Notebooks/Interact with files.ipynb

{
 "cells": [
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   "cell_type": "code",
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    {
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     "text": [
      "Thu Sep 22 16:06:36 2022\n",
      "3.9.5 (default, May 18 2021, 12:31:01) \n",
      "[Clang 10.0.0 ]\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": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "datafile.close() # Not need anymore of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   2.3  6   8 star\n",
      " tralala\n"
     ]
    }
   ],
   "source": [
    "print(data[0], 'tralala')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": [
    "# In python 2:\n",
    "for row in data:\n",
    "    print(row),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "# In python 3:\n",
    "for row in data:\n",
    "    print(row, end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "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": 30,
   "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()\n",
    "    print('N = {0:2d} f = {1:5.2f} type = {2:>10s}'.format(int(this_data[0]), \n",
    "                                                           float(this_data[1]), \n",
    "                                                           this_data[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 33,
   "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": 34,
   "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": 35,
   "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",
    "row = datafile.readline() # this reads only one line\n",
    "first_comment = row\n",
    "print(first_comment, end='')\n",
    "\n",
    "row = datafile.readline() # this reads only one line\n",
    "header = row\n",
    "print(header, end='')\n",
    "\n",
    "data = []\n",
    "while True: # loops until exit by break command\n",
    "    row = datafile.readline()\n",
    "    if row == '':\n",
    "        break\n",
    "    if row[0] != '#' and row[0] != '\\n': # comment lines are skipped\n",
    "        data.append(row)\n",
    "datafile.close()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
    "row = datafile.readline() # this reads only one line\n",
    "first_comment = row\n",
    "print(first_comment, end='')\n",
    "row = datafile.readline() # this reads only one line\n",
    "header = row\n",
    "print(header, end='')\n",
    "data = []\n",
    "row = datafile.readline()\n",
    "while row != '': # loops until exit by break command\n",
    "    if row[0] != '#': # comment lines are skipped\n",
    "        data.append(row)\n",
    "    row = datafile.readline()\n",
    "datafile.close()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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:\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": 38,
   "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:\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": 39,
   "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 change 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": 40,
   "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':['i4','f16', 'f16', 'f16', 'U10']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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', '<i4'), ('f', '<f16'), ('x', '<f16'), ('y', '<f16'), ('type', '<U10')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2.3, 6., 8., 'star')\n"
     ]
    }
   ],
   "source": [
    "print(a[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "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": 46,
   "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": 47,
   "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": 48,
   "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": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4'), ('f3', '<f4'), ('f4', '<U10')])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The names of the columns are f0, f1, f2, etc\n",
    "b.dtype"
   ]
  },
  {
   "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": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-e171fcb8a0c0>:4: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  c = np.genfromtxt('data2.dat', names=True, dtype=None, skip_header=1)\n"
