03 - Numpy Essential - instructor-with answer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Numpy and Matplotlib essential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As suggested in one of the previous challenge, the library *numpy* is providing a very similar object than the list called *array*. The main difference is in the operation you can realise on it which are oriented towards computation. \n",
    "\n",
    "In this lesson we are going to learn a little more how to use this fundamental library to do any numerical analysis in Python.\n",
    "\n",
    "Numpy extend the Python langage by providing new type (array, matrix, masked_array...), functions and method to realise efficient numerical calculation using Python.\n",
    "\n",
    "The most basic numpy type is called *array*. The most basic array is a multi-dimensional object which contains numerical data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Numpy](http://www.numpy.org/) is **the** numerical library for Python. This library is too big to be taught in a day. That will be extremely boring and not useful at all. \n",
    "\n",
    "This library is at the base of all the other libraries used in science and data science: \n",
    "\n",
    "- [scipy](https://www.scipy.org/) Fundamental library for scientific computing (interation, optimisation...)\n",
    "- [pandas](http://pandas.pydata.org/) data structure and data analysis tools\n",
    "- [matplotlib](https://matplotlib.org/) Python 2D plotting\n",
    "\n",
    "And more specialised one like:\n",
    "\n",
    "- [astropy](http://www.astropy.org/) for the astronomy\n",
    "- [h5py](https://www.h5py.org/) to interact with the HDF5 format\n",
    "- [scikit-learn](http://scikit-learn.org/) for Machine Learning\n",
    "- [TensorFlow](https://www.tensorflow.org) for Deep Learning\n",
    "\n",
    "We are going to learn some of the basic command not seen in the first course using some of these libraries but keep in mind that we are just covering the basics so you can understand how to start using this library and hoping that will peak your interest enough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start, we are going to import the two libraries *numpy* and *matplotlib* that will be use in this episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# Only on jupyter notebook\n",
    " %matplotlib inline   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A numpy array: [1 3 5 7 9]\n",
      "type of the object: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "odds_np = np.arange(1,10,2)\n",
    "\n",
    "print('A numpy array:', odds_np)\n",
    "print('type of the object:', type(odds_np))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**note**\n",
    "\n",
    "```numpy.arange``` is very similar to range but return a numpy array instead of an iterator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li> \n",
    " What is the result of adding two lists together?\n",
    " </li>\n",
    " <li>\n",
    " What happens when you add two numpy arrays?\n",
    " </li>\n",
    " <br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list of odds: [1, 3, 5, 7, 9]\n"
     ]
    }
   ],
   "source": [
    "odds = [ i for i in range(10) if (i % 2) ]\n",
    "print(\"list of odds:\", odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9, 1, 3, 5, 7, 9]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds+odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6, 10, 14, 18])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds_np + odds_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work with matrix like object\n",
    "\n",
    "*Numpy* arrays are not exactly a matrix. They are multidimensional object to store data. They are not necessarily numerical. In contrast, a *matrix* object has to be numerical. In practice, a 2D numerical numpy array is very similar to a numpy matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li> \n",
    " From the previous *odds* variable, create a numpy array which contains only the first 9 elements.\n",
    " </li>\n",
    " <br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  3  5  7  9 11 13 15 17]\n",
      "(9,)\n"
     ]
    }
   ],
   "source": [
    "odds_np = np.arange(1,20,2)\n",
    "\n",
    "arr = odds_np[:9]\n",
    "print(arr)\n",
    "print(arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array shape can be modified using the method *reshape*. Here we are creating a square array 3x3 from the previous array *arr*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The square array:\n",
      " [[ 1  3  5]\n",
      " [ 7  9 11]\n",
      " [13 15 17]]\n"
     ]
    }
   ],
   "source": [
    "arr = arr.reshape((3,3)) # notice the usage of a tuple (3,3).\n",
    "print('The square array:\\n', arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a new object called a tuple. A tuple is very similar to list but tuple cannot be modified. They are called *immutable*.\n",
    "\n",
    "We can verify the type of the array created above and verify that it is, indeed, a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the type of the element which composed that array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to modify the type of the element by using the method *astype*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "arr = arr.astype(float)\n",
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array can be converted into a matrix object. A numpy matrix does have the characteristic of the mathematical matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.matrix(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.matrixlib.defmatrix.matrix"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A multiplication of matrix with the operator *\\** will provide the expected mathematical answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.,  3.,  5.],\n",
       "        [ 7.,  9., 11.],\n",
       "        [13., 15., 17.]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 87., 105., 123.],\n",
       "        [213., 267., 321.],\n",
       "        [339., 429., 519.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat * mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li> \n",
    " What happens when you multiply two arrays together? How is it different to multiplying matrices?\n",
    " </li>\n",
    " \n",
    " <br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  3.,  5.],\n",
       "       [ 7.,  9., 11.],\n",
       "       [13., 15., 17.]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   9.,  25.],\n",
       "       [ 49.,  81., 121.],\n",
       "       [169., 225., 289.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr * arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrix multiplication is also possible for arrays -- use the *matmul* function from numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 87., 105., 123.],\n",
       "       [213., 267., 321.],\n",
       "       [339., 429., 519.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(arr, arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 87., 105., 123.],\n",
       "        [213., 267., 321.],\n",
       "        [339., 429., 519.]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(mat, mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Masked array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One very useful tool that numpy is providing to analyse experimental data is the masked array. When you are taking data from an experiment you always have some data which are not present, with bad signal to noise of that you cannot use for whatever reason. \n",
    "\n",
    "As example we are going to create a gauss function and adding some noise on it.\n",
    "\n",
    "Reminder, the gauss function is define by:\n",
    "\n",
    "\\begin{equation*}\n",
    "g(x) = \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}} e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li> \n",
    " Create a function called `gauss` which will take three arguments (inputs): *x*, *mu*, and *sigma*, as defined above. \n",
    " </li>\n",
    " <li>\n",
    " Create a numpy array using the ```numpy``` function `linspace` which will contain 1000 points equally spaced between x=-100 and x=100.<br>\n",
    " Hint: You can print the help documentation of a function with ```help(name_of_the_function)```\n",
    " </li>\n",
    " <li>\n",
    " Create a list using the previous array containing the value of the gauss from x=-100 to x=100.\n",
    " </li>\n",
    " <li>\n",
    " Use the `matplotlib` library to plot the curve with mu=0 and sigma=10. \n",
    " </li>\n",
    " <br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gauss(x, mu=0, sigma=1):\n",
    "    return (1./(sigma * np.sqrt(2 * np.pi)) * \n",
    "            np.exp( - (x - mu)**2 / (2 * sigma**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = 0\n",
    "sigma = 10\n",
    "x = np.linspace(-100, 100, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = gauss(x, mu, sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3UVoaTxnl5Rm1y0t1+wrJGIkkhONAXczz2qBs2jpmVgBUAt3B81rgKeAT7n4ouoG7Hw9+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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, g)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noisy signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to add some random noise to that curve. \n",
    "To do it we can use the numpy function *normal* from the module *random* provided by numpy library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hHsAfrhgUc7y+I/T6d9rFygvyfLDwLCWNnFPsKka9z973T8PgTqV47xfDHZXx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\nJd/rlmEV2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = 100*g   # To have something visible we are multiplying the function by 100.\n",
    "\n",
    "# Creation of the noise\n",
    "noisy = np.random.normal(g)\n",
    "plt.plot(x, g+noisy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the Signal to Noise ratio of the previous data set by dividing the noisy function by the standard deviation (the noise)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dxm52Q/OqRR+pbTB6QQj7NQD/WajfWOBACIuhAOCqsT3x1FXH43sju6Yl/osf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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rms = np.std(noisy) # root mean square\n",
    "SN = noisy / rms\n",
    "plt.plot(SN)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True False  True]\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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swODWqtesu93WP/59SKmpLjIVvVZSUaBs60HXyibOcE7iijh+47j/+MwQrvrZk9h3cCT+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = SN < 1\n",
    "print(mask[:5])\n",
    "print(mask.shape)\n",
    "noisy_ma = np.ma.array(noisy, mask=mask)\n",
    "plt.plot(noisy_ma)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to learn some commands to deal with images. Since most of the scientific domain are using their own file format, we obviously not learn all of them and I will use it an astronomical image using the format used mainly in this domain, the fits file. \n",
    "\n",
    "In the *data* directory you should find a file called *502nmos.fits*. \n",
    "\n",
    "We can verify that the file is indeed here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/502nmos.fits\r\n"
     ]
    }
   ],
   "source": [
    "ls *.fits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to manipulate data in this file, we need to import a library which will be able to open the file and put the data in a numpy array. \n",
