE9ZNwX9F/fH+7ve9z4DfnDmTN24mV4+X4x9689j96ozrhux+6FdVmGhtWSuuo2MvVrLg8bZbOxMFDwY4NQrtjcgH/ekqXJzq3qdd3zddx2YlJWV4fJl9yU6x8u8mAjzf1OAje8Cn/WXLf7j3B8AgG8Pfuv5toAa90hyVr0WjG05HOW15KqPJiGtaQFiNRsZ6z0s3QPg9QS1RBRY7CZexxiNjovSS7Y7HlHXV6QaxkP6Cu97Fb333nsAgGeeeQbx8fFO05WbvNj2iT+8zkcgFBXtC3QW7Pm1d3lNA7paaHjNAIeoTuEvto45kOH4Bl2w4oQHa1tvIoIAlHkxUJxXtw9JdFBFZX939LQz0P79+11+ftRNCY7BZHPzLK5mIGhTBJV9yIPeRDUMBkpLj9ZsA37h5qBqEGPUiaodD0rrRHZXJwoaLMGpYyrKiqsdloqym4jzi/XPWRdgNIm4p/sN3mzc9ec1uOH79uZX8239MmevD/Lhe0dy/NxI2sENPiBzhTpUC4GFIsz/+yAin2GAU8fUqG2GB0+XZXojXliSBQDo16qxzX7dbNrFtiUHJToAnBbhrFy5EgcPHnSzx+AmeVhtVlhY6LN9PvzVds8SmqrZhd3BH7EmsWf5YZuSMCfbKS0tRXh4OFQqlctt1U4JEBsZE9UlDHBCQAOl1mfbKrYZ8OxcgbXrrndVVOaLvEl/DKLxAky6LFw4MQwJ6u4erb5nj3y+purcNAb/v/Ver+NLnub5xAlPqhY9U1DmYuLPS0ds3sjztjfPsxIpqaLc5ecivCtczLcZHsBRgLN//34sW7YMCoUCb775puu8OfmGHi4px+kyHW6Pa+hFzpxgCQ5RncI2OCFgdMpE2ftrgrMBydyHKbbPqKJNtZNY5en9x6M/YvDSwdhxcYf9XsrKYTKUw1D6P5h0mQAkFOTbl8gU5/t+zJtKZ/JrPhieWOr92CwOt+NNTx1/WTDK6Ud/XfNXjzZhzLP/e/mriurMmTNYtmwZAA/nUHNSgnjrzqP468Ez2HS1uOaZEhjgENUlDHDqHPd3lK1KZw1U3XcTV9h8I0w2N42qD9hvb3sb+RX5eGbdM+bPbW5CRatWwVSlGsRReYZC6aAAMYiK+C9Oe81tGsld26NgUX7V6Ud60XnJT40maK2BAwcOuE9kw13V7czTPuhl6KQXlRgkM5kTkRwDnDrm8qXTsveCg0DFWZduT6pNFLZfCZv0zi7hepP9zVE0GOBRewUPb56m6+1Zavtma7jgfjTho9s8mPSzxr8y699hX7Hzkil/xFqn77zT88Q1+ftUybxC4eVJc/Pd9sn0Fk6qqHJLg3viV6L6igFOHZYQ0RJ3JD/tcfprBe4boWadL7D8f9WBS5b/u7p1iaKI80+Z85Eb3wtbev8fCvKqzillfwNyFHCl791kn6esrOv7qd05mEwl7uceuurBFANe36xdeCDrpM+25Qn9iervb/23X1R73bAw59VBoiiiuPiQ08/9x8FAh/oS3LfyvgDkhYjcYSPjOmxQwgOW/5sgIsxJvNqkyRn8sa41PGmD89t+a1H+2fxSyzdEctHYoiwjw/IEfaj9EwAAfZHzsWkECJAg2ZXIFAil2F9g3+hWp6vcVi3H494MIAjgwlHvbrqel0hZA8FCo/cDJlbHf2fugiRJ6FSDbWSt+dXjtFVjXVdB4bbtQ1FefrbKUu9Lj3474GW11cFlQC95e6Xj147L3gfjCNRE9RVLcOoQo17v8PJ5ICwb32jW45ziCgD7i2z7DpthfwPwbtAyV7cPSV/NbsdVbvAVcL2doqKs6u2nmgw53t0Ad/z8X4fLjfmOex/lOZm8MtBKrlXg0uki5J0phj48ym16k/4oKgo+x751v/ssD64CHPvgxvEwBXk6a/Wpo9/NpO/3OFhahe0M4hfte5spOLoxUdDir7MOWTX3AygV4XbLt4WbnyI3hB9EnlCISwrz2CpKZQUaN86u9v5MovNGxrYkR9NHeDDmjrdPu/sPPOtVek85LUnxsgTn1G77HmUAcGWh45Kdzz77zKvt+5rx6lUYi+wHB7QtTREVrgt5s9Zmw1D6KyCVYv2CefLtVDmv3rSh8rZaz9F3qctW1yVqHn37VthUATt4JvAmwCn5808U/u9/HqcnopphFVUdcu7QfkQYI5x+rhOMWKm2zrDcrftqREQ47h7rrPymwqYKxLbdp8tbU5Ubl6E0HSa9q+kVBAASJFFeTeW8TOn6uDom1+OwCGVGLN5+Fn/p3dyyrCkKMCJsJ1DUA4hJdLje3svux4EpiUzETx/uwfBxjittzh92PDChsUQP4yX/tx3ytgG2saQEx/v1N5/ZV8Nk/b3DlNabtuSma/SFowXWN1XqmWzbWEWHx+Lia38i94YKrCvPxB133AGtPLFs3RIn7Z+MxqptuyrXr1kD9DCYzN/jqoFVkU1Dc539b6lqgOMsaBf1epwbN968TmQkom+7rUb5DWZFRiPW5RfhzqYNfdr+jMhb/PbVMc4GNJMTER19WRbclCESOtiMBuukTc2q/dYeIbZVVCab9EV6+VO/ZDCX4IjXL/aOghtHF35JEpH77k6bJfZ5atr0NPr0/QHZ576FJLmuwlJvvoRpKw5g41Fr1c9i1b/x7/BvgK9HOF3vsdWPudwuAOy4+XWc3fsHFr462+HnS6e/4nB57ruOS3VcuXBhqdfr/L81x5x/aDLaLSrPzARgPuNhVb5SCqX17yC5KKHY8uPxKksElBUW4NKZU3Zpu8cOAQD8L/9PlJWV4ccff5QnqBLgHDlyBI4cOTrNSW6qH+C0Ei7gpCYNeLsRMF0LnN1q86nrqlxns8Hb5U5vrS47P8k/JZHBos+2w5h4KBuTDle/9JjIFxjg1DGeDBPTrPl+dOu+2vI+D/GYIHyHvwqLcQ2N8DmexWGpjcN1TTbFNrb7qvzv6cLT6L+4vzxP1yft3N/Jsx5dlluCJEEs0tstj4q6guSUAxAEEe3ab0F4uB7Hj7/tcFsnSu2f6Dcesw5Id6Piovk/BfbtNrwhicUwlm9Eaf4m70ZWNjp5ondR4nIl3/tRmLeevOL4A1EE/t3UfrlNtv6xRJ6XnOMF1mQOApy18+fhs6cfQ9baKj2sBGDeU4/i+1eex6VT8sbingYClUxOqgfLy895tP53F5ycjyq6Cifwh/pl+cJvbAdFdP23VgryQnCn341y16WPgLnt03evb8Vnz23A1RznvfNKCq7i98/n4FqO62EMDJfLcPFfGSj41T7g9KerBvPfbtVl301D4m8F//0vzk16FqLR/mGA6i4GOCEoOVleXfIBXrX8f7LwFTYJt+FtTLcmuH5RrnrTFR2Mg/PZXgftRq6XEOQ39q7PjSHT8QWwe4/fkJqaicREF6US1w3YYf+kfzzPvnpDBIBNH9gvdxBo6MuM2PTIGxBtbl6SZDveT817ypw+fbrKEgl5l9dCFI24ciXd4+0UlOkhiiKMTgbBeWrxIHzQMKbKnoCSP9Za3nfKlq9rsgnKjMpIu23u/f1XlBZchbG8yrAD11dTCGHY+sN/PD4GwD5wr6ioWVXUy8fOu/x8y3FzEDxPNdtxgspSLwfVUrZOF1b9Ozp24RXrb1BwMq/Wzx9loehKBUwGEYvfcj6kw+I3Xsb+db/ju1enuNznlW8PQiw1omSz+/GcPHHh4n+xJ/MxGI2eVbkGzTysHsh5/Q2UrFuHS//+d6CzQj7EACcEKRTym0Axou3SCKUGKHLLoMgth+b3i1Btv4xWr/2KlsJFVN6pZAHO9auVQbSvJrr07ky3ebox2joPVVJka8RpmiEs0wARtkGU/C4X2aDA5Tb1lcGJJEEotuZr83H507sewP03JOD1fXPlGyi7ihHL7Kuuvpy6Cfujb8WGWz50smfbUi4RHz/5oMNU27dvxy+qXcgX5DfJkpJjWLv2DSgU1jynpBzA/v1PY8fO0QCAPbgJ8/EUDNebyVXojZj6vwOA3qbNUrEB3d5Ox0szP0LvS0sgVKmmOVd8DhnGAixoGIOC620htmnUuDXlBqw97Lyxq22hzaF2aU7TmXQ7qyyREBEWjQdavISWl9tV3ard+icVuZaef5V+/vlnbNpUORaSg2pNJ1VRopdtcP5vWQY+D5+FJMHJCM//agzs+Q7Idz1X2Jtb5XNk7b6022E6vU1Aq4iJcZimON8+qNM7CPSK8szjUxnczA0mlftmSIGysrM4d34hjhx5Bdeu/YmDh/7m0XrBM9O853RHnI0CT3URGxnXJR5WjQhClSdy2DcUVW+Rd1FWFOhxWvMoAGC28V7MNt4vCzik6zeoo1ePomGJhIpwoEJtXiYWFbkt00iN7mz5f//4uwEAO5UnsFdZterIuqXERGsbDyPC8A2ewgZhKABgqjQDj2zcB0gSlIcLoTxnLdIPhxFDFbuxXWwPANgUGYHjKhWOq1R4Nf8aoiRzi6AfDixAbpl8FNrml2yOxEn7E2PZeoRpukER1gSi4RQaGKMwLDkN1/SXkJH3s6W90W+//QYogBXqHRir64cioRxJYiNs234XbmytR1T0FRw/1g8A0CI1CwCQX5qNZXgMq4S7AABJ0gWMwq8Yv3AXtpzIh6qBEvoB8QAA5Slz4DRLNx0IBwqkKCwXb7Hks9xgvQG+2rQxPrt0GRMSzeteiCzHTTbHFHdNwqD9IlbfpIB01Rp0CF6WVrWM7gIAaBxubdCtFFRIiGhhl3a9yqak8Q9ghHIEci8tRlT0TgwYKKC4uDH2Zo0AoEBFRQ6OHnsLxcXy9l15iMMxtENnmyqtacv2QRB0kBqpneZzRMkKjFDucvr5tzHR2J/xT8wE7H49V8qu4IPdH2Bcp3EoN8qDjI3nNzrcntF2yAEn1ZMmg3z5+UP7sfStaWic3AxPfPCp07w659nfTq/X4/jx42jfvr3DRsEZ24bItlVW5tngj3UwvqEQwwCnDqkoKYY6XOP1egY4LhK31UE4Y/n/FOVyc4Aj2ldRFedk44uPTdArgUdftn59iqOtPZcgRAGS+1GA7YMb4MbW9kXzuUjE3wR56cssYRoUF8qgOnBNtrwprmGnRt6IU2/zKNm3RQr+WlCIrxtqoTj4NUbvlFAcAZyJF3DLARFjdkhYN9i67pXYjmhy9SBsL9cm/T6Y9PugjLgVxvL1GJFqbmAcFd4Q2ZGtUWTIR3R4LGwrz5aqzQ1Xo0QNugvm6q6EhJNISDiJc+c6WNKNE+RVO+fRDF9gErafyAcAKEqNCDtRBFNqlN30BrNUn2FLRWeIUOAKtCg7bz2Xf0ZG4IW4Jpb3Q7Lk6879zBwgPPCnCYURzwC9/wkAKItMQElkIjS6q1CadNj5q7xKRjTly94LDgqFb0v8i90yR9asWYOBt5hLhQRBQkzMFTRufB4tUjPx59bvAJi/C4uRhvGYh0tIwD+Fd83LsyswrzGweEc2Fu88BzUAU1IEDG0bAir7PDWB8/YhFYKA9xs3srx//7L8GNN+S8P5kvP43ynXXb5FUXTci8jDHm9L3zI3qM4/b26sa9Tr8f1rL3q0LgCIpa7bk4iiiLLyU3j//30PwDx69BtvvOEgZdVAybPQxdE0MsHOq/Z1FPQY4NQx1Sn21Qnug6IUwX7QOdsfuwTgqkKBljnmi7Pq+rWzcZG5vGJXz79b86iIhGRyH+A4YltqU6lqcFOpanADwC64AYCSKjeZrxuaOygvmem+CH9fl0lodXKFw8+M5evtLuL94++x/P8I/nCQF/sqh5QU83gteYi3+6yyxEoDazuK8JPFCD/puG3IDpvjP/WLEkhOsrxf18DanibKSRMXwH7U6h03v25984s8wNEXfStf2cH3s5E6wfnO3GjeIguRkdZee5XfhV3oLUu3okCDQTlXseSEdXqRsIvlkAQBxk7mYGXH6Xz8fugSXhvVDm0UztvoLI6xDm64JqoB9mnUmH3pMjroDcgpycH5EufrVo7QffK2ITAVFCDyznuRtzYDDWzTXA9wMlacwN515zFiQieoI8zlRCbdIUBQwFC6ym7bS6a/gvxzjhvLlx24gtKMi9BfLEHTp7pCLHE+gWql7TvuRlnZQTRtOgCXL6fKGnZnn/sWRsNVtGxpH1AFU9XT7ku78fqW1zGlxxSMSHXeU9JzDHBCCQMcAgB87qixpW0JjgAMap6MW/fKnz7nfWJCYUxqlRWD6yLxryaxNVr/ZKt7AJN9MAUASoX70jFPzcNz1VjL+bk+FW4/KKRHW6zJ6Lw22YnTNMdVXc1m8W7QwPOeOFOOZENZWi67qCkvlEF5wdwo9sHrQeLSrUexP9xBWwshDJBMmBXbSLY4R6nE2BsS8WXOJUxYNhwAEF0m4dlfRKzrJuBgMwEVKqDdOQkjd5tw9J2OlnXXnWqB0i790bDgOHpkzYYEwCQKKC3UYc8ac8nMmi/2w2SUIBovwlC2Go7oKypw6aR98C+KIsRrOlz9/rBlWd5HrkdorixZKiszVxGmttyNy5flv+HKXot5l+0bvF82xTgvnbJR7WkrdnwJrHkNuO11oP8LLpM+k/4Myk3leGnTS74JcGyveUYRUPh2LjmqXQxw6oEbpaM4IbT1ap3Hw9bglcKF6N0oBQCQrVQCkoRnVlkDnB9mmItxdvd4qcratRDgmGo7iPL/Ra4C3lc/NoLzXj4LtfaNyytdjgGa2g9iDACQanCsDcKtDWhvTXzI7vN4UWsZadsflE5KtmztD3/Cbllqxfc4/e4Y4PgfwNYpDterbL8EAI+uF9HjlIQep1x/D0ujbgAAFDRsjXWDP7F+8Mqflv+ajBIkSYK+eInT7Xz8+P1QCipEKqPRIqoTzpUeQUJEKi59sxem4/LS0rXh+3Am7DJ6GFJxIiwXtxo6IX/vXlRUVCA9PR1GoxFxcXFoe70duFpdjnbtN0GjKcbVq8MRFW3tDVlaKg8E/8QAfKp7EZ13H0d6L/M1pTLYuVChx//yCixpy22Chcn/2YP0Q5ewYlI/JMREoLBcj9SmUThxqRgF5QakxEYiPub693/V9etJ+pvWAKe8AOLOr3Gty71o3LAFyvRliFRFotxm8M8KYwXOFJ7B9IzpmNJjCmI1sThdeNou8DEWmPOobNgQYlkZFJHWks3y/QfMaYwivpqyEWFKBcbNGugwyHEW5HkS/FHtYIBTDwzCepyAdwHOW+HfYq9Nd9b/xkSj/0H7tgOnm99uv7KDemyTZESYUL2vW2PpMvIF+VguYWc9rwJrp9PjiLqmJS2Ob2S+bGcwHL/hK0zyap2bFY4HxAPsq+ZsnUgS0LTI8THVpATH3d84WNtlWIK61kOQusGA0yrXpV8NPfj6SR4eqySWQlf4udt097WwVhe1b9gHAOyCm0KhDGfCzF3g94SbqxN/Vu8EVsh7vOXl5VkCHABo2tRc9ZWZ5bjX3B7chA8E6yCL+0vKkbA+y8kBSQjfeQVh1/RosUbeRf32OVvskicLl7E4/F+Ausy+Yf9063jX05o2xqrT3zjeJ4Bei3pZ/j8hfQK6nxAx7UcRlWVbkf36oWzrVscrAyiLiMO23v9Ext82o9foFjAZJZiMJnzx/Ea07B4Hk0FEbFIDnMq8bBmnKLVbY+gKf8LFY0dwzytv4uCmddi3djXaD7wVV86cxuVs899g3Jyv0DC++lW1VD21EmZ++umnSE1NhUajQc+ePbF582aX6Tdu3IiePXtCo9GgZcuWDufsWbZsGTp06AC1Wo0OHTpgxQrH7SRCj/c3CE/KOuYZx9gtezRJ/oO8f4uDACf1Do/yYBKNdu/VkvUmEi1q0FSUd5/N3GMOnjphn2XZ89L/AwAoSjyf4PMxB/Mt+YqjRrVA9Yrno+F9PtUuJigtcxGoKFxlrwYBjrfdtWtbGOzbXT2hlw/0F+tmegoA6O6m5AYAdGqt2zQAPApuVArPSvcuCb4vHduIW2XBjUuiBM3vFxF2zX0boETk44zmYWxRv4AUxRXAUAbonUeOq6IaOP3MkWk/yr+LroIbADjW+gEAQEWpAZuXWqsDTUYJx3dewqmsy9i16oxsEMbj2zfg1J6dqCgpxuI3Xsa+9N8AScLhTesswQ0AzH9+vFd5J9/we4CzdOlSTJkyBf/4xz+QmZmJgQMHYtSoUcjOdjyM9+nTp3H77bdj4MCByMzMxGuvvYbnn38ey5Yts6TJyMjA2LFjkZaWhr179yItLQ0PPvggtm93PjhWfVb5dKqWrhfnOihhET0InJQe37vcX/x/OTcPRpubjQABd+l7ydKUlDTGn1usPXDGSt+jN7YhXsqR7ULX21q6c14y9xS6S/c2WlQsQruKb5w+Se9rIX+/tpuAHW1selxlvIGYwspRYK07DFP3QHjUfQCcT7boaYCzLeMB5OWZMyI66M7vzgWpidPPShTO/6aCi+xVluCE64sRl+doXBfnpTRG0fWNrXLogQGGdogV3c9U7msKB3+XDWJ32fsbEnvZpamOw20f9cl2AEAT5tnNPQrOu8ZX1xfCZI/TatIvepz2v+rp1ciN/6j03j9gmHQH/JAT8hW/V1HNmjUL48aNw/jx5gh29uzZWLNmDebNm4cZM2bYpf/ss8/QrFkzzJ49GwDQvn177Nq1C++//z7uu+8+yzaGDRuGadPMTxXTpk3Dxo0bMXv2bCxevNjfh+SUKIq4Wqa3XQCUX5V9XmywPmFFqcMgFFnbDIjhWugqrBdglQ7QlVufaBRCmOxCJ0JCBeQ3FKVSJ3tfATUKYG40GQ4DdJIG0MkjlUhUIKHKgGf5Vao3wkwSzsQJiC+w5s/koIGtJOkgSeYnnB5DRmDf1goIFRlQhcmfQCtQAZNgO9GmADFMnncAMEjhyBebAGGAABEqVRw0+nIIFTajDF/vgRKBCsTB3Bi4tVbC3gIBFVDjVWkswrBWtt0wk4SuOQrLwHEnEoEvRoUh3KTGuOvTRylNOtyU+QGuNWyN3Z2uB1qCGuGRgwEAioYvoIHNYH2rz8/HyORxAAC9g5ICALhV3wG2ZVmiGIajRwbi5ImbcWpQL8AItJUO4T4sRUucgAlKPC0tgKQUIBgl6LvHQmyqgeb3i9f3Yy0F22TqjFvCzOPEfGMcgcKww7a7RpcKHRbkXMJXDWPQ6ooWuJ7HV54Iw+lEAR3OimieBwwa/CawtgzhxjJ0OvQ1cOhrSABOtLoX2cmDHR5XpVKj8xIECRLKYf4bR0gq3KvvjQKhFLuUJ3Em7DIEwaYXT3YnCIKElBTrWDkNYu/D9T8vbpK2Qw8V8tEEF4SU6zuQIDZSQXG99EBSAMa2WpgSIxGWU4bww4VIEuQDC3ap+NLy/yM5RWgTH4Vz139To0pK8X+X8zExIQ4nVOFYeT4HRx76Fr1ajMRv33RFS5shlDZ3FLC+i4D7t0jopG0L8cAR6DSOG7c3u7geFb1GIu+CDpLopDubIhrj58yHtmkk5jz+IFSS+xIcE0S7wTIdExEebv9bc6QYHgahkiQbhNIVASIaoxjnpaa4Qch3m75AoYCnEygIkoSY6wMt65XW3p7OSBBgCG8AUaFEZJUxsZyuI+kAybxhhTIZJpP7hvRhQjguHbYfNFLdIBJRsY4fUkRRRHm541GjhQil03Y+oijCaCywWx4ZqXHZNkgURVw1Ov4bKpWNIFSzXVFjZVjA2iT5NcDR6/XYvXs3Xn31Vdny4cOHY6uT4sKMjAwMHz5ctmzEiBGYP38+DAYDwsPDkZGRgRdffNEuTWVQVJVOp4NOZ/1BF/mpyuJqmR43/dvaNTgCFTis+avlfZkgYEiLFMt7tV7Cdx9YL+omhQobbUbPNVbsgrF8k+V9cmQbWTfkCujxH428TrtzF3mvhz8wHCuEB2TLNBvlP+QJYb/ivjD5dsbYdC8GgFY5QO9j8ovnhaSBsvcqXQFKjTsByXyuw9dugDL2WfSItX8i1sWlyN6LkHAtZa1durzoRjgQ1hWAuXIuJqYrVFf0UBTYBHbqMOh7NMYTJ5dApbs+nkuvWPz3+qkIi7PfbqscQLIJ9PIamks7OudYB8rT9O0L0+a1aFRwHA3Pf4E8bQMobBo1CkIYOkRYgzyjzWSgx8Lsn2SjJA1SVErYdrSuLEwzGtX4zZAKCEAEytER1ifD10pexyyjuW2OFK6Q9dOdELUalbVUtiVVbxkfRzTkv7u2enM49ExBEQ5ftd64Tiea1zvUXIFDzYGwnZlIRltURFurKAUArU8uR5OLq5DRxtxwVlBoIYnmgEbdcAp0BbPRJXaQ3XFXqoABxVW6yTeUGmCooQu+CvsDWq11qIJrV29AUVEc8vOT0a3bGhiN4fjxSG9U9qTvja3oB/N31iQpkI0WeF16zxLciA1V0NuU7JmaRSH8cCFeVVofgF43PIkimw7cIz/ajFPvjELmFXOQGAYgHMD8XGu+eq/+J8pvaykLbgDg4zvNQfaBFsAtNyRh8uQjKIu0Nkq+bYN8+IIG8blInvc5PnpCfh0Lj7ofxvLNCI8chh9m7MKEWbdg4jvzkfdxlixdubEEK8+ZGy33H5uGPveOxZY1G7E2w5pufMUQy/+/0pivS4Ig4LnnRsra2mzLuB8GQ4Tl/YsvvgitVgtRFNF3w8+y/cZKVzACqzAaPyND+0/sUfTDjZFqLDyTB80G60kxJUbA0MUc4KnXXoRgkrBycn+0iYvC1h3bcNtaeenW+4YHUAEVdqIjfr71CjDgRUATA0zX4umEpjiktpZMfZqbh4Hl179Hz+0BGrdCib4EUaooVJw9i9MjRsq2PfN+BUbtktDljPnH1v6IOfAvWvM7ijVx+Gmx/OGu6eVMdD74FTRdOiP1hx8AAHq9EV9N2QxJlCDq0mEol08jo1C1gypqFPRFH0M0GtGu/y244/m/Y+1Xn+LA2nTc32IqDN/aB0IGACVwPHCiASZ8q9ng8DNXFAoD+g9w3mDdmQqo7cbhsvIs+HNkf/+OaOpgLKra4Ne9XrlyBSaTCfHx8vE94uPjkZvr+ITl5uY6TG80GnHlyhWXaZxtc8aMGdBqtZZXSkqKw3R1gd1AVFUf2CQBkgTodJE4dNB8sxEkESpJh67IdLgKAIiSgCLJ2pugzEH1RmU4kNHO/rO4vF1oYPMEFKnTo6llUEIBkiThmi4Xq89/jZ/Oflx5MJb0RsEkz5moREREhCVdpKkUkZdTEB93O+JgHesEAG6NjYbYVANTcgOIEHA4MhVx3axBsmiIgSQqMeWqtav3lzmXLPszRanx9bDrM6ELJkgQkRd1Fjd+/pH1ePQGQJLQ2AhZMyjb5x2TZH1cVEoK8+ZtBnUbq+vnoLLMuiT56iUoRBGtNPLSHy0KIUCEOswEdYy81KyzaL04HpdukH0mifIqL09/7HkNsiFBQlwLR72wbEd6tt4UBUEBTaOpdql3XlmNnZd/k60eJdm3t6pKpTIHksVFcdiz+w7s3HG3PI82YwaFQUQqTuHDBt9Z81PsuG1SsmCdiHWRaYjDNJVuKXM0FYKHg9zZ9MxxRqFUQB1h/U4qI4ciLLwZ1DGPQKGMc7qnc6VHsD7X/gYmRHn/vKpURsNgcFw6VPnULUgilJIBd2kO4GM8jdEwBz0PNbyM5d1vxHttU/BDe3k3c2OKNXDUD07Aysn90SW5ITQqJW5rbS2xMEkCXjc8ibmme/CV6Q4cFloCQ/9pDm4qjwvmkhlIElIMBnTV2Zc+RamiZHkGzL/NyzHAiUQB+Q6+yjEjhiOqw/VJhyUTwkw6qHSFiK+slrV5kFCplJg4dxAmzLoF2iZV96+EQpkKQVBgwsfzcesTT2HUJPNvofuoMVAovK96Jt+olV5UVWcSliTJ5ezCjtJXXe7NNqdNm4apU60X36KiIr8EObGRKux63eaiKYq4Un7Q5q2In6pWUQ2WV1GNtqmi0qoehULxmOX9/Ncm4ocz72Hs9ZFzI6HGeN0Qy9OZVoxEZqa80e8d+A534BcAwCF0xJ/CIOhG3ACYJGjWmksZPjLdh49M90EQgN2qpxArlGDPmXPomtrMsp1jyQIemmb9usw1SJi+6A/ctsFaYpXZZTLCGwxDeINheOaz2wAAra/f4BUKBXJWLkPhIvMNRpN7Fv/46BNMnz7dfCxNotF14mv4Y91/AZiHJHnlFfNxWptgmmcxzz1zGBUjdBCKDUi/qQ06JVU25uwG4P/QHnKpZe/if88PBN6KxbjC6+e7CRB7xNoD6ZGsT7H38l7MS/tQdpFM/vwzXP36a4yZOROqBHOJhmgUMW/yBgBAllFCx4duhDJWA/ENazBz4swvePP7RThw4ACWL19uPiYICK9oihtvfA0nTrwDAJAk63d25/3mJ89DhxYjxyZW79aiA07fZW4E/pe9J7H+ajEMHRoi7GIZtM0HAUfNN7ufoh+GWKzArybzIHj7Ht+DVWdWYdpmDxuIXjf/TWvV8eH58s/6ZWZh71OPoqyoEKro+6AvWgRFeCsAwLOf3Ybzr8o7EJwq3gsAyCk/hQcGv4bxp4ZA0SAcok4egPRo2RlHLzUArlclipL1b1BaGmv5niy63nOnAeSNUYfcZg70pv30q8Nj+m+3Vnh0zQX80/AEll9v+5GkjcCFQvPNSiEAp2aYfzv7H78+HYRNDx6L5/cgosqi/3evNa8DbhiAT4Z+Av2qXKgX/Yy12W3RIjUM2GBN3zYrEwqNOaiY9OVCfDDWPP+YAHnPrfGzzKWJqhuikfB6b+S8ux2lmmL0+vfj2PqIOchIbt8Jfe4dCwDo378/OnbsiDlz5iA2NhbIVQDXqxzCleEwGA1ISUlBbGw/y/kCgAP7P8Hly+bfZeV5rjRDmAbpeslkUqOHcfF6zDdwwE6oVNYquFuaxeLMu9ZrT2UPqxvU4dh9q3VcIABAfDtguvk6+L+sC/h+iTntRw91w13d5EE6YlthycWTQJuRQI/HgCUPm5c/8C3Q8W5UpUpJsZTQdP7WPD1MQmQCxr33BbIffxzaB+Sl2dGNNXhm7mCcuedu6I6bGxbfuHEjwuPj7LatUCigilTg8fc/wfL3dyPnhPkYnr1+ravUY9Sdlv83viEFk7//AWKZ97OUi6KIF8u7OPzMfRXV03bLPami2uOnKqpA8WuA06RJE4SFhdmVrOTl5dmVwFRKSEhwmF6pVKJx48Yu0zjbplqthlrt+8Z3VSkUCjSJqvI0FCN/krP72TjOskNKVTj05UaUGYsQqbR/Ak4RG6NQ4Xym33Y4hB7STuwRegFhNlMPNNXA0D0W8xrGoccPXyAaZdivkbf6n3q99KP3Y79j7P/GwhAu4KsRYZi5wFzacOPGjch87ke7fdr+oG6+8z5sXiTv5vnII48gIyMDd999t/sTUIUUHW4T3HhLHgxP6ua4e3b0oEGIHiSvdlEoFebVJWDMc10R1drcxskUZsRVXQ4EKGAI00OhUDgMuqOiqk5EKXfjjf9ATu5ym6xaz+H8jqlouXkfTCkNYEppgFib8Qc7tGqOd3Y9AgC4uUUjKBQKjG452hLgdK+wPnmGNWwI0/XxQGxFOpg9vKqnP1sIo16PL6dshVr7V/mH188LAORVnLMsLjeVoOlTXSDqRUAJXHztT9lqw3oNxp3t78P2HRtQXn4OxUXNAQcTa94X3xAHL23HYAejRLtyg9ocPChsttm1WSNc2F/9ovdKEyZ9iZ1/PH19++a/lSohAal/exoTrqcp7vkFzk94yuH6kdqGKCssgELV0uk+lFEqpPx7oNPPKzVs2BCvv/46FAoFrv5wFGV7zNVrz4x/GpkH92LgQPfbsJWYcC8u5iyFRlMl8PBR4b/bUZEnbQNOpANtRgHHbAZB9LKnnzo1Fa0tk7haKRQKQGEdXRoABE+GlPCiM6tCoYAiqnrDVKhiHJeuuee6hNSZJPdJ6hS/VlGpVCr07NkT6enydiHp6eno16+fw3X69u1rl/7333/HTTfdhPDrI7M6S+Nsm6GiQSNzgFdkkNcZtzUmQSEJ6Gh0XSqlgIS/4V3cJFXtbSYBgmDp4luMSNyrm275tHtFBZ4sLMaThcXo0Ng6b1Kxzb0wPD7O2kvLC61bt8Zjjz2GmCozLKvVziM/n4ykMr2gRqtP+PAWPPxWbyS1to56e9dL/0D6xYX4/eICxDQ1h7IOA2tZV2r7o1GpGlZZYk0Tqaz6k3XcqHTBX2+2/P/zYZ9jRt4V3F5qDX6bLfgGysRExL/+uqPVXVIoFFBp3F94N+c6CHhVCigUCkQPdvxd7X3zrxg8aB9at3YcBH7SoQX+Tz0P4e6anVY5rQpBQKJWI+tFpQqzPlnWZNjIvsnW646zXnUN+vaFIioKYU2aWEpvKj392UI89+1/IQi+eQhz9JTesGFDDBkyBCqVdzfa9u3fQY/ui9C3zzr5BzUZ7doJh8GOUgW0uwNQKKrs0/OrgKvaAgubAEfhwTkyVPhmpnbyL7+3/Jk6dSq++uorfP311zh8+DBefPFFZGdnY+LEiQDM1UePPWathpk4cSLOnj2LqVOn4vDhw/j6668xf/58vPSSdbTcF154Ab///jtmzpyJI0eOYObMmVi7di2mTJni78MJqJgm5kaTJVUCnIHG9nhCNxjRdoXnjr2A9xEOE0yNzRdUU4q5/tq2ec8eqY3l/866kF/RCph3uwKx48xP8aPmP4+EVjEY9tcODtN7onPnzxET0x3duy1ymqZDtGfH6U8qjRKN4uVdd1O7WefnvvHmvgCANm3aIK5pHHoYqk5nYebJ3H5Nmw6V79v2gt28v+W/thfySJW1cLZfUj+MLi2z/hXVMdC0a4fW69ch9tFHZIPvNYu2VksCgOrGVu4zaKPhvTdCZyrHtrxfZI2uq9KObCFfUOVE3HvvvU7XvbHVy04/c2Xzy7fivYHWoObN0TbfU08inA53u03SvnHVClIzhVKJtrt2os0W+zHAPA0YA6VRoz5QKPzfmkHhLmixDXC8qC7xZHBJyWgTMHvwt7hyrnpz7VHt8vu3duzYscjPz8fbb7+NnJwcdOrUCatWrULz5ubZp3NycmRj4qSmpmLVqlV48cUX8cknnyApKQlz5syxdBEHgH79+mHJkiV4/fXX8cYbb6BVq1ZYunQpevfubbf/kHL9Irzv2iYIUOBs6UHclmiuklB4EasqICIp3ICzPRrDoDMBEeavgSbccV2pq2v/+q4KzHncfMOJjFHhvpdvcpHavbimQxFX5YZe1Zx2zVBmEnFPXMMa7cufbIONSc9OkrVNkTwYDE+liodefwkRES2QlHif7DO1QoC+cqqKno8D+SeApO4YFZ6ApTvPOdhaFVHyilKFoIBJMj+R3tdGvq/oocOQf8JxLw+Hm+6ViAWfToGurNR9YheUSiViY2Nx9epVu8/i4++AUhmDrL1PON9AlS+tAoBSqUCLhtZ2LrFRKkuNWoLWgwDD5m964pZU3LjpNFb0FdAewPS+07Ejdwcmdpnofjt1lvWkKnw046ZtUON2m7IBGL3YvydJbUtwPAieYhMbyAb8o+BUK42MJ02ahEmTHLdxWLBggd2yQYMGYc8e1xPG3X///bj//vt9kb06o3LwOIOow678NV6tq1BoINqMuTEn1YgnTzfAVZveUrd3TgAcDCMkBtno+powBb7v4ry9glMNmgAll9yn87PY2P5QKDRQKNRwdvXt22cdCgt3onFjD9pMDP8XAGAwgLl/6Y6WTd0MCtdQXkoToYxAicH8RDowWb6/mNtHId/BSOK1oUmTJg4DHABo3HggoqO7oLh4n2x5xxQtDp4rxL09bsAmtRIXqjRmRmJX2dulT/fBx+tO4J17O3uVt1NPj8D7nb/E1WjgNZgDw6rBYVDxeVm97y8Kbmu9bAMPL6rIPHn4s22D44moWDUDnDqAM4LVJQ7qM04X73eY1F29c+sIBQ4NkF/UnbbKr/K+obqhy20Hrcd/AeI7mXtg1DabP4dCocSgW/Zj4IBdlg+USvmzhlKp8Sy4qWJ01yR08LLh9dwhcxEVHoW0Dmm4IUremFTTpo2TtQLPUdXDr88OwB9/G4QP7+uKRTZBcHjl7yF1IDBoGnD3pwCAm1Mb47txvZHSyEHj6sjGTvedEJWAqzGCB61kg5/d0BMe8f2tw20VlS0ftwFSxjoelJHqNgY4dYijC9GhgoxqbUvw4gJRdaqDlXevtPxfq6puL6baY7kHNW0LPPOnw+6lvhLZsCEEhQI977jLZTqFQlG7o3s+aTMWTZW/Z8/4nsh4OAN/7/V3v+1eEVZ7XUVbNTW3KWsXFYEuURHoEROJBI1Nw9FbXwW6PeJ+Q0+uAVoMsL63+c080PoBdI/rjgfaPOBgxWDkg++aH+YZU8iG/vBiRS8Se9LIWNWqGiXCFPQ4m3gdUr0nrUryH3m4qollqbutVp2/p5HG2nMoqIvlr6vRafPS0/MWQhSNUCqr9MRwkwdv/rbVOpzmge1hOHT8s+4T+cHvvdpWf+WmrYEnfrUZD8emvYhCgYWjFtYscz5Qi1/tKvxQguNVYZhvGxl7SzQF7syT51iCU5d4cRN099QS1eBGt9torhwBBRSYVuhkvhwgkFfYoKRQKOyDGwAR3cw94JRN5T3AKkdr7tixo906PhfX0VwKMXqW//dVRbgm8D3fgl3TZrU/+Wh1+aP00aPu3NYMeL5dPwQ4uSd9P2s7+R5LcOqQmsQSYWGREEX7cWpcleDc0ng8Xhr+HhTXzgDf3QPc9KSDPAV/hBMMzSQaP9QOFb0ToWouHzN+ypQpOHnyJNq1cz34n09M2mruLeKjm5Mg1G7pWFWaiBQUXR8puVZoqw5251sPvnYz5v9tMypKnXevr7YgfZS1rSn3KsDxZh+ebNfL77EoBv91j4L2a0+ONHQ4UrPrH9qBA7fi2rVEdO3ypct0zigUCqBxS2DKXmDAFLvPRQejzZJjmlSt3ZOvWq1Ghw4dvHoinpBsLg1KVIe7SelALbT76Tr8dgdLfX9DaN9uBqKjO6Nly5fcJ66J3hOBxG7AbW/6dz8AlL6clNDnp9x2gwGuovLDQIMUeliCU4cMnfAsDm7wboj6a1eTce1qMu6/z7Z7rO+elGrWLoiq45WWiRjVVIsODYJzcLiBf3kcqd1uQkRMDBZMfcZv+1EqI3Fzr5/8tn2LUTP9v486xw9VVF4l9qKKypMSnCAo5SXfYxhchyiVKjzwxv/VeDvR0d6N+UHBp0t0JJS12QsLgKqlvKeJq9A2uX1HNEq0VulIDor0I7vbT2hI9YtXXcNteVGd5c0gqBRa+JevY5p1kg9UVjkCrSdatXoZEREt0KWzdeA2V5eJRpHuq0BGthjp8f6pbvNoEkIbttVujkZvbvhAa9f7C4bGU/WAp6Wwtu3tPK1S9eYvqHBXRyVrsOPjEhwKSayiqoPUkQ0sQ+GXm4pxuvQAUht0crtei+YT0aK5Z0PJCwDGDXA8fxIArL1/LXJLc9G5KUuD6g/eKOqsAD3KehNc+K2Kyg/f23B1GHRlbiZ8pYBjCU4dNPmbpbL3+yu2+Hwf797X2eVTWnyDeHSN6+r0cwpBfBImH7P9Snk3v1Vgb10N4x2MfE1BhwFOCKjRbafKytNGtcNd3ZIwtlczx+lrqHFyCgAgLtW7Waop8GpS1O92rh+2Va/3