Basic Python Packages for Science - Empty.ipynb

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    "![Python Madrid Logo](./static/python-madrid-logo.png)"
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   "source": [
    "# Basic Python Packages for Science\n",
    "## The Aeropython’s guide to the Python Galaxy! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Aropython_logo](./static/aeropython_name_mini.png)\n",
    "###### Siro Moreno Martín\n",
    "###### Alejandro Sáez Mollejo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Instalaciones cedidas por\n",
    "\n",
    "<img src=\"./static/ciff.jpeg\" alt=\"ciff\" align=\"right\" style=\"width: 200px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who are we? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Siro Moreno "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/siro.jpg\" width=\"175\" style=\"float: right\" />\n",
    "* **Aerospace Engineer**\n",
    "* Working at: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Álex Sáez "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/alex.jpg\" width=\"175\" style=\"float: right\" />\n",
    "\n",
    "* **Aerospace Engineer**\n",
    "* **Flight Test Methods Engineer** at AIRBUS Defence & Space on behalf of ALTRAN\n",
    "*  Co-creator of AeroPython --> PyFME \n",
    "* Member of Python España (come and join us!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python in the Scientific environment  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Principal Python Packages for scientific purposes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Anaconda & conda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![conda](./static/conda.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://conda.pydata.org/docs/intro.html\n",
    "\n",
    "Conda is a package manager application that quickly installs, runs, and updates packages and their dependencies. The conda command is the primary interface for managing installations of various packages. It can query and search the package index and current installation, create new environments, and install and update packages into existing conda environments. "
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf\" width=\"700\" height=\"400\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=\"http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf\" width=\"700\" height=\"400\"></iframe>')"
   ]
  },
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   "source": [
    "##### Main objectives of this workshop \n",
    "\n",
    "* Provide you with a **first insight into the principal Python tools & libraries used in Science**:\n",
    "    - conda.\n",
    "    - Jupyter Notebook and preview of JupyterLab\n",
    "    - NumPy, matplotlib, SciPy\n",
    "    - SymPy\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Other common libraries that will not be covered here are:\n",
    "\n",
    "- Pandas\n",
    "- scikit-learn\n",
    "- Numba & Cython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![jupyter](./static/jupyter-logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.\n",
    "\n",
    "It has been widely recognised as a great way to distribute scientific papers, because of the capability to have an integrated format with text and executable code, highly reproducible. Top level investigators around the world are already using it, like the team behind the Gravitational Waves discovery (LIGO), whose analysis was translated to an interactive dowloadable Jupyter notebook. You can see it here: https://github.com/minrk/ligo-binder/blob/master/GW150914_tutorial.ipynb"
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   "source": [
    "### 2. Using arrays: NumPy "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![numpy-logo](./static/numpy.png)"
   ]
  },
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   "source": [
    "#### ndarray object "
   ]
  },
  {
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   "metadata": {},
   "source": [
    "| index     | 0     | 1     | 2     | 3     | ...   | n-1   | n  |\n",
    "| ---------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
    "| value      | 2.1   | 3.6   | 7.8   | 1.5   | ...   | 5.4   | 6.3 |"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* N-dimensional data structure.\n",
    "* Homogeneously typed.\n",
    "* Efficient!\n",
    "\n",
    "A universal function (or ufunc for short) is a function that operates on ndarrays. It is a “**vectorized** function\"."
   ]
  },
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   "source": [
    "Let's have a look at the efficiency of a ndarray against the efficiency of a list:"
   ]
  },
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   "source": [
    "# List \n",
    "my_list  = list(range(0,100000))\n",
    "# Array \n",
    "my_array = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.41 ms per loop\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 95 µs per loop\n"
     ]
    }
   ],
   "source": []
  },
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   "source": [
    "Another example:"
   ]
  },
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   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 52.7 ms per loop\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 856 µs per loop\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [1, 2, 3, 4]\n",
    "one_dim_array = "
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [1, 2, 3],\n",
    "# [4, 5, 6],\n",
    "# [7, 8, 9]\n",
    "\n",
    "two_dim_array = "
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtype\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.],\n",
       "       [ 7.,  8.,  9.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
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   "source": [
    "# Other interesting functions for array creation\n",
    "zeros_arr = \n",
    "ones_arr = \n",
    "eye_arr = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# range\n",
    "range_arr = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,\n",
       "         4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,\n",
       "         9. ,   9.5,  10. ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linspace\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic slicing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`[start:stop:step]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
       "       34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,\n",
       "       68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess_board ="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Drawing: Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Matplotlib-logo](./static/matplotlib.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe1491d95c0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1519cc3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operations & linalg "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# numpy functions: sin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe14b7644a8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nnbGTJMuECbBokZ/TK1JsNPKXKpk+HY47zncR3HLL2GmS4ec/h8aNfeQvElN1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\nkRKk4i8iUoJU/EVESpCKv4hICfr/b1/w2IUFqTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1491b49b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# another function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe148bf99e8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0EZEMoYQuIpIhGpXQzSzPzBab2cdmNipZQYmISMMlnNDNrAUwDQgARwC/N7O+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe148c26c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some linear algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 40, 77],\n",