     ]
    }
   ],
   "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": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1,  2.3, 6, 8, b'star') (2,  3.5, 7, 9, b'galaxy')\n",
      " (3, -4.2, 5, 7, b'cluster')]\n"
     ]
    }
   ],
   "source": [
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('N', '<i8'), ('f', '<f8'), ('x', '<i8'), ('y', '<i8'), ('type', 'S7')])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.3,  3.5, -4.2])"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "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,  -10.5,  5,  7, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-2c7c002a02b9>:1: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  d = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1,\n"
     ]
    }
   ],
   "source": [
    "d = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1, \n",
    "                  delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1,  2.3000e+00,  6, 8, b' star'),\n",
       "       (2,  3.0005e+03, -1, 9, b' galaxy'),\n",
       "       (4, -1.0500e+01,  5, 7, b' test')],\n",
       "      dtype=[('N', '<i8'), ('f', '<f8'), ('x', '<i8'), ('y', '<i8'), ('type', 'S7')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The missing value has been changed to -1\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-ade99b5a0e80>:2: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  d = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1, delimiter=',',\n"
     ]
    }
   ],
   "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": 60,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['x'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-61-d9e9ff67078e>:2: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  e = np.genfromtxt('data3.dat', names=True, dtype=None, skip_header=1,\n"
     ]
    }
   ],
   "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": 62,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1,  2.3000e+00, b' star') (2,  3.0005e+03, b' galaxy')\n",
      " (4, -1.0500e+01, b' test')]\n"
     ]
    }
   ],
   "source": [
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ons 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": 64,
   "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": [
    "### Using recfrom to obtain a record array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/christophemorisset/anaconda3/envs/ML2/lib/python3.9/site-packages/numpy/lib/npyio.py:2369: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  output = genfromtxt(fname, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Uses the same keywords than genfromtxt\n",
    "f = np.recfromtxt('data3.dat', names=True, dtype=None, skip_header=1, \n",
    "                  delimiter=',',usecols=(\"N\", \"f\", \"type\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([(1,  2.3000e+00, b' star'), (2,  3.0005e+03, b' galaxy'),\n",
       "           (4, -1.0500e+01, b' test')],\n",
       "          dtype=[('N', '<i8'), ('f', '<f8'), ('type', 'S7')])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 4])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixed size ascii files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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  relat_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": 69,
   "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": 70,
   "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'), ('relat_erro', '<f8'), ('r', '<U2')])"
      ]
     },
     "execution_count": 70,
     "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": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-71-fcc153fc5519>:2: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  obs2  = np.genfromtxt('data4.dat', skip_header=1,\n"
     ]
    }
   ],
   "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": 72,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(b'H  1  4861A', 1.      ,    4861., 0.08   , b'An'),\n",
       "       (b'H  1  6563A', 2.8667  ,    6563., 0.19467, b'An'),\n",
       "       (b'H  1  4340A', 0.4933  ,    4340., 0.03307, b'An'),\n",