vApwvOhypfCktIffv5DEKqpQYHNh6GRMwcmwS+jSqzu2b9/uftUq758e5N/A49EZH+Foxma07e/9PEvVxTr4IOCgnUetTlURqjxsP+OTc12NHpM+nX1BFqgEtoqK6gZeYUJMH2MbPKwbgBEjRgQ6Kw4pVSp0HDTE4Wi/FORqECiKHE6AHLAtiPFuLiovbl2ebJcxUEhigBOCBAh8Oibfq3oHYswSeLVaOunfP7hXVVQBLpXl+F91A++CoUAABHX1Zmtm8S1VV3L7Ru4TVeINwT9q9bz6fl+CbDZxbwIcVlGRewxwQgJ/wFQLqjTsHPNsVzRtFu0ksRyfeGufr+eJq872vOoX5bYNjm19VoADHH6d6wQGOCEisidHhSV/k98oFEoFWvfi966u82fw6dMAR7Zh9o8h9xjghIiIzk2rtd6jSY0BAM00bPRLfuRgJGNbgqp6VawU5NwNTuxNFZVtIOZVNOSeoOD3LxQxwKnnZrRJxv96tMbW3u0CnRW/YQWeb9RoHBwnhQSq1Bgo4yOhaW3fnofd+4OMn6sZ/TXQnyffo7i/vwyEh0Pdpo1H22QNVd3Acr4QIKBmnQpu0jbwWV4o9AgaDaSKCjQeP97n2457mqNhhzKfhqi2pYBe9BL1pA2OMjYW7ffvq06uKIgxwKGQx6etmmmzLQP6CxegaVX9QSAbJSZ5vU5ERES190f+4PtGxrY1TW5rnWTVnN70uPI8qcd4UakTGOCEBIH1MOQ3Co3GaXDjrtai/9g0FF+9gmadvC+pGTVqFE6ePIlmzfwzbQgFGe+mHvdis7w41lcMcEIBf78u8fQETp97x1Z7XZVKhalTp/owNyGoFrvf+7rbeVUK7yZ28CIlrwD1FRsZhwBBEAI+sicRUVXuggvbTxXu6qhsJ2z1Zhwcv1wbWUdVFzDACQVOnuKUShbQEZGv+P6mbrtF9zejagY4fijBaRjPjhl1Ae+AIUJw8PSjUqlgNBoDkJsgw8Itv1FVc4oQqnuqMyCgd/NnuivBMVn/rwjsreu2tLYouVaBlt2qN/4Y1Q4GOCFCmcQnCqdYmuw3HQYmYc+abEQ3Vgc6K1RN/hzJuNhkcvm5aDt2nzcb9mY2cT9QqpS496WeAc0DuccAJ0Rw9nAKBIVCgcfe6RfobFCQuqR3XYJsG1y5bYNj203ci3Y1HDCy/uJdMYQ1bNgw0FkgIi/4tDDF5wUz3m8wxYspYNz2orKtogr0ZJtUJzDACWEPP/wwkpOTcffddwc6K4HF6xuRD3gf4ESFub7FyKrH3N6NbNOy8oHc47ckBCjCHP8Zo6KiMN4Pw+sTEfmC6M38mVIwdROnuoAlOEREQSIQ92J/NjL2htsqquoGOCzCrbcY4FDIigg3d2F+emD151AiCma1GZoEPBCSqldFdc+N9/ghM1QXsIoqBPh7CPW6KvONoTiTX4Z2iTGBzgoROSCLWdzWUXlXgrP2/rU4nH8Yg5sNrlbeqO7zawnOtWvXkJaWBq1WC61Wi7S0NBQUFDhNbzAY8Morr6Bz585o0KABkpKS8Nhjj+HixYuydIMHD4YgCLLXQw895M9DoTpIo1IyuKGQVruVL/59kHJ7LF72oopvEM/gpp7za4Dz8MMPIysrC6tXr8bq1auRlZWFtLQ0p+nLysqwZ88evPHGG9izZw+WL1+OY8eO4c4777RLO2HCBOTk5Fhen3/+uT8PJaixjpkoNHhbC1S7Zbe+35sI23FwvFiR436RB/xWRXX48GGsXr0a27ZtQ+/evQEAX375Jfr27YujR4+ibdu2dutotVqkp6fLln388ce4+eabkZ2djWbNmlmWR0ZGIiEhwV/ZD3opHbvg3MF9gc4GEZFT3nSMUrh73g50GyCqc/wWBmdkZECr1VqCGwDo06cPtFottm7d6vF2CgsLIQiC3aB1ixYtQpMmTdCxY0e89NJLKC4udroNnU6HoqIi2auuS27f0fqGBThE5HfeX2i8CUncFsqIrqd9IKrKbyU4ubm5iIuLs1seFxeH3Nxcj7ZRUVGBV199FQ8//DBiYqxtKR555BGkpqYiISEBBw4cwLRp07B371670p9KM2bMwFtvvVW9AyEiIr8QbUpl3IdPLMEh73hdgjN9+nS7Br5VX7t27QLgeIAlSZI8GnjJYDDgoYcegiiK+PTTT2WfTZgwAUOHDkWnTp3w0EMP4b///S/Wrl2LPXv2ONzWtGnTUFhYaHmdO3fO28MOOpLIHzsR1Sb/zibuthfVjUO93j/Vb16X4EyePNltj6UWLVpg3759uHTpkt1nly9fRnx8vMv1DQYDHnzwQZw+fRrr1q2Tld440qNHD4SHh+P48ePo0aOH3edqtRpqNWc7JiIKJgpvRjaMSQSeywRUDfyXIQopXgc4TZo0QZMmTdym69u3LwoLC7Fjxw7cfPPNAIDt27ejsLAQ/fo5n324Mrg5fvw41q9fj8aNG7vd18GDB2EwGJCYmOj5gRARUUAH8BO93Xfjlv7JCIUkvzUybt++PUaOHIkJEyZg27Zt2LZtGyZMmIDRo0fLelC1a9cOK1asAAAYjUbcf//92LVrFxYtWgSTyYTc3Fzk5uZCr9cDAE6ePIm3334bu3btwpkzZ7Bq1So88MAD6N69O/r37++vwwlurK0iIr/zb28GDndBvubXwQQWLVqEzp07Y/jw4Rg+fDi6dOmC7777Tpbm6NGjKCwsBACcP38eK1euxPnz59GtWzckJiZaXpU9r1QqFf744w+MGDECbdu2xfPPP4/hw4dj7dq1CAsL8+fhEBEFl1otffH9vryqoiLykl+naoiNjcX333/vMo1t8WiLFi3cFpempKRg48aNPskfERH5j7v4xesqKiIvcDjIOko2/xQfgojqpyAvAfEmfuHgxORr/EoREdVVLAEhcooBTigI8qc4IqqfvLk08WZEvsbvFBER+QULmCiQGOAQERFRyGGAQ0QUNGq/yMOfA/15VUXFVsbkY/xG1VGBHH2UiOofXnOormGAQ0RERCGHAQ4REbkl+Lm3Jm9G5Gv8ThERhSIf1yixiorqGgY4RERBI7TGtPLmaNjGmHyNXykiojpKCkCvK28Ed+4o1DHACUWh9RBIVI8wJCDyFQY4dZWD+vDoYc0ApQKNxrYNQIaIiOS8q6Li7Yh8SxnoDFD1CDYXA23TePO/Q5pDO6R5oLJEREQUNBgyhwCBTz5EREQyvDOGAnbfJCIikmGAU0cJbElMRETkFAOcUODnEUaJiIjqGgY4dVSwj39BRLXAB9XTHKGYQhUDHCIiCohoDTvykv/w20VEVFfVavV0dUp6XOdvUNs4dEyMQaMG4dXLEpELDHCIiOoql9VLdaPq6dcXBgY6CxSiWEVFREQe8L60qH0DjR/yQeQZBjhERHVVkPagnN2uGYY1jsEH7VICnRWqxxjg1FHNOnWzvmEvCKL6ydVv3+eXBc83+FBiLL7r0hIqjrJOAcRvXx3VrGPnQGeBiIIZn3uonmOAQ0QUiliyS/UcAxwiohAkib7eYnC29yFyhgEOERF5gCVCVLcwwCEiCkWsoqJ6jgEOEVEoYnxD9RwDHCKiOspVIY0Qzss71W/8BRARhSDtXa0gaMKgbtPQZTrOJk6hyq8BzrVr15CWlgatVgutVou0tDQUFBS4XOeJJ56AIAiyV58+fWRpdDodnnvuOTRp0gQNGjTAnXfeifPnz/vxSIIbL1BEVJUySoUbpvdD079yzCyqn/wa4Dz88MPIysrC6tWrsXr1amRlZSEtLc3teiNHjkROTo7ltWrVKtnnU6ZMwYoVK7BkyRJs2bIFJSUlGD16NEwmk78OhYiIiOoQv80mfvjwYaxevRrbtm1D7969AQBffvkl+vbti6NHj6Jt27ZO11Wr1UhISHD4WWFhIebPn4/vvvsOQ4cOBQB8//33SElJwdq1azFixAjfHwwRERHVKX4rwcnIyIBWq7UENwDQp08faLVabN261eW6GzZsQFxcHNq0aYMJEyYgLy/P8tnu3bthMBgwfPhwy7KkpCR06tTJ6XZ1Oh2KiopkLyIi8garwqlu8VuAk5ubi7i4OLvlcXFxyM3NdbreqFGjsGjRIqxbtw4ffPABdu7cidtuuw06nc6yXZVKhUaNGsnWi4+Pd7rdGTNmWNoBabVapKRwhlsiIqJQ5nWAM336dLtGwFVfu3btAgAIgv3Q3pIkOVxeaezYsbjjjjvQqVMnjBkzBr/99huOHTuGX3/91WW+XG132rRpKCwstLzOnTvnxRETERGnaqC6xus2OJMnT8ZDDz3kMk2LFi2wb98+XLp0ye6zy5cvIz4+3uP9JSYmonnz5jh+/DgAICEhAXq9HteuXZOV4uTl5aFfv34Ot6FWq6FWqz3eJxFR3VDzaqPGjRujpKSkVvZFVJu8DnCaNGmCJk2auE3Xt29fFBYWYseOHbj55psBANu3b0dhYaHTQMSR/Px8nDt3DomJiQCAnj17Ijw8HOnp6XjwwQcBADk5OThw4ADee+89bw+HiChoBGLEh4ceeghLly5Fjx49an/nRH7ktzY47du3x8iRIzFhwgRs27YN27Ztw4QJEzB69GhZD6p27dphxYoVAICSkhK89NJLyMjIwJkzZ7BhwwaMGTMGTZo0wT333AMA0Gq1GDduHP72t7/hjz/+QGZmJh599FF07tzZ0quKiKh+qHm1UUREBJ544gl06dLFB/khCh5+6yYOAIsWLcLzzz9v6fF05513Yu7cubI0R48eRWFhIQAgLCwM+/fvx8KFC1FQUIDExETceuutWLp0KaKjoy3rfPjhh1AqlXjwwQdRXl6OIUOGYMGCBQgLC/Pn4RARBRlWGxE549cAJzY2Ft9//73LNLaj8EZERGDNmjVut6vRaPDxxx/j448/rnEe67LYpGRcvXgetz35dKCzQkREFFT8GuCQfz354WcwGvVQKlWBzgoR+YCLDqbO1vBHNohCAifbrOMY3BDVZ6yiInKGAQ4RERGFHAY4RETkVoQmOdBZIPIK2+AQEQWJQIyD46kbb5yGwqIsNGrUJ9BZIfIIAxwiInJLoVCi103LAp0NIo+xioqIqI6Ka9Ey0FkgCloswSEiqmNGTnoRJ3Zuw5AnJwY6K0RBiwEOEVEd03HQEHQcNCTQ2SAKaqyiIiIiopDDAIeIiIhCDgMcIqIgEdc8JtBZIAoZbINDRBQkho3vgI3fH0XbPgmBzgpRnccAh4goSKhUSgz7a8dAZ4MoJLCKioiIiEIOAxwiIiIKOQxwiIiIKOQwwCEiIqKQwwCHiIiIQg4DHCIiIgo5DHCIiIgo5DDAISIiopDDAIeIiIhCDgMcIiIiCjkMcIiIiCjkMMAhIiKikMMAh4iIiEJOvZxNXJIkAEBRUVGAc0JERESeqrxvV97HXamXAU5xcTEAICUlJcA5ISIiIm8VFxdDq9W6TCNInoRBIUYURVy8eBHR0dEQBCHQ2QmooqIipKSk4Ny5c4iJiQl0dkIOz6//8Nz6F8+v//DcVp8kSSguLkZSUhIUCtetbOplCY5CoUBycnKgsxFUYmJi+EPzI55f/+G59S+eX//hua0edyU3ldjImIiIiEIOAxwiIiIKOQxw6jm1Wo1//vOfUKvVgc5KSOL59R+eW//i+fUfntvaUS8bGRMREVFoYwkOERERhRwGOERERBRyGOAQERFRyGGAQ0RERCGHAU4I2LRpE8aMGYOkpCQIgoCffvrJ8pnBYMArr7yCzp07o0GDBkhKSsJjjz2Gixcvyrah0+nw3HPPoUmTJmjQoAHuvPNOnD9/Xpbm2rVrSEtLg1arhVarRVpaGgoKCmrhCAPL1fmt6umnn4YgCJg9e7ZsOc+vY56c28OHD+POO++EVqtFdHQ0+vTpg+zsbMvnPLfOuTu/JSUlmDx5MpKTkxEREYH27dtj3rx5sjQ8v47NmDEDvXr1QnR0NOLi4nD33Xfj6NGjsjSSJGH69OlISkpCREQEBg8ejIMHD8rS8Pz6DwOcEFBaWoquXbti7ty5dp+VlZVhz549eOONN7Bnzx4sX74cx44dw5133ilLN2XKFKxYsQJLlizBli1bUFJSgtGjR8NkMlnSPPzww8jKysLq1auxevVqZGVlIS0tze/HF2iuzq+tn376Cdu3b0dSUpLdZzy/jrk7tydPnsSAAQPQrl07bNiwAXv37sUbb7wBjUZjScNz65y78/viiy9i9erV+P7773H48GG8+OKLeO655/Dzzz9b0vD8OrZx40Y8++yz2LZtG9LT02E0GjF8+HCUlpZa0rz33nuYNWsW5s6di507dyIhIQHDhg2zzIcI8Pz6lUQhBYC0YsUKl2l27NghAZDOnj0rSZIkFRQUSOHh4dKSJUssaS5cuCApFApp9erVkiRJ0qFDhyQA0rZt2yxpMjIyJADSkSNHfH8gQcrZ+T1//rx0ww03SAcOHJCaN28uffjhh5bPeH494+jcjh07Vnr00UedrsNz6zlH57djx47S22+/LVvWo0cP6fXXX5ckiefXG3l5eRIAaePGjZIkSZIoilJCQoL07rvvWtJUVFRIWq1W+uyzzyRJ4vn1N5bg1EOFhYUQBAENGzYEAOzevRsGgwHDhw+3pElKSkKnTp2wdetWAEBGRga0Wi169+5tSdOnTx9otVpLmvpKFEWkpaXh5ZdfRseOHe0+5/mtHlEU8euvv6JNmzYYMWIE4uLi0Lt3b1k1C89tzQwYMAArV67EhQsXIEkS1q9fj2PHjmHEiBEAeH69UVhYCACIjY0FAJw+fRq5ubmyc6dWqzFo0CDLeeH59S8GOPVMRUUFXn31VTz88MOWSd5yc3OhUqnQqFEjWdr4+Hjk5uZa0sTFxdltLy4uzpKmvpo5cyaUSiWef/55h5/z/FZPXl4eSkpK8O6772LkyJH4/fffcc899+Dee+/Fxo0bAfDc1tScOXPQoUMHJCcnQ6VSYeTIkfj0008xYMAAADy/npIkCVOnTsWAAQPQqVMnALAce3x8vCxt1XPH8+s/9XI28frKYDDgoYcegiiK