    "\n",
    "This is a good occasion to install a new library. To realise that, open a terminal. On Microsoft Windows, can you start the **Anaconda prompt** software: \n",
    "\n",
    "![MS Windows terminal](images/anaconda-prompt.png \"Anaconda Prompt terminal\")\n",
    "\n",
    "On Unix system, you can start a terminal or having one started through the Jupyter notebook:\n",
    "\n",
    "![Starting a Terminal with Jupyter](images/jupyter_terminal.png \"Starting a terminal with Jupyter Notebook\")\n",
    "\n",
    "![Terminal using Jupyter](images/jupyter_terminal2.png \"Terminal with Jupyter Notebook\")\n",
    "\n",
    "We are going to use the command **pip** which will allows you to install any package available on the python software repository: [Pypi](https://pypi.python.org/pypi). Here we will install the library [pyfits](http://www.astropy.org/) which will provide us the tools to open the fits images files. \n",
    "\n",
    "**Note:**\n",
    "\n",
    "This is only an example to teach you how to install a library not yet available and how to open a specific image format. I am not expecting you to use this specific library in the furutre. Each domain, has their own file format (most of the time): \n",
    "\n",
    "- [NetCDF](https://www.unidata.ucar.edu/netcdf/)\n",
    "- [HDF5](https://www.hdfgroup.org/HDF5/)\n",
    "- MS Excel\n",
    "- SQL\n",
    "- ...\n",
    "\n",
    "All of these formats can be open using python but you will have to install an additional library to do it.\n",
    "\n",
    "In the previously open terminal:\n",
    "\n",
    "```bash\n",
    "\n",
    "pip install -U pyfits --user\n",
    "```\n",
    "\n",
    "\n",
    "The *pip* command will look at the *pypi* repository if the library *pyfits* is available, the *-U* option will look if there are an upgrade available (if *pyfits* is already present on your system) and the *--user* option will install the library in the user account not on the system wide (you do not have to be administrator to install a new python library)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li>\n",
    " What is the main format for your data?\n",
    " </li>\n",
    " <li>\n",
    " Find a python library which will allows you to open this format and convert it in a numpy array.\n",
    " </li>\n",
    " </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some available libraries:\n",
    "\n",
    "- FITS, VOTable...: [astropy](http://docs.astropy.org/en/stable/index.html#files-i-o-and-communication)\n",
    "- CSV, HDF5, MS Excel, SQL...: [pandas](http://pandas.pydata.org/pandas-docs/stable/io.html)\n",
    "- NetCDF: [netcdf4](http://unidata.github.io/netcdf4-python/)\n",
    "- Matlab mat: [scipy](https://docs.scipy.org/doc/scipy/reference/tutorial/io.html)\n",
    "- Tiff: [Pillow](https://pillow.readthedocs.io/en/latest/), [pylibtiff](https://github.com/pearu/pylibtiff),[matplotlib]()..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to the data directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#Windows\n",
    "#os.chdir('C:\\Work\\Python-advanced\\data')\n",
    "os.chdir('data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with binary file\n",
    "\n",
    "We are importing the new library and we can now open the fits file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import astropy.io.fits as pyfits\n",
    "\n",
    "try: \n",
    "    import pyfits\n",
    "except ImportError:\n",
    "    import astropy.io.fits as pyfits\n",
    "    \n",
    "im1 = pyfits.open('502nmos.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "\n",
    "By default pyfits is opening a file with the opion *memmap=True*. This option open the fits file without copying the data in memory and allowed to open very large file which will not fit in the physical memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fits file are composed by  alist of HDU (Header and data unit). We can list the information with the method *info*.\n",
    "\n",
    "Here we will be only interested on the primary HDU which is an image and is called *PRIMARY*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: data/502nmos.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     290   (1600, 1600)   float32   \n",
      "  1  502nmos_cvt.tab    1 TableHDU       353   1R x 49C   [D25.17, D25.17, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, A1, E15.7, I12, I12, D25.17, D25.17, A8, A8, I12, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, I12, I12, I12, I12, I12, I12, I12, I12, A48, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7, E15.7]   \n"
     ]
    }
   ],
   "source": [
    "im1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check what is the type of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.hdu.hdulist.HDUList"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(im1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**note**\n",
    "\n",
    "HDUList are not object that you are familiar with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the first HDU (the one of interest) which contains the image as an element in a list using the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "HDU_copy1 = im1[0]     # As mention an HDU is a list and we want to have access to the first one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to access it by using it's name (or key) as in a dictionary: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "HDU_copy2 = im1['PRIMARY']  # HDU can also be accessed through their name like dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verified that both copy are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HDU_copy1 == HDU_copy2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fits data, as most of scientific data images containers, are composed with a header and a data part. The header contains metadata relevant to the observations and to the data itself. You can print them on screen by using the attribute *header*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / FITS STANDARD                                  \n",
       "BITPIX  =                  -32 / FITS BITS/PIXEL                                \n",
       "NAXIS   =                    2 / NUMBER OF AXES                                 \n",
       "NAXIS1  =                 1600 /                                                \n",
       "NAXIS2  =                 1600 /                                                \n",
       "EXTEND  =                    T / There maybe standard extensions                \n",
       "BSCALE  =                1.0E0 / REAL = TAPE*BSCALE + BZERO                     \n",
       "BZERO   =                0.0E0 /                                                \n",
       "OPSIZE  =                 2112 / PSIZE of original image                        \n",
       "ORIGIN  = 'STScI-STSDAS'       / Fitsio version 21-Feb-1996                     \n",
       "FITSDATE= '2005-07-01'         / Date FITS file was created                     \n",
       "FILENAME= '502nmos_cvt.hhh'    / Original filename                              \n",
       "ALLG-MAX=           0.000000E0 / Data max in all groups                         \n",
       "ALLG-MIN=           0.000000E0 / Data min in all groups                         \n",
       "ODATTYPE= 'FLOATING'           / Original datatype: Single precision real       \n",
       "SDASMGNU=                    1 / Number of groups in original image             \n",
       "CRVAL1  =      274.71130937177                                                  \n",
       "CRVAL2  =     -13.816000630104                                                  \n",
       "CRPIX1  =                386.5                                                  \n",
       "CRPIX2  =                 396.                                                  \n",
       "CD1_1   =          1.878013E-5                                                  \n",
       "CD1_2   =         -2.031193E-5                                                  \n",
       "CD2_1   =         -2.029358E-5                                                  \n",
       "CD2_2   =         -1.879711E-5                                                  \n",
       "DATAMIN =           0.000000E0 / DATA MIN                                       \n",
       "DATAMAX =           0.000000E0 / DATA MAX                                       \n",
       "MIR_REVR=                    T                                                  \n",
       "ORIENTAT=            -131.9115                                                  \n",
       "FILLCNT =                    0                                                  \n",
       "ERRCNT  =                    0                                                  \n",
       "FPKTTIME=     49808.8637043663                                                  \n",
       "LPKTTIME=     49808.8638721904                                                  \n",
       "CTYPE1  = 'RA---TAN'                                                            \n",
       "CTYPE2  = 'DEC--TAN'                                                            \n",
       "DETECTOR=                    4                                                  \n",
       "DEZERO  =             311.2883                                                  \n",
       "BIASEVEN=             311.3258                                                  \n",
       "BIASODD =             311.2509                                                  \n",
       "GOODMIN =            -1.921564                                                  \n",
       "GOODMAX =             4128.058                                                  \n",
       "DATAMEAN=             8.284751                                                  \n",
       "GPIXELS =               559916                                                  \n",
       "SOFTERRS=                    0                                                  \n",
       "CALIBDEF=                80081                                                  \n",
       "STATICD =                    0                                                  \n",
       "ATODSAT =                    5                                                  \n",
       "DATALOST=                    0                                                  \n",
       "BADPIXEL=                    0                                                  \n",
       "OVERLAP =                    0                                                  \n",
       "PHOTMODE= 'WFPC2,4,A2D7,F502N,,CAL                         '                    \n",
       "PHOTFLAM=         3.026684E-16                                                  \n",
       "PHOTZPT =                -21.1                                                  \n",
       "PHOTPLAM=              5012.91                                                  \n",
       "PHOTBW  =             48.40781                                                  \n",
       "MEDIAN  =             6.800608                                                  \n",
       "MEDSHADO=             3.649954                                                  \n",
       "HISTWIDE=             6.501746                                                  \n",
       "SKEWNESS=           0.01308636                                                  \n",
       "MEANC10 =             10.52821                                                  \n",