       "       (b'H  1  4102A', 0.2907  ,    4102., 0.02229, b'An'),\n",
       "       (b'H  1  3970A', 0.18    ,    3970., 0.01253, b'An'),\n",
       "       (b'N  2  6584A', 2.1681  ,    6584., 0.08686, b'An'),\n",
       "       (b'N  2 121.7m', 0.004462, 1217000., 0.2    , b'Li'),\n",
       "       (b'O  1  6300A', 0.0147  ,    6300., 0.00325, b'An'),\n",
       "       (b'TOTL  2326A', 0.079   ,    2326., 0.2    , b'Ad'),\n",
       "       (b'C  2 157.6m', 0.00856 , 1576000., 0.2    , b'Li'),\n",
       "       (b'O  1 63.17m', 0.13647 ,  631700., 0.1    , b'Li'),\n",
       "       (b'O  1 145.5m', 0.00446 , 1455000., 0.2    , b'Li'),\n",
       "       (b'TOTL  3727A', 0.77609 ,    3727., 0.2    , b'To'),\n",
       "       (b'S II  4070A', 0.06174 ,    4070., 0.2    , b'To'),\n",
       "       (b'S II  4078A', 0.06174 ,    4078., 0.2    , b'To')],\n",
       "      dtype=[('label', 'S11'), ('i_obs', '<f8'), ('lambda', '<f8'), ('e_obs', '<f8'), ('observer', 'S2')])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-74-039df3e0929d>:2: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  obs3  = np.genfromtxt('data5.dat', skip_header=1,\n"
     ]
    }
   ],
   "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": 75,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(b'H  1  4861A', 1.     ,    4861., 0.08   , b'An'),\n",
       "       (b'H  1  6563A', 2.8667 ,    6563., 0.19467, b'An'),\n",
       "       (b'H  1  4340A', 0.4933 ,    4340., 0.03307, b'An'),\n",
       "       (b'H  1  4102A', 0.2907 ,    4102., 0.02229, b'An'),\n",
       "       (b'H  1  3970A', 0.18   ,    3970., 0.01253, b'An'),\n",
       "       (b'N  2  6584A', 2.1681 ,      nan, 0.08686, b'An'),\n",
       "       (b'N  2 121.7m', 0.00446, 1217000., 0.2    , b'Li'),\n",
       "       (b'O  1  6300A', 0.0147 ,    6300., 0.00325, b'An'),\n",
       "       (b'TOTL  2326A', 0.079  ,    2326., 0.2    , b'Ad'),\n",
       "       (b'C  2 157.6m', 0.00856, 1576000., 0.2    , b'Li'),\n",
       "       (b'O  1 63.17m', 0.13647,  631700., 0.1    , b'Li'),\n",
       "       (b'O  1 145.5m', 0.00446, 1455000., 0.2    , b'Li'),\n",
       "       (b'TOTL  3727A', 0.77609,    3727., 0.2    , b'To'),\n",
       "       (b'S II  4070A', 0.06174,    4070., 0.2    , b'To'),\n",
       "       (b'S II  4078A', 0.06174,    4078., 0.2    , b'To')],\n",
       "      dtype=[('label', 'S11'), ('i_obs', '<f8'), ('lambda', '<f8'), ('e_obs', '<f8'), ('observer', 'S2')])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs3['lambda']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "new_obs3 = obs3.view(np.recarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'H  1  4861A', b'H  1  6563A', b'H  1  4340A', b'H  1  4102A',\n",
       "       b'H  1  3970A', b'N  2  6584A', b'N  2 121.7m', b'O  1  6300A',\n",
       "       b'TOTL  2326A', b'C  2 157.6m', b'O  1 63.17m', b'O  1 145.5m',\n",
       "       b'TOTL  3727A', b'S II  4070A', b'S II  4078A'], dtype='|S11')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obs3.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-79-ed3c65ebb4cc>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-79-ed3c65ebb4cc>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    new_obs3.lambda # lambda is reserved!!!\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "new_obs3.lambda # lambda is reserved!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obs3['lambda']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using masks on the structured array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(b'H  1  4861A', 1.    , 4861., 0.08   , b'An')\n",
      " (b'H  1  6563A', 2.8667, 6563., 0.19467, b'An')\n",
      " (b'H  1  4340A', 0.4933, 4340., 0.03307, b'An')\n",
      " (b'H  1  4102A', 0.2907, 4102., 0.02229, b'An')\n",
      " (b'H  1  3970A', 0.18  , 3970., 0.01253, b'An')\n",
      " (b'O  1  6300A', 0.0147, 6300., 0.00325, b'An')]\n"
     ]
    }
   ],
   "source": [
    "mask_observer = (obs3['observer'] == b'An') & (np.isfinite(obs3['lambda']))\n",
    "print(obs3[mask_observer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line b'H  1  4861A', wavelength=4861.0A Intensity=1.000 +/-  8.0%)\n",