       "       [25, 25, 68],\n",
       "       [33, 16, 91]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_dim_array = \n",
    "# Transponse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3641,  3119,  3733],\n",
       "       [ 2632,  2713,  3176],\n",
       "       [10497,  9813, 11910]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix multiplication\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 240.4,  250.8,  783.1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_dim_array = \n",
    "# matrix-vector multiplication\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.05372256,  0.00140303,  0.01923512],\n",
       "       [ 0.10898393,  0.07381761, -0.05250057],\n",
       "       [-0.03598099, -0.05634759,  0.03394433]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 133.6946629,  -17.266221 ,    9.5715581]),\n",
       " array([[-0.29580975, -0.74274264,  0.0661375 ],\n",
       "        [-0.24477775,  0.65983255, -0.79576005],\n",
       "        [-0.92335283,  0.1138173 ,  0.60198985]]))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eigenvectors & eigenvalues\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Air quality data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://www.mambiente.munimadrid.es/sica/scripts/index.php\"             width=\"700\" height=\"400\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=\"http://www.mambiente.munimadrid.es/sica/scripts/index.php\" \\\n",
    "            width=\"700\" height=\"400\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estación: Barrio del Pilar;;;;\r\n",
      "Fecha;Hora;CO;NO2;O3\r\n",
      ";;mg/m³;µg/m³;µg/m³\r\n",
      "22/03/2016;01:00;0.2;14;73\r\n",
      "22/03/2016;02:00;0.2;10;77\r\n",
      "22/03/2016;03:00;0.2;9;75\r\n",
      "22/03/2016;04:00;0.2;3;81\r\n",
      "22/03/2016;05:00;0.2;3;81\r\n",
      "22/03/2016;06:00;0.2;6;79\r\n",
      "22/03/2016;07:00;0.2;24;59\r\n"
     ]
    }
   ],
   "source": [
    "# Linux command \n",
    "!head ./data/barrio_del_pilar-20160322.csv\n",
    "\n",
    "# Windows\n",
    "# !gc log.txt | select -first 10 # head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the data\n",
    "# ./data/barrio_del_pilar-20160322.csv\n",
    "data1 = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dealing with missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  nan,  nan])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.33717277,  29.79581152,  55.47643979])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [0.3371727748691094 29.79581151832461 55.47643979057592],\n",
       "             mask = [False False False],\n",
       "       fill_value = 1e+20)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# masking invalid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data2: \n",
    "# './data/barrio_del_pilar-20151222.csv'\n",
    "data2 = \n",
    "# masking\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Maximum values ** from: http://www.mambiente.munimadrid.es/opencms/export/sites/default/calaire/Anexos/valores_limite_1.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* NO2\n",
    "    - Media anual: 40 µg/m3\n",
    "    - **Media horaria: 200 µg/m3 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 220)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vB/FZw2dqD4MRgSiJ9F8FsEJg+zMcxy0c//cxABBCZgO4CcBsAJcB2EhCbDHMcFhERc7e\nYQupSBMOidxqYzWqDFVqD4MRgciKPsdxuwH0CuwSEvNrALzBcZyN47hGACcAnOnXCANMOCyXOGSV\nt3dCRbxCEaGJWcB4uas9NKqeLFYLOgc71R4GIwLxx9P/D0LIYULIy4SQ5PFteQBanI5pHd8WMoSD\np8/sHf8Ih0h/2DaMjsEOtYfBiEB8Ff2NAKZzHLcAQAeA3wduSJMHx3F0RSol1Tsqe/q6GFa94yti\nnn4orZFrsVnQOcQifUbwiZI/xBOO4wxO3/4FwNbxr1sBFDjtyx/fJsi6descXy9ZsgRLlizxZTiK\nsdltoouTOBOtjYbFapnUsUgxNDqEtPg00f1sIRVppOwdGxciom+1sEifoYidO3di586dAbueUtEn\ncPLwCSE5HMfx79jrAXw7/vX7AP6PEPIsqK0zA8A3Yhd1Fv1goKRGH6CRfv9IfxBGJAyzd/wjXOyd\nIeuQ2sNghAHuAfH69ev9up6s6BNCXgOwBEA6IaQZwFoAFxFCFgCwA2gEcA8AcBxXTQh5E0A1ACuA\nNVwINYmx2CyCYuCO6p6+TaZ6hy2kIkk4lGxabBb0WHrAcZzskyeDEUhkRZ/juFsENr8qcfwTAJ7w\nZ1CTxbBtGPHR8bLHqe3pK5mcFSriFYoM24YRpw39SN9mt8E0YkJKXIraw2FEEBE1I9ditXgsoSdE\nKNTpM9H3nRFbGET6VgvyEvOYr88IOpEl+jaL8khf7d47rHrHZ0bGQrv3jp2zY3RsFEUpRaxWnxF0\nIkv0lUb6avfeUTA5i/XeESfUe+/wTyI5+hwW6TOCTmSJfhhF+lKiz9owSBPq9g5fUJCty2a1+oyg\nE1GiL1bK5w7z9MObUC/Z5J84WaTPUIOIEn1v7B21q3dYP33fGRmTmJwVAq8bf1PK1mUzT58RdCJL\n9L2wd1ikH76EfKQ//j7M0ecwe4cRdCJL9L0o2VTL07dzdtn5BKwNgzShPjmLfx9m67OZvcMIOpEl\n+jZloq9mpM9HqRoi/qcJFfEKVUJ9uUT+b8wifYYaRJToK07kqujpy83GBVgbBjlCvWSTt3d4Tz+E\nOpUwIoCIEn2LNfQ9fTk/Hwgd8eJ57dhrqOuuU3sYDkLd3hm2DSM+Kt4xRjXzR4zIw6fWyuGKxWaR\nrIrhUdPTl5uNC4SOePH879H/hcVqQVl6mdpDARAGiVzrROO/uKg4+mQicJNiMCYDFukLoGbDNbnZ\nuEDoiBePadgUUt60ZD/9EHjdnKvIYqNiMTI2ovKIGJFEZIm+wkSumm0YlNo7odSGoW+4D11DXWoP\nw4FU751QWCPXuQsoH+kzGMEiokRfaSJXzTYMZqt0L30gdCJWHtOIKaREX8wu0WpCY7lE5yfOWG0s\nRmws0mcEj4gSfaWTs9RswxCO1TshF+mHg70z/sTJIn1GsIks0Vc4OUtNTz/cqnesY1aYreaQ8vRD\nPZHrPD7m6TOCTWSJvtJIPww8/VAQL4BaOwQktCL9EC/ZdLZ3WKTPCDaRJfpeRPqq2TtW6WZrQGi1\nYegb7kNBcgF6LD0hkSQFwiPSd7Z3mKfPCCYRJfpKE7lajRaEEFVELNwi/b7hPmQkZCA5Nhndlm61\nhwMgPDx9h72jjWWRPiOoRJToK7V3ABrtq+G1KhH9UFpExTRsQnJsMrL12SFh8dg5O2x2G2K0MR77\ntESLMW5M9bYHzu/DuKg45ukzgkpkib5CewegEZgaFs/Q6FBYzcjtG+5DSlwKsnRZISH6I7YRxGhj\nQAjx2EcIcQi/mrgnclmkzwgmEdeGwatIXwWvNdzsHdOICSlxKYjWRoeG6IskcXn41y5Ko95b3zn4\nYIlcRrCJLNH3ItJXK5lrtoWX6PcN9yE5Nhm6aF1IrAIll7cJhdfOpQ0Dm5zFCDIRJfpyi5M4Exul\nor0TZtU7KXEp0BBNaET6IklcnlAQfecbE4v0GcEmYjx9O2fH6NiopCA4E8qJ3FAQLh7TMLV3QiWR\nGxaRvtMTZ6yWTc5iBJeIifT5fixCCT4h1ErkKqreCaE2DH0jfUiOS0ZKXAq6zOqLvlJPX03cq3dY\npM8IJhET6Xvj5wPqJnLDtXonFDz9cLR3mKfPCCaRI/peVO4A6iVyw62fPm/vhErJZtjZO6xkkxFk\nIkb0nae+K0GtRK5p2ISk2CTJY0JBuHj46p1sXWh4+uFg73hE+szTZwSRiBF95yXqlKBGItfO2dFt\n6UZGQobkcaG0iApv7+hj9CCEoNfSq+p4wiLSdyvZZJE+I5hEjuh7ae+okcjtG+6DLlon2ELAmVAQ\nLh7TiAnJcckghKA0tRT1vfWqjifUPX2b3QY7Z0e0JhoAi/QZwSdyRD8MErmGIQMydZmyx4VK7x07\nZ0f/SL/DjipNK0V9j8qiL7JUIo/aos8/ifBVZMzTZwSbyBH9MEjkdg11ITNBXvTVFi6ewdFB6KJ1\njpYGoRDpiy2VyKP2a+cefLDqHUawiRjR9zqRq4K9YzAbkKXLkj1ObeHi6RumNfo8pakhEOnL2Dta\njVbVvv/uwQfz9BnBJmJEPxwSuYYhg+JIPxTaMPBJXJ4ZaTNCItIPB3uHh03OYgSbyBF9bxO5KpRs\nGszKPH21hYuHr9HnKU1T394ZGQvtRK6gvcMSuYwgEjmiHy6JXAWRfqi0YeBr9HkKkgpgGDLAYrWo\nNqZwi/RZIpcRbCJH9G3eiz6L9KXptnQjLT7N8b1Wo0VhciEa+hpUG1Ooi777EydL5DKCTcSIvjdt\nlQH1ErnhVL3TNtCGvMQ8l21ql20Ojg5CH6MX3a/2a+f+xMkSuYxgM6VFv9s8sVB32CRywyjSb+1v\nRW5irss2tcs2h6zSy02q/doJJXKZp88IJlNW9DsHO5H7TC5qjbUAvLd31EjkelOnHwptGNoG25CX\n5Brpz0iboWqkL7cIjdqi7946m3n6jGAjK/qEkFcIIZ2EkKNO21IJIdsJIbWEkG2EkGSnfQ8TQk4Q\nQmoIIcsna+ByNPY1YnRsFA/veBjA+GO1l5Ozgum1chwHo9kY9pF+UXIRmvubVRpR6Ns7zjOYAebp\nM4KPkkj/VQAr3LY9BOATjuPKAXwK4GEAIITMAXATgNkALgOwkShdtSTANJmacNmMy3Cg/QB2N+8O\n+USuacSEuKg4RRZUqLRhaBto8xD99IR09Fh6VBpR6Ns7A6MDSIxJdHzP24gcx6k2JkZkISv6HMft\nBuDeOvEaAJvGv94E4Nrxr68G8AbHcTaO4xoBnABwZmCG6h3NpmaUp5fjqUuewh3v34H2wXavE7nB\n9FqV+vmA+sIFAGP2MXQNdWGafprL9tS4VFVFP9wifQ3RqLZ2AyMy8dXTz+I4rhMAOI7rAMD3DsgD\n0OJ0XOv4tqDTbGpGYXIhVs1bhUtLL8X2+u1eJ3KD+UFUWrkDqC9cAM0/pManIlob7bI9LT5N1fbK\nsp4+CS3RB9isXEZwCdQauT49m65bt87x9ZIlS7BkyZIADYeK/pJier3fr/g9+kf7UZ5ervj8YCdy\nwy3SFyrXHBqioq+2vRPKkf7A6AASYxNdtrHF0RlS7Ny5Ezt37gzY9XwV/U5CSDbHcZ2EkBwA/JJJ\nrQAKnI7LH98miLPoBxo+0gfoB/3Va1716vxgl2x6G+lb7VZwHKd4ofdA0zrgmsS124G8PKCtjVpo\n3ibOA8Xg6GBIe/os0md4i3tAvH79er+up9TeIeP/eN4HcPv41z8E8J7T9psJITGEkBIAMwB849cI\nfcRZ9H0h6PaOwhYMAPWBCQjsnH2SRyWOe6RvNgMmE3DypLrRfqiXbA6MDHiIPivbZAQTJSWbrwH4\nEkAZIaSZEPIjAL8FsIwQUgvg4vHvwXFcNYA3AVQD+BeANZwKZQlDo0MYsg4pFlEhYrWxQSule/f4\nu9i4fyMqcioUn6N2BY97uebQEP2/tlY90R8dGwUHTnLlMbVFv3+k36V6B2Blm4zgImvvcBx3i8iu\nS0SOfwLAE/4Myl/4KN8f6yNYkf7B9oNY/cFqvH7D67io5CLF5/HiFQvxjpKTSdtAG87OP9vxvdlM\n/6+rA1JzU9E7HPxkLh/lS/3dQ0H0PSJ91oqBEUSm5Ixcf60dIHiJ3IbeBpydf7ZXgg+oL17us3FD\nIdKXK9cE1H/dBkY97R3WioERTKau6Cf5J/rBSuR2DnUiW5ft9XlqL6Tibu+YzYBGMx7px6WiR4Wy\nTbmJWYD6ot8/0u9ZvcM8fUYQmbqi72ekHyx7p3OwEzn6HK/PU1u83BO5Q0NAeTmN9FPj0rDh5R78\n9a/BHdPQqHS5JkDbP6v1unEch8HRQebpM1RlSop+