       "MEANC25 =             10.84973                                                  \n",
       "MEANC50 =             10.78265                                                  \n",
       "MEANC100=             10.71748                                                  \n",
       "MEANC200=             9.907546                                                  \n",
       "MEANC300=              8.59161                                                  \n",
       "BACKGRND=             3.285868                                                  \n",
       "DADSFILE= 'U2LX0507T.D0F'      /                                                \n",
       "DADSCLAS= 'CAL     '           /                                                \n",
       "DADSDATE= '02-APR-1995 15:25:12' /                                              \n",
       "                                                                                \n",
       "                 / GROUP PARAMETERS: OSS                                        \n",
       "                                                                                \n",
       "                 / GROUP PARAMETERS: PODPS                                      \n",
       "                                                                                \n",
       "                 / GROUP PARAMETERS: DATA QUALITY FILE SUMMARY                  \n",
       "                                                                                \n",
       "                 / GROUP PARAMETERS: PHOTOMETRY                                 \n",
       "                                                                                \n",
       "                 / GROUP PARAMETERS: IMAGE STATISTICS                           \n",
       "                                                                                \n",
       "                 / WFPC-II DATA DESCRIPTOR KEYWORDS                             \n",
       "INSTRUME= 'WFPC2             ' / instrument in use                              \n",
       "ROOTNAME= 'U2LX0507T         ' / rootname of the observation set                \n",
       "FILETYPE= 'SCI               ' / shp, ext, edq, sdq, sci                        \n",
       "                                                                                \n",
       "                 / SCIENCE INSTRUMENT CONFIGURATION                             \n",
       "MODE    = 'FULL              ' / instr. mode: FULL (full res.), AREA (area int.)\n",
       "SERIALS = 'ON                ' / serial clocks: ON, OFF                         \n",
       "                                                                                \n",
       "                 / IMAGE TYPE CHARACTERISTICS                                   \n",
       "IMAGETYP= 'EXT               ' / DARK/BIAS/IFLAT/UFLAT/VFLAT/KSPOT/EXT/ECAL     \n",
       "CDBSFILE= 'NO                ' / GENERIC/BIAS/DARK/FLAT/MASK/NO                 \n",
       "PKTFMT  =                  104 / packet format code                             \n",
       "DATE    = '02/04/95          ' / date file written (dd/mm/yy)                   \n",
       "                                                                                \n",
       "                 / FILTER CONFIGURATION                                         \n",
       "FILTNAM1= 'F502N             ' / first filter name                              \n",
       "FILTNAM2= '                  ' / second filter name                             \n",
       "FILTER1 =                   23 / first filter number (0-48)                     \n",
       "FILTER2 =                    0 / second filter number (0-48)                    \n",
       "FILTROT =                  0.0 / partial filter rotation angle (degrees)        \n",
       "LRFWAVE =                  0.0 / linear ramp filter wavelength                  \n",
       "                                                                                \n",
       "                 / INSTRUMENT STATUS USED IN DATA PROCESSING                    \n",
       "UCH1CJTM=      -88.34862518311 / TEC cold junction #1 temperature (Celsius)     \n",
       "UCH2CJTM=      -88.80734252930 / TEC cold junction #2 temperature (Celsius)     \n",
       "UCH3CJTM=      -88.34862518311 / TEC cold junction #3 temperature (Celsius)     \n",
       "UCH4CJTM=      -88.85844421387 / TEC cold junction #4 temperature (Celsius)     \n",
       "UBAY3TMP=       13.61565399170 / bay 3 A1 temperature (deg C)                   \n",
       "KSPOTS  = 'OFF               ' / Status of Kelsall spot lamps: ON, OFF          \n",
       "SHUTTER = 'B                 ' / Shutter in place at the beginning of the exposu\n",
       "ATODGAIN=       7.000000000000 / Analog to Digital Gain (Electrons/DN)          \n",
       "                                                                                \n",
       "                 / RSDP CONTROL KEYWORDS                                        \n",
       "MASKCORR= 'COMPLETE'           / Do mask correction: PERFORM, OMIT, COMPLETE    \n",
       "ATODCORR= 'COMPLETE'           / Do A-to-D correction: PERFORM, OMIT, COMPLETE  \n",
       "BLEVCORR= 'COMPLETE'           / Do bias level correction: PERFORM, OMIT, COMPLE\n",
       "BIASCORR= 'COMPLETE'           / Do bias correction: PERFORM, OMIT, COMPLETE    \n",
       "DARKCORR= 'COMPLETE'           / Do dark correction: PERFORM, OMIT, COMPLETE    \n",
       "FLATCORR= 'COMPLETE'           / Do flat field correction: PERFORM, OMIT, COMPLE\n",
       "SHADCORR= 'COMPLETE'           / Do shaded shutter correction: PERFORM, OMIT, CO\n",
       "DOSATMAP= 'OMIT              ' / Output saturated pixel map: PERFORM, OMIT, COMP\n",
       "DOPHOTOM= 'COMPLETE'           / Fill photometry keywords: PERFORM, OMIT, COMPLE\n",
       "DOHISTOS= 'OMIT    '           / Make histograms: PERFORM, OMIT, COMPLETE       \n",
       "OUTDTYPE= 'REAL    '           / Output image datatype: REAL, LONG, SHORT       \n",
       "                                                                                \n",
       "                 / CALIBRATION REFERENCE FILES                                  \n",
       "MASKFILE= 'uref$f8213081u.r0h' / name of the input DQF of known bad pixels      \n",
       "ATODFILE= 'uref$dbu1405iu.r1h' / name of the A-to-D conversion file             \n",
       "BLEVFILE= 'ucal$u2lx0507t.x0h' / Engineering file with extended register data   \n",
       "BLEVDFIL= 'ucal$u2lx0507t.q1h' / Engineering file DQF                           \n",
       "BIASFILE= 'uref$fcb1503du.r2h' / name of the bias frame reference file          \n",
       "BIASDFIL= 'uref$fcb1503du.b2h' / name of the bias frame reference DQF           \n",
       "DARKFILE= 'uref$f461210tu.r3h' / name of the dark reference file                \n",
       "DARKDFIL= 'uref$f461210tu.b3h' / name of the dark reference DQF                 \n",
       "FLATFILE= 'uref$g640925ku.r4h' / name of the flat field reference file          \n",
       "FLATDFIL= 'uref$g640925ku.b4h' / name of the flat field reference DQF           \n",
       "SHADFILE= 'uref$e371355iu.r5h' / name of the reference file for shutter shading \n",
       "PHOTTAB = '                  ' / name of the photometry calibration table       \n",
       "GRAPHTAB= 'mtab$m1p1255om.tmg' / the HST graph table                            \n",
       "COMPTAB = 'mtab$m4i1217km.tmc' / the HST components table                       \n",
       "                                                                                \n",
       "                 / DEFAULT KEYWORDS SET BY STSCI                                \n",
       "SATURATE=                 4095 / Data value at which saturation occurs          \n",
       "USCALE  =                  1.0 / Scale factor for output image                  \n",
       "UZERO   =                  0.0 / Zero point for output image                    \n",
       "                                                                                \n",
       "                 / READOUT DURATION INFORMATION                                 \n",
       "READTIME=                  464 / Length of time for CCD readout in clock ticks  \n",
       "                                                                                \n",
       "                 / PLANETARY SCIENCE KEYWORDS                                   \n",
       "PA_V3   =        0.9299789E+02 / position angle of v3 axis of HST               \n",
       "RA_SUN  =  0.1077934379409E+02 / right ascension of the sun (deg)               \n",
       "DEC_SUN =  0.4635995603524E+01 / declination of the sun (deg)                   \n",
       "EQNX_SUN=               2000.0 / equinox of the sun                             \n",
       "MTFLAG  =                    F / moving target flag                             \n",
       "EQRADTRG=                  0.0 / equatorial radius of target                    \n",
       "FLATNTRG=                  0.0 / flattening of target                           \n",
       "NPDECTRG=                  0.0 / north pole declination of target               \n",
       "NPRATRG =                  0.0 / north pole right ascension of target           \n",
       "ROTRTTRG=                  0.0 / rotation rate of target                        \n",
       "LONGPMER=                  0.0 / longitude of prime meridian                    \n",
       "EPLONGPM=                  0.0 / epoch of longitude of prime meridian           \n",
       "SURFLATD=                  0.0 / surface feature latitude                       \n",
       "SURFLONG=                  0.0 / surface feature longitude                      \n",
       "SURFALTD=                  0.0 / surface feature altitude                       \n",
       "                                                                                \n",
       "                 / PODPS FILL VALUES                                            \n",
       "PODPSFF =                    0 / 0=(no podps fill), 1=(podps fill present)      \n",
       "STDCFFF =                    0 / 0=(no st dcf fill), 1=(st dcf fill present)    \n",
       "STDCFFP = '0000              ' / st dcf fill pattern (hex)                      \n",
       "RSDPFILL=                 -100 / bad data fill value for calibrated images      \n",
       "                                                                                \n",
       "                 / EXPOSURE TIME AND RELATED INFORMATION                        \n",
       "UEXPODUR=                 1100 / Commanded duration of exposure (seconds)       \n",
       "NSHUTA17=                    1 / Number of AP17 shutter B closes                \n",
       "DARKTIME=       1100.000000000 / Dark time (seconds)                            \n",
       "UEXPOTIM=                48592 / Major frame pulse time preceding exposure start\n",
       "PSTRTIME= '1995.091:20:41:39 ' / Predicted obs. start time  (yyyy:ddd:hh:mm:ss) \n",
       "PSTPTIME= '1995.091:21:01:39 ' / Predicted obs. stop  time  (yyyy:ddd:hh:mm:ss) \n",
       "                                                                                \n",
       "                 / EXPOSURE INFORMATION                                         \n",
       "SUNANGLE=       96.98478698730 / angle between sun and V1 axis (deg)            \n",
       "MOONANGL=       116.8501663208 / angle between moon and V1 axis (deg)           \n",
       "SUN_ALT =       22.06604003906 / altitude of the sun above Earth's limb (deg)   \n",