      "line b'H  1  6563A', wavelength=6563.0A Intensity=2.867 +/- 19.5%)\n",
      "line b'H  1  4340A', wavelength=4340.0A Intensity=0.493 +/-  3.3%)\n",
      "line b'H  1  4102A', wavelength=4102.0A Intensity=0.291 +/-  2.2%)\n",
      "line b'H  1  3970A', wavelength=3970.0A Intensity=0.180 +/-  1.3%)\n",
      "line b'O  1  6300A', wavelength=6300.0A Intensity=0.015 +/-  0.3%)\n"
     ]
    }
   ],
   "source": [
    "for o in obs3[mask_observer]:\n",
    "    print('line {0[label]:4s}, wavelength={0[lambda]}A Intensity={0[i_obs]:5.3f} +/- {1:4.1f}%)'.format(o, o['e_obs']*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple \"write\" method from \"open\" class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "f = open('data10.dat', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.write('tralala')\n",
    "f.write('trololo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat 'data10.dat' # the writing method put everything together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": 88,
   "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": 89,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tralala\n",
      "trololo\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": 90,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n",
      "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": [
    "a = 'Smith'\n",
    "b = 3\n",
    "with open('data12.dat', 'w') as datafile:\n",
    "    datafile.write(\"\"\"Hola Sr. {0}\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 {1}.\n",
    "\"\"\".format(a, b))\n",
    "print('end') # This one ends the indentation\n",
    "    \n",
    "!cat \"data12.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pickle (and cpickle) python specific format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 92,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pickle # The module we will use for this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump((a,b,c,d), open('Demo.pickle','wb')) # Writing the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "res = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[3](5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "a2,b2,c2,d2 = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": 104,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.6 µs ± 811 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit res = pickle.load(open('Demo.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import gzip\n",
    "pickle.dump((a,b,c,d), gzip.open('Demo.pklz','wb')) # Writing the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "with gzip.open('Demo.pklz','rb') as f:\n",
    "    a, b, c, d = pickle.load(f)"
   ]
  },
  {
   "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": 108,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.2.1\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": 109,
   "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": 110,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "hdulist = fits.open('n10017o.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "len(hdulist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 114,
   "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": 114,
     "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": 115,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'107 Psc'"
      ]
     },
     "execution_count": 115,
     "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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\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/ML2/lib/python3.9/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 : A list of `Card` objects, 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\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hh = hdulist[0].header\n",
    "hh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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": 120,
   "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": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2048, 2154)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc2dc8623d0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc2dafc7a30>]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A column from the data can be plotted with \n",