+PTTT92mlyQJgiBY3tv+31ma+mb37t346KOPsGfPHq/PA8+va6IoAgDuuusuvPjiiwCAbt26YevWrfjss88waNAgp+vy3Hpmzpw52LZtG1auXInmzZtj06ZNmDRpEhITEzF06FCn6/H8yk2ePBn79u3Dli1b7D6reg48OS88v77BEpx6wmAw4MEHH8Tp06eRnp5uKb0BgISEBOj1ely7dk22Tl5enuXpIyEhAZcuXbLb7uXLl+2eUOqTzZs3Iy8vD82aNYNSqYRSqcTZs2fxt7/9DS1atADA81tdTZo0gVKpRIcOHWTL27dvb+lFxXNbfeXl5Xjttdcwa9YsjBkzBl26dMHkyZMxduxYvP/++wB4fj3x3HPPYeXKlVi/fj2Sk5MtyxMSEgDArpSl6rnj+fUfBjj1QGVwc/z4caxduxaNGzeWfd6zZ0+Eh4cjPT3dsiwnJwcHDhxAv379AAB9+/ZFYWEhduzYYUmzfft2FBYWWtLUR2lpadi3bx+ysrIsr6SkJLz88stYs2YNAJ7f6lKpVOjVq5dd19tjx46hefPmAHhua8JgMMBgMEChkN8GwsLCLKVnPL/OSZKEyZMnY/ny5Vi3bh1SU1Nln6empiIhIUF27vR6PTZu3Gg5Lzy/fhaIls3kW8XFxVJmZqaUmZkpAZBmzZolZWZmSmfPnpUMBoN05513SsnJyVJWVpaUk5Njeel0Oss2Jk6cKCUnJ0tr166V9uzZI912221S165dJaPRaEkzcuRIqUuXLlJGRoaUkZEhde7cWRo9enQgDrlWuTq/jlTtRSVJPL/OuDu3y5cvl8LDw6UvvvhCOn78uPTxxx9LYWFh0ubNmy3b4Ll1zt35HTRokNSxY0dp/fr10qlTp6RvvvlG0mg00qeffmrZBs+vY88884yk1WqlDRs2yK6rZWVlljTvvvuupNVqpeXLl0v79++X/vKXv0iJiYlSUVGRJQ3Pr/8wwAkB69evlwDYvR5//HHp9OnTDj8DIK1fv96yjfLycmny5MlSbGysFBERIY0ePVrKzs6W7Sc/P1965JFHpOjoaCk6Olp65JFHpGvXrtXuwQaAq/PriKMAh+fXMU/O7fz586Ubb7xR0mg0UteuXaWffvpJtg2eW+fcnd+cnBzpiSeekJKSkiSNRiO1bdtW+uCDDyRRFC3b4Pl1zNl19ZtvvrGkEUVR+uc//yklJCRIarVauuWWW6T9+/fLtsPz6z+CJEmSf8uIiIiIiGoX2+AQERFRyGGAQ0RERCGHAQ4RERGFHAY4REREFHIY4BAREVHIYYBDREREIYcBDhEREYUcBjhEREQUchjgEBERUchhgENEREQhhwEOERERhRwGOERERBRy/j+7AHXZVgVaIgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The data are available using (this example is NOT the right way of plotting the data, it's just an example) \n",
    "# and don't forget to import numpy as np to have np.arange working]:\n",
    "\n",
    "data1 = hdulist[1].data\n",
    "DTs =  data1.ABS_CAL\n",
    "WLs = data1.WAVELENGTH\n",
    "DWs = data1.DELTAW\n",
    "for WL, DW, DT in zip(WLs, DWs, DTs):\n",
    "    plt.plot(WL + np.arange(len(DT)) * DW, DT)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing FITS files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Creation of numpy array with the data. \n",
    "x = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('SIMPLE', True, 'conforms to FITS standard')\n",
      "('BITPIX', 64, 'array data type')\n",
      "('NAXIS', 1, 'number of array dimensions')\n",
      "('NAXIS1', 100, '')\n",
      "('EXTEND', True, '')\n"
     ]
    }
   ],
   "source": [
    "# Creation of the HDU from the data. \n",
    "hdu = fits.PrimaryHDU(x)\n",
    "print(hdu.header.cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#Adding additional keywords to the header. \n",
    "# The automatically created header contains only the required minimum of keywords. \n",
    "# If additional keywords are needed they are added with:\n",
    "hdu.header['testkey'] = (0.001,'some test value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('SIMPLE', True, 'conforms to FITS standard')\n",
      "('BITPIX', 64, 'array data type')\n",
      "('NAXIS', 1, 'number of array dimensions')\n",
      "('NAXIS1', 100, '')\n",
      "('EXTEND', True, '')\n",
      "('TESTKEY', 0.001, 'some test value')\n"
     ]
    }
   ],
   "source": [
    "print(hdu.header.cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "hdulist = fits.HDUList([hdu])\n",
    "hdulist.writeto('new.fits', overwrite=True) \n",
    "hdulist.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to deal with FITS tables is to use the ATpy library, we'll see this later"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python_Lecture",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}