       "FGSLOCK = 'FINE              ' / commanded FGS lock (FINE,COARSE,GYROS,UNKNOWN) \n",
       "                                                                                \n",
       "DATE-OBS= ' 1/04/95          ' / UT date of start of observation (dd/mm/yy)     \n",
       "TIME-OBS= '20:24:17          ' / UT time of start of observation (hh:mm:ss)     \n",
       "EXPSTART=       49808.85019742 / exposure start time (Modified Julian Date)     \n",
       "EXPEND  =       49808.86292890 / exposure end time (Modified Julian Date)       \n",
       "EXPTIME =                1100. / exposure duration (seconds)--calculated        \n",
       "EXPFLAG = 'NORMAL            ' / Exposure interruption indicator                \n",
       "                                                                                \n",
       "                 / TARGET & PROPOSAL ID                                         \n",
       "TARGNAME= 'M16-A             ' / proposer's target name                         \n",
       "RA_TARG =  0.2747039166667E+03 / right ascension of the target (deg) (J2000)    \n",
       "DEC_TARG= -0.1383091666667E+02 / declination of the target (deg) (J2000)        \n",
       "                                                                                \n",
       "PROPOSID=                05773 / PEP proposal identifier                        \n",
       "PEP_EXPO= '4.2000019#001     ' / PEP exposure identifier including sequence     \n",
       "LINENUM = '4.030             ' / PEP proposal line number                       \n",
       "SEQLINE = '4.200             ' / PEP line number of defined sequence            \n",
       "SEQNAME = 'M16SEQ            ' / PEP define/use sequence name                   \n",
       "                                                                                \n",
       "HISTORY   MASKFILE=uref$f8213081u.r0h  MASKCORR=COMPLETED                       \n",
       "HISTORY   PEDIGREE=INFLIGHT 01/01/1994 - 15/05/1995                             \n",
       "HISTORY   DESCRIP=STATIC MASK - INCLUDES CHARGE TRANSFER TRAPS                  \n",
       "HISTORY   BIASFILE=uref$fcb1503du.r2h  BIASCORR=COMPLETED                       \n",
       "HISTORY   PEDIGREE=INFLIGHT 13/01/1995 - 11/04/1995                             \n",
       "HISTORY   DESCRIP=not signif. different from f1j16* but generated from new data \n",
       "HISTORY   DARKFILE=uref$f461210tu.r3h  DARKCORR=COMPLETED                       \n",
       "HISTORY   PEDIGREE=INFLIGHT 27/03/1995 - 04/04/1995                             \n",
       "HISTORY   DESCRIP=dark,full mode,serials on,gain=7,-88C                         \n",
       "HISTORY   FLATFILE=uref$g640925ku.r4h  FLATCORR=COMPLETED                       \n",
       "HISTORY   PEDIGREE=INFLIGHT 01/05/1994 - 01/10/1994                             \n",
       "HISTORY   DESCRIP=Improved Cyc4 flat, fixed errors at CCD edges - now < 0.5% RMS\n",
       "HISTORY   SHADFILE=uref$e371355iu.r5h  SHADCORR=COMPLETED                       \n",
       "HISTORY   PEDIGREE=GROUND                                                       \n",
       "HISTORY   DESCRIP=                                                              \n",
       "HISTORY   PC1: bias jump level ~0.135 DN.                                       \n",
       "HISTORY   The following throughput tables were used:                            \n",
       "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
       "HISTORY   crwfpc2comp$wfpc2_f502n_005_syn.fits,                                 \n",
       "HISTORY   crwfpc2comp$wfpc2_dqepc1_005_syn.fits,                                \n",
       "HISTORY   crwfpc2comp$wfpc2_a2d7pc1_004_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_flatpc1_003_syn.fits                                \n",
       "HISTORY   WF2: bias jump level ~0.113 DN.                                       \n",
       "HISTORY   The following throughput tables were used:                            \n",
       "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
       "HISTORY   crwfpc2comp$wfpc2_f502n_005_syn.fits,                                 \n",
       "HISTORY   crwfpc2comp$wfpc2_dqewfc2_005_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_a2d7wf2_004_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_flatwf2_003_syn.fits                                \n",
       "HISTORY   WF3: bias jump level ~0.239 DN.                                       \n",
       "HISTORY   The following throughput tables were used:                            \n",
       "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
       "HISTORY   crwfpc2comp$wfpc2_f502n_005_syn.fits,                                 \n",
       "HISTORY   crwfpc2comp$wfpc2_dqewfc3_005_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_a2d7wf3_004_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_flatwf3_003_syn.fits                                \n",
       "HISTORY   WF4: bias jump level ~0.120 DN.                                       \n",
       "HISTORY   The following throughput tables were used:                            \n",
       "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
       "HISTORY   crwfpc2comp$wfpc2_f502n_005_syn.fits,                                 \n",
       "HISTORY   crwfpc2comp$wfpc2_dqewfc4_005_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_a2d7wf4_004_syn.fits,                               \n",
       "HISTORY   crwfpc2comp$wfpc2_flatwf4_003_syn.fits                                \n",
       "HISTORY   Ran task WARMPIX, version 1.1 (Sep 28, 1999) at Tue 17:21:22          \n",
       "HISTORY   30-Jul-2002, rej_thresh=0.10000, fix_thresh=0.00300,                  \n",
       "HISTORY   var_thresh=0.00300,                                                   \n",
       "HISTORY   fix_dqval=1024, rej_val=INDEF                                         \n",
       "SKYSUB1 =                 0.89 / Sky value for chip 1                           \n",
       "PYS_VERS=                  1.7 / PyStack.py: Version                            \n",
       "IRAF_I  = 'IRAF V2.11 May 1997 release:2.11.3b'                                 \n",
       "STAC_I  = '$RCSfile: imstack_imshift_imcomb.cl,v $ $Revision: 1.1 $'            \n",
       "PYS_ITER=                    5 / PyStack.py: Number of iterations               \n",
       "PYS_HEXP=                    2 / PyStack.py: Value of exponent                  \n",
       "SKYSUB2 =                 7.68 / Sky value for chip 2                           \n",
       "SKYSUB3 =                 5.37 / Sky value for chip 3                           \n",
       "SKYSUB4 =                 2.74 / Sky value for chip 4                           \n",
       "NCOMBINE=                    2 / Number of exposures for this association       \n",
       "ASTR_I  = '$RCSfile: image_type.cl,v $ $Revision: 1.1 $'                        \n",
       "XSHIFT  =         -3.967285E-4 / Y shift in degrees from USNO stars             \n",
       "YSHIFT  =          3.557205E-4 / X shift in degrees from USNO stars             \n",
       "OCRVL1  =             274.7039 / Old CRVAL1 value before USNO correction        \n",
       "OCRVL2  =            -13.83092 / Old CRVAL2 value before USNO correction        \n",
       "FR_LOWF1=            0.9954308 / Fraction of flux in low frequency for chip 1   \n",
       "FR_STAR1=          0.004569176 / Fraction of flux in stars for chip 1           \n",
       "FR_GAL1 =                   0. / Fraction of flux in stars for chip 1           \n",
       "FTOTAL1 =             195.3842 / Total flux in chip 1                           \n",
       "FR_LOWF2=            0.9223479 / Fraction of flux in low frequency for chip 2   \n",
       "FR_STAR2=            0.0249364 / Fraction of flux in stars for chip 2           \n",
       "FR_GAL2 =            0.0527157 / Fraction of flux in stars for chip 2           \n",
       "FTOTAL2 =             979.1025 / Total flux in chip 2                           \n",
       "FR_LOWF3=            0.9117381 / Fraction of flux in low frequency for chip 3   \n",
       "FR_STAR3=            0.0116048 / Fraction of flux in stars for chip 3           \n",
       "FR_GAL3 =           0.07665708 / Fraction of flux in stars for chip 3           \n",
       "FTOTAL3 =             2060.833 / Total flux in chip 3                           \n",
       "FR_LOWF4=            0.9916226 / Fraction of flux in low frequency for chip 4   \n",
       "FR_STAR4=          0.008377461 / Fraction of flux in stars for chip 4           \n",
       "FR_GAL4 =                   0. / Fraction of flux in stars for chip 4           \n",
       "FTOTAL4 =             2126.442 / Total flux in chip 4                           \n",
       "FR_LOWF =             0.948407 / Fraction of flux in low frequency for all chips\n",
       "FR_STARS=           0.01250294 / Fraction of flux in stars for all chips        \n",
       "FR_GALS =           0.03909005 / Fraction of flux in stars for all chips        \n",
       "FTOTAL  =             5361.762 / Total flux in all chips                        \n",
       "IMAG_I  = '$RCSfile$ $Revision$'                                                \n",
       "EQUINOX =                2000.                                                  \n",
       "SEXC_I  = '$RCSfile$ $Revision$'                                                \n",
       "HISTORY   Ran task WMOSAIC, version 2.1 (Jun 1995), at Fri 16:17:53 01-Jul-2005 "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im1[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the data itself (here an image of a nebulae), it is similar but the attribute is called *data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "imdata = im1[0].data\n",
    "print(type(imdata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use matplotlib to show what the image look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f27529fb550>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LYdMpe7cC54vIeikiz+e7tCRz6yZXjbEdOXKkUbS1qRCmOkdMUMp9yrQqN9bP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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imdata, origin='lower', cmap='gray')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is difficult to see anything on that image. This happen very often in astronomical images where very bright objects are saturating the CCD and a linear output will show a limited number of pixel because of contrast. \n",
    "\n",
    "We are going to improve the visible output by doing some simple analysis on the image which will help to side step the contrast problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean value im1: 6.237272\n",
      "median value im1: 6.7385635\n",
      "max value im1: 2925.8718\n",
      "min value im1: -12.439324\n"
     ]
    }
   ],
   "source": [
    "print('mean value im1:', imdata.mean())\n",
    "print('median value im1:', np.median(imdata))  # Note: median is not provided by a method!\n",
    "print('max value im1:', imdata.max())\n",
    "print('min value im1:', imdata.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous result are giving us some useful informations. We can see that the range between the minimum and maximum value is really big and also that the maximum value is very far from the median or median value in a pixel. That probably means that a very small amount of pixels has very high value.\n",
    "\n",