    "plt.plot(data[:,1000])\n",
    "# where I am plotting the column number 1000. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc2db100a90>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the same way a line from the data is plotted with: \n",
    "plt.plot(data[1000,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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": 128,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#but now hdulist has 2 elements (2 header/data units):\n",
    "len(hdulist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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": 130,
   "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",
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      "('', '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": 131,
   "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": 132,
   "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": 133,
   "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": 133,
     "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": 135,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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224EJwendUisrbg5x+G0WWDi3Zx0wFlNWuF0eFjy+lumzh5M7JAvj8fDkzddw1PHTmf3jX7Q2RxGXeIOX/yrFq1ud3/bi8dQ0v5JS3YCOLI6SqbefHhZUnom3aSi8xuWeNpTMf774XPZvb929gsZ8xOIPfmjvEXatP8jif2y0923tfdsKX/NVQ2k19UWtuzH9u217uWl9oV9KdM1ZLfHh839nxxdtG8doTPw9OU4lJg0EURARjiy15s9xV7SuWaTx6WZtrf3vWLWy2XW+3PRLtmz9IwAe0/6Fqr/9f15JySOrvO997f3BOfB9Ao/uKmFeSZn3/ardVu+rsur2ezrb8vmv8fo9d7VpW22/V/FCA0GsROjh2NwVgYE2tdc01DbfLLFnzwvs2vVUQD4aC+LaBjerd5e1+rjWTqxfJopenQcOLAagsnJ9xHXm/G8bAMsKD7V6/3U7dmA8bcug8RhMpJs7AbRbq4oPGgjaW4Rao6PCGg8QdmkLapqHi60eukV3fsTB5zc2nw37d2PIufO1Ncx+9BP2V7Rk5HMgX62+7TXiI9Xb2rxtc+q2bGH72edwYM6c5lcOY+9vPmPfvcubXc/jad1V4MwH/8fFT3wWmOhugCdOgW3vByTH75QiqivSQBCVJmryzRSWcmBTE0vD7CbMoeu2lwFQs7b5sQEuO7hsPmIV/KuLrGaXqjqXtcLhQqgq8a7/l8//wsr94ZufGvMUTctIScmC5ldqo4ZiK8jWfP5FQPqjNy7ms3nNByBT58ZdXtfseps2/apV+dq8v4qlO4Kubir2WDfA/3Nr2G1Cm9KUij0NBFGoqaxodh3/srLer6lC6o+ELA8v0gyl4A56oH1FRQUfffRR2LbrL6usALC2KrBJybv3hyfAn0Z6059c+yRX//fqJrMUfJguU4ttovD8fOHOmB3mcFkTEwrGiN6HUB1BA0EUXEd8zSr+ZY9/LW5Hra9nSW2YduewvYZa8OWvqaqgbkdgIHr11VdZtGgRJSUlEbbyP0TLC5gjS5YGvN9vH9fjtgNbVy2rulMh2o2yquKPBoIoZEjPsOn+hewL+301cAcw74siCu58C2llIWU81QHvizasw+N3RbBjxw7vnP9ud5hujUHxpvCgtb+WND3U7wysRb//D2skbbXdU8rtsnv1NHFOlZUdOcdR25tTIgVIl8tFRUXzV4CRrNx5uPmVlOokGgiiMC3pq97Xu+sCa+F1dVa30oD544CH39sC+D54/2Ln4z0fU1VfFbCfhtpSag8/QF156I1P/30/++yzfumhhVnAmKzy1g2EMiEPk2nMmxVwKg8eDEhvWY+bNmgIzfeHRR9GXP3IJ5+0+hC1m0MLbJfLxe9//3seeOCBMA/WCbWsrIrfbN0TkFZR28x2eitAdSINBDFyxOPfTBT+Wy0iuIP68zfeNdh3ZB83vXcTd34U+IC3I4ebGOwUofYaLhBUunxXCfvK/fIaee9eJfff3+Ty4NP11LhasNdA9S3p6lm2KyTp15/+OiSt8t13W3TM3RtCp7d2l4XeJN640dcry+Vq/tzO+2Irj+8u5YjflVmzTXHaNKQ6kQaCVtj+ua9L4fjep0Zcr6HBV0P2/34LsNuepTS4T1G1y2qq2Vmxk7J/vYpBWDv2Omorm+jeade8D0vgc4D37t1LdXVhQNrzxeH74rvC1N53VgQ2BZm6pnvQhJRxbWibP/fzLc2vtPSJkGN4wgxmKHvllRYdc9HfHg9J87Rw8FptbcgEuyHKG/wDQaS17P+EI4FTq//845+3KB9KxYIGglbYt807cSpjsqezx3GITc7AAqF37z1s2ep7QFuk77+3aUgAEd7c9iYAhRWF7P/DH2hIzqA0bxJNDVpyV1u109dSA3uvbNu2jQZXebhNwjDgDqzlBjdPNee9Jx8J2aU/j39vqQhXS2sqW9BcVfhxSNKhWl+A27R0H4/euJia1Jywm3tce3HXb/K+P1gUeoVREaZXUbg8f/Lpyc1m1/9juP65CFd2HvuzdwWe/6Jdi5rdv1KxooGgFT579cWA92+nfMFHyRt5M8Xqb5+dXcwxxy4OWCdSTdDRWKO1a4TPbXguaA3BeGpw10ee4rhhd+QbsHv2vBhxWaMUGhBXHQTNmfPK5vA1arcjmbowNebgJpaDz20IeP/b3/622by0yAFfIY4xlL36Kpk1vg94sz3dR3Vm/5BNN3yyl/rKl2g48lbIsiRJoWb9AYqKiqLuAvt5he/qzH9PEa8IPv1Lk/sLl5+Df3+GmnWRR2Qr1VoaCGJgn6OMnr32c+z49wBYzzEcpjceHLj9vsg7a0ObWBrrynXuwGWHe4+mrvxxIPzEZkmSEjY9N3cH2b3nUFz8r5BljtJa0hbuYct+q8a/JPVmRswdYY1u9fP6ltfD7nvZpJ/w+I2/xxhfXte9H9ge7zpcS30TAcoYg9vd/GCtR99velK92i+/pPgXv+S+p32fjyfMNNjG46Guptrb0wngwK5Cykv2e99PzT2bdf/8lKeeeoqPkkIDb/AVQXn5FyHrNHr3gK9nkTuo9M/nIIVpl8FdvWDF363Ew60f11By770UXnhhq7cLx7g9eOpjN3ne/23aTf/3V8Vsf6pjaCCIkQkTrKd31ZDG3fIbbpGnuEL+RVFdY197Q63b10Ti/8F/2RDajLN+7LVNHm9EVuDs3WPHLWbqtFc5eszHpKQUhd3GUWI1P/zfa2sAyJEqDGDe+aV3neCbmgfXFdKQlA5AZfJB3HUrcdX4pklYuzjwqWVVnzbVdm6oqyuiqqqJ2qwxVNY28PCiwHsGn6x5htWpKfZeoHrVKgByK0Ds+xzVy5Z799Fo0dOP88jVF2E9NsPy7E9u4Zlbb/K+z0rK4aDDCo6bk0Lzv2RJYNNbdfWOiNl/cKcvwDTefmn8TD9L+75vxTdvt5rkDrTg3oifhj17wqZXHqrl0RsX8+iNi/EE3fdZ+/47PHjZbNxhbnSXzlnD3l992