k6nJf3snSIncjsEOZOu9j/TVXEhlYGQAZqsZ\n6Qnpjm1mM1BcTL8mw2k4drIHzzwDBDONzy/ULoWaN8sh6xDiouKg1WhdtjNPnxFMpqTon+o/hYLk\nAvkDJQhapD8UfpH+wfaDqMipgIZMvH2GhgCdjkb7+3enIq+0FzYbsHt38Mal1N4Z49RZGF0oiQsw\nT58RXKak6HdbupGRkOHXNfxJ5Fa8UIHB0UFFx3YMdvjs6asl+vvb9mPRtEUu28xmICEBKCsDvvo0\nDTklPVizBti4MXjjCvVErlCNPsAmZzGCy9QUfXM30uPT5Q+UwNdErnXMiqOdR9E33Kfo+HCM9Pe1\n7UNlXqXLNj7SLysDOHMaEtJ7cNttwEcfAd3dIhcKMHITswB1XzehGn0guHNCGIwpKfo9lh6XtVt9\nwdflErstVOGGRodkj+U4zmdPX42F23n2te1DZa6r6POR/hlnABdUpmJwrBcpKcAllwDvvx+cccn1\n3QHUF30W6TPUZsqJvsVqwRg35rI6kS/wyTZvBcJoNgKgAiTHwOgAtEQrK1RCpMSlqDIBqtvcDcOQ\nAeUZrs3r+Ej/ssuAl56bqNNfuRLYvDk4Ywv1RK5QjT5ArUTm6TOCxZQT/R5LD9Lj0wPSiMyXZK5D\n9BVE+r5G+QCQkZDhsgZwsDjQfgALpy10SeICVPQTxu+zqXGpjvbKV1wB7NkD9Abh/qSkZFPtSN+9\nRh9gkT4juExJ0ffX2uHxxeLhRd9sNcse62uNPgCkx6c7flYw2dfqae0A1N7RjQfZKXEp6B/px5h9\nDImJwNKlwbF45DpsAuqLflKMQKTPSjYZQYSJvgS++Obe2Du+zsYF6LKEfP4gmBzsOIgzcs/w2O4c\n6Ws1WiTGJsI0YgIAXHcd8MEHkz+2IWtoJ3KlqndYIpcRLKac6Hdbul0mDfmDL2WbhiEDAOX2jj+R\nvhr2TkNvA2akzfDY7hzpA64WT2kpcOrU5I8t1Es2xewd1oaBEUymnOj3WHqQFhfASN/LBJtXkf6g\n75F+RkKGKpG+WIsLPpHL49x0LTMTMBgmf2yh3ntHLJHLJmcxgsnUFP0A2jtee/oWIzITMic/0lfB\n3pFap4Av2eRJi09zVBdlZQFdXR6nTMr4Qj7SF6jTZ4lcRjCZcqLfbQ6gvePDpBmj2YiilCJlidyh\nTp+rd9RI5DabmlGQVCBYGSUU6fP2U3IyMDwMjExyMBvqJZtidfoJ0QnoH+lXYUSMSGTKib7qkb7Z\niKLkIkX2jt+RfpA9/WZTM4pSigT3uUf62bpsdA51AgAIATIyJt/iCVd7pzK3El+d+gpjdnV6AjEi\ni6kn+sMBLNn0IZFrNBtRmFyovE7f1+qd+ODbO1LrFLhH+jn6HLQPtDu+D4avH66J3LykPEzTT8OB\n9gMqjIoRaUw50Q9E3x0eXxO5xSnFspH+6NgoOoc6kZ+U79PYUuNTYRo2BTU6lFqnwLlkEwCmJU5D\n+2BwRT8ceu8IRfoAsKJ0Bbad3BbkEYUXj3/xOE57/jSc/fLZeO/4e2jsa8T33/4+9jTvUXtoYcWU\nE3017Z2nnjXDNmZHli5LVvQb+xqRn5SPaG20T2OL0kQhKTZJcWO3QNDcLy767iWb0/TBF/1Qj/TF\n6vQBYHnpcmz/bnuQRxRebP9uO+47+z48cv4jeGjHQ6h4oQKH2g9h76m9ag8trGCiL4E3iVy7Hfj9\n80bEjWVAF62TtXdO9pxEaWqpX+NLT5jcZO76netxrPOY4/umPuHFaTjO09OfljgtqPaOnbNj2DaM\n+Oh4yePoxGv4AAAgAElEQVTUjvSFqncA4IKiC3C44zBMw6Ygjyp8aBtow7kF5+Kq8qtwZPURNP6s\nET9a8CO0DbSpPbSwYsqJfiAnZ3kT6X/5JdA1aETUaAZ0MTrZ6p36nnr/RX8SfX2O4/DnfX/GRyc/\ncmwTs3eGh4GYGEDrtCCUUKQ/mWWbZqsZCdEJHj2B3FFL9G12G4Ztw6KJ5vjoeJydfzY+b/o8yCML\nDziOQ9tAm2Nd5hhtDFLjU5GXlIe2QSb63jClRN9itYDjOMRHSUd7PM8+C1RVie/3JpG7ZQsw/ywj\n7EPjkb6MvVPfW4/SNP8jfX8reLZUb8HTXz7tsb2uuw4GswH72vYBAMbsY2gdaBVckcw9icuPbWBk\nwPGkNNmRvpK+OwAw2K9FS2vwRb++px5FKUWSN6Xy9HI09TUFcVThg2nEBC3ReiTCcxNz0drfqtKo\nwpMpJfo9lh6kJyjvsPnmm8DBg/TrQ4eAF15w3R+jUZbItdup6K+4zojRvgwkRCfI2jv1vfWC7Qy8\nIRCzcv9x9B/I0mV5bN/dvBuLchdhf9t+AHROQWpcKuKi4jyOdbd2AEBDNMjSZTnKNrOyJk/0H9j+\nAN749g3ZJC4AfFcfhZ6+4It+taEaczLnSB6TpctC11AQZrGFIc5RvjO5ibnM3vGSKSX63ZZur/z8\n1taJVZ2+/hr43/913a/U3tm1C0hKAnJnGDHck4EYoiDSD5S940ek3z/Sj08bPsXV5Vd77Nvdshs/\nWvAj9Fp6YRgyiPr5gHCkD7j6+pMV6XMch437N+L5/c8jOS5Z9vj21ijYYUV/kOdCVRuqMSeDib6v\ntA20IS8pz2N7bmIuWgdawXGcCqMKT6aU6HuTxLXbgfZ2wDieBzUagbo612Nio+QTuRwH/L//B9x/\nP9A7YkSiNgMmg3Qi187Z0dDXgOmp0xWNVQx/Pf0P6z7E+UXnIyUuxWPfrqZduKDoApyRewb2t+1H\nXXedZOWOe6QPuPr6kyX6PZYexGhjcOzeY3hn1Tuyxxta9UDMEFqD7AhUG6sxN2uu5DFZuix0mZno\nC9Ha3yoY6etj9IjRxgS1ii3cCTvRbzG14Gcf/UxQjPkFVJRgNAI220SkbzDQbT09E8coifTff58u\nEHL77bTDZqYuA12t0onc1v5WpMSlKPKgpfC3emdz9WbcOOdGx/emYRNWvrkSO77bgR5LD+ZkzkFl\nbiX2ntqLp796GreedqvgdUQjff3kR/p8cjlGG4PilGLZ4zsak4FYU1C6fjqjxN7J1mWjc7AzSCMK\nL9oG2pCr9xR9AMhLzGMWjxeEleibrWZc88Y1+PDEh1j94WqPR7q2gTbFkT4f6fGiz0f8ztG+kkVU\nHnkEeOopWrnSMdSBgpRpaG9OgMVmEX3krO/139oB/Iv0B0YGsKNhh4u1o4/R46Lii3DDmzfg3MJz\noSEaVOZW4tm9zyIlLgXXlF8jeC3RSN9pglZqKjA4CIx6v+ywJFITxoQ41aADokbQ2GIN7EAkGLOP\nodZYi1kZsySPY/aOOGL2DjBh8TCUEVai/9OPfop5WfNwePVhHGo/hI37Njr2Wces+OPXf3SJXKVo\nawPi4lxFPz0dqK2dOEZuRq7dDhw/DqxYQb9v7W/F9KxcNDVqEKuNhcVmETyvvsf/JC7g35KJ/zj6\nDyybvszlJqnVaPGTM3+CE/95Ahsvp69tZV4lBkYH8PSyp0UT5EoifY0GSEubuLkGiiZTE4qShfsB\nCdHYQBDDJaH+VPDq4Rv6GpCly5KdOMZEX5zWAWF7B6BtLFikr5ywEv0P6j7A40sfhz5GjzdWvoF1\nn69ziN5LB15CcUoxVsxYoehara3AnDmunv7ixa6Rvpy9YzIBej0VNIBGI3ML8tDYCMkKHl8mZplM\ntMTUmfSEdJ9Egk9+3rvoXsH9mbpMR2lmYXIhan5Sg7PyzxK9nnsLBp5pidPQMdQxcd1JsHi8ifRH\nR4HOTkAfnYzG9skX/W+7vsXLB19GtUHezweApNgkjI6NwmIVDhamCoc7DuORHY94lXwVq94BgFx9\n6FfwbNgQnDUllBA2ot9t7obZanb0qpmVMQsrZ6/E47sex6H2Q3j0i0fx1LKnFF+vrQ047TTXSP/c\nc93sHZlEbl8ftS0A+gjfNdSFiuk5aGgAdDHiFTz72vZh4bSFiscKAEePAs8847qtPL0cHYMdXr/h\n97TsgXXMiqUlSxUdL2dLuLdg4AlG0zVvRL+lBcjNBZJjk9FimHzRf/ngy7h769343Ze/k63cAQBC\nyJSP9h//4nGs+McKbNy/ESd7Tio+r22gDXmJEvZOkGv1P2v4DJ82fKr4+A0bgP37J3FAXhA2ol9j\nrMGczDkuFsO6Jevwt8N/w6X/dyl+t+x3WJCzQPH12tqA+fNdRX/xYk97RyrS7+sDUsYLX7qGupAa\nn4qZpdFobIRoKwbrmBXftH6DxQWLFY8VoJVGXV20WognNioWV5Zdibdr3lZ8HY7j8MxXz2D1otWK\n5zPIIWnvOM3KnYzFVLwR/cZGoLgYSE1IRnvv5Iv+tvpteOmql3Cw/aBsEpdnKot+VVcVnvv6ORxd\nfRTXzboO2+qVNZizc3bJNuRqzMr9zRe/we3v3q74qcxgCM6SoUpQVfStY8qTadWGaszNdH1EztZn\nY/sPtqN6TTV+UPEDr352ayswcyYV0e5u6s+ffjpw8iT9GpBP5DqLPh+J5ObSG0h8lHAFz+GOwyhO\nKUZqfKpX421vp/aEyU2rbpxzIzZXb1Z8nQ3fbMCJnhO4c+GdXv18KcQSuTn6HBiGDI5OoKefTudD\nBBJvRL+hASgpAbISU9Dl/kIGmGZTM4xmI358+o+x9469WDVvlaLzprLo//cn/41fnv9LZOuzaYO5\nemUN5gxDBiTHJSM2KlZwf6Aj/c7BTqzfuV5U0DsHO3Go/RAqcirw3NfPyV7PaqVawUQfcIkC5ajq\nqhKMlhblLvKp105bG5CXRxf3qK2l/+v11K7h/zhyidze3gnR5xNNWi29RqzIBK3dzbtxXuF5Xo+3\nffylco+Ul5Uuw9HOo+gY7PA8yY09zXvwxO4n8P7N78smFb1BLNKP1kYjU5fpqKxYvhzYHsBGkiO2\nEXRbujFNP03R8Xykn5mUjFFiwpD8kgc+8+/6f2PZ9GXQEA3mZ89HQrTAXVGAqSr6u5p24bjxONZU\nrgEAXDL9Enze9LmiyY9S1g4Q2JLNj09+jHnPz8PTXz2NKoNwj5a3a97G5TMvxzPLn8HTXz6NXkuv\n5DX5vCETfQCn+pW/CtVG+Tpnb2htpf5uejqtwMnIoNvLyycsHjnRd/b0nd+Yej3orFwBe2d3S2BF\nPy4qDlfMvAKvH3td9hpb67ZizaI1KEkt8frnSyEW6QNAaWop6nvqAQALFtAPQHNzYH7uqf5T9Ear\n0cofjIlIPzk2GcnZJkfZbn8/8Ic/TDzhyXHceFy29/22+m1YXrpc2QWdyNZlT0nR39e2D1eXXY0Y\nbQwAWnlWll6Gr1q+kj23daAV0xLFb+w5+hx0DXUFZG2JTUc24fGlj2PZ9GVo7GsUPGZLzRbcOOdG\nzEyfiXlZ83CwnfZy2XtqL9Z8uAY//einLjcCPo/FRB9eir6CyS1KGR2lgp2V5Sn6OTkTwirX0Mzd\n3uGrC/R6IBoJHpE+x3HY1bQL5xee7/WY29upsAp54g+e+yCe2P2E480nRl13nWxS1hfEIn0AKE0r\nRX0vFX2NBli2LHDRvrc1+g0NNNJPjkuGPmNigtaTTwIPPgisW6fsOn899Ffc8vYtorNAx+xj2NGw\nwyfRd+5XNJXotfR6WJrLpyuzeL7t+hazM2aL7uefKAMR7dd116EiuwLFKcVo6G3w2N811IUDbQdw\n6YxLAQBl6WWo66bVH5sOb4JpxIS9p/a6/F4GA9WXlha/hxcQwkL0+4b70D/SL9jh0Rfa24HsbCpC\nfG0+L/o6HZ1EBAD5SfmSY3QWfedp4jodEMV5Rvq13bWIj4736fdob6eJZyHRn589H89f8TyufeNa\nyUfNuu46lKWXef2zxWhoAH7xC2DPHmWRPkDnNKgh+hYLcOwYrdhKjk1GfIoJLS00+nrhBeCrr4C/\n/x149135a+1r24csXRb+Z9f/CO5vMjVBF60TLTGUYqraO73DvUiNcxX907JPQ213rcgZE+xr24fK\n3ErJY4pTikUjc6VwHOf4jJSklIhe78UrX3Ss21CeXu4Q/WpjNe44/Q7cNPcm7G7e7TjeYKD5LBbp\nQ7no1xhqMDtjtmyvdKW0tVFrB/AUfb0eDq83LzEPp/pPidYTO3v6bYMTMwb1eiDK7unpP7XnKXx/\n/vd9GnNHB1BRQevMhbhhzg2YnjpddJ3VMftYQDp7OvPRR8C+fcDq1TSCF2JG2gxHpA/Q4z75hLbA\n8BepNXvd2b2bvn7JyTTSj0k04dVXgVtuAe6+G1i4EPjNb4DXZVwyO2fHgbYDeHPlm3jl0CtoMXmG\nb3XddSjPKPflV5qyot833OfR4ylTlwmDWb6Gd1/rPizKXSR5THFKMRr6PCNzb2gfbEd8VDxS41NF\nr5ely3JJypell6G2uxYcxznyjucVnoddzbscxxgMtGjEbkfQG/0Joarot/Qre96pMggncX2FT+IC\nVOzr62kNOUAFm4/0E2MTEaONQY+lR/A6UvaOdsy1eudY5zF8eOJDPHjug16Pl6/amTtXuuSxOKUY\nzSZhw7zZ1IzMhEy/+/24XLMZuOwy4L77Jl4/d0pTS13qsXNzqa++a5fw8d5wqv+U6NR8d7Zto4lk\ngEb6OSV9uOIK4IYbaCsNgEZjR45IX6euuw4ZCRmYnz0fl8+8XLDssK67DmVpvj1RZeunpqffO+xp\n72TpsmAYkhb9zsFODIwOyAYrUpG5Upxv1iWpyq5XnlHuWHuCA4dsXTYWTluIkz0nHaugGQzUSs7P\nD41oPywi/UD6+cCExwbQSN9mc430edEHgILkAtFxOidyW/tbHYlcnQ4gNld758FPHsSvzv+Vova/\n7nR2UlF1zjcIUZhcKLoIR213bUCtHQBoagKKZDog8J6+89PSypXAZuVVpqK0D7YrrtzZvn2iXUZy\nXDLs0SY88ADws5/RvzkAzJpFb2RmiUXP9rXuQ2UetRoqcyuxr3WfxzG1Rt9f66ka6fdaPO2dzAT5\nSH9/234syl0kO6ckEJF+rbHWcbPm7SK5WcMlKSU41X8Kh9oPOeYRxWhjUJlXia9O0SR1Vxf9/BYU\nMNFXTfRNpgmxTh+v9nT29J1L+aR8fT7SH7GNYGB0wFE6qtcDsE4kcr/t+haHOw7jnkX3+DTe9nZg\n2jT5yU1FyUVo7heO9Ou661Ce7pvlIEZzM1Ao466kxadBQzQujeFuvBF4+21gzM9ii47BDsmqDp62\nNlqttWjcIUiOTYZpxLNOPzqaVm99+634tZz95crcSsfKYs7U9fhu72QkZMBgNsDOKSwlCgJmqxlP\n7n4S63auw4E2YftQDqFIPz0hHb2WXsmqGyV+PhC4SJ+/Wetj9NDF6GST6tHaaBQmF2Jr3VaXWdfn\nFZzn8PUNBir6LNIHfXRTsl5poEW/r496u4Cn6LtH+vmJ4qLPe/ptA22Ypp/myDno9QBGJyL95/c9\nj7sW3uUoV/MWpaJfmFwoau8EOokLKBN9wDOZO2MG/X1275Y4SQFKI/2PPgIuvnhiDd/kuGTRBcgr\nKqQtHmcRWpCzAMeNxzFsG3Y5xp9IP0Ybg7T4NNVaLA/bhvHk7ifx2BePOQT+6S+fxgcnPsA3rd9g\n05FNPl1XKNKP0kQhOS5Z1D4FlIu+WLWNN7jfrJUmh8szyvHu8XddNOq8wvOwpXoLHvviMTQMH5w6\nok8IaSSEHCGEHCKEfDO+LZUQsp0QUksI2UYIEfUzMhIyZN/c/SP96LZ0K+qVrhSTacKLlxV9BZG+\nezMovR6wj9BE7sDIAF7/9nXcdcZdPo/XWfTFErmAtOgH2t6xWulYchUUqLgncwH/LR47Z0fnYKfo\n1HyexkbgV78C1qyZ2CYW6QPSom8ds+JY5zFH36T46HiUZ5TjSMfECWarGQazwavOn+4EohLFV451\nHsMfv/kj+ob7cPlrl+PLli/x3NfPYdO1m/C9ed+TFGgxOI4TjPQBaYuH4zgXO02KguQCtA+2+7Xo\nvfvNuiSlRNGNpCytDK0DrS6if2Hxhbhl/i34uvVrNKS96BD9UCjb9DfStwNYwnHc6RzHnTm+7SEA\nn3AcVw7gUwAPi52cn5Qvm8ytMdRgVsasgFXuAK6RPi/2QiWb/BhPDVDRf+891943vKd/rOuYy5tF\npwPGLDSR+/z+57GkeImjUZwvdHRQPz8tDRgYEO9JX5BcgBZTi4s18GHdh2gxtaCuuw664XIcO+bz\nMFxoa6Nlr9HR8se6J3MBGnn705Kh29wNfYxedGo+QMs0r74aePhhYMmSie0pcSk+RfoNfQ3I1me7\nLM7tbvGc7DmJ6anTFU8YE6IkpcRvf9pXDGYDTss+DU8vfxprL1yLC169AD+s+CGmp05HWnyaT6Jv\nsVmgIRrB9ZUzdZkwDBnAcRy21m512fdF0xfISMiQnI3LE6ONQbYuW7CaSgnWMSuaTc0u3W+9ifQB\nuIh+XFQcfn3hr7H6jNUYim4OqUg/ys/zCTxvHNcAuHD8600AdoLeCDyQq4MHAm/tAK5VN3ykz//v\nXLLJj7HF1IKBAeDaa2mnvDPOoJHu8DAV+F3Nu7C0eKJjpV4PjDXpsLVuK450HsHbNylviCZEezud\nzarR0JuT0SgcYSdEJyAxNhGGIQOy9dl4p+Yd3PPBPbBzdgyODuLtV4tg6gVefdWv4QBQbu0ANJn7\nedPnrttKadWUEEeP0r+P1PXbB9tl/fyvvwbi44H//E/X7QnRCbDarbCOWRGtdb1rVVTQn89xgHvu\n8GTPSY8qksrcSuxumfCp/LF2eNSM9A1DBmQm0FKsNZVrkBCd4Fg8Jy0+zadFe4SsHR4+0m8fbMfV\nb1wN00MmJMUmAQCe3/881lSuUdwYkK+48WXG+Xe93yEvKc8liChJKcGhjkOy55allyEpNklwXkZe\nYiGsCc1IT6eJ3KkQ6XMA/k0I2UcI4Tt4ZXMc1wkAHMd1AMgSO7kgSbwyhkfJgtLe4mzvpKQAf/kL\nEDv+txazd3iB2rKF/s/fOAih/XTOL5qYZavXA7reM/Hsimfx7b3fYn72fL/Gy9s7gDJfv8nUhG+7\nvsXdH9yNf33/X/j6zq/xp8v/hC93awO2NqySyh2eiuwKfNP6jcu2jAxaNdUjEDg+8ADw5z9LX7N9\nQN7Pr6qiN0t3zSCEICk2SdDiSU8HEhOpLeSO0GL2lXmV+KzhM2z4egM+bfg0IAnzQPjTvmIwT4g+\nANy+4HaHLZOekO5TpC9m7QAT1Ur8k2CNoQYATdJvq9+GH5ymvJGiPxU8jX2NHmtWK735npl3JjZc\ntkHw5qS3FQLJzdBoOJSU0AmN/hYw+Iu/kf65HMe1E0IyAWwnhNSC3gicEa15qtlSg69Hvkbfx31Y\nsmQJljg/g49TbazGPWf4VvUihrO9Qwhwp1PDSbGSzZMnORQXE2zeDPzP/0yIfrOpGRarBTPTZjrO\n0ekAa3+6o7mUv3gj+kXJRWg2NeODug/wwOIHHJNasmNKsfogrU4JBN5E+qdPOx1Gs9FlBi0hE9F+\nmtMKlxYL8MUX1MaSQknlTnU1XShHiORYmszNSMjw2FdWRsdV4hYwCi1zOS9rHm6Zfwtqu2vx3NfP\nwWg24pkVbgsfeElJSgneqnnLr2v4StdQFzJ1wpMufLV3ZCP9IYMj0V9tqMZZ+Wfh5YMv46Y5N3lV\n4uxPBQ/fO6u2lj4dFhbSv+3FJRfLnpsQnYDbKm4T3DfSnwwNNOgd7kVaYhpycoATJ2h5sFJ27tyJ\nnTt3Kj9BBr9En+O49vH/DYSQdwGcCaCTEJLNcVwnISQHgKhE3XP/PfjHsX9g3ap1oj+jqqsKaWNz\nUF9PRSIQONs77riXbCbFJkFDNKiqN+H661PwzjvU87Va6TX4rpnOd3l3i8gfOI4KUHEx/T47Wz7S\nP9lzEu/Xvo/Hlj7m2P7111TMAhXpNzfTlgZK0BANlk1fhu31211aOs+YQX+3Sqc83a5dwOzZ9DW2\nWOgHUAgllTvV1dSSEyI5TjyZm50tvNhLfW89lhQvcdkWpYnCby/5LQBa+fLC/hewolTZ6m1iqB3p\niz2ppMalwjRswph9zKucRe9wr8dsXJ5MXSbquutgtVuRGJOIakM1AODd4+/i2RXPCp4jRnFKMXY0\n7PDqHB6+S+5vf0vfd2+8QQO+B8/zfjKlMwYDED9KA7G0+DRHzsgb0XcPiNevX+/XmHy2dwghCYQQ\n/fjXOgDLARwD8D6A28cP+yGA98SuIff4NDg6iK6hLuzYUgI/f08XnO0dd9wjfYBaPEebGzBasB03\nrOSweTMt10xNHbd23BqoCV3DV1pagJgYKkSAsgqevx3+G2ZlzHJJHu/eDVx1FU0CB2Js3kT6ALC8\ndLnH7FUhX3/7dirU8+bRFg9iKLV35oqsUshH+kKIvcZyaxvHRcXhvrPvUzxLWIyilCK09LcEpGuk\ntxiGDKKRvlajRWJsoujNUgyhZms8vKdf31uPFTNWoMpQhYGRARw3HseZeWcKniNGQVKBz4lcvgKv\nthZ46y1aPBEIDAYg0T5RVSdXEhwM/PH0swHsJoQcArAXwFaO47YDeBLAsnGr52IAvxW7QEmqdElU\nXXcdZqTNQF+vVlIAvME5ASsEH+k7t9nNT8rH+6mX4KX+a5BzwVaH6Cel2LCzcadHq+RAiv6RI/SN\nwpOePrHalxCFyYWo7a71WCB+1y7g/PNp+4lARPu+iP6O73ZgxDaCnY07wXGcqOivWAGcd550qwa5\nRK7BQG9w00QOSY5LFu2SKWSh2Tk7GvoaPHzfySAuKg7p8emqrPtqMBuQpRNNw4laPAfbD4r2xhdq\ntsbDV+/U99Tj6rKrUW2oxtetX2PhtIWSlVlC5OhzfO5Q2jpAZ9TX1QGXXw68/LJPl/GgqwtIi5oi\nos9xXAPHcQvGyzXncxz32/HtPRzHXcJxXDnHccs5jhP+ZIE+Lto5u+iHr9ZYi/KMcvT20qZogWhW\nZDJRP1+sIECrBeLi6CMez+pFq5G66y/404Vb8HLTgxix2vDmV19iZ9ki5Cfl4/Rpp7tcw73s0x/c\nRT85Wfp14D3zG+bc4NhmNgPffEOXgwyE6HOcd4lcgK5ulJeUh9l/no2lm5ai2lDtIfrOM2fPP196\n8lb7YLtkjX5NDfXzxf7OUrX6QqK/82ArUmJTFS+G4i8lqeqUbTpX7wiRHi/cbvyWt27B542fC5yh\nrHrnZM9JLCtdhq6hLnx88mOf1pzI1mf7PKmtbaANOnsurFbaYvvFFwPTFLCrC8iJmyKiHwgIIZL+\nJV8J0dNDheaAbzPAXZCydnjcff0rS6+Hae/1+OE5l2OafhqsPzgf70TfiHPsD2LbrdsQpXFNjQTS\n03cX/aQkzyUTnZmbORe/vfi3DvH/9FNqldx0E02YBkL0h4ZoBUKyl22E1l64Fo8vfRx3LbwL2+u3\no7SULk8J0Cere+8FbruN3njPPZe2OxardJCzd6SSuIC0vSOUN7njv+ph7SxVvNCKv6hVtmkwi9s7\ngHCkP2Ibwcmek6JzbvqG+8TtHV0mvuv9Dla7Fdm6bJRnlGPTkU0+iX5afBoGRwcxYhNf+EiMtoE2\nWLryUFZGG+/pdNQe9Jf2dqAoZUL0i4tpkYLU0/pko/rC6FLd7PhZpD099A8RCIvHuXJHDHd7prGR\nTqyIiSHYcNkGrJh5CbgNNThH/z3hMq1JtHeSk6VFXxejc0k+/fznwKOPTjyu5uX5P0Gkt9e14kYp\nK+esxPfmfw8rZqzAtvptyM+nb36Lhc6a7eujC5oAtFdJVhYVb3c4jpO1d6T8fEA6kevu6Q8PA6fM\n9dCYSrF2rZLf1H+Kk4OfzB22DWN0bBSJMYmixwiJ/omeExjjxsTblUjYOxkJGTBbzZiRNgOEEMzJ\nnINuczcWFyz2evwaokGmLtPrhnU2uw1dQ10wNmU7qttmzwbq6rweggft7cDMLFpGDdAnz9NOUzfa\nV130i5PFa2v5SL+3l/q8+/f7//OURPruou1cOTQ3ay7+euujKMhKcjRtcycujvrJ/tbjDg1RgXYu\ns5Szd9xpb6ezX3ny8/2P9Ht6fBN9nqUlS7GnZQ+s3DCKi4HHHqPVEm+9RZPWPJWVwn/zwVH6x5ES\nJ7lIPz8pH9/1fie4z93eqaoCUqafxI+vm4E//EG+nDQQlKSWeLSu8Ja2gTavSix5a0dqMlR6fLrH\nBC2+4kZS9EUi/RhtDFLiUhylsHMy5mB+9nzRah85cvQ5itaLdqZrqAvp8emor4tG2fi8uvLywIn+\n3HzX9ihqWzyqi75YpM+vYjMzfSZ6emgv9H37aCK2psb3n6ck0ne3d9zLRQmhQrVYJBghxPMavnDs\nGC3tcm51IGfvOGO10qjcudd9IOydnh6I3vCUkBKXgtOyT8Oupl0oLQX++Efa4iLDrWS+slL46Y4v\n15QSp6Ymzzp7Z84tOBd7WvYI7nMX/SNHgIS8elTkl+Kss4AAlkyLcmHRhdjRsAN3vHeHqA0lx5oP\n1+DZr5SXPUrV6PMIRfpVXVVYkLNAXPQlPH2A+vq86F9ZdiXuP+d+xWN2J1uX7XUyt7W/FXlJNInL\ni35ZWeBEf35xLgxDBkei+5JLvLdGA4nqoi82i65jsAOxUbFIjUtDby9w5plUwCoqXPuoeItUjT6P\ne6R/8qTnHIHbbqOzPZVewxfcrR3Au0if78OvdSqpDoTo+2rvOMOvj3r77TTCny8waVlM9Bt6G2SX\nSezvl/47z82ai25zt2BUqNPRHBJ/0z5yBBhKOoS5WXOxfDldkAWgT2GT5fHPTJ+Jmp/UwDRiwmNf\nPIRlmU4AACAASURBVCZ/ghv9I/34+OTHONKpPKR0n40rRFp8mkcit9pYjeXTl/sU6QPU1y9Nox+w\nipwK0YlOSsjR53idzOXLNevqJp6qy8tp8Yg/2O00eMibFoVpidPQ2k8/eNdeC/z4x/5d2x9UF32x\nWXS8tcNX0eh0wH//N7B2LY00ff2w+WLvNDd7V6kidA1fqKuj3qIz3kT6zjN5eQLh6ftr7wDA5TMv\nx9a6rVi5ksPy5RAsT1ywgForI255uSOdR1CRXeFxvDP9/fS1EkNDNFhcsBh7mj2jfUJoMpf39ffW\nfgd71ADmZc1zrPE7MEB7MP3rX7K/qs8kxSbhyUuexN+O/M2jfbMcH9Z9iJLUEu9Ef0i6XBMYb8Uw\n7BrpVxuqsbxUQvQt4pOzAOCWebd4THrzlWxdttf2TttAG3L1eTh5ki5rCNBIv7bWtcGitxiNNEiL\niaE6569dFyhUF32+esd9hRrnJC4vML/8JbBqFe2N0idaCCqNL4ncri7a5dIbAlG22d7u2ViNT+Qq\neTMKiX5ODn0zipWjNQkvvOUCPzHNHxblLoLFZkGVoQptA20o+kORwxvm0enorN2jR13PPdJ5BBU5\n4qI/MkKDgliZMm/3tUyd4S0ejgO+tWzHxcXLoSEazJ9Pbyhr1tC5AEI9egJJaVopFuUuwuYq7/pQ\nb67ejAcWP4AeS49oSbQ7SiN9Z3vHOmbFd73fYXHBYlhsFpfV4nicE7mtra7l0ADwkzN/ErC2377U\n6rcOtEJnz0Vq6sQqarzV6E+VjfPnb07mHI/3t1qoLvrJccmI0cbgo5MfOVaVByYW/RCyEjIyhKfJ\nK0GJvePux3d1URHwhkCUbQqJdkwMEBVFK0qUnO9+s4qOpq+f0IzDjg4a4cglKgMR6RNCsHL2Smyu\n2oy/HPgLOI7D9vrtHscJJXOPdEhH+nyUL9ec8bzCidWN3OFF/9QpYKx4O66ZRxfY1Wjo4u6bN9MF\n4ZuFly8IKPcuuhcb929UfPzg6CB2NOzAtbOuxbyseTjaeVT+JEjPxuVxr9M/0XMCBUkFiI+OF+ya\nO2wbxph9zDG/4frraQnxNtfJ2QEjW+9bpK8152K607w7Qvy3eJjoS3DrabfisS8ew4V/u9AR8TvX\n6LtHlXx7YV/wxd7p7PRN9AMR6QvNKFVq8YidL+brNzXRqiO5RKW/iVyelXNW4p9V/8RfDv4Fj5z/\niKjob9tGa/YHB6mI1PfWS7bblrN2HNfOrcRx43EMjHje5XjRP3DYirHCT7Fs+jLHvrvuAp59lk4g\nC4boXzHzCrQNtOFQu3ybX4BaO4sLFtNeL9kVLou8SOFLpO/c+lxI9DsHO5GlywIhBCMjtDjh6afp\ngvRKAhdv8TXSj7LkeXzG/U3mMtGX4I+X/RF7frwHds7ueNMc7TyKOZlzRCN9X0XfW3tneJg+jsrd\nKNwJlL0jJNpKk7kdHeKiL+Tr8wK23VN7XQhEIhcAzso/C4OjgyhJLcF9Z9+H3c27PbzrSy6hf+tV\nq2iVT42hBjPSZkhO0e/vV1YdERsVi3MKzsFHJz/y2Md7+lu++gbp2hJk67Md+y64gE4kKywMjuhr\nNVrcvfBuPL//edFjBkYGYB2zAgC21GzBytkrAdC21kp9fbmJWYCn6B/tPIp5WfMACIu+8zKdx45R\nu+666+j/k1G26Kunj4Fclyo3wP+yTefP35zMOagyVMkutB4MQkL0Afq4z69C1GJqwZB1yMPT5wlG\npM9bMwYDjfoUruMgeA1fMJupNy00VrlIn7e+xG4aYrX6zc20Mkru0TsQ9g5Ak6mPnP8I1l24Dqnx\nqZibNdcjsTpjBm3H8NhjdMHyQCRxnblr4V2CYspP0Pqo5jMsKxVur1tYqCwHEgjuWHgHNldvFi3f\n/K/t/4U1H67B0OgQttdvx7WzaHvRihwvRF+mBQNAy237R/odzeD2t+13tO8WWk/aeZnOffsmOqqK\nzcHwlr4+1ycGX6p32gfaYeudJhjpB8reydZlg+M4ryeOTQYhI/oAfdze37Yfe1r2ONoVCyUNJzvS\nd47SffHzAf/tHX6JRKGbjVSk/913dCaq3e6bvXPllfSG0iAxGTQQiVyeeyvvxcXTqajyZZxCzJlD\nJ1zJ+fmAd6J/7axrcdx43OPROysL+Pe/geH0fbhq4VmC506bNtHYbbLJ0edgRekK0YXJqwxV2HRk\nE57+8mmclXcW0hPoUnDzs+aj2lAtu3asxWpBbXet7KpTWo0WSbFJ6Bvuo2vYOi1czq894YxzpO8u\n+oGYYb9qFX2/fzT+sJYSlwKLzaK42mnMPoa+4T4MGtI8Iv1Zs4Djx30fm/PnjxCCuVlzQ8LiCSnR\nX5S7CPva9tEe9QW090agI31v6/R98fPdr+ELYoINSEf6p05RIaqp8V70m5tpb5Dly6ngiRGoSN+d\ny2Zehg9OfCC4b9Ys+qh9uEO6cgfwTvRjtDG48/Q78cL+F1y2Z2XRDzzJ24fK8UjWnago+voGao0C\nOe4/53785vPfYP3O9R6iVtddh/vPuR/rPl+HlXNWOrYnxiaiIKkA33Z9K3jN/hEaPbxZ9SbOyjtL\ncMk/d3iLp8nUhGhNtKOVtPN60s7j4vvzB1r0jUZg717g978Hvvc9GigRQugELYXRfu9wL5LjktFt\niPIQ/bIyGvxYrb6Nz72QYk5GaPj6ISX6lXk00v+i6QvH8oNqJ3K7uiZ62XuDv56+lOhL9d/ha8u/\n+IJ+LVRqKuXpFxUBF11EzxcjUIlcd87MOxOmYZNjyTxn9HogK8eGg+2HcHrO6QJnT+CN6APA3Wfc\njX8c/YejvQMw/jdPbENU7CiKU4pFzw2Wrw/Qz8fBew7iaNdRzNs4Dx+f/BgArYMftg1j/UXrsWru\nKlw/+3qX8/gFbNx5/djrKHi2ADWGGmzcv1HxSm8lqSU42nnUxdoBhD193t4ZGqIz2/lJePPm0XJX\nf1pavPsubc9y7bW0FTfflTVbr3xWrtFsREZCBgwGeIh+bCz9+5444dv43D/DoZLMDSnRz9JlISk2\nCfW99Y4PdiATuXY7FUs5QXAu2fTH3vHH05cTfTF7p6ODzmN47z36v1CtupSnX1hIJ4SJeZk2G/29\nJmMauYZocMPsG7Cleovg/tzKb5CuLZZNNnor+gXJBbiw+EK8duw1x7aiIuCs6/fhnMJKyXYPwRR9\ngLbOfuumt/Cny/+EW966BU19TQ4LJUYbgzdWvuGxBOTyUk/bbNg2jId3PIxb5t2CpX9fis7BTlw2\n4zLRn+vsm99ecTtePPAi9rVOWDsAUJpaimZTs2M95GHbMNoH2lGSWoJDh6jQ872VoqPpDeCQsoIk\nQTZvBm4cXzbCWfS96b8jJfrAhK3oLRwnLPpVhgC07vSTkBJ9gFo8Z+WdhWgtbTjjj73jPgnkxAkq\n4FEyi0S6R/rhZO90dtIVsnbsEJ9Qxts7zoUEQ0P0X2am9GxEPieimaR3zo1zb8TmapGJSDO2o2Bk\nuew1vBV9gNbCP7//eUd1RVIScMlt+1CZVyl5XlFRcEWf59IZl+Li6RdjV/Mu1HbTdSfEWFK8BF+3\nfu0ycerP3/wZp2WfhuevfB6rz1iNR85/RHQJxBdfdO1YunLOShzpPIK3at5yeX2S45Lx16v/iuv/\neT1a+1tR31OP4pRiRGmicOQI7ZTrjD8WT3c3tXYuv5x+77z+Qo4uB+0D7YquM1miPzBA25/wk70A\n4LTs03Ck84gqK6I5E3Kif035Nbhp7k2O74WShpmZ8qLf3U0jWucI5cUXgdtvlx9DKIg+n8gVQi7S\nv+ACamGJ3TT0ehppOc9qbmkBCgpo4jg9nUZkQksGBjKJK8TigsUwmo2oNXo+anTptyG6ZXJE/5Lp\nl2BwdBB7T+11bNvXts/FvhAi2JG+M+cV0Mlldd11KEsTn9GaGJuIM6adgS+aqGc3bBvGk3uedKzt\nu3bJWtx1xl2C5+7cCfz61/S9wH/mYqNi8aMFP0J9b73H63PNrGtw9xl3486td7pU7gg1wLv4YuCv\nf/XN4lm/Hrj66okV8BYtojmYgQH6NCTW298do9mI9PgM9PR4NvwDqOi799WX8/ibmoAf/ICOyZls\nfTbyk/Kxvy0AZUt+EHKif1vFbVi9aLXje7FIX25GbkMDPXfPeAWg2Qz8/e/APffIj8HZmuns9N3T\nnyx7Ry7SnzaNPu6KnQ94JnPdlz8Um5gyWUlcHg3R4NpZ1+L92vddtvdaetE+VoXuQ/KLa/gi+hqi\nwd0L78bLB+nCAxzHYX/bfhf7Qohglm26w7eRcK6QEcPZ4tlctRkLpy2UnODG8x//AbzyCo3Sndth\n3LvoXqyau8rDSgKAh857CCe6T+D5/c87krhCy2tecw3tVHvrrfT9rlT8X36ZlhVv2DCxLTaWjnHv\nXir6fP96OYxmI/SaDCQmunaz5XGP9D/9lNpSYuX2HEdvZqefDnz8sed+qQq1YBFyou+OkMgkJ1NB\nlbrjuk80+uc/gbPPptUpcgSiZDM5mUbFvuJrIrejg96krrsOOEu40hCAZzLXvamc2BT0yUriOrOk\neAl2t7i2R9jRsAPnFpyHuupY2WZ7vog+AKyatwrv1b4H65gVhzsOIyk2SXKhFkDdSL8ipwItphbs\nPbVX0t4BgKvKrsLr376OFlOL4qTtyAjtMLtsmWcP+KKUIryx8g3B82K0MXji4ifwyXefuET67k0L\nCQH+/Geaazv9dPpaygVzPT3AAw8AW7d6FmTwFk9hsmv/eikMQwbE2DIErR2AVo2dPDnRq2rDBhoM\nidlS+/dTW2ftWuF8Gr+AkJqEtOiPjdG7v3vSUKOhNwKpZkjNzbQL4rZt9DrPPAP85CfKfm4g7J38\nfP+6WSpN5LoLIF+xc9ttNEqTGp9zpN/UpCzSD9RsXCn4njh2jv5yH9Z9iAf+/QBuPm0l0tPlm5z5\nKvqFyYWYkTYDnzV+hhcPvIgfL5Dvf8tH+mpMtIzSROHs/LPR0t+CmWkzJY+dnz0f959zP5ZsWoLW\n/lZcMfMK2eufOEGDpNhY7xf+WDlnJW6YfQPOKTgHgHCkD1AbcetWGqxccw3w6qvS133/fWDp0om+\n986cdx6waxe9ISkVfaPFiOjRTFHRj4+nAVJ9Pf08f/458NOfAluEaw2wZQuwcqX4ZM7zC8/Hkc4j\nPq+REAhCWvRNJlqBohXIL8klc5ua6Ivf2Ag89RSNCi69VNnPTUigf7T2dogmeOTIz6cLffuyepbN\nRiMasZsNb+/U1NCl13g4TrkdpcTeUSvSz03MRUpcCo4bj+NfJ/6Fez+8Fy9e+SJ+fPqPMXeufGLN\nV9EHqFi9cugV/LPqn7hz4Z2yxycl0feLUP4jGJxXeB5yE3ORGCu+ihjPfy3+L1xaeikeWPyAaNLW\nmerqiQSut6JPCMGWm7ZgXtY8jI7Sz5GU3QjQzqUvvCDdNn3zZvq5FmLxYhqBZ8XloW2gTVHC1Gg2\nAmbxSB+gFs+BA8DGjcD3v0/zgps3e97oOc61okiI+Oh4LC5YjM8aP5Md22QR0qIv5R/LiX5zMzB9\nOo0KfvUr2uRJaSsFjYZO9vj976nVI9eiV4jYWDp2oW6WcnR20mSq0M0OmIj0P/2UJpl4q6evj0Ym\ncXHyP8NZ9DmOfqBnzJjYL9Z3JBiRPjAR7W/4ZgMeW/oYlpfSBK67x8pxnmNVUpYrxso5K/Fm1ZtY\nNn2ZrLXDU1pKI0E1uHTGpbio+CJFxxJC8Ocr/oz/POs/FR3vvOTkvHk0CPBlolJrKxV8uaq5ykoa\nUIi1Aenro5H8lVcK709JoZ/56mOxSI9PV1S2aTQbMTYgLfrnn09zga+8Qt2Cigr6uxw86HrcoUNU\nO9wXPnJn+fTlONB2QHZsk0VIi75U1KpE9IuKgJtvBu64Q9rfFuLee+md3Rdrh8fXcr7WVirKYvCR\nPl+ixifYeD9fCc6e/r599CZy7rkT+0tL6VOSe9/9yU7k8pxXcB7+fuTv2N+236Way130Gxup4Dt3\nBlXacE2I4pRiXDvrWvz87J8rPqe0lPq+/rB1K73OG8I2uShn5p2Jf1z/D/9+uAhVVROin5BAnwR9\naUugdBEiQoCf/5y2rN661XM/b+0kSjzU8BaP0mSu0WzEaJ+06D/wALWZOzupx08IcMstwOOPu0b7\n77xDu4fKBZf3nX0fHl36qOzYJouQFn0pEVMi+oWFwE03AS+95P3PXriQWif+iL6vSb6aGvrmEoNP\n5O7aRWfP8o/dYjNwhXD29DdupB8059r7+Hganbn34AmGvQPQSH9Pyx7cXnE74qImHl3cS+h276ZP\nVbud8r7+2DsA8M6qdxxetBJmzPAv0n/lFeC++2hEKdX+Iti4Ly7v64Le7vkiKW69FfjLX+jr8cor\nrvukrB0efpKW0mSu0WyEpVta9IV4+GFq/z7qpN0ffzwxb0AKJdbaZBLSoi8lYlKib/n/7Z17bFVV\nFsa/VQSLYmspAtEilVBagQBBKQ4PAbHQODJFRaOpkcdgJoCORgSlghjEKGZ8IETAV0QBGXzCHyTU\nACoVtJChUKRVOgoCQqXAVEqBlvabP9a99ra9j3Mfvedcun/JTW/Pufec3d19vr332mutfU6ngqG4\nWnryxBPNtysMhlDd+Txtqd5wu5RevKgjC/eDGMxI/4YbdJT84osavettz05vi7m//RZeR2iVjE4Z\nGHzd4Ebuu4CKUElJg923oACYPLlB9GtrNQFa+/YtX0Y34Zh3qqqAefOAdeu0vRUUBP5ONKit1eR9\n6R5OQQMHAt9/H/y1fC3i+mLMGK2PefMaHCoqK3URddw4/991e/B0Swgs+jV1NaiurUbl74lBt+n4\neB3Zr1ihHjsVFfqs/MX6WME2HC36/kQsJUXzc3vjyBE9H27UaKizBDehjvQ9p9XeiIvTKe7w4Y1H\nX8GM9JOS1Kd5yxZdePIWmNJU9M+f1+8MGWL9bwkVEcF3U7/7c8NsN4mJars97Iq92bYNmDpVR/dH\nj+o03MquWZEkHNH/17/UZHHTTdrRl5fbtyjsSVmZBut5rg+NG6f5boLdnzpY0Qe0PkaN0nU1QM09\nI0YENtulpOg6XPyFwKJ/svokktsno+KEhOSs0bUr8Nhj6na6ebOWz51mwsk4WvT9iVhuriYFKypq\nfi6Y6WRLEqroN51WeyMxUaey/fppJ1FXF9xIH1AT0pdf+u7YmvrqFxTogl40zDv+cNv1KypU6Pv3\n1/WIgoLwTTuhEKpN/9gx9fteuFB/b9NGO9Rvv/X/vWjgbeDRu7cONgoLg7tWKKIPqM18yRLdR+GT\nT/x7xXgybBhQ+Wtg0Q+UgsEKU6ZoR/jRRzpDiQUcLfr+RCwhQad/s2Y1d52yunDU0oQi+mfP6t/t\nuV+nN1JTdYSYkKB1VFYW3EjfCk1H+vn5mtXQbvr0UVHavl0D7i67rGFab4fod+misyArW1h6Mn++\nioZnwKBnDhk7KSxsyIjpyb33qm09GEJ9HlNTgcWLNd3Cli2BTTtuMjOBY6WBF3JPVJ9Apys6hZw+\nHdAZ8p13qonUiH4ECCRiDz+sDaqpi1eoI4tIE4r3TmkpkJYW2L3tm28aHkq3iSfUlBG+aDrSz893\nRsPu3Vv/5wsXascHqFhu3WqP6IsEb+LZv19HiHl5jY+7vU/spKZGU5bk5jY/N2GCjrqtBqOROvPu\n1i20suTmqqdMVpb1GeagQUDZrsABWhXVFUhs2wlnzoQ3WHr0UV3vSPMfH+cYHC36gcwVbdsCixYB\ns2ereWPhwoZc8E4Q/Y4dNZQ9mIRSVkw7TcnMBCZOVCGM5AynWzf11qmq0v/FoUN6L7u56SZdPJs4\nEXjyST02aJCWdefO6Is+ELzoL1ig7bapkA0apG0g3P2Vw+Gzz7QNevMg69tX3Td37LB2rYoKtXOH\n8z9ZuNB3BKw3+vcHftrbEXX1dX63J6yorkDb2muQlhbe+l9mprbHaK4jhYNjRd9qdGlOjtq3c3M1\nEVNOjtr50/2nIokKIsGbeAJ57njjqafUq+bUKe9T8lCJi1N3xAMHtEO57bbAM5BoMHCgpuCYMaMh\ngC0uTj2Z3nvPPtG3atc/e1a395s8ufm5+HjNQxOKl0ykWLZMo2O9IaJxLytWWLtWcbF2FOESjKBe\ncQWQ3kuQ2TG7WeI+T9yBWZHQilgRfMBm0fc3RfzjDxUYd+pUX4ioB8SmTWpXe/xxDfkePjyyZQ2V\nYEU/kOeON+LidMTYEmLnNvF8+ilw992BPx8tvI3MJkzQRT87RD8jQz04rHi2bNyoaxHJyd7P22ni\n2bdPO/mcHN+fmTxZA6Ws7GmxZ0/gCNWWYNAgoPvZCT435AGAg/87iAsnu3jN43MpY6vo+1uwCmZR\ncvBgTYzmbly+0hfYQY8e1gNa3OkQgh3ptyS9eqnJ5KuvfIe/O4Vhw3RmaIfoP/igLubOm6czzqFD\nfS/sBgoysnMxd/lyXSvzlmbYTXKyLq4GSo4G2Cv654v/ih1HduDUuVPNzlfVVOGL0i/Q9r/jjehH\nk2XLfJ8LdlHSXyO1k2nTgNdfby4AGzfqw710aUNStqIi7bCc1AjT01XErPhI202bNhpbEar7XThc\nfrnawlet0kjSpCRdb2pKdbXOSu+6y/e1hgxR806oG3L74+mn1Sumd+/m0dZVVcCaNSr6gZg+XSO5\na2r8f85O0S8qvBJZPbKwvnR9s/Or967GyNSROLI/xRGm4KhC0pYXACYmkseP0yvr1pH33OP9XKwx\naRI5Z46+P3iQHD+e7NmTfP99csQI8vbbyfp6Mi+PnD3b1qI2Y/t2EiBXrrS7JNa4cIGsrbXv/mfP\nknV15OHDZMeO5K+/Nj6/ahU5enTg6/TtSxYWBnfv+nptW0VF3s8vW0beeCNZWkrOmEHOmtX4/PLl\n5F13Wb9fdja5eLHv8zU1ZPv2WifRpqaG7NCBXLZtLUe+P7LRufr6evZb1o+bDuSzQwfy9Onoly8c\nVLbD0N5wvhzWjQFOnaqN1JvwL1lCTp8eiSqyn8OHyaQkMj1dhWDBAvLcOT1XV0f2709+/DGZlhb8\ng97SnDypD8+pU3aXJPbIyyMnTmz4/fx5skcPcvPmwN+dNo189dXg7rdjB5mYSKamkr//rsdOn9ZO\nJj2d7NyZPHBAj5eVkZ06kdXV5PPP6/mEBGtlc7N3r17Tl2ju3avXtYuZM8m/P1zDtDfSuKls05/H\nt/6ylWlvpPHwkTp27mxf+UIlpkX/zBnyySfJa67REbAnzzyj4nipUF5OlpSQJ040P5efrw9g9+46\nWnMalZV2lyA2qawku3RpGHm/9hp5xx3WvrtmDTlmTHD3mzmTnDdPO5sBA8hdu8ixY7UDKSkhKyoa\nf37sWB3Z9+hB7t5N/vJLcPcjySlTyLlzvZ9btYq8777grxkpTp1SbZm7+lNePacf1/77Iuvr65n5\ndiY/3PMht24lhw2zr3yhEtOi72bSJHLp0sZ/2NSp5IoVYddPzJCd7TzTjiF8li4ls7LI9eu1Yy8u\ntva96mry+uvJbdusfb6+XgcNe/bo7PGtt8jkZDUd+jJ3bdhAXnUVuW+ftXt4o7RUR/vnzzc/N2sW\n+cILoV87Erz6Ktnu8np2zRvKK++dzjd3vMuBKwayrr6Oy5drpxVrXBKiv2YNOW5c4z/szjv1QWkt\nnDun9mjDpUVNDdmrF5mREZzphCQ/+IC85RZrs7/CQjUPen62slI7D39Ewp49erQ+w56UlKhpZ+PG\n8K8fDnV1Orsurypn98cfIp4DN/+s/4jcXPLll+0tXyiEK/qOCM7KytK0qW5PAFLTukYyj4zTiY+P\njQx9huBo21ajV/fsaUgZYZXcXI3o/uyzwJ9dvVrz4ngGCSUkBE4x3XRz8VBwbzgEaODZnDnqmTZt\nmv25muLiND9O5ys7Y/MjK5H07jFUFt2Gd95RV+SpgXfEvOQQ7ThsuLEIPe998826efmtt2pOkmef\n1e3HnORzbzBEm7Iy3czGX5DioUMapVxcDFx7bfTK5qa2VvdnSEnRQMRRozRgMtCeuHbw9dfaGR0/\nrp1xLLprighIhhwD3GKiLyLZAF6HxgK8S3JRk/ONRD8vT3vl+fM1bHvxYusbmRsMrYHCQvX9f+01\nDUZ85RXgoYc0rW9qqubzsYvfftNNea6+OviI8mjj3qjd35akTiZc0W8R846IxAFYCmAsgD4AHhAR\nPxsA6jRw+XLdeaZbN/unhbHGV56bxBrCxon12a+fBjoNGKBphvv00QyPmzZpinE7ufZaDSrzJvhO\nq8t27WJX8CNBS9n0MwEcIHmIZC2AtQD8ZPNQs87nnwMvv6wZ9WIpgZETcNqDFes4sT7j49Xs+cMP\nmoJ77lzNlbNzp//Nwu3GiXXZmmmpnInXATjs8fsRaEfgExHnJEkzGJyMp608Pt4ZacQNsYMjvHcM\nBoPBEB1aZCFXRG4B8BzJbNfvT0N9Sxd5fMYetyGDwWCIcRznvSMibQD8CGA0gGMACgE8QLIk4jcz\nGAwGg2VaxKZPsk5EHgGQjwaXTSP4BoPBYDO2BWcZDAaDIfrYspArItkiUioiP4nIU3aUIZYRkYMi\nskdEdotIoetYkojki8iPIrJJRBy+5Yl9iMi7IlIuIns9jvmsPxGZIyIHRKRERMbYU2rn4qM+54vI\nERH5j+uV7XHO1KcPRCRFRLaIyA8iUiwi/3Qdj1z7DCdxTygvaEdTBqA7gLYAigBkRLscsfwC8DOA\npCbHFgGY7Xr/FICX7C6nU18AhgEYAGBvoPoD0BvAbqgpNNXVdsXuv8FJLx/1OR/AE14+e6OpT791\n2RXAANf7DtC10YxItk87RvpBB24ZmiFoPkvLAbDS9X4lgPFRLVEMQbIAwOkmh33V398ArCV5keRB\nAAcQIOakteGjPgFtp03JgalPn5A8TrLI9b4KQAmAFESwfdoh+t4Ct1pxUHRIEMCXIrJTRNx5CKrf\nEgAAAdJJREFUAruQLAe04QDobFvpYpPOPuqvaXs9CtNerfKIiBSJyDse5ghTnxYRkVToDOo7+H6+\ng65PE5wVmwwlORDAHQBmiMhwaEfgiVmhDw9Tf+HxJoAeJAcAOA7gFZvLE1OISAcAnwB4zDXij9jz\nbYfoHwXgGTie4jpmsAjJY66fJwB8AZ3OlYtIFwAQka4AfrevhDGJr/o7CqCbx+dMe7UAyRN0GZ0B\nvI0Gk4OpzwCIyGVQwf+Q5HrX4Yi1TztEfyeAniLSXUTaAbgfwAYbyhGTiMgVrlEARORKAGMAFEPr\ncJLrYxMBrPd6AYMbQWObs6/62wDgfhFpJyI3AOgJDTY0NKZRfbqEyc3dAPa53pv6DMx7APaTXOxx\nLGLts6USrvmEJnArXLoA+NyVxuIyAKtJ5ovILgDrRGQKgEMA7rOzkE5GRNYAGAkgWUR+hXqavATg\n46b1R3K/iKwDsB9ALYDpHiNYA3zW5ygRGQCgHsBBAP8ATH0GQkSGAsgFUCwiu6FmnDyo906z5zuU\n+jTBWQaDwdCKMAu5BoPB0Iowom8wGAytCCP6BoPB0Iowom8wGAytCCP6BoPB0Iowom8wGAytCCP6\nBoPB0Iowom8wGAytiP8DZhkyA9miEikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe148c5e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://ccaa.elpais.com/ccaa/2015/12/24/madrid/1450960217_181674.html\" width=\"700\" height=\"400\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=\"http://ccaa.elpais.com/ccaa/2015/12/24/madrid/1450960217_181674.html\" width=\"700\" height=\"400\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* CO \n",
    "    - **Máxima diaria de las medias móviles octohorarias: 10 mg/m³**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.convolve.html\n",
    "def moving_average(x, N=8):\n",
    "    return np.convolve(x, np.ones(N)/N, mode='same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe148249080>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fTqDXquUE1ahRcMUVZ/fO69d3vlC1ajnj0XPnQlSUU26LFs5/CwqcXyEFBdCq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\nAJPio4yvFU8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe148b5bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* O3\n",
    "    - **Máxima diaria de las medias móviles octohorarias: 120 µg/m3**\n",
    "    - Umbral de información. 180 µg/m3\n",
    "    - Media horaria. Umbral de alerta. 240 µg/m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe148234c88>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aWdc/3hJbzm1B+0/bI+qrKBSXFdfYr5/iwh5C8CpqrsLmoE9E54moG8numl2J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\nnzHGNOT/AbtwPgv0ixBaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe148295278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Scientific functions: SciPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![matplotlib](./static/scipy_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "scipy.linalg: ATLAS LAPACK and BLAS libraries\n",
    "\n",
    "scipy.stats: distributions, statistical functions...\n",
    "\n",
    "scipy.integrate: integration of functions and ODEs\n",
    "\n",
    "scipy.optimization: local and global optimization, fitting, root finding...\n",
    "\n",
    "scipy.interpolate: interpolation, splines...\n",
    "\n",
    "scipy.fftpack: Fourier trasnforms\n",
    "\n",
    "scipy.signal, scipy.special, scipy.io\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Temperature data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will use some temperature data from the Spanish Ministry of Agriculture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://eportal.magrama.gob.es/websiar/Ficha.aspx?IdProvincia=28&IdEstacion=1\" width=\"700\" height=\"400\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iframe src=\"http://eportal.magrama.gob.es/websiar/Ficha.aspx?IdProvincia=28&IdEstacion=1\" width=\"700\" height=\"400\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file contains data from 2004 to 2015 (included). Each row corresponds to a day of the year, so evey 365 lines contain data from a whole year*\n",
    "\n",
    "Note1: 29th February has been removed for leap-years.\n",
    "Note2: Missing values have been replaced with the immediately prior valid data.\n",
    "\n",
    "These kind of events are better handled with Pandas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# mean; max; min\r\n",
      "3.49;10.87;-2.75\r\n",
      "5.10;10.88;-2.36\r\n",
      "8.77;11.86;3.80\r\n",
      "6.07;14.77;-1.36\r\n",
      "2.48;12.06;-3.68\r\n",
      "1.69;11.47;-3.81\r\n",
      "2.57;9.29;-3.81\r\n",
      "6.28;11.08;0.49\r\n",
      "11.66;16.24;8.76\r\n"
     ]
    }
   ],
   "source": [
    "!head data/M01_Center_Finca_temperature_data_2004_2015.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loading the data;\n",
    "# 'data/M01_Center_Finca_temperature_data_2004_2015.csv'\n",
    "temp_data = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Importing SciPy stats\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=4380, minmax=(array([ -4.21,  -0.04, -11.88]), array([ 30.59,  42.  ,  21.77])), mean=array([ 14.03399543,  21.22098858,   6.86335388]), variance=array([ 59.25648501,  76.46548678,  44.03205308]), skewness=array([ 0.07277802,  0.10781561, -0.0989195 ]), kurtosis=array([-1.09612434, -1.11634177, -0.94371617]))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying some functions: describe, mode, mean...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([[ 9, 12,  0]]), count=array([[199, 228, 393]]))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.03399543,  21.22098858,   6.86335388])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13.52,  20.62,   7.09])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get information about **percentiles**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  7.65 ,  13.665,   1.32 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1415525114155249"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022831050228310501"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.315068493150687"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rearranging the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![3d_array](./static/3d_array.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_data2 = np.zeros([365, 3, 12])\n",
    "\n",
    "for year in range(12):\n",
    "    temp_data2[:, :, year] = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Calculating mean of mean temp\n",
    "mean_mean = \n",
    "# max of max\n",
    "max_max =\n",
    "# min of min\n",
    "min_min = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's visualize data! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using matplotlib styles http://matplotlib.org/users/whats_new.html#styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark-palette',\n",
       " 'bmh',\n",
       " 'fivethirtyeight',\n",
       " 'grayscale',\n",
       " 'seaborn-white',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-deep',\n",
       " 'classic',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-talk',\n",
       " 'dark_background',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-poster',\n",