    "We can check it by plotting an histogram which will give us the number of pixel per number of photons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHUz1qGSZHuSp5JMJdkz6vlI0mvd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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = plt.hist(imdata.ravel(), bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram confirm our suspicion but we can improve the visualisation by plotting in logarithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nk3wpyckkv9G1T+vxWao/U3mMkjw9yWeTfL7rzzu69q1JPtPF9qFuFmKSXNI9nuue3zKw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\n4fh1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = plt.hist(imdata.ravel(), bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also modify manually the upper limit in number of pixel with photons (y axis) using the method ylim:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100000.0)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ytHwAeMPsTkm2Adva4n8neWiEY74S+M8RticfG2XrJTfyfCaM85lsL6T5vJDmAsefz4+d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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = plt.hist(imdata.ravel(), bins=100)\n",
    "plt.ylim(0,1e5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to limit the range in the number of photons in a pixel (x axis) using the *range* argument in the *hist* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100000.0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kbnZPSUvMripNM0NjHr6kUJK+laEhLSNbHZo2jmlIkrrZ0pBOEFsdmga2NCRJ3WxpSGNg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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = plt.hist(imdata.ravel(), bins=100, range=(1,30))\n",
    "plt.ylim(0,1e5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the previous graphic we can limit the range in photons in plotting the previous image and improve the contrast using the *vmax* argument: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f27391877b8>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NDUsnIAnoImIOgdM5p1qtZp/NaDRSqVSyz5jy91u/9VtX9n9CevAyvwj8ojhzwOReuHDB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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imdata, origin='lower', cmap='gray', vmax=25)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next plots does not provide a lot of informations useful in our analysis but it is a classical plot that you can obtain with some visualisation tool. It sum the columns and the lines of the images and divide by the number of pixels in the direction. with this plot you can distinguish the 2 bright objects (stars) which are located for the first one in the lower left part of the imae and the second one, the brightest, which is more central."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27390ff7b8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+CRXVvdZ9JaZA2MrtxdW21caSrfv9Yp6W7CT/zpO56ZSBTBzaNSpiXhcdMlK4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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(imdata.sum(axis=0)/imdata.shape[0], label='axis0')\n",
    "plt.plot(imdata[:,::-1].sum(axis=1)/imdata.shape[1], label='axis1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option to improve the contrast visually is to plot the image after a conversion in logarithm (that will flatten the image). The problem is that the image can have negative value. The  *imdata - imdata.min() + 1* assure that every value in the image will be strictly greater than 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f275252c668>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RCNNmOPkmaRAWA9018WQnnwbFY/NMhOh56pNhN+/25BkVZtTd3V1Np9MmA5uRuMeCwsJL\nibu7ux1D0amYDMz6sq5kYCIkk1cvJpPJiuMyg8Kc3oxPAXj16lXduHGjFynq7+5f5k1BYqTlmI5N\n0CAsBrpr8sTlMfiE39R2eeCtmcTfk2GZjrEL9AuY+XIFgo5Gv8eTwW8XuNSKByml6OjoqFsSbqGK\nk5MTHRwc9Mq04MtQbJ7ulSso0nyvDFGC65+rP0Q6dNBumgZhMdCZiFGMht0WDMlMrXMbGBHp/FrL\nli1fhilXT1oOSMd87Ozs9ByOdmRaE1sIMLDL8REuK/ey1Fo1mUxWfCB5hQD9JlxGpRCi8DAS4nOG\ntNN8oznX8nOcJw3CYqAzUQYSES6PRvNTqbhlnMycE5uMuu631kpKwnoyOeG6rxfgzlGen2EBMB6P\ne5GUudLCttvkIKJqrfS04j24jZ9tSVSUu0m5upTBYek32gQNS6cD3RNxyY+7MbnqQMYmbDazGm4T\nfbQiJS0QMujKZAifqwn+nmHXUn9/xnQ67cKpuSJik+f4+FiHh4c6ODhYQUatFZaW09EIpZSiz//8\nz19BXRko5nfSmUq60FvUBxooIwhrrRqPx51T0RqW285zMqfPgTa9GermzZs9k0fqbw13njQRKDDM\n1Cy75dy01rbQmk6nXbqdnZ1O0GSoe5oQGWDGtjqd2/Pxj3+89zvjPohALJRokrkePGZv02bIgCwG\nOhOZKSgcJpPJij+CsQFSf5u21PdNmFmMPI6OjrpdmBnElUKCZgXRi/0DfE4/Ra4gkPH9yTzsm0l0\nw2VMx2WkIGIerP/Ozs7KjWiuP003E52/DNTaNA3CYqC7Ju5ZMONI/R2T9CG0IDUjFU1mjhdffFEv\nvPCCbt261SuTTEYG4moMw6bpsPyqr/qqXpyENGc0ohS/2woAax2pl1GnbDPbm+dvcoXE+YxG8ztO\ntre3O/+DzTP3TcZ78Hku526CBjNkoDMTNzmZ+D93gVILk7HNNJPJpAuqajn7pH48BNEDlzMzItTl\n/fIv/3LHqMyT39P/4DwZXk6GpWPSMSe5VJyIiCYD0QTNMLfJQtk+oDRRKBhfjqXTAVkMdNfEEG5p\ndfu0mS8vA5L6S6D8LU8CJwzne/7e0uBZF36maWCBwxUMOl7pAOWGrkQ3LJfLriZeYcCdtK4X20oh\nxToSabg+3pj3xV/8xS/Lsqk0CIuBzkCesI5fkFYPo0nmJ2MwHsPPqGVTSJB5ksh4rbLS1k+fSa7S\nuH30KTAKs7UyQZ+FydGXXP0oZXmeB8/+YL8yzzTViNCM2H7v935vbd+cNw3CYqC7ptFo1N1d4XMr\ncomUMRfW6kdHR70j58gcplyOJOXqgLT+MNyWKbNuCZKIIv0Jrgd9Ei6DMRW51Ou6+m97e7vrM/tA\nSpnHdpj5eXCPUYr7km21aeLvLcG4CRqExUB3TWYaOjelVfucGtJLkJzQGVPx0EMP9UyT2WymL/zC\nL+yVnT6SpIT4dKiS6NvIoDHCfe7uzPItNJk3lzWdLi95Zto8GyQFROs3ruywLtnG86ZBWAx010QN\n508yG5nUROHBnadEGKPRSNevX++ugBiNRvrYxz7WfWdeUj8Wwc9zVYC/p1/FWpyrKWTINIHI8Jlu\nXTn+7jMoWjtS2Qfuy9buUpfXyqd1deJ507AaMtCZidGbphQa6aCsterKlSs6PDxcWTZ1Pnt7ezo9\nPe22oUur28ilZYyFg76Oj4+7uA4jB96nenR01BM0RBWJWBKN5PInd9GmTyX9GzRVshzv9fB7XHam\nryRRRO7yzWjWTdCZ7jotpfy3pZTfKfP7TP+3UspD+O0dZX7X6e+WUr4Bz9+4ePZkKeW7zr8pA71c\nxGCjtOGtFf0sHXSz2awTFHSAMqpSkq5evaqHH364Y4zt7W2Nx2Pt7e317hSVlpDdwU28YWx/f79j\nwO3t7c7BSJ+C62Zfgf0K0hIRsD2sk8sjo667pLkl8Licyj8Ly+PjYx0fH/fiKbwSUmvt/BcXZSPZ\nj0n6IUk/gWfvk/SOWutJKeX7Jb1D0neWUr5E0pslfamkz5H0L0opX7R453+U9HWaXx3wq6WUn6u1\n/tb5NGOgl5O4dOoJnKZJ3l1q4UBGYkyB8027+/r16yu2vrQUWGakXLZ0uQ7TZvwDy/AWdjIsTQdv\nEac2p0B0mtaJXxaGFFYur2UyEY3RzPFlyYmK2JYLgSxq467TWuv/U2u1kfQBzS8NkuZ3nb671jqt\ntf6B5lcCfMXi78la68dqrUeS3r1IO9AlJQYXkVGpiaXVi38Iz1srFCRCeD4zWWh5lYFCgP4RRpb6\nzwJgb29PV65c6aERa3ZJPSduy7nKMz24mkEzxe30Pad8rxV9mWYK+5TLpv7f5W0aWZyHg/M/kfR/\nL76fy12npZQPlVI+tOlLUwa6N+KqR2uiJuMwqCmFhvPwCoTU377dKtvEfNOuNyP7jAhqfq5SjMfj\nldOoLDQYBk4fCqM6/T8P4+VGr8lksrJ/Iw/HoSmWgs9ohIKCwolb6jdF9yQsSin/UNKJpJ88n+rM\n7zqttb621vra8Xh8XtkOdI6UzjsSmZunaEurN3lxFYHORmrytOXT3KHTMB2qNJdavxFB8A4Up6NZ\n5Py5aS1NAVMiIgqmPF4w/Tn+n/3qPqGpk2bRy2GGnHk1pJTyH0v6a5LeUJe1XHfXqW7zfKBLRvQ9\nEGpL7ct//U7uZyC1/Bt2/lHYSMtVEP9OJEBmTwYiwztdKaU7ZLe1LyWFIR2jNiEYodnqKzNz3gPC\nI/uct7Q8LT1P5qKDNVHXOnR3nnQmZFFKeaOk75D012utB/jp5yS9uZQyLqU8LukJSb+i+ZWFT5RS\nHi+l7GruBP25e6v6QPeL8iyJ9PqTrPkJnddpwBbTmbmZlxkl7fR0bvoZn6cJRD8L60qBI6lDFNT+\nZFwTUYnLdxqnz7tK3CabPIxDyaA3O3RzFYrt3xSd9a7TH5L0gKT3lVJ+o5TyP0lSrfUjkt4j6bck\n/bykt9daTxfO0L+j+WXIvy3pPYu0A11C8nIdGU7qn9uwDk5zMxUZ0s8SjqdTNHd9WgObuNIgLc/g\ntJBgXcyk/p950NGZKx+sBwXhbDbrAsr8f5obiYIsJEpZXgeZyIj+F8eRtPw9990Mqe27Tn/0Num/\nR9L3NJ6/V/OLkwe65GQGaWl1ackILU1POzsFymg06iA4Q7EzTdr2KQQoKMjIZDDuXXFEJC8jJupg\nPtxp6zTeml5K6TkyLVS44zXRENvDE8VYh3WIgf16Yc2QgQZqLfl5ordWMdInQeLqQUaFtsKunUdC\n/tTKkrozNSWt2P9+79atW5pOp83AJmt2rn44UMp5WNjwEB32A9FR7qdpmUz5lxGgzo/5Zl6boCHc\ne6C7JkL/XOGQtBLRuE5IOC+pjwLWRYia1mlQChUHStF8SbOJ/1so0Izhfo3d3d2uLRZoDMbyJ9uc\nppa0NGWcJldGaJ7Q4ZkXJGW+LwcNyGKgMxEjNz2JrVkZ92AGyhiGFqXfIJ1762A3Ha2E/BQQ67S5\nJO3v76/4GqRlQBaP2acm5xJwtpuan3WjryWdv+4D+m6Muviby2ZbLoTPYqCLS9euXZM0nzC8mMaH\n0nACMS5hZ2end0RbQmFuj/ZKAcOqf+RHfkTf/u3f3nvH+fCwGEZ2rlslYTmZhn4Lau/UxokU0tZP\nIcS07AOiHJop1PauR0ZntoKr3B92XCb6YBkZ3MZ8Wd8UPoniNkmDsLjPdP36dUnL2IXZbHm7NvcD\ncPJPJhNdu3ZNJycnvbTWQDzKjXn7FGtOLh9II/UvrDk+PtZ4PO5u3PI5kCcnJ/rWb/3WFUccGaTl\nAPXzXBp0nlLf/CDZ0ejv/mxpVS5NpqM0kQ2dmLVW7e3t6eDgoHtGfwHzJGKiT4VChW1xnbh64TJb\nwVQtVJXmVAaatfwt502DGXKfiXaynWj+bg22s7PTaSdpfjs4j7O3JrQzr6XJyRiSOqG0v7+/4jcw\n8/KgWDOMEUsuOXri0zFHhOLfaKpIfcRDTZ71dp35rIUq/D3LJ4OZQXNlo9b55cQPPPCA9vf3tbe3\np93d3ZVVFbbJf0ZSGbDlOrndR0dHXbu9rJtH/3t5lMIhzRWaPGzbJmkQFveZcqnQfwm5vTTHdXmb\nH3a+2WTwORAtKO6tz7w9rJTSHUNPreflRB7jxh2QrfqQSTjJ+ZtXBVrwOWMspFUfQ5oIfC/9KPxu\n5jIKa5lIFIJ+b922g/TXZB5OQ8Hg391HKXw8J7IvnBcFW9Z7EBZ/xonCwJOXTGtma53BQPi7t7e3\ngkik/klRPvuRuzAZjWn7+vT0tGOQ3OGY+xOk5T2bPN2JjMiJTQ3NZcRkGjM1hV6iGTJH2vbpeGR/\n8ei+VtnMg9cL0inJ8HATTRUTBX4ppUNmydgUpO6/bFNrPCyMOC82RYOwuM/EoCMGB3HSWJs7aMhk\nWDubzbpToOgbsFOSTjZOfE/az/mcz+nMEu/QdL42PXyFH7WjpB4CaoVlS+vvtUgnXqKEtOdpWiS6\nSP+J05D5KVyNnFxf+hsybWp3t5uCsLXkSrOEQpTtdxi586ZQTSFJosB0XTZ9tN4gLO4z0VY3KuCt\nVAkvrdG4/ToZxILiypUr3WTkPgTC9Vqrnn766Z5ZYLt6Z2dHe3t7mk6n3SlTGW4sLQUGT5TiJ9EO\nNaCFkduTlwQ7D5aTp3DRrm/FUkirWpp93xoPCmp/5zmjbpPbkM5XUzqo06fDNllYt0wM+zFYXwrY\nlk9jEzQIi/tMhv/cKi2pd8S+T8VOk8Ubm7yi4N88seyboKBIpxwnrY+Ik+bnYHrSGml4Uo/H4x7j\n0KufSIBMYqL2JcrxO0Rb6a9wOaw/Ba7fYznJ5C1UkijBfZIoxwLO79Ik4SG6RhOZln3k9uX2+OxH\nO51bDmD6Slo+oPOkQVjcZ7KPgmc+TiaT7vednZ3uJnKvjEjLqEILBam/M9ICho61luYyOmGZZL70\nurs8ajJqYuZBTWpinAKFBJnQZMHVch6mfc960DRKRs9nfr/1LO/kaPk2UhixnjRzuKPWwi6jYG1q\nppD0O63grJYA2RQNwuI+kyeJyadVe/LSnuWOR04Yr4ZIy23e/s71eAuXDARyHp6kZDZutvIBsq4D\nmYyohQyXAsTPyTgtpEENn45N52FK9ELEREGXArDlKGX+/J3mk/swV1Xc5xQqNCctJIwEuVTud132\nbNY/mFdaCnvPE86ZltP0vGkQFveZ6BiU+qdJGyUYzpqheegtHWR8n1o0mYd2MyeZmcIOTS75OY3N\nGmpGExmz9dztpeYks6SDM/vJn6xzy0majkq+RyTQcnqSUqPTkev2p3OSY0nTxAKEAj+D4egk9bja\nuU0lkX2aKGNTNERw3meiyeDBPz4+7piSE5i3knNSMoLTE9aMQHvYAkfqO+gcsWnNd3AwP8/IKyzb\n29uaTqcajUadL8Xv0ofQilGQ+gzMdktLIcBIznX95PSJONyeLNd5U9iZwXOlJZ2D9EWkucTv3Dfi\nfDwGFvKJNmw2SvMYDp816/fYBtc1d85muS1hd940CIsLQHSo5eEwZninsfOSWpJOOyIAay9/puYz\ngxDB3Lp1q7PXx+OxTk5OOn+GBcXBwUFv78l4PO60bQsKU4ua2VtxESY6+1omRqIJ58PfiWD8v5mO\neVPgtRy1aVqxDRQ8HEPmzTBvjyVRSAptmmR+RoTkpWyOuft+08hiMEPuM3Elw0FTtGnNoBYgp6en\nOjg46JyiZDxevJvLc3ackcjYLtNaS1pGa3LPiFdDnG8ppXdzWItaJ1kxfZoKhOfJ9GmiMA3zqrV/\nV0j6WSgk7H84OTnRdDpdubjn8PCw50j0d65+uP4sJx3L3JqeJo7zcz5ewWL9Wb59GhY4Dv/fJA3C\n4j6TtUcp81OWPHGpUQhN7ag8OTnpDnydzWba3d1dcYjZxDHjMA860I6OjvTiiy/2Vk+8J4ImCiep\nJ386DRNG+zfSOkSQ6dcJICKVPOuBAoUM1MrfFwhJ/eP3KDxu3rzZC4YjouFmM+fLlREimlzhYH/l\nb+5bohTn68A9Ci8ve2+aznp94SOllPeVUj66+Hx48byUUn6wzK8o/M1SymvwzlsW6T9aSnnLZppz\nOYnMYV8F/ROEr1y7Z1i4Q7QdK+G0NEEIfy1sDg8PO2ibTErzwlrak1VaMqA1m7XyZDLpMQJNjQcf\nfLDnm2jZ2dy3wjqZqbjFns6wMsvJAAAgAElEQVRZl0UNf3x8rMPDw+40LLd5Mpno6OhIN2/e7PqA\nDMwl6DSDiCyk/qoSzQrXqeXLoVChqWWBRdTCT66IJNq4CEFZPybpjfHsuyS9v9b6hKT3L/6XpG/U\n/ETvJyS9TdI7pblwkfTdkr5S89vJvtsCZqD+kiIDrbisSsekzRXa1TY90sN+69Yt3bhxQ4eHhzo8\nPNR0Ou2YmtCYWth1sCOOk5ROQ66W5DWAk8lE0+m0Y1ILv09/+tOSVpdB6X9J56fL83v0u1gI0byY\nzWZde+1v8W/crZsMTQZeF56dKw92+qYPhX3FfCggKAD5jgWiieiFZmL6ae77akit9ZdKKY/F4zdJ\n+trF9x+X9IuSvnPx/CfqvPUfKKU8VEr57EXa99VaPyVJpZT3aS6AfuqeW3DJiRPQpkTa1GYOw16b\nKdZ6hMl+h0t61LaekAwtpxMvl0NdD5sjx8fH2t/f75jW79P0cbtYVu6BkObIhUvDUjt+gH3E36Sl\nkDPDU3C4fqxb+jf4rKWhM1As+4jIjT4R5kU/C1EWy3d9qTj8Hn069j/5eZpxL7WqdC901tWQV9Va\nn158f0bSqxbfz+X6Qs1RSe+osz+rlMyYGssogprIp0D7ff9fStF0Ou20kh2U0nIJzrY8g7Voq9sf\nYiRgRjaT+P3cpm7ipOeEN9MY2kvS4eGhJPUEXfoaqP3JcP7NZXBjl+tjQcH65XLjOm1MoUeGTKjv\nwDmu7vA95uPfKQDZh0SF7EOXSxOEeXBurPPznAfd89JprbWWUs5tgbfW+sOSfliSHn744ZfnvLD7\nSKk5GXyUSCJXPlpMur29rclk0mlywunRaBmqnYFgNnkM2x0C7pUP2tDc2k7hxkmfpgQddCQyBJmO\nzES/DImMQwHo+qc/hPEhiQLSUUlEQIYlgmDaFCrJtC2h2uqHdfPB/d5yhiZa2RSd1SPy7MK80OLz\nucXzddcX3u5aw89oIkNIyyU/am4uhdpkYATl6empbt26pePj405QeEWDkFxSD6nQ/+BNZ3Sq2kwg\nU+ayLBnN29opKFLb08dARjC6crsyviGXOulfMNFk4/KniXVrMSbHICMv/b7fydBsMqmFpdvJIDn3\nFSNj6TPhyo7rnysnRFGlFL3jHe/oCcpN0VmFxc9J8orGWyT9H3j+bWVOr5P0wsJc+eeSvr6U8vDC\nsfn1i2cDLYhw2sSgqdSyniwma15PbiODdJjZv8GNZnaouh5O5/p4EnpTG52ELSYhpROOf6SMwGRs\nib9bwLhN9EfQR5DOy5bpkO/x/zQ7/ElhxoN5OIbXrl3rOZoplMngjMTNOjsdx8Rlccexf/ve7/3e\nnlDZFL2kGVLm1xd+raRXllKe0nxV4/skvafMrzL8I0l/Y5H8vZK+SdKTkg4k/S1JqrV+qpTyjzW/\n81SS/lFdODsHmhMnt6Sek8pMure3J2mJPqydtra2dHR01IPu9j9QI0+nU+3t7XUTy9Dd4dwMuiJq\nYbg3BYzrTabNZU2W77pL/bBtP0+nH4VK2upuN6Mp3S63PR2G7GuTmTDjQ9IMcr4UHiaaEDdv3uw5\nUFv1d79mWRYijPKkj4OCj8KU5uF9FRa1fX2hJL2hkbZKevuafN4l6V13VbvPAOLkN3NL85WCo6Oj\nbtLv7e11y6Y5kexnyC3ddoh5r8ne3p4mk0mHKrgnxQJJWl7Ay+hS18NOVxOZh5A8nZFOkxrfnwx1\nd3q/Q9SRkJ9mDvPL7eVp12dsBIUK/2edyJgMmnI/uO9yBYTtTxMskVgiIaZn++gfoql2EX0WA50T\nGeZzYxDtYg8+ob+FAr3+uXKRt145rbe/+/1S5isoLo8Ci5DcCOPq1atNZx3r7ndJiShKKXrooYe6\nd9OHkM5GMzHNFKZJEyj9EVyVoQ8hmdj9QM3NdtE0SiSXeTF4je2k05dCo2WiURgyfwo/p9+032IQ\nFveZuMpA5541F+1bx1WYuQ07LUis9W9nu1KjeqLaEcooRp6ulce+8dQsTk5q3lxKpAAwE/3pn/5p\nj6H9XqILlsONWWQSMmF+Z7v9W8s3wnqnxqc5wXco6EopnZOXJhKvVrCwYp+lUOKcYP/TD8K6s86D\nsPgzTrS1/b+k7jyD6XTa+Se4vZnM7v8ZCu6J5Inrd+mNt8PSGpUBP/Sb0NEo9Vdt8jqDdb4Gv2/0\nwmeE3+sY1e3wd/6e6CZ37/q5Pym4WF8K52wDx4uClGaAER7HiPETNv+IFtKRTOSYz9hejkWaVpug\nQVjcZ/JA0ydgZ2Ip/aVKLqXNZrNuq7iRiGMojo+PO8FAzSMtV0Ps6PTkTs3ViquwkPGSqp2bPAQm\nkYC0egQeA45YTkZlppZs2f2E5KQM1ybMl5aClQgonY+5SsLv7gv+lsvZHF+PQ8vhmuldf6ZPE4t9\n0nKkboKG8ywuCBna2zfg8yRsWhB6e+JxPwjjCvw/1+cd2OXj8ewspX/DsRaM8RiNRivMy5UHp6M2\nZn1N1NzU7AwcMzpxu/wete46Z6Tr4lWS9P/QP+Ft/4mUpL5gy/NF0lnL70QzGSOSaMttTYftOsdn\nhuCb0h+zSUEhDcjivpO1s00JIwXv+KSG5GGuk8mkmyBeLTFa4AHAZmRHYnoSTqfTLq20tOktnDzh\nPakJjSeTSc8JaJOGjkPX2XVIqE+HHSE0oXlrCdX5Op3PFeVOWyIXlseo1vSBJLPxe0ug+K/lyyBi\nSn9M+lsozNJx7LKcPpdbnTb9LpuiQVjcZ7IW9NFqjsKkv8AakwJgPB53TMJQ6AcffLC3C5IT3KaK\ntFwV8fZsmyPWvMlURBf7+/s9cyM3pHmC056n/4BH82UdW8KBCMDkpVEjKJ5T6bwYvOb25aEyrC+F\nIpGI1If7ZE7Wl4yfqMVC1Xn7OZda2Wb+z705fo91c5oMhz9vGoTFfabcMs3zKLzaYWbmZLTT0xPO\nk9yxGdJyAtlEaQkEMztjLOhPsHnk/Hd3d3Xr1q1enaX+ydMt5pdWl4S5ikEH5+00pPPj/aOuQ27P\nT4jP/TStFRGjj0QkTsM20MSj0OCnyyVTJ8PTfEmBkwKEztj0W3Cz4aZoEBb3mYgWeCrV6elpd3KW\nJ7OZ3pPDafzMQsY3ie3s7HQXHkvLFQxqPxMFCZ+Nx+MuWtQrKnt7ez1Gc9p1Kw9mBguedBCmc49a\nmKdx+bdcfjQDOiI1VyXMYEY03I2ajki3ieiICMZ1ILJg3dK3JPUPNzJibK3scEXJ/9NEYZ+mmfRy\nODgHYXGfKZ1aV69eldQPvuJ+DId2z2bL+02l5WlVtP15w/r+/n7vEF+bDbxzpNbahZR7VcUQ3xPd\nqzaelLnEmJDcAkpSLwSbfo7UwkQZFlRplqXD0UTm93f6XWiysI9p+rANXPXhOLXMJVKaLH5mQZX9\nR+HUmhtZDuuSyG1TNAiL+0wJde085GTn5DSDe9WEkD+9986XR+KNx+MObVB4cKekhYGXZM2oZmSe\nN0GGXOesdP3M9F5t8YSn87UlbNielhMyhUtSK5Yj/QRe9mxp7zQtWoKA9WEZpHV9kv4Ppl+HLvi7\n69Qq8zxpEBYXgLgUafLk5YnbZjRGWtZau01k3iPCSceJ62Co/f39Tmjs7e1pf39/ZSmP+0coeLyH\nRVo9oyKjK1vH2NtX4TqNRiPdunWrJxjdJ2Zo/3FLdxIdhczH31vmARmQzsR0gEr91QeueORKT8ab\nMH0yO/02rTnR2lTWWnJttXkTNAiLC0BGCSYzP1c7GCdBVECI7MnHbc/pLLMZQ6elBZPjI0xm9tFo\n1G00S+ht9OHJSwZwuTaPzAA0N9IhRzOFzl//5Q1g/H3dakALplNosD0UZIlmuJqR/orsMwq7lm8m\nEQQFgrR6gZLT5ZKskWFrxei8aRAWF4A80c38DIe2f8KxE/Yd5LF20+m052ewP8CT0O9yUlkbWxgY\nZfgGdWnpYJxOp7py5UonDJxXXppj52Rul24xTEuDu0w6F5mHmcgCh2iCqCMdgBRgfsbf19WFphWd\nsywvV3wSJXL1x2noc2iZGGwj657oyGUP94Z8hhCDrziJHKxVa38XKRmO/g4yiicxGdgT06YLy3MZ\n3nma+xh8poXr63pQg9J+pt/D7aFfwJOebecKAevldPm+fzcRvbTCvY2e1t2x4bISzqdvgKaLmZXx\nGwz5TjMlVzUS3aS5lEKI3/23TkieNw3C4gKQB502upmT3nGihoS71HI8IdyTjlqx1tpbUjW5LJ99\nkUfoWfiMx+MV+9lMnHEMZmCjEGphvse+4D4U5mXmof+EZJRDBOI6JAJxPhRyREyk7KdEJ2mu+HsK\nmBYj05fRcmTSaU3fBIWQhfomnZvSICzuO1H78LQrTywzvqRelKTfHY1GvSVUh2tnfAJ9IAmTM6zc\nTOel1729vQ5t7O/vr0BeMhidmc7PE9plU8DkygDJ2pppyKD0qUjLE9HZZkm91Ry2L0PEW/eWcmXH\nz0hkUKIN1pNLtTm+RFAWksyTq1wsm+aL/2/5gM6TBmFxAYgORjMpUYPTeELTD0HnorR0sJGRODnt\nKHV0JiepNSIP4vEE9VF7yQjpnKNdns/NNGTQVnwBn2cQE8sjFCdqcH/Rges8iTocQu9gLt4Vm0vL\n9hmlQEuThWYFhaSJZobfZ1qiFKKwlr+ExDiQTdE95V5K+S9KKR8ppfzrUspPlVL2SimPl1I+WOZX\nGP6zUsruIu148f+Ti98fO48GXHYi00jqnIl+zu3hTp+eekJ8p/HEojDJFQALKQZGceeqy/U+Ei+5\n7u3trUB2rki0oDzTJEwnM5Po5KRJwtiP9PWYEqlQkPFZXhNJ8yrvnOUYvNR5l8wnV22y3/x/oo5c\nTna6XBZm3TZJZxYWpZTPlfSfSXptrfUvSNqS9GZJ3y/pB2qtf07SpyW9dfHKWyV9evH8BxbpPuOJ\npgQjKz1ReFFQChZPKpoWdDZS+5o56Wj0sqZvQuPyacJkT2Kn827PhOm0ua25EyoznUPc3fZcuqR2\ntalmrU/HJ4Ut/SYm96mfuZ+57Nzyc6Sj1GN2u1u/aCpyz0aiEqfJvGheuE0t08X5u80XfSPZtqT9\nUsq2pCuSnpb0ekk/vfj9xyX9+4vvb1r8r8XvbyibNLAuCXkyXb16tbdk6Aln+MsYA2pC2+k8m8JM\n7eAp3l1aynI51sQdm5503A4v9RGH/Sh2hF69erWLwhyPx90fz/McjUa9PSXUvDzCj9qUJoXRkoPF\n8u7VRDgWIK3lSh5sTLOKKzt+h89ZNzpmWSYRChGh80uTy0RhlMuxvFxoHUokCtsUnVlY1Fo/Iem/\nk/RxzYXEC5L+laTna60WlbymsLvCcPH7C5JekfmWUt5WSvlQKeVDXqr7s0yMyhyNVi84JrLwGRfS\ncvkyN0/5NwsRTmAzGhmTcNuH4jgfCw7C37TFrd2l+f4Tmy+llM5kuXLlSs+sojYn9JdWzQmaVDS9\nXD8T86CJ5L6g2cWlWr/LMzzcxnReesnVPh/n0TIvuO8l8yEaSgHjcllvtpf+D6INjtem6MwnZZX5\nZUFvkvS4pOcl/a9avW39rql+hl1fyENlzLAUFkYU1tJXrlxZ8SvwMJqWf8MTV+pPMEZsugxJ3X2p\ndHZ6ee7w8FDHx8cdEjJZ6CTzP/DAA7p582Z3MA9jCOiwNTP6fy7/+nkuuVqIWBCR3O4MaDKTOu9k\nZL/Lukr9mJbcJevvaW4ZCXH/hxGbTS6v3jCIzkI132E9GG/idpyenurg4EBXrly5+4l4B3Qvx+r9\nFUl/UGv9pCSVUn5W0tdIeqiUsr1AD7ym0FcYPrUwWx6U9Kf3UP6FIWtU29OkhJKEkPYZEFJfv35d\nzzzzjB5++GG98MILeuCBBzSZTHRwcKDr1693kZrcZ+FJyp2nnNgtiE9fg+viScht85701JRXrlzp\n3T/idhIduIybN2+uOOD8u00sM3ut83MqjCipeS0suVLCdkt9/wAFCk2eDOpyOzlW68yXPGAnl0lb\n/huWQx+IheL169d1eHjYE1wMibcQpXDjZzqiN0n3Iiw+Lul1pZQrkg41v3ToQ5J+QdI3S3q3Vq82\nfIukX178/i/r7dzJoN3d3U7Tkdm4DEeNZfJvjKfPNFL/XEfC8nTGJXGAa11u7yatWxXwUqJ3kFIr\nfdZnfZZqrXrFK17Rtd9leLIxYIr2Pf0KacO6Lwy5KXScP/uBfZNbuTPGwb9lv6XdTlvcZTz66KN6\n5plneoLBzOR0bjsFndvCsacZwDLcV3Qk5+qD01EAcAnTfhf7TFpH/Pt9LhO35oHH7ObNmyt9z/r6\nN7f76OioQ1rZl5t2cJ5ZWNRaP1hK+WlJvybpRNKva24+/F+S3l1K+a8Xz3508cqPSvpfSilPSvqU\n5isnd0T0qJvJOAnILBxcayIPQsJOk6W0IaNtUtrUft/M0LKtqXG8S5TMS0edJz9hJQd+3bJhQmOn\nd/34ibHq+oXMRAFA4cAJnlrTdcub3Vkn5kVGJsJg3s8++2wPUu/s7OjatWs6PDzs2pArKe4bLv2y\nXl7uJUqw0LOgcb0yRiHHOVdJ+A4D6RhBSjOIiI6xGy2zx/1A3wUdnP7eGrs71L1npns63bvW+t2a\n331K+pikr2iknUj6lrOUQ8lOxxq1OoWHPxNFML3TWAtQWktLKGgh4CW+ZMRcznS+nijptfckzZO1\nnZcRgYlnYVIosq0tSJ19RobJFQfXlwIsmZ9MTycj80/zID8TeaSw8zM6TJ02namJssyoeTVjBntZ\nwLjuRFLZJpfDNlIo2CHt9zwXXI7NOZoVLottZhQvTS+3m0KLgjiVSQv9niddiqsA3Lk8Np72f9qX\n3JHp536HDkV/JvSmJjQRzfD9ddCPA0/ms1ZiCDLf4ZZ0t9HnarYYkQLOebAvEnX5/2QIPnOf+v4S\n50e0Q3ONebDvqPEzf/cNbfHWZM8xsY1+eHjYmRPsA2pkO3iznz1HaNbkfCIDOi/2HUPszdzO0+Yo\n+5i+H2kpWFqmit9jeWmmmCioN02XItybzjdqUTvHTMmgaY9K/ZOd/GkNn5OVzqoWpUPLk4YCTOrD\n5qxDmlO2h2kO8LAZ/xGqkvg7BZnRD3+j1z41K/vXnnnXl+n8neiK9jwRiZmDAiPHgn1rSjNAUhfb\ncXx83OXrNCkwW+PqdGlCUXAQEeSnhbfrZ4HCWI2cU6WUbgNfImD2C+vJ+vI5BW+rzzZBl0ZYpL1L\n+N/qZMK6hOm0fykMEha3gmeYljCeE4dxD0QyhKsZaJOQmVDZ5bEM15PwX+qvfhAFkFmdh4Vbmhns\nC9ctNSPHJaNKXV6aQvyfDEOfEvuWY7ZuXNzOjD9oCUzOiXzmvuAco4CgGei+SHON84eohkSh4kA2\njhERmcePbaOJwnq77E2uiFwKYUEnFgdU6k+MHED6N9LeNxn6G/7z/RblbwnNXa60aqIQppPhuf+D\nZkt6x6XlLlSjIdrSNE1cPmF+yyRqmWvZRpeRyCBtcfpCiNbIdH6fgUktgc3viRZoIhj2ewwl9TaB\nUWi6HclwzJf91NLmib74zGjCfdFCpaUsY0hKKV2kK9vp9yxUvOrDYLJc7bqdYjsvuhTCgkxEDUcB\nYDuVHU7m53ZnDrD9A8nYHLTUmuk7IPxOxvEqCCc47+twOYyPSHOFWp8ChAxMoqZmhGf6ZpwmNSTj\nE1hPbpEn0/qZ/Qee5Am1afIkI94OSbit/N1/PMiG53BYiKSZwQA1v0PTkePpdyhY0jnr9lMJ0DeW\n5XF82BbX1XEurBedqO53n9ZOh/Gm6VIIC2sDMnoOrJ954rY0B4kTmPCPE9Hvt+BkakA661iel2Fz\n41VGVZrxsw2uY25Fp5Bk/S0YqCV9dqbTp81LX4bUNy+yD9nPNAUpiFOgUlMSCVJ4tAQZx4r5pLBL\nJeF+d1r/lmHmUv9yJBIRD9tCQZwohxGjdGCnA5J5W9hbyDKKk+PLvmhtfhuQxYLSwUmGoVBIrcAg\nGV6/Ry3b0rjp4yBS8bPUdPSquy5cfs1DbUn0inN5luHQXCbLyZNmi5l5nQnSamc6OlsalasbuQxJ\nYZNQnWWwT/geEYDJbUj/jcnvMa80xdgWt4MmBOu9ru4UVomMWsuw/D+duJ4DHCsKY8eHsAzODwp4\nHmicq2KboEshLAjv1v3OyZADyd9NdAqakW+HQmhKGBXkJJvNZr21dTpgWxrT9aDgSR8CQ6+lpcYk\nI6cj0+nNaBQUZB46YllGMr3bkz4GfzrOgaZeS2hSYxIlpJBPam3hXocGMjKTyIL7UJw+fROt3ywo\nWU6aUZw7/kvHLQVHml10JLMcSb255nGz2UffzKYjOC+FsJD6EjhtbAoCD5IHxIzA9OmZJ3w2tSYj\nhVAiBGoBD1z6Fyxw0pvNPNKH4OcpDKTVlZIUCuvSkunZfiKsFnqhNnSerjODh8yUXEXJcWuNI5nS\nzE3Gb624OA+X7X0lFKJOQxMvYxxcD5oVHkf/lmiohdT4Dt+rtfb8KykwaKb6d/cl60OhYoVks2TT\npsilEBYpcdMfQWhniMwJbPLk4YRedxxZIoF1mi/rlQyV9eRnai0yL7Un7XLmQUZjXhSWbgfPoKBJ\nRiHZYgBq5QyySib2M6KfdDBywjNvjk8L5VFTJ7G/uMzIecM2mtES0bQUBAUof2/5WGj28fk6BUL0\n00JaFDYUbkR5HJvbHchzHnQphAWlrdQPU+Zkpa3udKaEwIbMPBBX6jM2tS4ZgFqWwiEhOutFhxe1\nQNq06+pOwcfPRARpA5ssFOk3sHChxvZ7iQCsFdm/nvz0K3CCkwlSwIxGo951BLPZrOdnYD3yz2nS\nf0BqCYFk4vyfaVMwsE+skIhQyNwUkFQaubrFMaIZnc9tjhodsY05T1oC77zoUggL2mQJG8kstNlu\nZ0Z4sD1oeS6D8yWcpr8ilyI5OW7nZCJs92TzGjuZisKQGrU1wdg//u12sN/1zpUBwnm3kwyZ6CVN\nqIyiTOTT8nd49yuZPpFOjlvLxPK79I20nJ7sj0QvHBMqlfSxpCBbp2hSCHGsU2B5DhIhZ/5WcBbu\nnLPp5N0UXSphwWjFnFD5fR1ZOtPv0FqrTu82HYWcaK5fagTWh5MnHWXW9BQQ9AmcnJx0Z16kj8J/\niUoYLJXCgH4N14HP0zeU7crPlgbmO86v5XxjX9IkcT7sf6dJwcl+aJmK7leXQ18NNX0Km5Yz0v3j\neiRTp3OafZ5187jQZKZJnMv4nPucT0S0aVadN10KYUGJKvVXJzhZOUD8LvXX0xNNpJZsDS4nmcta\nxwCsD/NJjcyJQXTCMt1+TpzUqq06JNTNd1r1TIREAUfh0nL0UZCmiUihlXmzH/wsV6dSw2bbW+jB\n76fwc72IENkm9qE1ePqJ6CNIxZHvkzj+fCfRZSKTFLZcjcuyP6ORxbVr11TrMgLTg9s63MXUskNp\nU6dQyfQJOU25ovBSA2Pt4/fI9JmO2oH19m8Ujhm9msyc8JjOsUQWNO/IfM6/deeFo03dr6lRs2/Y\n/y3HoNQOjuJY8X+2gQI50VSrThxzokzHtxBppYDOJW4jAftzKBzTfCHRuev+93GK7ocU5myn69wy\nCT+jhYXU1/SGa5zQHOSEzpwcRCQOq22hCJfZ0lbO15PCE4h7UKhlWz4MesWJWHI3YkJ0TtpWhKK0\nevJ3Qvp0knLFooXSuLriursM5uN32Eb3HSMTKeSOj487Jm0FvJFBsu+dd44zfSB+r2UqZV40L3Jp\n2GVltKrbREFHh7c/2dccK/YHHch+L9NRuHgcsh2bpAsvLFragJGZ1IrUCDl4iQrIHC2GbtmYJjKK\nUY/Ul/g5ISmkWO8W02U5RAZp/pARktmZT5pnyXR5HQDztEA10iildH4UohuWmzZ99vFsNtN4PO6d\ngpZj53TspxyzRGSt72mOZT9kWiIZIgwKEafzmNLfQMdo+qhyvCncXB7nUO5bYt+47E0HY5kuvLDg\nJJdW19sp+QnF+L+hs5nZf7RHqT2pObNMIhE69HJi8sKcJGosO7f8vRVzkemtddK/wDTsOzJLttVt\ny8N1aNZkXjQ/Ev0QUfFdojv3V/pJ0nRJ+M36k0Fa/obs51QoTpdmh/uGQt3puFTMtPZtuL9yjlKQ\ncCcu20klRqFC4dIyh4gwNo0u7klYlFIeKqX8dCnld0opv11K+apSyiOllPeVUj66+Hx4kbaUUn6w\nzK8v/M1SymvusIwVBw87Jb3WCd38fWtrq7sXg3CSk75F6fRrOTmtcblfgpF3/mQaaXkOZq11JUyc\n2pwMTKjNOpISwqYPw0zNfOhA9qfr4RWK3Jdiocu6JWrJMt0/RGHpH7BAbPlfWFZrDrT8JUnsGwrO\nNGcdws4Ym0Rd/OSGNo5Dy+RrBe216ubv7EOasSncW+09L7pXZPFPJP18rfWLJf3bkn5b0ndJen+t\n9QlJ71/8L0nfKOmJxd/bJL3zTgrggJJpc6Lxu00DwkVObKkfmiv1DwXmpJb6jsl0qhJBmPGdHyec\nJ5ov4vUzC5V0GrqdknqQlqYCNRvrRY3Nv9SKnFj0g7i9/mRfEz34k2HVRBhpUng8KZTSH0JBwN84\nbs4n20Jkxjq4j1pBUYm82I5EDU6fpo8/PRe4PZ0rLomQsl88Rp4XjrRlf7nf0/Ro5XnedGZhUUp5\nUNJf0uL07lrrUa31efWvKfxx9a8v/Ik6pw9ofr/IZ79UOTnZreGs7ajtc9D9necseEAzEs4D44mf\ng5EDxKCmNIEoWHJ5ziiCdfakoubgJHYeraAjOtnSXqdwZTuSsZ2eDmO3xWUYeZCBU6NmGfwjsR0U\nLi2E4br5/xaEJxpg21guHZcpEJiOdSKxT6igEu3NZrPuekg6NzkOzDvL85GEzp9Czm1ItNkyuzZB\n94IsHpf0SUn/cynl10spP1JKuSrpVbXWpxdpnpH0qsX37vrCBfFqw45K4/rCRBa+nIbETpOWa/V0\nIDkdpTw3FzGv3N3ZskPN2akAACAASURBVPNbjOBnDC7a2dnpXcjDd7yBiELCx621bO6WQ6zFnCkw\n3S7+30qftjOFtPeVtKCuhUlqbJpShPsUdq4LGYHm3jqvf44Hx4ACKE0atpHLzhQ4LI/55/who3MZ\nk36V3H+U9a21dmeJUvCmWeXf2X4KK47xJuhehMW2pNdIemet9S9KuqWlySFJqvOa31Xta60/XGt9\nba31tWYaEzfUZJBWOiX5PbUwnVicnJTQrYnFg2up5ZwfhZp/o03P7dNEEs6TSIRtNdKh+eF65sTl\nsXtEGS1N5P9NTMMJyHLWMaX7OZEBfUlf9mVftqL12Q+E3fl+jjfrnu1l/dIxmeW3bH/C/aQ0URLN\nUvjlSfKsH52/NI88f6zs0kfm39MByrptiu5FWDwl6ala6wcX//+05sLj2bIwLxafzy1+9/WFJl5t\nuJayg1NzU4NJ/cg7M6p9BFxLT+jJ+ASaKOk/oKDh79JSgxj50AfhspmeGozQ1m3lRPBRcdlm1stl\nrWNqtoOMQ4ZPodISUKndKPyyPI7bhz/84e63NC2I3BJqU5C6bikwSLdjmkQO+ZxtYJnZXymA02Fu\nhrdQkVYjdVMopW8lHaTOg0qAfbxu9e286MzCotb6jKR/U0r584tHb5D0W1peUyitXl/4bWVOr5P0\nAsyVuyYu9Vmim1lSAnPvR17eS+3OyUOkIK16zunU46RiudQAhMEsizdncSKin1eeuXz/RqRF5qcA\nbU0wt6v13e/xHaIY/k7kw3zYV+wTf88+N9EH4XLSf8Hf0pRwujRjXDe/w7Gg0GsJWiqqnAstwUVU\n1BLgrgOd1iaaMXndBZdJ0zRin2+C7vWSob8r6SdLKbua30T2tzQXQO8ppbxV0h9J+huLtO+V9E2S\nnpR0sEh7R5QSNjs2paqZg1rLnZvONeefNmiaMa2oS0k93wm1D00Jl+3v9JPYB0DThgLQk/j09LS3\nipIOT9a51Qb2VwtR8D0v6bqNXOUxQxE6t/qfGtTv8QpHjhO/Z37sk2Q45p0MzvSpuT2mWQeXlxHC\nmT/fY5+lMDRxKTzHh8iCc4b1oeBJNJHt3STd6/WFvyHptY2f3tBIWyW9/SzlcLWAcRGOW6Aw4EEq\nftfvpbaiFiHcRJ277+vMEWn1TIrt7e0upiM1k9/L3ZVmJJojNreMYrwcm/EPppyQJk5qarpkCE/c\nNK0SYlOIcSKv82e4PAod92kL6dDWp9BoKYFk9kSHHB/W1eV7vuzs7HT7M5wPBTjnAxWNn9GJmnMi\n0ZjH0+WxT6iUqFyIgLPu60yu86YLH8EpLe1kD0gyiSWuJwMlfAqBhILriNqITCwtz8F02YThtdbu\nslzCXUJlXn1ncpwHhQknjvPjBPFvvi4vkYnfI+LJZ+wrU8s+l9qRkkRJNC1Yl/QHtEyrFpSnH4Rl\nphBYN44tpyzJCMpjkmWmEuAcaPVpolGnT9QnLc8VpT8mN59ROPt/l0UzqtVPm6BLISzcwdzU5KUm\nUy5PMayWA2wiMshypH50IhnBn9zIZTIzt3wFLXOB6fz76empJpOJdnd3V2xVogynH41GmkwmK3ss\nWG9qM5Pzz30nLbhtU6s1aekv8Xf2CxmM7VyH4FgHOgazP5lHauckCozpdNrbWn50dKTj4+Pu+dHR\nURcQZaFHRNLyt+TYsj5El+m/cp42RTP2h2iFJkiafBnnsim6FBcju2MZnJLQl3AtfQK07U3JHK3J\nTRhKaMjLgKntXQ//llo20Q7TjUbzY+ZyxYPlUEC5XKdPz3syKM2e1Orux7SlKaSlfnCY+yfLaq2K\nsO7ZZgvIRIppXtFc4+5c5pf/