qo8uFxVHDjwfthlz+09GDa9vR1ZsoSNR4+hfldoc59qngaCGKvF74ljxpC86iBpC/eQ9s5e7rvnEX6e9E/A98F7gJJ9q7ybDNvXfNPExcPuYEDfyWxwFrEkySpI+vTZQ2qqr525hH4s5Gz208+qodW4SCqybkgn4yID6yb0rEEDmFGy0Mou8JcvfE0VPaoNLz2ynWVT7BuX9o1Z4z6IMW487oMc2rab/unDvdv8Y9k85qUsxYUbDx7WrnuAAQOsXjd5edtZu+48Fq68kbv4A3/nOwA8/L+tOOzxFs4dVVzymyd40fFLUvHV8G/84s98e0B/PMAZgwew6Nnfe5f1qoasakNZrxEAFOef4F22+t23AXDX+5qrRvaczAUFPyAjyRoHkp2aR5LfV+F/yetZ7SykuLiY5577GXX1H5CVZd3MdburOVK93btuDWnUYQWoytoG8Hu+sse+Giw+WGZdCQT59P6BVFUGFuyHag/h8kTumVT2+ryw6SWFviuRmopKVr2zwBuA3pnzFzxuN/VhJips6uotnH375vO/Dyewes31VFfHbpR2tMrn/RuA6hXNz8qrQsXkwTTKx+HfpusB535fr58XUu4GYK7rXF/3UYGDB/bxyt+TeOxrDnpXQk2adbPTmXo87jqrlpviSA84zvxU383H5OTAL/jl8pr39XNA2kJfYTNadrEw1ddFdW+y9S9w7LAhAPzorid4ZbNh/RA4al8vPvkK1KX1ZtfgM3yn5dpJXdnDOCWJbxb8CID9NTtJdqTwumwAgWfSPgDg5JJ/MOIoGHGUld+D5HCrzAVgC0fzHrNIe3sTKYA7Nw1naS0PpTzGSMceNqVdzV0NV7LIM5Ey+9gT7Hwe6Olrzpj7V+v14tOsArkkbzK7Kgrpe9B378JV/Z739eDMowHIcPYk1f5c/Z/ctsW5D5yw/IltnHzKy9708vJcli77BzsZys/kNX5o/sgD8lMA9gHH3vUOaUDD2GzcgzPplWR9ts7q0Lmg3s9I59Z+1rO3lxTuJtMYKuorOPXl0N5oxuOhdt060seP58CjjwYsqzhQQ0avFIq3WVeVxriZ810r6PQbPoLy/fv8dmTfl6p3Y+rcONLDf/1driPs2PEOzz+/mvz8fL773e96l63f8APva4+n9RMWtkZFfQU9U8IP2gwRiwmwEpgGgpjzDwS+1yn42uEzpcY7snhdaiplRSkcjYfvvWXVJhef9jsAxO8+QFZy+J4wANNPeLWJ7AR+MfyDQLDkBsO0zdb643bBwZyB3mVbR5wPtZ8HrN9Yowbolz7UfhV4ozjYrfJkYILLV4N2lloFy0iHL3Ddlfwcd/EcxzIkMK9hKs0Z1fupzuhn5feoC9h61AVw+IGQ9dKcmQAU9BjHoMzRACQZZ5P5BujVy7oq+JlY+2wMAgD931/lvRZM3lBG8oYyptgB+I3cJ+gXtK/GIAAwvWAwV5RX8I8XTyStzjB7iYdPxjrIqoFZKz18+cdxAKwafzOHTnuUkz/+MQ1JmezbXs5r961kxKQ8tq7cR13ZQwHHKNtXzIK//sn7vmjjOvrXDaH8Latpy4Ubh18A9Hg81NfXs2TJdbg9y0lLm409fx+LFo8gLW2Qd916Uthb52FUj6Y/s3qXh5SkCA0Pm96GIwdg0hUhi5bvW861C6/lsTMe4+RBzffQwmEdw2Nf9dRU1ZOWmayT9rVQTAKBiMwCHgacwFPGmHuClou9/BygGrjaGPN5S7btbox/a5tfIOiBr9b+QeqP+F6SVRC8ntWDV972lWpbh/u6nvoHlVp34FiBlpLylj/UJSmop6oz6Kau8QTey5CglsU29biJYQUuq3KXNxA0uV5ybwBG9DzOm5ZC84GgLXpSxYTK/3nfX1z3S16++0ec9+hw5mf5StF/9LKC6gWfepi9xHDBp6E3cA/ljAXgo5Pswv0+qxlk64qddseCQG//9c9cPOwOX8LrbsqxgsDC5NXsdvpdqdzla/OfcvxG0tPh+KlvALBo8T8AqK0twgAbGccf5Lewpg5YFXhQY3DsreaWN9fy5seh7fWCh5e+nsm0PjXwkt1ctv51qK3gv2UbeCwnhx1+geN7i77nfZ1/0PDwXDc9zz2XtGPGUXLPvQA4e/fGffgwLmcqS577nK+c+U2e//USBozM5uSLR5GWmURZSQ1vPPgF3/rpZA7vXUPBhMlsXvoxg44eR9n+fRRtWMvJl10dkt9EEXUgEBEn8ChwFlAELBeR+cYY/6rh2cBI+2ca8DgwrYXbdiueCIHAEVTifZQR2NTTaNeQmX7v/LaXwEK3h0mjSqwadE1NFunpldTVpQfcJwBw7gkMIMs8o5nq2EQ4Dk/w+8Ag4nD2j9CHyeJuwYNaxHgwEv4zao3aMJ2mMqr3haQ5kkfjaQh/vv487TS0957kpwLeLzVjQIQJks78MOvnlYXfT21qdsRjhAsCAH39avDBAoJAkPT0yONIvu3X7BjCGNLesW62v+ltzPO5O+lJLkt6H4IHfm+zulz/ZNiQkG38PTzX+u+rePNNKt5805vuPmxNC1I49Gx2DTmLHb+2bu7v3VLGy79fFrCPf975fYwn/OBKA5ySoMEgFlcEU4Gt9oPoEZGXgNkEthHMBp4z1t2rJSKSLSL5QEELto2Z3/xnPRv2WjfVriqfw1DXNp7tVUNhsvUPdvYHLgaUGnA4Ke17IXXJAxBpQLBusmYmZXv3dUh8X5bGG4kAWxjtfe047LvZOcWv8F2alup93e+QrwCqT870vjbG4G4otPaTMoacZN8lbi0N3iAAkJ5u3fArL+vP6m0z4EQ4wXzEzTzEtRWBhUQeZVSn9GFsxV8RxxF68Dvvsly/Cv8D33CQ5MznmIMwfs1jNCRnsnb4RACSMr6KIymfXlg3C6tdFWQk9QzIE4CIL2ysWH4eQ4as5dh+q1nDREaYzZzEhzxXeyXBKkw6PcUX0CocgZf3R9XXM/CgL5i89hXhgk8NFQXjATjpkzvYUXAuewaeDKZl8yqV2aOzz62bTImjnGXJW0lKar6bq1eELphDxDcH1fG1Vvv+5v2VrBo1A/aGzpN09O7wAamyR/hC0nhCC+3jz7uG5fP/Tt/UgWG2sP5/IklNjXzlWUpuxGU0eHAWRd72ONlqBYEwqkVYlZoadhlAZo1heBOdKFzOVCp6DsOVFL5yBeBx7ceY2ohBACBtvYPdD/jmp0pOTcORlBywzsflqzlgD9aUFCdJfdIIlt37HVJSfJWSlJQUcnL6hKw3t+Z0trvzAHA6M0jPGBqyTjjH9EjndyMjB/m2iEUgGAjs9ntfhFXrb26dgS3cFgARuQG4AWDIkKZrDrHkch+mss7qXjcu+0Rv+uqkQu/rYcN9PRVe5nLv6+Qtvp4ctyX5alIP5mR7X5+6zleLrszy/0cwGHcxSW43SZlnMyLT13Sx6PB8yM8mmMORwYEevQBI83jon/91HBXWl944hYaJfShYt5/ClGHW+qmB0xqct7Ue7CaSTYOEY0usXjjZs2eS9PcHKUkpZE9OFg5nHg5nDsMyrIKhuHo7I3oeR6kEzs55qXsyu3kBABEPmzadxI5eo8jOaOC3xmpfd5U38BrnUX9cDkk7q3Acrg8IAgATzR/IwDfp3rw9+9hYNMDa3gEvn+rk5VPh4nUp5DU4SWmoYvSWlxi95SUWTBgRsK+UnteEfG4AhxxWIdbDpNHfnU2V1FLc25p+u74unaVLL+SokUuorOgL1r1mnjcXAFBGNt8/bI2uNklC3RlW3pI/P8gxlYXWZ2RyKMVqknryw+18wSaSjeGTnUWkN97HmXUvG4887M3TRT+1vp5/GHoL+5+1CoxBRYsZtdX6X8q9/XbWpOfxhf2Mn5SsKzCew+zePJAfvfwmRXd+5N3Xx/tfZ0/1Fq68/xHqalxgjze8vtbqBPBUmjWS+ZJLx7B9O9TV5bNs6ZkAnHTSSZx55pncv/5dsOPaOeYNZvI2/QZ/n37553P354V8vNm6oVA/qQ+evqmM2lbDX2eNpaBvJj2eux/s+w3X1v+YEsnjzdtOhbyj2fuHvnw33z6/hgbeLiqGaTdhZv0Rl3FRt2wFu6+2ulOvKRBSGwyj90DBa6+SOmwYB7bs58PH7S69xsNJn/6MIb/4Mb0vvogj5XW8+7f1HCh8i/L9VmVMHDkMn3oL1Qde5tQrryevYBiPXXcZVYcOUlrl+57lDBpMRs/ssP8v8