p6+L9aKiScXCDVqt3cSZvjW3nNb9RfOBfepn3H3rfuZY7u7uNs2O\nnKubRBiXAllksIrU9tCnT4OMzQEktL/dpCNj+DdOnmQ6atXUiq6nEQIjUE2uu8sgU1rw5Oa3UkoX\nn0CbPjURhasFk5nCTJU7JdO0Szs+7Xu/Q/OD0Yh8h8I33yVx8qfQSaJm9//uA/ebHcUUhAzyy/lB\nNJRmCdNwDnqeMW36JXLe+RrDDAb0d0eFss+oVFro+bzpUggL3kVKppdW7WhCQPs3cnnqgQceWFuO\ntHpmQy6nOY0nUEJvHmHfguysm9/LSZ3az+9QY/k9LyOnvW8iCmkhj4TzGcSW9Ur/gvuAk9nClChM\nWvpmElJvbW314lSIWlrmRsukoQ2fEJ5bvcnAVDr+JOrxVvxsO6nl2F1nInHVJPsg300fBstzn1Mw\nb9pvceGFhTuWoa7WVnR85oA5KIp5WBrfuHGjp4FS4PiYs3TecZJxyZTr6JT60uoZlxZcXJ4z0nDd\nE93UWntH+adWpgCgKcD6v/KVr+wYicLM/bOzs9PzDUj9KwCNduiozT7MPmo5XI+Pj3Xt2rWmIM4l\nS64CpMlDJmFkrcvOqE2/4zR0MCajub+57Zy+Ga7MOK/bMW7rGfuOeXpcU1GwT3N1xXlc2KCsl4sS\n/lE70E/htNaiuVadcN/k5xYunjxkWAY8mewY293d7aEMM7MHPAOC7P3O6/XSpneeZlLmm0LO5bZu\nBXN+N27c6PlMqJnT/5F+GjIsUV1qapoq29vbunbtmq5evdqb3KPRSDdv3uyZXLu7u7p69WpXBoO4\n0j9hhnQ6m2auk52Vx8fHveXrdYFN3FGckamcO+lo5DmcRr0UPBRwrjfzMsJKdOT6te6zoXDPOfpy\n0IV3cCa08gRJGzE9zNLqKVktRxc7nNqLEtvP7Rtw2lJKZ0t6nd7pLHzoDXdcCFcvqOkyOi/rSfvf\ndXDak5OTLg6DfobUeFJ/j0w6ZAnzXXb+b2HHuAkLbsa1TCaTHhNxC/xoNNLjjz+u4+Nj7e/v6+Tk\nRM8//7yOj481mUw0nU578RmMn2E7WIfpdLp2TF0vO0nddvqOeKwBzd0UimkWZN2IGFp1Yb/TmWmB\nlf6lFP4U5umo3iRdCmThTqNtTCZOG95M50HkUtk6245QMScF/RLUMLdu3erKpZPR2ope89ls1nm8\nx+NxzzHmPKjhPUm9rGaUktqFfhwKEteF+XLCsX3sZ+edWivLTfhONGQhxfJcD7937do1TadTffmX\nf7muXr2qRx55RI8++qi++qu/Wvv7+ysh+kYSyUxporjc9G2kMKQZRLPCabkhz+k5DzgX/LuRjE0Y\nj2MeWWiB5TxYf5q3nuvrlkZZPsd8U3ThhYUHkRCOdishaysE2VA+hQXzSL8ENQkFkVGE//b29noa\nyPlR6LAuknrmSUvgUVPaO+5t7KaEoYm2kgHS7LIDMFEDJ5/rY8bx/+mz4ESn34JOOrbf/TKdTrW/\nv6+DgwN9y7d8ix577DHdvHlTH/7wh1eC7hhbIvVRGIXY/v5+Fz9ChEH/Bf0MXG1iuyT1rnlcZ8Jy\njlLwGkFNp9MVU9Rl51hS+HrOpi+O/e3/04G6Sb/FhRcWJDIm/QKmRBR0iKYjkLYnB7sF+6QlzE5t\n5ndoQ1PwUFgZNnMSW4PRH5DmQnrQ+XsKprxm0QgnJ2PGrKTA48Tc29vrHfZDAcc+oEA2Y9Ic8zu1\nVu3u7uro6EhHR0d65pln9NBDD2l/f7+ngRMduBxGOzKN28kT4In0Wv4O9x0ZM5WTx87tcBmcHxQ2\n3F7OseJ7dJqyH3kVQKIJIpMUYJ6fmzRFLrzPwp2TUjZXAjjZPSAcXKeR+k5Foo2EimQyowvb6ly9\nyNDd9Fjbh+H2sP4+FMdajCsCND04eSgIaMKwf2jrk7mYV5pkRCQtoUlHIP0gLfOHvgKiOH8eHBzo\n2rVr+sM//EM999xz2t/f16OPPqpPfOITXRtzSToFnMtnfRhAZrif6JR+F86bdaelUWjQNCViTScs\n+8ICwateVDqMBaLTM+vc8pvk99YKynnShUcW7lijiJzgklaYJAeUz1PgeFJwMhhxpDORsN2MxahL\nqX9ClyfZeDxeOdhFWtrYdriZuf0uz/d0utwByjqxDg5ZJlwnc7M9KThoclB45NmcuTOVfZv1c740\ny7xCMxrNV0g++tGPam9vrzcOZNwMsX/ggQd6aCfhfJqeiUopdCxA6EAlM3Kusf+z79f5aPIdf/cR\nBZK6PUXsO/pWsi5UpDTzNkUXHllIS7OCmoGowr+ZyFBkPkpqaXW7toULtaLTkcn5PCM6JXUxCxYQ\nDIiyNjFy2dnZ6ZguoS3JjMPJkPYwmYJtcr3pE6Fpk3DXeeTvu7u7+tIv/VL9+q//esdwzJvMkYKC\nNBotnYF//Md/rCtXrnTw+/j4WHt7e93KhuvTWiK/ceNGE+0wTQbFtRAR+5TCjGPPeeJ0nAd07hLt\nJOqhEKEw45z0mOVc4FKx0SKFtdu9KbrwyMIDTjst18MtAMjoHoR0hGW0nImQ1sTTruiXMHEipEBi\n2dIyYpPMld/p4ff7ngT+7kliTej3MtaAjEsNTW3kPKh93Rd5hqk0F3o3b97sTXTa+fyjLU50RtTn\nWIWjo6MuNmI2m+nmzZtdYBwPK3J9WR7Hj3WxmcmALTp16Rsgg3P+EKUSjXHu8He+R5PRz4k2U7gl\n6mWeRH40Yz0vGLezSbrwwqJF6YMwpVNJasf6+7u0uqJCuDebzTrNn3CS2i4ZxROcYcfSEvEQWqYW\nNqN78toBenp62jGU4zU4Ia3d8uqBdMqmUHI9nAdtevaDkdDv/u7vrjjh0tRwW/0+zSP7aMgIjF9x\n/RjpuQ5tpRng+lCD21xKwc04F2roFtPRLMs5xf+puCjEKWAYccz+pwByObl0ynlP5z2F3CbpUpgh\nhG2eDOwcog6pf1gOtYMHyHd3pB2ZTE8oyMCbRAJ+1/9T+nMiMBKUk5tmA52RjCvw8qnz5mnh7J8U\nBOnjcf+x3TSPUnv7czqd9rS0BUZC69T2ZEjGj1DLc3zc7pOTE926dau3hJnjRXOB5OC0NDmIylJg\n8ZAfavDsB37SlKOfg9G3HhOWybHhHCOyzbalSUyh6z7a9GrIpUEWnPA5QRKmZYjw1tZWtxpBzWMh\nw9WMLM8TjNI+dwam9vUz2+bUSml7M4iH0JzOTqdlPVsIimHC6ZQj1M6dm4T4iShYb/9vE8PvEJ6b\nKLTILGSCNJEoMLncnO2hH4X+Is6D1vygaeR6UxlQUKQQIPmZ0Qn/bP6wT9IkyfpQoHlesu89ZkSA\nFDDpjN8U3bOwKKVslfm9If/n4v/HSykfLPNrCv9ZmZ/PqVLKePH/k4vfH7vTMqgRUkO3ILBhLKEb\npXr6Exb1W4GTR0dHmkwmnSbkqonJzxL55OClNvG7PpCVporLYrsZoiz1vfIkmh8ug+VTIOZEdzr6\nidLXwYntfm3lnU5hjgOhfq1Vk8mk62sziT+n06mm0+mKbyRNj/S7ENJTOVAgEGERiXK+cH6R/H4L\nwdCMbPmDnC6X01vjyfJYv/Q7pT9tE3QeyOLvaX5toen7Jf1ArfXPSfq0pLcunr9V0qcXz39gke6l\nKzhannKcnUUtR7uzBfdMHNAkO9t4xFoGRuWEs2M1fRm063NXKVdGuLvReXJlgKaI2+183Qe1Lo/y\no6PTlKYY+yTNBqahIGE9qAE9SdfZzKkJJXU+F9fBW+yluc/Gf/m+5wDrlv9nPWiiksFa79E8dB8x\n3TrThOnI1OnToWDNlS0K12wH/Sr8MxGBbJLuSViUUj5P0l+V9COL/4uk12t+h4i0en3hjy++/7Sk\nN5R1YhtEWG/mpCOpNTmk9gG8fs5BJCwnjJfUY9b0f1BgeKdjCiVPDjogsx6so8s1BKf/wrsoLXiY\nN/sq4bwnP30ELnMddHW/5MlMeUIYhTP7n/3A39y/XPJ1P3j1w/mnQJ/NZppMJiuBdImI2JeJNPyd\nS9QtJqcgYP4tAZECIcfUioxp3L/sH/ocqIgsJGgeUnFZAL4cTs57zf1/kPQdkjwjXiHp+VqrcSav\nKOyuL1z8/sIifY9KXF9oid/yFVBi+3lCsfSMp3PN/g0G/xAmE6oS2rLcra2tLriGKxgk+g3oO6BW\nph1NuM6VDwsuCw+jCfcNhUkLTaQdnv6GXEqlPyXzc3vSd5Ntp8PXfhg6B/0OfQAcX9Z3Mpn0nqUZ\nmsvq2W9mPp5ZSpTDMWoh0kQMWUc6wIlk2Ld+j34SntzO+ci+HY1GPbPVf6zbJunMwqKU8tckPVdr\n/VfnWB/VxvWF7Ew6yRi3IC2lLr321Kyp4T14Pvw3tQoltcs9PDzszJ90RPE4f9ebEDxhKB2akrql\n0ZxQo9HyLNGcFM7T9aEGYhxD3sjt+uUzmnb8PX0+fu52pFZrCSV/Jjqjpk0BSo3u3+kwZTlkLLYh\n9wZRATmfREnpr0jzhMgkhTnTeo5SULpeVGwM96fpmwKaPigiXtd9k+jiXnL+Gkl/vZTyh5Lerbn5\n8U80vx3d7nZeUdhdX7j4/UFJf/pShVB6pme8Zc8v8u8FFXEychI4CIjf2fmU9kYM3l5u4iBy8Dlo\ntdaOCdget4/OSE5453N6etptsqLQLKX0UEzatcw/mYLmStrUeWoXtacZkQgnNVr6RdiebJfHiGaZ\nV2V4Gxf72YIwhVfC/Zwz7FuON/MnGkli3s7TCsH9kSs7LbPEfZ6owfM0fTycN6wjlUOu7myCziws\naq3vqLV+Xq31MUlvlvQva63/gaRfkPTNi2RvUf/6wrcsvn/zIv0dBbK3JHGiCg/SG9/4xhUtakZu\naTRTKaVn5pyenurg4GBlk5gFESU7BQOjLnnwrvNhsJKZn2votNcJq90mt4X2KrWb68O/FABEUu5f\nkyduarR1MNeTmP4U1tF5pMPOebpNNLv8jPXy/+k45srJyclJF0ZO256Cij6AdDLSL5bl+i/9My3B\nQqGXDmkqKo+DY2hagoLP2Xd+TlP1DtnpzLQJzPKdkv5+KeVJzX0SP7p4/qOSXrF4/vcVN67fjnx0\nnbQccDrxTKPRSD//8z/fMZE7nveiciLQD+GJQ5vTjE8N5ShEl+cJwInhATw8POyVkZ8UYERPRiIW\niBYU6ZTMCeK60Fxz3gl/yWAs30RU5d9dB/YdGYD1YlQm65LpSIyPcb5ERhboqaXTHCGxXTkHiGxc\nZgY2paM8BV/6SPxJAZ7vzGazDmly5Szzb5lurFtu3luHiM6LziWCs9b6i5J+cfH9Y5K+opFmIulb\nzpD3SgRfRra1IJztfG5XzwlFxqG28h0NLTs8nW/Sqj1rwbO3t9fLhxqKcJQ+irTBCW3dHhO1FCM6\nW85VQmhrfsZusD7SqvmyTsg6LSe5y6JGzP7iOHLLP9O4Pd7Z29LQLt/9y/+TQZ2nb4RzJC8ZkdGf\nLROGwtpCnUIr5wLrREdnolyn405h/06BncKSpvgmTRDpEkVwSn0HTjp2Wh3FqE066MjwZhQOJPO0\nNuMEJbO4PnnwK52gnAhO4+cug4xMT7nLcB1bDi8inRSohOTuN7/LE9DdLn9aOKWNnaYFzTPmw/pa\ny7bGqNUHkjqzxm3J/s78Ob7U9vQROQ3Ns9w1nKtn7EuWx/+dhn4hKgCimVRaaQ5nPmyLFUJuGny5\n6MLvDaFmo/RMTcWJSB+AmZeaKTUvg6+cz3g87tnfzo+bxKh9fH5EaozUQvk7nVzUEDs7O724CaOH\n1J6M9vR7kno+ErbfdXEenryJoiT1hBDbQ3OGTNtCFC6PAparBxxntodE9JPzwmPBuvk3b+XOOlko\ncAWJ40Rtz7JTWbR8HuzP25lGHIckzgP6pDgfnEcea7BJdHHhhYW0lMbZEYRhhM+cSFJ/7Tu1B9ez\nU3v5ueG68/IE9S3YhIkWapb+6eDypGNshMsy+V0zutEDTw1f549w+4g8ctWoxahs4/b2dgf9XQcK\ngwwo43fmzTanKZNQncgqKZECy0lkxLF03Vlmnr3JOZWK5HamFNtA3woFc+ad5REl+rf0Q7EMp6Uv\nJH0ZLZPuvOhSmCG58YlQu6WlMi39AXRK5sTI/23XckKmbZ4axCsCabbkhGudLEWBZIFB6JuTMNuR\nyCMRBcswEYFQC/OQYJZDgWshkkzG8WJdk3HT/9DSxKnRkxJRsH5UGB43CmDWz/1OhJf9a9MghYAF\nofM0IvX/NAHZL0Y19ItYQLdM7NYBN+zrTfstLrywsDZwB9FeszSX+hGSLY81tT5jLpiGzwjNeUOZ\nJ5T3j+ThwB5wp6dt34L6bAvRQe6DMHHynJ6eroSG8zczPiezJzL3o1Aju85uH+19MnMKTdaPbeO4\npb+B7U9mT6GRfgTXIfuS5hJRCBEMfRAMM3cdGdfgPmwpnNZ3Kgai0Gw7BTPr67GyMmmZwDkXBgcn\niBOHB9qS6AugVqGEb/kszEScQNTm/vPyrfNK08aUdnwydwsCZz5pP5Mp6LhkW1N7+Uj8bBcn+Gg0\n6p0ByXx5FgNNHaeR+rEuKQCMhLJ9iQJa48zfKEyS+VNxULC5j/0/rzPwb2bAXB7lu6wLPxM15O8m\nLk37k22ij8bCgcIjYyzSTLOJ2jLfzpsuhbAgDLT0TtudE8GDv27CMq0le8ZxmGj7J7MwNNfag9oi\nNZ8nRtrwnJSOXCTsd5p15ywm45GxXc9kRPsp7Aj0Z5pVtI0p+LL+bIfbzn4wY1q4kWlupxEpdN33\n/qMASt8Jl8StqV0+hRbH03WhiZTzxvUhimg5RzmerhPnoylNHp585f6lIvQ88ji7PuyLTdGFFxYt\ns0Dq72FgOu5VYIATV0NyqY+U0JKdn8fi2SnoqEEHI1G4teqbzNTyqnPw03ySlgKBmpHOXbclbfcU\nBMzLn4TdLUak8Gj5IchYFM68T5a+Cmrx282DVhoKMZdDgUDTK9/JWJoWsmuZWm4/kRU/W+0nIiIy\noUCwWck6WUEQ0dLkSeXXMnXPiy7FaggnbAv2kdJ+SzPB+ZFBTO54eptzkFsahxGiLot2bzrxzPh5\nrkMpRZPJRFeuXOkxODeoUSAwtDz7I1eIpNWdrxlQ1LLJk0ltSlGr8fc04+i8YzttQrRWEFjHNHPy\nGccz5wH7hEyZKNCUQjPnBeeT0W0iVCotz0ULdT/3J8ugMkt04D5sIbE8j3WTdOGRhdSHzinJUxBI\n/RgEMyYZjUyR7xNa17rcok0t68lJ/wHLto3M/EieELlhy6sQXGVwPXOFxGVRU7ciA7NfqGVp89PX\nYoFJjeh62/FKlMbxYB+T+RL1jEajztxKxsl+Yj0SIZGBKKBoAniM0mQlM9OhyT7MPvb7FEIuz6gg\nx8m/0YxMQZfIJecT03he09/RMnHOmy68sOCEMCW8k7SiZan5WidKO29+SksGytgCv0ebnVCy5etI\nm955MM/0YTitYzhSMFio0JuejkgyMCdey4fCttGRxgnNdhq6M00GNVHzpa3OcSEDk8i4OU4UCiyL\nmj8dnayH82dZRJHJ5Czbv9EMsXlpn4gRm9HTunZQ2KQQ93OPJecX5xl9G69+9as3aoJIl0BYSP2N\nOQln/TsnHiektISeZo4W80p9hEBnpAd3e3tb4/FYDz30UO+2a5dJAcFoTtqh/HS9qIX39vZUa9V4\nPO5BZ4aT046mRvJETaTDerltiSCcNk0Lvuf/00mYJkyaDu7btOWdJ80/P0tEQQanoEifAhER0UdL\n6aSpwT7l3HK7sk/ZJy0EYuRE7c9+z/mT89BpaYJwLFiXp556aqPOTemSCAtp6eE2sfMoFNK+87up\nkfx8nRlD2/30dHmATK1VBwcHHWMS/nOwKExoVjANV21cn4ODg57p5LzsRGU7kwFa7aFdnQwnte1h\na0tCdf7m9y3A1qG/FG40SXLfjrQMXSZaIZrgeaUsx32cz1oC0fVoCZHW/xTG6Zex1k/k4/qkgqNA\n5VxO84HBYR5rht7nGCYa3hRdeGGRjpv0+uantD7is6XZmGadAMo8WJ61R3qx72TQ0pRyPmYWOzZb\nqxPpUCNDcvJxo1FqYfYl25UmVNbTz2nbUzNScHrSEz1J6k5Np6lHR2eLYSlckym5SuAykzFTsPo3\nCrU0O10u503LnG2hKwtUIolEcq6vtBqNSSGdQjLHI8d4E3ThhUUSOyknpT/pOCT85YClmeK8qYmY\nfwoYIg9ek+f8UhjRNqYDlkQBkPekWpCwvi1B4aVCTjaW6TyzXmQwPs/+ybFo+UzMcGSi7Asu9zod\nEQ2hv23/RFYp/DzeqbndBxa8rjvnRAqoHJcUEPbfcH5RWKVAp1OSgpnOc/o4/IxKKfvQ5eU5IJui\nCy8sUuN7EuSaPaMVSZy8DL+mJnA5rQ5PJJPwmvY1YzzIDH7P5fKIPUJlMhn3arh+3MeRms5CJWNI\ncmmV2orMyLalY9CMmkImIzhTcDBtC6EkgklmltYfb0+hYiHD3yjsafe3TIl15obrkT4Wt911Jzqh\nEEpE47xyqZNo0IxP809aPf2bdbHw/Yx3cBIWSstlIx9gYqJQIQyk5jQM5jOW4zxe9apXdXkmGmA5\nfodOSg+6j0wjkSmpOck8/I0a2BOb9eQkdPrU7jyLlEyxzi7nnhFCeQrZdDS7PC7lpTZOUyQZ2sxP\ngd8y5VpowuVz1Yb1TqTAccxlcfYNKQVwy0yzb4tmUvYxBYMVB8eSd8xk+93vqdSMRFso7jzpwgdl\nJWPy9Gtp9ZQqThAPht+3dCYUTGfhaDTSs88+2+t0wl5GTLJumZf/50T04FrrtKLyqFHYLtbPz5h/\nmmctaiEG59WaZC6DwpLIxAKJJ49zTCwgjJTo5U9EkQ5bjl3myU1zriOXx4kGyJzsAyoC5p/KKQUJ\nzQsKd2kpRFym03CsOEass/9yThD9sC/YFi/dcs5sgs6MLEopry6l/EIp5bdKKR8ppfy9xfNHSinv\nK6V8dPH58OJ5KaX8YJlfX/ibpZTX3EVZPRRBc4Iw3mlpz5HJeA8oTYnWJEmG82+5zyI1CCdfQk3+\nRi1opmodQux3CXUpvGiGtSgFGidTS9j5k/3RMjXYTzzz4vR0ef4mJz9PCTPRd0R04fxpw6dvw2Ph\nW+5ZTxPRVo6BhUG7sgAAFo5JREFU0+aSbov4Dtu/TkgnOjTDuw5+x4qPgq8V6McxY3+RiC42Rfdi\nhpxI+ge11i+R9DpJby+lfInmB/G+v9b6hKT3a3kw7zdKemLx9zZJ77yTQlq2KwerdaGPiZM87VYO\nMJmeQoSUEYAkvtuyR6V2LEiaN36e7adASTPDEyevLUxfSUJYtpVlW6imD4DpU1O3zs/ke9LSrqeQ\nzLrezub2OFHgk2k8D4hOGLSU48K+TASXdSJSSZ8QUQPzltSZyuxj5p0CnkqOQoOCtNUvRh4XNoKz\n1vp0rfXXFt9f1Py+089V/5rCH1f/+sKfqHP6gOb3i3z2S1YQ6CC1kjsxpTgHKCf3or4r5o0/rc2c\nznXgBKWtbcrj9sjQvizJz/PdWuf7Sxhn4Oec1BRsZoR1Qi6ZkSZF9gUFp2MZ1jnMbIr53RbEbzEP\nnYIpGFt9wvIYs0C/DNvgYwjX+WdaJlq2henTt+Lnab60hLzT2MzwvEoTw3MmhbjzsCBIfwr7LZXQ\nJulcHJyllMck/UVJH5T0qlrr04ufnpH0qsX37vrCBfFqw7V048YNlyGpL+m3trZ6kY607TwwhLIe\nlNR8nISpbU2coJ4AaV+7jhms44NkpH4gDbW9NV+GYPvT5XMZlQ7BZAZOKv/uCci6cJLu7Oz0Tgbj\nkmUiHFNrtcKM0PJLMN+sQwqnFIYt4eVxoEZOpk50RZOAfhG+x/qloKVAz/HjM/bTaLS8etB50Hzi\n6gnnUa6KUHAQVdwOlZ0X3XMJpZRrkn5G0n9ea73B3+q8R+/K41LirtPFsxXfhNRfIvP/0uo6PO1o\n2sdkAk6u2zkME1m0JHrWkyaQ/zfTE3qyDVL/VGmmp5ZpIQBPVgqTFkxl+VKfibmUy7J5n0gKVOZL\nRjGqSBOEgqDVh9m3rb7mmLGMFvS3wODFUYlEKeT9WyoZjksqMfZxCg8KXvqgUiCkkOGVjhQg3lzI\nvTmbpHsSFqWUHc0FxU/WWn928fhZmxeLz+cWz7vrCxfEqw07qo27TqnJU/NPp9Om083v2AmW6+ml\nlN5xeYuyexok1/xTK+fzDPDxILu8/GvZrpw06aehc8955rMUYAwMWgfz0wY307RMOPsGWrZ4Cg8y\nQbaZ9U0myzrlOJChKRwcTZtMz3nD9zmGfs709AXRN0MBy7lGJUHBTtPITM8rCDjfOH4WbBzjVEIU\nFJuOtbiX1ZCi+S1jv11r/e/xE68pfIv61xd+W5nT6yS9AHPlTsuU1I8A5ME2XI6UltrRXmdOBg7y\nutuzOKnSj5H1Sjudwol1zOAmTjqWkzZwajdSRgWSCM3ZL0nUpome+C5RDZmx1VdpPmX/u34UaO4L\n2uW5LEompIZmm+nncV1SWZBSiHG+uN9TsPj/loPR7WV/Scs4lrwXhmPrft/a2uqdEcqyW7QugO28\n6F7E0NdI+o8kvb6U8huLv2+S9H2Svq6U8lFJf2XxvyS9V9LHJD0p6Z9K+tt3WlDLJkwkkVqOzh96\n4ktZBioRjkqrqybJEKlFW3CcTj4yit9hpGkijSSaQ2xvy4tOjeo2uS1cSaHvwb9REKYgJuXyH9tA\nGzwnM/uEDNv6zURNmgjFdUj04LJbTkn6o5jeZfO+Fc4HIoCcQya3ieXST8Z6t4ROrmR5ntTa9/2k\nY9ftWYdGz5vOHJRVa/1/Ja0LF3tDI32V9PazlueJmoKC/7e0stSf0B5Y7+TMwUN9OwagCZRalkya\nzjhOTKdNGzPRkInMlxO9dRxcCh1OVjIin7XeYcg6I07TIUlhxMncEnr+jdo+IT+FNOuUfUgnKZnU\naclo7l+XT2ekA5nIzIT16bvwexSkLQanwCCaYeCalYzraWe1zT7m4XMycp7QVBmPxysKdRN04cO9\npdUQWcLYdBz6mT/p+Mr3kvlNZKKWze/3OMFdvrV1K0jMk8GTPhnZz3I5kpOshSLYDjoR6QyjkCDS\noJYkkyWjpIb099YKQbbJ1EIHFAo0mWgOJvLxwUBmaPZZohYzPgWk1L+Dg4xmIULGSxMmTbSWUnB/\nuu42JzyeRG28aaw1H1smHudKHpyzKboUwmI0GvX8Ci0omU6xljPP6VIrc2IzbJZCguiEDMyBdB7S\n6lo6fyOjsz3+nr/nRGBdnG8rICcjJNlPdKoZRaTzMvs4TQX2cfoMWC4neZoh7BcKY27tznHNPTJZ\nL5P9Bsmg9J8QmUjqrkbgWOWyNseavikqBqkfe5PKg/2Wu4lzjFptoCB0my5sUNbLTa2TqahN3HFp\nZ5OsRanZqTVqrd1GIKl/RmUiDCKIjHhMFELtTHMkJ3j6YVoCjw6vXLWR1NSwTktTyQKJwslaykKO\n1ILW6wSey6D2bvmFnI4XGjkNw8PJ0OscvymEUkunL4Gmhk0+7vhkXsmI6bzkXEozhmUxXZqAGetj\nRzgDzYyS2baWwtoUXQphYenu7y246/8dH58IwswtLZ1m7HwPEiGoGZO2bNbDGjmhKR2stKOplZLh\nWv6LnAg8CJgCyW228Eq0lL4Ct8Hl0gygv4R9QwZgTAbb4+/uZ+efwmI06p/+dHravywnGd35c7zY\nh1QA6a/gXKBvgoKBSNL9kAFyfC61HcDZT36X9aBAMVlYWWBl3syDJi3bmfPpvOlSCIuWZuZvUl/a\npx19O3uOUJUOu5Tcubxn52jLUUftSQRkZxYdZWS4NKukfhwBo0SdhnUgOuBkbNm0PA+E9XX+1Px8\n5k+2O3/LtmSYNusrLTeEuV9yyZBjwDZbCOa8cB5Mk2PE9DQ9qdldRweipcmS6NCC53bzlI5L1jXb\n6rlCJcV+4zxKlLQpuhTC4nbkjuZJy2Z+CwA6zfiOtIS81Dxpb/u5kcRsNutWU/wbISRhIuvnAW2F\n8VLzJ6PmhHIefMdCgnlJfRuf5XFPRAvO+1krSjY1dbaD/UhKX0hrWTDNNoZI02ZP7U1lwTKIytIP\nZQS1zg/DOB3WKVc8bhcMRXSRphHLpGDLPvBVlDQlXU+OY+uy7fOkSyEsUsNJfSb0b+7cdOj5d2qY\n1JKtVQ1PqnRU5qcnPTWPJ6a1qut3u7M6+T2XinM5zTbtaLQ83s3Iw9u2iWpazi+nTwZPG9ztcf1b\naCIRlInPidiyzWmmWCi14lgyrbQ0Ld0O/ub/OQ6p6UktUzCD6TgXW2aP03DuUlBRQFOIcmyyzjQt\n3a+ecyl8N0GXQliMx+MOqo7HY+3t7amUor29vZUJys5LqNwiLm+1vOrOjxDQE8fMxrVx5kHvOQcx\nGdRlc3KZ+aXVg1w52XPSJirKs0GJCvKYvJY/w2WwLZy4Lf9Rwm/6fFrmFOl2KwKpIIigWL91h9vS\n3HA/pRbnn8fVCMTlUfD6WSnLJWj2ZUZgZrvZb+lYtXDlkYmuFxHzurqdN134k7KkeYc++OCDkpYR\nb6enp5pMJpK0MoEpialBiCJMFATO13DXkjrNBjobkzFs39KOzElNyO86WDjk8pwZn0t0x8fH3cQ/\nPj7WeDxegdouh8gmJzn9FSwrBZH7PBER+5kIhqZgrmK0zMF1SIXPJPWYwr95nJwf01vQu+9YF48V\n/VruMwZBUWDQiZzmE52mabZxLrbQFxUS+67WunKlROtEsJbw3gRdCmHhDvmCL/gCPfroo3ruued0\neHio3//93+91XMLVXEZswT2ndToKDn/nhMuBovnh32kmsAzfiZo+lFrnlwpZCPA9TnRPHNrtDKRi\nf9FeTwefqbVjMWEsBR/zTkdqy7GWfpj0bbA++b7Hj0zJOBC+Q7TlMaez1ExIZMHjETkvWE8KAfYx\nV60oVCiQmHad+cL+SFONiMft5OoRfTytcdsEXQozxBr/ypUrev3rX68XXnhBn/zkJztmycnuSWNm\nSWdhOr1MOXFzIFoMQfuTjJGTmhOOk6qU+eE4hNi51Ejtl5Mx90HwedrmafcSzmYcC21qOlO5k5ZL\ni+zr9N+s67fZrH9COMfQTHO7d030VbkePAE9b7anACUzMn9+N7IgWqKAbPliaO7RV+U+TEozz+/5\nM/s5ndZp6m6CLoWwkOad8clPflI/9EM/pOl0qsPDw+55a7AoENiJ6260cjpPNKl/PkZCT5NhIaGx\nqeW8c14J34+OjjQej7slxMwv7XwiG9afPhK2P9EG4zW4iuRn1FruB+9hYN9Rm3sCk8FcV7clBXsK\nqezDbFu2NwWSGZI+INaLS8bsR9azhVxaAoVLqn7Gd9MHxnTsG9bZ/d0aa+aRvq5W/5w3XQph4Y54\n+umndePGDU0mE+3t7Wk6na5MPmtaQjlpudbPZ+nw42DRBElbmYzLMPS0vZPpXK88ZMZ5GmrbZ+I6\npPBiW50/taoZ2GW0jsFj2HA6EQnXMziJ7UvHHE00afXwniQjC/oS0kSj444Mniah250rUznWDKAj\nI6eyIXpIc8D95zlF5JECd53ApO+BKNPvso70LSVCTPN7k3QphMXOzo6ef/553bp1S4eHhzo8PNSN\nGzd6m3PSByD1L6LJCc8JkIzAieEJSB9ErrYwT2l1mZIDPBotDz6h7etyc2UlEQLzTIcl6+BnWc/M\n0+mo2Y6Ojnqw2MgjzQXnTacdhSPrR4GwDjm43Yw3cL5HR0c9xMB2pZBPH1UKn+xL91uiOQodIxL3\ne/pg2L/Mk0IiY1RYhtNQmTEdfRgsi4KkZVL9/+2dQawWVxXHfyfv2S4qtWBNw0IFTDVhZZEoi9qN\ngQLRVuMGY1K0Jm5sYmOMwbDp0mo00WhsUElaU21isJGNaZEYXVHbIm2hlfLAmti8QqQiiPIQPS7m\nHvp/p/Me74OZed9r7j/58s13v5m5/3vuveeec+bMTJdYEsriwoULlwftxYsXmZmZ4dKlS7PuKcjB\nKu2EtgColkcnTk5OzkrxzgFTTYFuW52ytRBl6ovGgM8KQ7loLEG5tE3WyCJUPrrdZmLnbNTseukk\nywM3ZKsrZ1YOYZ1EG7L/3nZVJvz6mFRqAWisSY/JV1o0ppCfWZIfNaAyDIWgyie3TeMryivHLnJs\no8010Eu/amXm+JKOqbb4jcaUhgh0LglloRMaZr81OtyE7Nep1lXLQwWux8RNO5pUFQM9r8468fNK\nrt+5DWqe58GkMYwoV/M+DyhdRWLi5IBYlGniWnyrdZEvDearSIFsLkfdOU4TSkwneI5/ZKsg+jPL\nXs+r51CeanFoLoIGZnXVzjIMZRm/Q2nldqmCVG5hCUUmsC5eKqeoU88fFou2TxWFXmpX+bYpj75d\nkiWhLLIPHmXReXmV1AGqfq6a1nP5q9nkb3sEHrw5iUmh5mzbCqgv4cm+aOzXFnDUqyE6eON82q4Y\niGFCa3luvw5GeOPZj+puZdnrBNSJpZOkzdJq+62KKs4flxzbZBxjIccJVJmptaAKIz/UNxRafmpa\nG+K/nAsTbdD4Q1sbo1yVQOaT61NXVa0wVbZtY6gPLAllEWjzxzTAlGMNMNvMzJpb/1ezW6/Va7me\nR5VXtjrUEtEVIbgqbx0IsbqqKZstIl2BdYKqtaJ+tVpZWRkF1MrIcZ9cjw7MkJFOyJCVDmadRG2x\nnjY/PpAnnfLRFVkXDuWuCVs62dTty3VlJaQWqsom+kUtsra4Qdu4zclyeUzqwqIWUJvcspvbF5aE\nsgghqcWgd/xpZL9tZQszU/37fH7VyjqwY2BFZ7UNau2g3PExmaJOvTEqXiwEzHrRs66EuuLmCZ/f\nb6lKLvhu3LjxspsWPnes2LGfPuPD3d+UlxC3Tqu8JicnOX/+/GVOeTDnIGS+5T1bElFXTGC9qqHI\nMtffbT69ujPRzpyvkt2NiYkJZmZmZr0+IgcqNb9FFbVyyttt7VCZ6rYuGrF/m9WQlVifMYvBMzjN\nbDPwXWAC+LG7f+MKh7T6iZr+rEpETdgoU2WRTXxdCdUq0cmjprgqJB3kqlR0hdXzRX0RxIzBG5fh\ndMCov6+Wxnwrifrosc/+/ftnTdx8rhxAa7vC0mYdBL8zZ85csc8r3hoYVFmY2QTwA2AjzRvJnjaz\nve7+4nzHxVvJusSyZctaYwaqfNTEjcnWFujU1bPNz855G1lhRWCszb0I5MBsPm/mo3GTNmSFkWMS\nfci8YmljaMviw8CUu58AMLPHaN6BOq+y6APnzp0busqKiiWNoZVF2/tOP6I7mNkXad6yDvDPPXv2\nnAb+Ngy9OXHzGHCAyiOj8piNq+Xx3oXsNHZ3nbr7LmBX/DazZ9x9/SJSGgsOlUflsdg8hr4asqD3\nnVZUVIwfhlYWTwO3mtlqM7sO2EbzDtSKiooxx6BuiLtfMrP7gCdoLp3udvcjVzhs1xX+HwLjwAEq\nj4zKYzZ65WF95pJXVFS8dbAkMjgrKioWH1VZVFRULAhjqyzMbLOZHTWzKTPb0XNd7zaz35rZi2Z2\nxMy+XMofMLNXzexQ+WyVY75euB01szs75PKKmb1Q6numlK0ws31mdqx8Ly/lZmbfKzyeN7N1HXH4\ngLT5kJmdNbP7h5CHme02s1NmdljKRm6/mW0v+x8zs+0d8fiWmf2p1PW4md1UyleZ2b9FLg/JMR8q\n/TlVuI50W+gcPEbuh07mk6Y7j8uHJvh5HFgDXAc8B6ztsb6VwLqyvQx4GVgLPAB8tWX/tYXT9cDq\nwnWiIy6vADensm8CO8r2DuDBsr0V+DVgwAbgqZ764jWaxJ3e5QHcAawDDl9t+4EVwInyvbxsL++A\nxyZgsmw/KDxW6X7pPH8o3Kxw3dIBj5H6oav5NK6WxeW0cHe/CERaeC9w92l3P1i2zwEv0WSbzoW7\ngcfcfcbd/wxMFc594W7g4bL9MPBJKX/EGxwAbjKzlR3X/THguLv/5Qr8OpGHu/8eeL3l/KO0/05g\nn7u/7u5/B/YBm6+Vh7s/6e7xgJMDNHlCc6JwudHdD3gzmx8R7lfNYx7M1Q+dzKdxVRZtaeHzTd7O\nYGargNuAp0rRfcXs3B3mb8/8HHjSzJ4tqe8At7j7dNl+DbhlAB6BbcDP5ffQ8oDR2z+EXO6lsRQC\nq83sj2b2OzP7qPD7a088RumHTuQxrspiUWBmbwf2APe7+1ngh8D7gA8C08C3B6Bxu7uvA7YAXzKz\nO/TPskINcr3bmsS5u4BflKLFkMcsDNn+uWBmO4FLwKOlaBp4j7vfBnwF+JmZ3dgjhUXph3FVFoOn\nhZvZ22gUxaPu/ksAdz/p7v919/8BP+IN07o3fu7+avk+BTxe6jwZ7kX5PtU3j4ItwEF3P1k4DS6P\nglHb3xsfM/sc8HHgs0VxUcz+02X7WZr4wPtLneqqdMLjKvqhE3mMq7IYNC28RKh/Arzk7t+RcvX/\nPwVERHovsM3Mrjez1cCtNIGsa+Vxg5kti22agNrhUl9E9LcDvxIe95SrAhuAf4i53gU+g7ggQ8tD\nMGr7nwA2mdnyYqJvKmXXBGse3PQ14C53/5eUv8uaZ7VgZmto2n+icDlrZhvKGLtHuF8Lj1H7oZv5\nNGpEdKgPTaT7ZRotvbPnum6nMW2fBw6Vz1bgp8ALpXwvsFKO2Vm4HWXECPc8PNbQRKqfA45Eu4F3\nAvuBY8BvgBWl3GgeJnS88FzfoUxuAE4D75Cy3uVBo5ymgf/Q+NZfuJr208QUpsrn8x3xmKLx/WOM\nPFT2/XTpr0PAQeATcp71NJP5OPB9Stb0NfIYuR+6mE813buiomJBGFc3pKKiYsxQlUVFRcWCUJVF\nRUXFglCVRUVFxYJQlUVFRcWCUJVFRUXFglCVRUVFxYLwfwk5QKMP/fyBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logim = np.log(imdata - imdata.min() + 1)\n",
    "plt.imshow(logim, origin='lower', cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masked array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy is providing an extremely useful tool called **masked array**. This tool associated to a numpy array another array composed only of boolean value (**True** or **False**) which indicate to numpy to use (or not) the specific element.\n",
    "\n",
    "We are going to see how it can be used in our case study:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 0.0 0.0 ... 0.0 0.0 0.0]\n",
      " [0.0 0.0 0.0 ... 0.0 0.0 0.0]\n",
      " [0.0 0.0 0.0 ... 0.0 0.0 0.0]\n",
      " ...\n",
      " [0.0 0.0 0.0 ... 0.0 0.0 0.0]\n",
      " [0.0 0.0 0.0 ... 0.0 0.0 0.0]\n",
      " [0.0 0.0 0.0 ... 0.0 0.0 0.0]]\n"
     ]
    }
   ],
   "source": [
    "immasked = np.ma.masked_greater(imdata, 25)\n",
    "\n",
    "#mask = imdata > 25\n",
    "#masked1 = np.ma.array(imdata, mask=mask)\n",
    "\n",
    "print(immasked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is aware of this numpy object and will plot the image by looking **only** at the pixels with the value **True** in the associated mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2752cdf2e8>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kMGRMzPRCWFAmo6WDuGCXCD1GNIbS4qKu1+uWpY7HY5VKJQNexMaU8OgJAaZQ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\nZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(immasked, cmap='gray', origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the mask created above, it is possible using the attribute **mask**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f27526c37b8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1XN1rFmcAgxGxIyJeBdZQ1EA1s4ar+wzO4eqdnlnuMKR84S+BF4EXahndkU1vwBjA4xjK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\nbvbVNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(immasked.mask, cmap='gray', origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original average: 6.237272\n",
      "Masked average: 5.976717048232941\n",
      "\n",
      "original max: 2925.8718\n",
      "Masked max: 24.981937\n",
      "\n",
      "original min: -12.439324\n",
      "Masked min: -12.439324\n",
      "\n",
      "original median: 6.237272\n",
      "Masked median: 6.725283622741699\n"
     ]
    }
   ],
   "source": [
    "print('original average:', imdata.mean())\n",
    "print('Masked average:', immasked.mean())\n",
    "print()\n",
    "print('original max:', imdata.max())\n",
    "print('Masked max:', immasked.max())\n",
    "print()\n",
    "print('original min:', imdata.min())\n",
    "print('Masked min:', immasked.min())\n",
    "print()\n",
    "print('original median:', np.mean(imdata))\n",
    "print('Masked median:', np.ma.median(immasked))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='background:#B1E0A8; padding:10px 10px 10px 10px;'>\n",
    "<H2> Challenges </H2>\n",
    "\n",
    " <ol>\n",
    " <li>\n",
    " Use mask array to calulate the log of the image.\n",
    " </li>\n",
    " </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f27528b9b00>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISqWSOFrRkYt+Fu12Wzgh3NXBqQinGc8995ysS2T7QkEdR7KGYQil3bIsnJyc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OwAqdVqKhaLyuVy1lmQ4SHJ5N2g+s1mU2tra0okEuZ/IUIQ2pFTFgOY1xxF4YSmi8ViKhQKGh8f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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "immasked2 = np.ma.masked_less_equal(imdata, 0)\n",
    "\n",
    "immasked2.mask = immasked2.mask | immasked.mask\n",
    "\n",
    "plt.imshow(immasked2, cmap='gray', origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We already spoke about the ```dtype``` which stand for data type. \n",
    "This is not only an information but a very powerful tools especially to load data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gruel/Documents/Cours/Python advanced class - Jupyter notebook/data\n"
     ]
    }
   ],
   "source": [
    "cd data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this tool we can load a complex file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "%more informations.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Firstame    Surname     Age     Phone     \n",
      "\n",
      "Bob         Doe         18      077778778\n",
      "\n",
      "Jane        Smith       24      077776666\n",
      "\n",
      "Alice       Jones       35      066556657\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('informations.txt') as f:\n",
    "    data = f.readlines()\n",
    "\n",
    "for line in data:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(b'Bob', b'Doe', 18, 77778778) (b'Jane', b'Smith', 24, 77776666)\n",
      " (b'Alice', b'Jones', 35, 66556657)]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "data = np.loadtxt('informations.txt', \n",
    "                  skiprows=1, \n",
    "                  dtype={'names':('firstname', 'surname', 'age', 'phone'), \n",
    "                         'formats':('S8','S8','i','i')})\n",
    "\n",
    "\n",
    "print(data)\n",
    "print(type(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the shape of this numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access to each row using the indexes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'Bob', b'Doe', 18, 77778778)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can access to th different columns by using the keyword as in a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 24, 35], dtype=int32)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Bob' 77778778\n"
     ]
    }
   ],
   "source": [
    "print(data['firstname'][0], data['phone'][0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}