SYWgSDc3Zjg8B1pnZZsayUaMxeYCzBlypQ2Xcf/+uvj/N5ZXQzv81/hhtBtaqoqOVT0DQDm//Z3HNP7JAAmuoaxzWn1Gd+6ZRpOp4uqqhxuPflPeIyTh/gJhybmkPqZVdj+oOFmknDxZuov+M2BQ1w4MB+ARRMcfDEcCvvD15bv5JwvHiCjej8ffeUeUrIu5qLLB9JrwvHsXVcOycLLD30X43Dy9W//iP8sWszRo0aTkzGDQ9XvM3niaUw4YTaza+oZn30JA/tkUHfSlzh3HWHN1cex35PKhrH/xtG7AB7egLuuP3dMeITz35hNmdNBbo7B9ddHSTlhJudv+ifHpE4kzZ3B0cdcQ2V+Oifihn79SD36RP71xxWsqXEz8LRBbHrlKUb0PI606lpmzzyDNz6wCpX0hl7ez1HE+pM9MTKf/j2h2B5KcHK/JVz0lVt5sjaDt7IO4txeCXZX9G/X/5QkXPTPHsa1E37BQ6t9XUYblfkuojjrmnGMyRvNnv/60o478TRKDpRSumcMmFoczt4B27+84156JPXmtGnfYVrmcWRssQLnJNdwNgxIBhZzpDrb/jtP55vf/CYcCvz3y6aMT04fwIxVh8DtWza1VyaLyiZyhvMLnnec503/3oyjqHc8Ssb+DaQXXu3b0djZmKRHEFfg1cVXpl1A1aE1rC7sxcSCvhyxx9r1mn0e0zPS+WLB3wBwJOUCuUw5pwCAPleO5eC7mzGnZrDnj1Y34+y8/vQwhunTp1NUVAT2vrIyetArJ5sB+efSO3s8GRkFLFtqBbcpU6YAMCvbTeb+nzE2dzLuUivAnzDkDNJS05kzbSRfDulHncAF23aCCM9cPpnhGXZNf/Zfof4Ixz+2jVKyee2mEyDP6gAxcMRXea6uBJIyGF34PzjhFjj+ekSEZEnGMe4Yhr7wPFe+fSV7cuCxEx8g861lpI0Zgzgc9B4zhPN/3Jtdl15CckMlI5+dQ8rgwQBk9krlGz+cxIHdfXjxN9YV2Ne+dxK9+ufSZ8CfvZ/x1Q88TtWhwDEQvQcMJKNnr4C08xlP88JXNoI90fwqHSYWgaAIGOz3fhAQPCon0jopLdi2U6X3yGLg0dZNump3JUtL32Ja7tfINpn08mSQQhKl3kcFGAooBOBufsyvsu7hYEEPkgqrWDvqGL5/VD4Fbw3nkobFgDUQ691D1gXRpKQhLJxYy2UfbKPHmWeQVbmPqvSB9JtxOgDDT8myDvEQiMfN5JNP4dhp03E6nZSUpnNow/tkZg0lP39wY4XVylFmMq4x2eRkZJMDMHYGACIbMJ40Lh1/Cs5/GzJcbrhpAckF1lXP7ZNvD/gccq68Av9+SzOvG8fA0b3J6JnCyX2u5cMn/0Vp7W5uO22eNxCISSIr6xgqK9d5A8HpI60xB42BYMTAkzl25DhGNbh480A5rmN6g31v9WPPsQB8+YOvkpbsxFE4n6kbrDEPw9/8D9vP/Tp3XuOrwU0bexwA/j3zz7j1x4D1mMpGkubE1LpZXPw8AFWuw4y+9RQAby06jWTO+9ZVlFccx7atR1i31vp7jRo1ii3JKbz14Yn46589EFgXULW5dOIgdhdaV01jB2SD/ZTMYX0zgdFQGTT4qmc+9cMGkLplN6/88Dhrf0Df9L70Pf90CgDjmciXzzxB3p13kJyfTzJw2e//zIq3i9m1wbq67DPQio7pY/swaOwJVB7y3Q9ITksjGZg1a5Z1vj/9CAzcdsMtJGVbzRypqXkB2crOzgbgmAEzyTYl5PY9k09KXyApKYu0VGs6jx6pSUwpyKG8wQXbrQ/BGwQA+lt/y1I7yk8e6vtvSr/keSYCvHS51cttyHTIGeZd7szKImPSJDattfY7cfxMGO+7l5ac4iT/qGzKKq2myoxJkwjWd/BQHElWU9nwScNClvfK60evvOY7GsSrWAwoWw6MFJFhYvV3vARC7oPNB64Uy3Sg3BhT3MJtu5SSWl9PiG/Vn8Ds+uP9lvpKgR5U8SduxTU8C9egDNyDMnHYbd0vuWd410sGkh1WO2RNmvB/1zgZeP/9XPzYRVz0c/99h0pJScHpdNK/32wmTXyB/v3PD1lnbGYad40YEJIetrt1wYlhEsMbeXw/Mnpad2yHHjOB4prtSIpVKN9www2cX2e18Ikk2b/D30geMcIqqHsn+9VJZv4ejrmQrDQrLS3Z2u81PUYyrt6qsacedRRjvtzItLEzaa1+t0xkaelblNaGjsBOPyawLbdXzwlMnOgboJaenk5WkpOZx/m6paalDfTe83b4dVe8cPIgrphmteWeOS4/TE4k5HXdMVagdOf0Ylyfcdw5NbC7rzgcjPlyI32uvtqblj9yNKkZfSOeb6yIOBg86Eocjsjt+Y5oumvqGIBOE/UVgTHGJSK3AAuxGpifNsasF5Eb7eVzgAVYXUe3YnUfvaapbaPNU3uqdlXw8o57ObnfBQzIOKrJdR14eGHycC5LLgTgtFG5PMaXhLSI9RsL2D0f+guO9HTSgLTMwBtVkYgIvXuHvbXC4qlHh033bgswciZseafJ9ZrJAADOFCswDBgwAI+xqr9jx9zL9u0PUVUV2CRz0klLMZ560tJCgxRfsaZieH9WHYeO+M0flBR6Y25q/6m8u/Ndrh53tTctddQo6jZvDlnXu5u+6RRWrWvRqUH42Udzcr7CuHEPsX797fTseRzpyU5GDujJjacN5+aDvnsvzmMvgJVPkzJyBs9c05vhff063jv8vn4pVnrld77Jr/t+zNF90nhpxkstzmNQjjtoGxUvYjKOwBizAKuw90+b4/faADe3dNvu4NOSN7jQHlXblBk5Wd7XkWtLwvBew9levp2+6e1fswtxyYvgidyTpDmpGVZTxMmXXhWyLDPzKI499hFee+2uwG1Smj/Pvj1S6dvDr/Z54m3wwR8D1rl49MWcOuhU8nv4atyZJ5zQZCBoSsqQntSsa9l8OeL3nDmHQ3j3VmvgU31xGl9U2O2FBSfBXVYvk9OCO90MnAyTr4aVz3iTThk6g8VTz+PWSbe2Kf/h89mCQr4rxAEd/NVpdK6hNnKbMENbw3IwOjO0JgswzmPH4eGnceoga2qBb4/5dgxy13IigDMJkiN3vWuOMymJH738JuPP8D2605HRDoPWk9O9bc2NRCQgCNiJbT6Eo4VXYdZxrK+PCXo4zqX5fbhv9OBwWwQdzAFnBU7RneJM4e6T76Z/mKm0mzJwdO/mV4qZyJ9vVEW5Ng11Gp1iohV65vajonR/s+v16HEKVVVWDwURv/G3ft+S309YwNfH5Vq1wanfwXz+EBA6cKy76n/H8RiX74v9k5/8JDY7/s4HNDscuYNqllk9xgDQL+/stu+kse/78U3PNtucsScNYOn87dRUhrmya8HnoXXxxBYfpU4HufDnzT9g5dNPLmZAfuBzdL0TzPnVeJwOJ47kNJh+Izic3kcutugyPobaay4WR2oSTr/adWZmJpmZmU1sAR9OPZol08c0vWNnEjibrrUn9Yk8L1Ojq/70aNh0R1rL60YZGQWcPmML/fqd2+JtQiSlwC8Pwpm/afs+sP6OjTfv27aDqA4fG9o01Gk0ELRC7/zwIzX9ud0pOBzJ9Mgczdgx1kPfw/1/9+sZ2Fw0Mc8atTumTzMFYRwblZlGQXrkHikt5ehpzdvT64LQXlSN+g4eynk/tqbXHjzO1zc8bWzzQcSfxOIKzpnUroVgk8E+xq0x2jTUPWkgaKVrH/INA/lwX+RZP6dNW0B+vlUQNdby/b+QU4YGtumeOfRMFn9rMdPyw/f+UW3QTOHaWIgn+01v0J1nq4yuHO2+562ip4GglfyvCoprtoVdJ7gwaXyXkuLkvgvH88mdp4ctcHIzmpjLxTb5a98gJT2j5RlORC2dm75xeTcu/MNp0+m0Ypt2C5Zx9nfoTvRmcQxcUDeNtGHZPL/XGvUaEgj8yqWLprSgN0kTTrvyek678vqo9hHvWlpQNU6b3Z2vAuKSNhF1OA0EMdDb9CAtvVfE5Y3FjP57d7DmPnBvgaOBIGZi8VFqYO5w2jQUI2kjsr2vI9Uw2/TgFtV6YZqGLvnV1ND14rNlKESLrni6wmegVwKdRgNBrDiEvLy8sIuuGmCNoh2SFn2PGNUSoaVanwFNPVOxK5SC3Ul7fV56hdZZtGkohowJ3+Z82YA+XDYg9MEUqn2kHmU9gyB90sQm14t0hdbvh5MRR/crjCJWqDvwkqejx8H4G/zkkyQPDDN3lWqWBgIVd9InTOCo9xeT1L/paRr6jxgJwDEzzgpIT84L7ZU1duxYNmzoJk9QbVOvoe4X+IL1OPmkzs5Ct6WBIIYiXRGojpecH27a50A9++bxo5ffbHY9gAsuuIDzzjuv+RW7gg5ratdupPFCA4FSLeB0OnE6nc2vmFBCI46OLO6eNBDEjO+fWK8Iuq6xJzZ/pdDtteHfr3X/su39/90++7/oZ8eTkq5FXjj6qUQpY1Ie1Z9bT/Q2WqPp0r7711NxOhOvo5xWTCy5Q7KaXylBJd63IsYk2fcRXnjhhYwdO5acnNZNXKY6RlKys1v2Buq6Qj/L6D5drUh1Fr0iiKH8/Hwuuuiizs6GSlSRrkhbNKCsLUV4jAvuOJ37qTuI6opARHJE5F0R2WL/DnlMkogMFpH3RWSjiKwXkdv8lt0lIntEZJX9c040+elc3euf90/fmsDEIdmdnQ3VDtq/L3/n/K+fPexsZg6d2SnHjnfRXhHcCSwyxtwjInfa7+8IWscF/MgY87mIZAErReRdY0xjp+wHjTF/ijIfXUD3uqy9cPIgLpw8qLOzobqKGJXt7VmZv++U+9pv5wku2nsEs4Fn7dfPAt8IXsEYU2yM+dx+XQlsBJp/wotSKiZif4XQXpUenWKis0QbCPoZY4rBKvCB8JPt2ESkAJgILPVLvkVE1ojI0+Galvy2vUFEVojIitLS0iizrVQc67ByVAeUxYtmA4GIvCci68L8zG7NgUSkB/AacLsxpsJOfhwYARwHFAN/jrS9MWauMWaKMWZKbm7zD3BRStlaUq62qeyN8YCy4+3nbORPiGYvqg2avUdgjDkz0jIR2S8i+caYYhHJB0oirJeMFQSeN8a87rfv/X7rPAm0bLx/V9S9bhGoONRxj6pspxr7qK/CXeXts2/VpGibhuYDV9mvrwLeCF5BrNEsfwM2GmMeCFrmP8zzm8C6KPPT8fQyVnVxsX8egdZ64k20geAe4CwR2QKcZb9HRAaIyAJ7nROBK4DTw3QTvU9E1orIGmAG8IMo86NUwur4Okm4AWVaMeqOouo+aow5CJwRJn0vcI79+mMi1DeMMVdEc3yllE/kpqH2fpCM6u50igml4kxbrgy61qRzqqNpIFBKtVLkKwENEd2TBgKl4lzLavtteqxZG7ZRXZEGAqWUlukJTgOBUvGu3boThRlQpgGlW9JAoJRSCU4DQZQyjrOmu0g9KrtzM6ISXqQn5LWob3+bavJa/Y8X+mCaKKUW9GLQPSd3djaU8oo4krgD2m00NHRPekWgVJxp27OztQhPZBoIlIp3HTj7qOqeNBAoFWdaNMlcex27046soqH3CJSKd+0WGFq/36eunEKSU8NFV6OBQKl4EU1LTYzL5jNyeoZNP3Nsv9geSMWEBoI2yB06jNKdO8jold3ZWVGqWe03NXS4AWXC0uljyEtJbqdjqvag9wja4MSLvw1A3rARnZwTpWIjlvcVhqanku7UoqU70b9WGzgczs7OglIhOr4Pj7b1xwsNBErFm+DyWScAUs3QQKCUUgkuqpvFIpIDvAwUAIXARcaYw2HWKwQqATfgMsZMac32XY3RgTSqG2nqgqDvd46l9stDHZcZ1SVFe0VwJ7DIGDMSWGS/j2SGMea4xiDQhu27HL3gVl1KG6aWSBuRTfbXhrdDZlR3Em0gmA08a79+FvhGB2+vlGqWVllU06INBP2MMcUA9u+8COsZ4B0RWSkiN7Rhe0TkBhFZISIrSktLo8y2UnGomZvC7TeeQHV3zd4jEJH3gP5hFv28Fcc50RizV0TygHdF5EtjzIet2B5jzFxgLsCUKVO0kV6pYG2adbRpqampTR0w5sdTnaPZQGCMOTPSMhHZLyL5xphiEckHSiLsY6/9u0RE5gFTgQ+BFm2vlGq54AuDtg4Wu/3220lJSYlBjlRXF23T0HzgKvv1VcAbwSuISKaIZDW+BmYC61q6fVeUmZ0DQN6wozo5J0q1n+zsbDIyMppYQ5ua4kW0cw3dA7wiItcBu4BvAYjIAOApY8w5QD9gnl0rSQJeMMb8t6ntu7p+w0Zw+d0PklegvS1UN6DltWpGVIHAGHMQOCNM+l7gHPv1dmBCa7bvDvqPGNnZWVAqAi35VevoyGKlVKs4nekADB3y3U7OiYoVnYZaqTjRXKehWI2IdzhSOOP0bTHZl+oaNBAoFWdCew05OP68Cxg1/aTOyZDq8jQQKBXnRIRTLr+ms7OhujC9R6BUnEhK0edkqLbRKwKl4sS5N49n87J9ZPVJ6+ysqG5GA4FScaJn33SmnDOss7OhuiFtGlJKqQSngUAppRKcBgKllEpwGgiUUirBaSBQSqkEp4FAKaUSnAYCpZRKcBoIlFIqwYlph+ectjcRKQV2dnY+WqkvcKCzM9FB9Fzjk55r9zfUGJMbnNgtA0F3JCIrjDFTOjsfHUHPNT7pucYvbRpSSqkEp4FAKaUSnAaCjjO3szPQgfRc45Oea5zSewRKKZXg9IpAKaUSnAYCpZRKcBoIoiAiT4tIiYis80u7X0S+FJE1IjJPRLL9lv1URLaKyCYR+apf+mQRWWsv+4tI8OPHO1+4c/Vb9mMRMSLS1y8t7s5VRL5vn896EbnPLz2uzlVEjhORJSKySkRWiMhUv2Xd8lxFZLCIvC8iG+2/3212eo6IvCsiW+zfvf226Zbn2ibGGP1p4w9wCjAJWOeXNhNIsl/fC9xrvx4LrAZSgWHANsBpL1sGnAAI8DZwdmefW0vO1U4fDCzEGuDXN17PFZgBvAek2u/z4vhc32nMK3AO8EF3P1cgH5hkv84CNtvncx9wp51+Z7x8X1v7o1cEUTDGfAgcCkp7xxjjst8uAQbZr2cDLxlj6owxO4CtwFQRyQd6GmM+M9Z/2XPANzrkBFoh3LnaHgT+D/DvdRCP53oTcI8xps5ep8ROj8dzNUBP+3UvYK/9utueqzGm2Bjzuf26EtgIDMQ6p2ft1Z7Fl+9ue65toYGgfV2LVWMA659ut9+yIjttoP06OL3LE5HzgD3GmNVBi+LuXIFRwMkislRE/icix9vp8XiutwP3i8hu4E/AT+30uDhXESkAJgJLgX7GmGKwggWQZ68WF+faUhoI2omI/BxwAc83JoVZzTSR3qWJSAbwc+BX4RaHSeu252pLAnoD04GfAK/YbcPxeK43AT8wxgwGfgD8zU7v9ucqIj2A14DbjTEVTa0aJq1bnWtraCBoByJyFXAucLl9+QhWzWGw32qDsC65i/A1H/mnd3UjsNpOV4tIIVa+PxeR/sTfuYKV99eNZRngwZqYLB7P9Srgdfv1v4DGm8Xd+lxFJBkrCDxvjGk8v/12cw/278Ymv259rq2lgSDGRGQWcAdwnjGm2m/RfOASEUkVkWHASGCZfTlaKSLT7RrmlcAbHZ7xVjLGrDXG5BljCowxBVhfkEnGmH3E2bna/g2cDiAio4AUrNkp4/Fc9wKn2q9PB7bYr7vtudr5+huw0RjzgN+i+ViBD/v3G37p3fJc26Sz71Z35x/gRaAYaMAqCK/Duqm0G1hl/8zxW//nWL0PNuHX0wCYAqyzlz2CPeK7K/2EO9eg5YXYvYbi8VyxCv5/2nn/HDg9js/1JGAlVq+ZpcDk7n6u9jkZYI3fd/McoA+wCCvYLQJyuvu5tuVHp5hQSqkEp01DSimV4DQQKKVUgtNAoJRSCU4DgVJKJTgNBEopleA0ECilVILTQKCUUgnu/wH+9ZUoWDJI1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing FITS files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": 137,
   "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": 138,
   "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": 139,
   "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": 140,
   "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"
   ]
  }
 ],
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