       " 'ggplot',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-pastel']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 365)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K1UNXDVFI5y8Xw34hrhLJ6Lx5IYGgPzU/b6kf70kPyuJMqiZpzSKlWyRzPvq+9MQ30Z5p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\ntAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe171b4e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days = np.arange(1, 366)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if 2015 was a normal year..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 365)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cP/86QpZcHIc6xvdCFBNC1pa51Wrlhhtu4P777+f73/8+L774Iq2trTz77LMs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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1419f8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 365)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z0qpPwx8viJQxPDvf5nzTZ6kdpNUn02sEmvmmZ0MglU6SkwbPIZRBtqNEexwf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wVXOjlcBUVRiCI5DykQET5giShACBERX8jjJHxIjE6MbnR1oquTgKIASYAYETnh3pjDyyRg7MH2\n2J5Hd9W+H3ZVdXV39Wseds+wfxLC3V1dtWZ399q71l7rv3QWbQBX3izDtrViiPVvFvD6U2BvnYWY\n9aI7bsGoTJLwziZ++WaIjYP6JwHIqEe4OF2xoZv+mKMHgEuCtnv/TdfjFMngv7QPV70uDIY3SHX/\nxbGgxd9LQfOPOsIabkZBZn389UNX49/5Z3CIXQYAmPD+AgB1/VwJCbJuOIWfM+Fn9FgjYxp1XPLN\nallVEQts12q/tEPDWZsS+eUIX49rugMorWsss9AK32DQOYXjatiQt0AAFB0NjkZgMYJ+1wAnQNrk\nGA1SJ/OxuwlTo0GBUuzWnTbfKMuafNGx8WYYWvNmInL+j03OiyC8m2l2nk4uUXsspQCB/Hs6OQ+N\nTT61q/AobIX6UIwW6PJI9WlRNUkExeJ11FbL1n4foueDu4zNvTb+eaKIvFl/MqdoI9RnqPo9BHIn\nZixuH046GqUoJGSrNSRYNNGzHkCCQqtax13yUR5yqvbQ6ck5AKTqDr8hQejJd/UoeUMEejx+xqj0\n1GhCVf+JBnIecbrS0Q+J1wEALwVpSa7MbMchdhn+3fif+FtN2yzBCf6iTySe61W6NvkipKIx4WV0\n+EUHJ51evFHTsLc+dCMdtnA0lMYyEJaGQH5CxuCDJsRShMmvfOhxtb2FeLJmmu5pDcJgyFkanBpJ\nhPKGej2ftjEZDJPD0Rl6g7h/uMAhRPZppUTeHhdsDkqAXCx2qjMZx62K0dPmZf/uMq7mpU20aSYN\nQWW1206Lv2YwIle/S+Xoa7NzKILQDTqL08dj9LW2hY9rK1+B2Oo/eDruxDlJduC1k7rBSNX3ITqO\nEWRMDRYX2JDTEydj09JkdzNSX8VaHrRhxlb54SQlhKhafLSDoAQz7xuUFad9dlXXOjInV+Cljdnq\nO+Uw883WWidReTJBwk9pMi0yzNQTUoako0hcQoeuJLrS0RuYx1ukr6qfahkMbya03QMAUIoj1kZM\nw8Vx0oPf8atQBsNzbBs80ig6JQAqG3LMb5FhG9/W8GagMR9ygvTUvqvySzG4jKmFXyqdyfx8k8G3\npKxqtJnTSJujHYRI7o4TUBpOwXc1jGRNfHBDgvzvAhEGg5HSkNIZ8kFVYvwH2JfSoFGgzzWgUyK7\nPcX0QnQqm1jEF1+U1KswxuGL6OjUDs0ybgBEjjP892IINyybfdqdXKM25ZHShVXHxmP0tXNu9Dyt\n1wSqnaC1us3Y+mvVv4fUbbwCcq9gQ8GCTknDlHCdERkaIaQuA23+4h5kYplkUcNzioaNaBoxu6sf\n5fEsSpty8LIGSCmehQOc+/AovJxZ588FgcxQa9KDFkBU2FXus6NjhUaDDl1aZ8kaXr3SaxLLHqN/\n/vnn8eMf/xhCCHzgAx/A9ddf39b7/souwsmY3ssrdF1jp80ISqaFR4x/hQBBmWg4xN4T5cgnIYLQ\nzfxEAd6ITIny0zrOHa9Ut/ogeJv0Vr+RoGoZFpc0EEEXHGFwqTMz78c2YGNvamdFH94uUgIEX4xG\neEMu/LkyUjrDhryBj2wswAfwf/7fieiYy4bSOPTG6dbXhYyrzpWl7fZQCgYViUuCMEd60DXACDCY\nNqti7jqnsHUGEsvTlu36ljE204JWUTOZ3CTtXQozzdrm1TV0co3a89DgPxKFYtqbJOMpmbXvi7cQ\nqA3dhGmRNDg+7rBZENPnlKAcC3XU6dbT+jRRAOixKHauqZcNj6Mzgg2X9GItp7C29OE//nwssvzi\n8TzG1lXeH15Co6QuhbQVfr+cMOauHASZnq/67guDRS04qz48KvfnREpr2KkqOgdkNa9w9chJC4PL\n83IqK9r9oH1nkwUegLpU00Ys64re93384Ac/wDe+8Q3cd999ePLJJ/HGG2+09d4jbBxTsXSi34XS\nBUnIYChKRK9sUJDWRQQgBPPvKUbHef026JkSjkDG9Z/muyrnCD88WTteOUfYtxKQO/B+0ANTZ4Dn\nw8/Jdn1VJdta4531+LVC7Rri+YlVsXEbREqHrVGkNILtgzbG8hXBr5TOsLGnPV0YnRH827YBGQLS\nGFJDKRkDThhLi0vHXbA5dE6wrqZ2wGAEtlYfKulwgbWktJpj445pyRx9kwVHJ9eoPZYQEq3OO4kG\nclb5PGvPGa2ESX1FbziJhyGd+FhxKo+vdey14R+dkcQwEyEEmRb+yuAEez45jss+OYaxvI7RQCwv\nb2vYM5arCvtF1biMwFzAnliIsHjV4PoWj+3vBc8LqWMVLshabcaSIC1bpLSoj0NY0CivKf9Pz5Ra\nxvtJPJ26Ccv6kzty5AgGBgbQ29sLzjmuvPJKPPPMMy3fdwIFnCMOZoiN/6V9HI/qe3GaNpjtAzXJ\nsDioXQhQ6aAU4PVYmL28D4/jA/jf2j/hORarevOErF6r/7XFWusFIRyDQegURASbofO+zJENTQ5F\nmBrZVhaYuXakYp+HqK9qM+IrrPim52DGjHLV42RNXncbPdGXQtGiuHpdFsLmGNw12PB6LFhPmZzC\nZKRu00tjBE5CXnBSjPZ80SruXuXoF5kYKYQIukI1/qw7CrkkHMtDR9/BieLjX5crH4bhSX27wDDi\nFg/vhP+nhCTm0teFf2h9fn67mIxEstScELxnUK68d45kUFs0Hn7OGqHJfQ/ahVO52g6o0nYKzysA\nL2fIFX1tb9skgpg8KJH6UQCgV7rPCZMj0tRqEQaKjm91yZZHLIKpqSkUCpUG2Pl8HlNTU03eIYkr\nvb3C1uEftLGzCcXJhE7bvXOtUPvjMDlKEwXMUBsvsfGqlbs3YEtRsYQRi1boLOzAxGSOvgC8kbRs\nY1YlwtQkjucLlEdS8LNmbGcMGO5PtfyBxHORdYagghFYkzWiFn1xdq3JYsCtXoVvCqSDM6Ysczeb\nyB9UriVXcrVpkTojsBPe366+y3LQSok2LnuwFGa2kjruZMM3KeTFwzh9B+eJf09qt0viexi18fXa\nTJ3QnFChktP6SbzW8RNCYC9WLS5gKK3DNRjW5OrrQ1i46GYy+6v+zqX92H25z5YLM8+vSreMMn88\nIYuegt9+K68aLUzjz2msuqXnvI/yiNs6A4c2rrGJc8Hz6A8fPozDhw9Hj/fu3Ys36BrQdpODfR/U\nlCtJijNoO6mYAkznib9mYtfcfnk+vKwF4s+CzPtgvKZyU2Mg8x58QwPRGKipgZgaiKOB9DogGgfV\nePQ+kjbABKla5UfxuLky5sfzYLoGElT8ObaOT793GC+8M4PfvPROwz/JsXS4rtxvsDwflqHhuov6\nUHA0GUOtsbvoGhjrTeGNM7LtmaVRrO1x4ToGesvn4Fo6sikbrt1cAM0qe2CMQjd96LEMAMPyYBgm\n3JrJpOz5iWXw7aLrlb+zUzzfB2vyHfF8P5II8H2//e9hAkIITAwQpFMmLI0n2t3JNWqPFUJACIGy\nL/CxyX74bd7RWpzCcRwQQurGI3wshMA1YwJXlH0wyuD5HrKmBte1o2M2lBluckwQAuRcGxqj+Nhk\nP7yYHT22fE/cZtMXi/r8QxzHx8cvBkYKbt0YmjNnYDCGlGWCM4qUZeDsfEXR8hOTvXANjp/86R+t\nk2QuKcJ46TR8m8Mfz1d+xzoHLQkAPlCwAfuM/O0zBpL0mQZ1NYIRMK3mDj1nAbYGxjm8zT3Q/nwC\npY158Ldmks8VommgaSuadQ8cOBC9NDk5icnJSQDL7Ojz+TyOH680tJ2amkI+X13GGzcm5CgGq9Qg\nm1L24HFIrbGy13A2JWdLcgYOV5jCg+d7iHIjY3g6rWosTmbLKOUMaKdmAQh45eriC6FRiLKHMgeo\nQVEmAvA8lHpMeL6HssMgSOV9Qifwyz6EX9G7oGfn4XMdBAKlrA6UyxAUEL6PnK3D1H1syOj4r3K5\n8RfTK2N6Wko3EELgcAKD+shyD6fmfBDfizbKOCXImAwZHZFdEwNpaP48pqfnoQmByaINUp7F9HSD\nMvAEao90CDA9XUo8dqG4rhv9nd1O0QDKszOYnl1eu9e5NVkCLThz5kzLY4ZtAKBw3VRgt1dlf44D\nubT8wZXnZlAGsLbODm9ZP6shh+Ls2bN1z/vlMhgFyvNzMk5PgdPB93w4Y2JDRqYDb8wbKPvAbNnH\nayer5fmuWJPF7189CaQ5pm9YHxUnIjiPRwH4PkjJR9mioCmOMhEQwofw6z8LOjULP2dAGLzOh4AC\nsJg8d97A2d0j8AsGNAh5jemS7OhWg7DkJByGi/bu3Zs4Tst6Dz02NoY333wTb7/9NsrlMp588kls\n37695fuqigFaQHwBX6PwTa26S3wNdbIDtZuq8WNrA35EyvHKYoiE4zmRt3YGk+lVQZgmLFYSab3q\nVk0Y1Rs8ZN7H7Hv7QWZ9+WUKN2WCW0PNoCCUwNUJ0g3icQTVMXohBNImgxXsG+iMVMUqiykdBUtD\nJna+dbnKytvWCDb32ouWAVAolpNG0RdG5HdeCwrWemMblu8ZcqOQ15qsibU5ExcVbfSljKqIyuZ4\no3NXr79YVKgkU6q9giUTI5J+M36QHVPyZTFlC7w1bqWDW9AECV7NolQ0z8aLs6yOnlKKz372s9i/\nfz++/OUv48orr8Tw8HDrN3aCgGysYdLmG5wyDy1mXGMHJgLxpOgxJRAOD5ph198BCI1GE47UrQhW\nOZvk3UtpOFUVR5PyyBV8R+4NCJ1GSpFApbRZCzaDTI2i362PR/Y6Oi4ZcOtiqj22HmUc6Kw6f319\n3gJjFAWL4fKRNGyNor9Go77Hav2FVCi6EUZlDUP4lS8E4UdGgMHYyjhrcWRNhn5Xw8cneqJMHo3J\nupBmNR9R0x8idbNKlxbQMAVqzsPclh6Zk9/G5imAIOZPQeZ8zF45CDJf49/Koq0WicB5iNFv3boV\nDzzwwPJdINRLbzSTBgiQ6jvKJsd6BQv8pVOVHFZTxvKlDkXCZKIz+bzBEgfeG3Cqd71q+l/6abmR\nc/b6DbLVX2hzuKIPd+OFwJqsiQ0FC//54nF8ZFMP/vP/HsdI1sRg2qhz9BmLwwyzFKhsqn38rAyj\n9Niy8MTiBB9cl8bGHgeuXv3+Vim8CkW3wgipquFwgoVWMWUgF1tRZ02Gsg9kTVnBPVl08PLUDFI6\ng8kI0qaGuZKH03P1Wjl+wQRePS39jkYrYoJawqKTU5TXpGE+dwxeA/2cJIQhterLa9IQL5wA4iHt\nsg+v32nrPF1ZGdsWvqiIhnGpT9GsCTNqS9GbHOsNpiohGiHgBbv6jdrm+Y4GUpKpVaWJQv0BJo+K\nsuS1ScXxl334hWCVrjOIrFEJsYQr+tgXc6zHwpqsAUdn2Fy0kbM1FB0NaYPVVX1mDB5lSAghsKFg\nY/e4tC8b67/JKcGatPLqitUDJbJ+JMygCjNsNvXaVQvutMHgGhQGk2mqfUEoJGNp0KnAJX0Orl6f\nLCPi9VpAWUj9q1g6V2IWjMGDXHtfThBtIjQqa3FC/awYhJKGomu1rFhHT+Z8kBkpPBSKjAlCKs+H\nO/9zHsisJ2fc0Ln6oum+lbC5jKsDILM+SuPygxYmS+zy5PdaMn7WSdpYsNIm8z7Kg5XS7ZTOsHNN\ncL0gbTPeFDmny3zioYwFhwnsHMkEX1ZW13+0tgnzRNHGaEaHwWmVVAGweElehaKboAQoxMKgeVvD\nh8bzWF9T1KdRRLn5QNCYhhIUHQ1CCFzcn0qs+9g2lMaeHYMgYVPvqoLIBB9hhk2HqLyDb5OZ3aOY\n2SPbmNb6HqGzKpnkZqxYRy+F+km0oocmS839jIZzHxwBmZO3OEQEJcecApyCzPtyh7rUPKnKK1qy\nvFgj8PrlpowwuNwgqcEvOrIIogNHH+XgMgI/tgmatzWsyZoyHTJoJKLHPmCZh0xwUVHatDZnIG1y\nOBqpk/w1azQzXA3otTn+ZaIXqYU0P1EoVgiUEAzF9rP6bIb3DjkYTDWPVod7WdkgDONqItHRFx0N\nlww5MFwd4KwqFJy0og+dtJczGkYGEomp0dbuHfoJvqgRK9bR+4FYv4Bc0fe6BlkEPwwAABTsSURB\nVASRMp/+oAMvYwTKbqQiLaxRCJ3i7PUbWpYklvsdkHlPZsGEOb+cVG2WRtBgMuggNzjcyPFrdvMH\n0wZMTrB1IIWxNRmwsoBVswIgRB4HADmDIG8yWAxgNRWYNYt2mZlDgU15veOuRArFSoIRIO+0HwsP\nMZjslxAWddVms4VYOoVOCXqLNmjNolp2rov9Fv2KTEFpPNu5qGGAlzNldX54nXeFo3c0CIvLuDyn\nuGZDHsQTMsUJwPxEDmTOh5/So9W80GRfVRgMM9cMNT2/SOuy6ja+Q04IyiPJhTpzW3obVqglyfIK\nTkDmfcxcVV31m7c4Upxgsi+FgSEXrOTDTqj8c4LULqmQiEQ9GtJxqbBCsTrQGEFPJzr0AZwSODqv\nqppNquQOX193zQh2/+vFVXH/uqSNkg8vaMeZuIfXJn7OkFl/QkRNTtplZTp6T3Z+ESaD7+jQDIbB\ntBwEL9jo8NZm4Ntc3u4Eov6C02hm9YvNRb58W6tr/g2gelO19vmEmVpjBFeMJmzmMIrycAoiVx0z\nTOkMBicoOhyDY1n5xattNA5IRUmFQpGIRgHehk57LUIIZC1epXiZtKK3gzj8xPZ+XLSjD8PZSrad\n72pVNTsklEhYJMLR5R5kWYDMejK83CYXXAJhIZB5D17RBj1bgp9h6EsZUuHT1qQeDQBQgtmdA6Cn\nS6BnSzJU40vhsrbQpW5Ns9sjW2fQGcV0k3h/SucYzNR/yF6fjZGtvfjvOa/qeTfIaTQZUEhpyKxJ\nw+ogHUuhUCyOvMWr2l2G3a/C8LjOSLT/Jd2DwGTRwavvyMrauFSx9FXW0jh6i2HmqkH4RQv8tWl4\no+13j+vqFT2Z94C5hAIlQuD3WPCKNkobcxgvWOBEgPc7gMHw/g2yUMnvd1DemK2EblIcXpOV/HDG\nxOUjgUpmIEXa7APa1GtjJCNX240i3lmLJ3ZNKl2Ux5axQl2aY/yW0eQEV316I4wl+JIoFIr2yFoa\nzBol2Hj4JmVwWDV374XYBmtU0QoZZZm9enjBcfmQvZf24cObCihvzsPoc5C7aqQj6dOudvTlgg2/\nNwhbCFFp6WVKpbfSRXn4eROjWUM2nd7eB1tnmOxzqhyv4DK1srQ+09RxXz7s4tI+pxJvowRek+NH\nMiYywW76ZUPpqlXAhryNjKmh6OhRLvuAa1RVtloaxfogdscpQcqoSBaEDE/kobUhUaxQKJaGjMmr\nMtY4IzBijj5vaXX7bmmTVeTpTV4RLGQ0MSW7U2yNohjsOfQ6Bja1WREb0r2O3pOFRL6ry11rzmQa\nYklusIYMpQ30p2TOa2F9Fn0pHa5GqlbKoLJpr99rN5xZ/2lzD9ZkdRQdin/bNoCBtAGvYDbVgc8Y\nLCpuGs4YMGOr8avWZvDPF8kVu07lTv54jx1ly0izBIaCx9eszyNraXUpkgqF4vxSJ7dNAT3m+Icz\nRl0znZRGkQm1ozitSA4bldTLxXRW41SGdSkBBtN6lUZVO3StoydzPrR1WZSLsuvT/EReNuNwNPjZ\niqPf1OtE2Ylpg2NN1gQnAuOxjkrC4k3F+XVGsCFvwNGkdvhgiuP967Iorc80zHnVGUHWZNFM3+No\nMOISvVymPaYNBluTq/W8zZG3OIopHVrg/LOBXesLJgZSKhavUFxoasMyjABm7LedTdi30ygwUUzh\npq390FhQsRr0jw7Zs6mAK9cmV9m2ghHZ7yFtcOSsigbPupyFkWzrStuudfSuo+F/7F4H9Fog8z68\noRTAKM59aBTzsRQlJzb7WhpFLvgQLh92sSlQn/MdDb7B8MEN+USRIktjMGpm29GMhus+sh6kQW58\n1tKR0ikMXlGHDJ22zojUyTCo1IInUi0yZ3JYGsVlgy5ytly920FTBFtjGOigYk6hUCwPBqtPrnBj\nFaiZBiJQlw2mUDCA7cMZeH02UPKjtoBAUGRVrA4rJ00aSTAqGwkNuAZSOovy/Df22vjYpkLLu4Wu\ndfTr1mXR4zIUiin4Dq+srE3ZVDckHhdP6QwDwWtZg+LqtRlolMB3OLSUhov7nbrY2lDahMlZfbd6\nSrCxYCLV4EMdcHUwInNsOZVVqengWFtnMINYey6oWto1mkHGZLA1hh5Hw2jWgsYodEaQ0jk4EehV\nsXiF4oKT5DJDPyDFAZN9gqvJepYem6M8lAIpVXo9OzpDzmCwdRKdS2MEezbKVoJDaROjDVbmYf9e\nIQTGeiyk9ErIOGMwuAZp6KdCujK9UhMCwxcXwAhw8WAKp27YiOcaaLHEb7NGsgbiFc5Fm+G9oxmc\nMDmuXJ+Fw0nQgV6mNGqM4Or1WfzpjWnQWAu5yrkJcpaO6bnqhgQAUAyKFUxGpdIdr3wZ0gaPNVKW\nx/c7wSQQrOCH0gYMDvg+Qc7mYEQEE4XKj1couo2wJWajLDqgkgTj6Aw8b2J4axGvBJLI6/IW7GDx\nV7ClT8mYGvpTOnRG8MGxLN6aLuG1k7N15w0bwAPA5h4TZV9mb1oaRdpk0CmJJBsa0ZUr+rzBMXp5\nEQBwab+N3R9di029yXKc8WYatTIWhBBs7rGwbVMB/T0mGKluYdbvmshZHBmT1Tn5kOGEHPiCrUez\nus4Jemw92nAFgJ4mpdeWRuHoMsSkUwKDEeSD96siKIWiOwl/2xlTaxkmcTSKAdfEJ764FRfvlBX4\nl/ZXhAtDnzKYNpDSgH+ZKGLE5VHYOeTykQw4JVFPXkBKNDgaga0R9KWMaOO42CIa0JWOPluwYAb5\n6SmNQKPAZF+9o5cCRM0HvehwDLpBj0dCkIrF2sYLFhgEMmbjQcrVNN+gBLh+sjeK2emMYFOQm28w\nim3D6aYhGFujSOk0qrwjwepeoVB0L2Fo19EbLwpDLI1ifcECpQRDeQtjBRsj6ZgCbeDQ+13ZOnQ8\np4MSgpwls2q2D7mwdYbxHgsTfSlwSut6mTAC7NmYgxMsdHMrcUWf7q0vakraALF1VqfQWAsliMIo\nQojoDkCGT3QYjES58Emk9OpZ1tQYMgZFPviwNEownJaTks4ILu1zok3gJMygSXwqdifS56psG4Wi\nmwllEBrF5+NYnER7hQWbY9uQW6WAmbM4CBCFgMIgQ8ZgyFk6xntsZE0OjRJc1GtJJduEu4hei0X7\nCfFCyyS6MkZfnMjVPefqcuNyPiYWFI+Ft0s4IDvXZDGS1kAJGvZhBYCMKQczvKrFKQyGyq0UJ0ib\nOry5GTg6Q8ZkaEdrSI+FaRxNxeYVim5GD7xxbeplEgarJGbkTFbV0QoAhlwNezYV0F/jKDQqq+0d\nnWM4Y8LmBAQsCN80v2YrR9+VK/qBi/J1z7k6RU+syhSobizQLlYgRjTg6pXNk4RGASF5i2E4thvu\nxro2AbLfgB2s+lM6Q0rrbOIBkltMKhSK7iFckScp0SaRM8OJQbbrjMMIsK3fqpsAAJlQ4hocg2kd\npiYXtzqT9T3N6Gmhcd+Vjt5KEB4jRFav7hipCPnkm8TWGxF+UPFbKaeJc+YE2DpQ2UjJWbxhjC6t\nL8xjd1jkplAozjOVepn2XGarvj6NOrr12BwZiyNnabAYAWekSkmzEY0ygUK60tE3GoQ+m1atvtsZ\ngFpCB69Vrcqbh03it1jNwjwL7cbHVNhGoehqOl3RL5SsLgXU0gaTzfOCBJJWG8Ct6EpH34y4NnQ7\n8bJawvRKrYNWehmDRdoS9iJV6BQKxcpDKliSjvzGQggbCIVrS51KtczFsuK8Vlwu1G6zMW7V+2n4\n//b/dJMTrMnJcJKuHL1C8a6DB7FyvQO/sRjC6UTj1SnhC2VRU8Xvf/97PProozh69CjuvvturF+/\nPnrt4MGDePzxx8EYw80334wtW7Ys2lhAznAmp9AYbZlamQSnFKyNXexaRjIGXvjHdJXkgkKheHcQ\n9pJd7tBNLYwQuBd6RT86OoqvfvWrmJiYqHr+6NGjePrpp3H//ffjtttuw8MPP7zoGFOIxij60wau\nHcsvyNFrTMbZOvXXfSkpTraQfQGFQrGyYUQWSyW1FVxOhBAtdWzaYVFea3BwEAMDA3XPP/vss9i1\naxcYYygWixgYGMCRI0cWc6kITglyFsdQWl9YjJ5S6IxWbca2Q7/DccPFfehrkcakUChWJwVHX2yj\nqAWxFFGEZTF7amoKPT090eN8Po+pqaklObfOCDIGR0YnC8o/16iMtXU6M1MCbMxfmA9aoVBceLYP\npmBcgKKXxTQsic7R6oC77roLp06dih4LIUAIwY033ojt27cv2oDDhw/j8OHD0eO9e/fCdd2Gx0/7\nM0jPCmTSjY9pxjkxi4xtIJ1KNUzj7BRd15va3K0ou88vyu7zy1Lbfb6GoNbu2elZuG7r5iIAcODA\ngejfk5OTmJycBNCGo7/jjjs6tRP5fB7Hjx+PHp84cQL5fH21a60xIdPT0w3P7ZV8aPCbHtMMTQB9\nDsOZM2cW9P4kXNddsD0XEmX3+UXZfX5ZLXYLj7T1d7iui7179ya+tiyBiO3bt+Opp55CuVzGsWPH\n8Oabb2JsbGxJzs1pa12HZmgUWJvrrLGuQqFQXCg63U9MYlE7i3/84x/xox/9CKdPn8a3vvUtrF27\nFrfffjuGh4exc+dO7Nu3D5xz3HLLLUsWJtEoSWwH2Al5W4MSEVMoFCsBTvxFn4OIpcp7XEL+/ve/\nN3zNBzA146PHWrizn/EIrIS+kAtltdwirhSU3ecXZff5ZaF2Dw4ONnxtxeWQMFKvBtcp5hI6eYVC\noeh2VpyjF0Is2tErVWCFQvFuYsU5eqDShFehUCgUrVmRjl6hUCgU7aMcvUKhUKxylKNXKBSKVY5y\n9AqFQrHKUY5eoVAoVjnK0SsUCsUqRzl6hUKhWOUoR69QKBSrHOXoFQqFYpWjHL1CoVCscpSjVygU\nilWOcvQKhUKxylGOXqFQKFY5ytErFArFKkc5eoVCoVjlKEevUCgUqxzl6BUKhWKVoxy9QqFQrHKU\no1coFIpVDl/Mmx955BE899xz4Jyjr68PX/jCF2DbNgDg4MGDePzxx8EYw80334wtW7YsicEKhUKh\n6IxFregvvfRS3HfffbjnnnswMDCAxx57DABw9OhRPP3007j//vtx22234eGHH4YQYkkMVigUCkVn\nLNrRUypPMT4+jhMnTgAAnn32WezatQuMMRSLRQwMDODIkSOLt1ahUCgUHbNkMfrHH38cl112GQBg\namoKPT090Wv5fB5TU1NLdSmFQqFQdEDLGP1dd92FU6dORY+FECCE4MYbb8T27dsBAL/4xS/AGMNV\nV121fJYqFAqFYkG0dPR33HFH09efeOIJHDp0CN/85jej5/L5PI4fPx49PnHiBPL5fOL7Dx8+jMOH\nD0eP9+7di8HBwZaGdxuu615oExaEsvv8ouw+v7zb7D5w4ED078nJSUxOTsoHYhEcOnRI7Nu3T5w+\nfbrq+ddff1187WtfE6VSSbz11lvii1/8ovB9fzGX6mp+/vOfX2gTFoSy+/yi7D6/KLsrLCq98oc/\n/CHK5TL2798PQG7I3nLLLRgeHsbOnTuxb98+cM5xyy23gBCymEspFAqFYoEsytF/73vfa/jaDTfc\ngBtuuGExp1coFArFEqAqY5eAKA62wlB2n1+U3ecXZXcFIoSqZFIoFIrVjFrRKxQKxSpHOXqFQqFY\n5SxqM/bdyq233grbtkEIAWMMd999N86cOYPvfve7ePvtt1EsFrFv375I4O1C8dBDD+FPf/oTMpkM\n7r33XgBoame3CNEl2f3oo4/i17/+NTKZDADgpptuwtatW7vK7hMnTuDBBx/EqVOnQAjBtddei+uu\nu67rx7zW7t27d+OjH/1o1495qVTCnXfeiXK5DM/zcMUVV+DTn/501493I7uXdbyXPGHzXcCtt94q\npqenq5776U9/Kh577DEhhBAHDx4UjzzyyIUwrYq//vWv4uWXXxZf+cpXouca2RnWPpTL5Qte+5Bk\n94EDB8Qvf/nLumO7ye533nlHvPzyy0IIIWZmZsSXvvQlcfTo0a4f80Z2r4Qxn52dFUII4XmeuP32\n28Xf/va3rh/vRnYv53ir0M0CEELUqXE+++yzuOaaawAA73//+/HMM89cCNOq2Lx5MxzHqXqukZ3d\nJESXZDeARAXUbrI7m81i7dq1AADTNDE0NIQTJ050/Zgn2R1qU3X7mBuGAUCukj3Pi+zr5vFuZDew\nfOOtQjcLgBCC/fv3g1KK3bt349prr8WpU6eQzWYByB9OXB+om2hk59TUFDZu3Bgd141CdL/61a/w\n29/+Fhs2bMBnPvMZ2LbdtXYfO3YMr776KjZu3Liixjy0e3x8HC+++GLXj7nv+/j617+Ot956C3v2\n7MHY2NiKGO8kuw8dOrRs460c/QK46667kMvlcPr0aezfvz9Rm2elVAKvFDv37NmDT33qUyCE4Gc/\n+xl+8pOf4POf//yFNiuR2dlZfOc738HNN98M0zTrXu/WMa+1eyWMOaUU3/72t3Hu3Dnce++9eP31\n1+uO6cbxrrX76NGjyzreKnSzAHK5HAAgnU5jx44dOHLkCLLZLE6ePAkAOHnyZLSh0m00srMTIboL\nQTqdjn6w1157bXTr2m12e56H++67D1dffTV27NgBYGWMeZLdK2XMAcC2bUxMTOD5559fEeMdErd7\nOcdbOfoOmZubw+zsLAC5AnrhhRcwOjqKbdu24YknngAgFT1DCecLTe1+QiM7t2/fjqeeegrlchnH\njh3Dm2++ibGxsQthMoB6u8MfLgD84Q9/wMjICIDus/uhhx7C8PAwrrvuuui5lTDmSXZ3+5ifPn0a\n586dAwDMz8/jz3/+M4aGhrp+vJPsHhwcXNbxVpWxHXLs2DHcc889IITA8zy8733vw/XXX48zZ87g\n/vvvx/Hjx9Hb24t9+/YlbiieTx544AH85S9/wfT0NDKZDPbu3YsdO3Y0tPPgwYP4zW9+A875BU1T\nTLL78OHDeOWVV0AIQW9vLz73uc9FcdhusfvFF1/EnXfeidHRURBCQAjBTTfdhLGxsa4e80Z2/+53\nv+vqMX/ttdfw/e9/H77vQwiBXbt24ROf+ETT32I32/3ggw8u23grR69QKBSrHBW6USgUilWOcvQK\nhUKxylGOXqFQKFY5ytErFArFKkc5eoVCoVjlKEevUCgUqxzl6BUKhWKVoxy9QqFQrHL+Pz8oa/xQ\nc17MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1419e5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib AGAIN, Now with 2D COLOURSSS!!1!1!11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Matplotlib-logo](./static/matplotlib.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, lets represent a function over a 2D domain!\n",
    "\n",
    "For this we will use the contour function, which requires some special inputs..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we will use numpy functions in order to work with numpy arrays\n",
    "def funcion(x,y):\n",
    "    '''This is the help of the function funcion.\n",
    "    This function receives two parameters and returns another.\n",
    "    Such sicence. Wow.'''\n",
    "    return ????"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 0D: works?\n",
    "funcion(,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 1D: works?\n",
    "x = np.???(0,5, 100)\n",
    "z = funcion(,)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(x, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In oder to plot the 2D function, we will need a grid.\n",
    "\n",
    "For 1D domain, we just needed one 1D array containning the X position and another 1D array containing the value.\n",
    "\n",
    "Now, we will create a grid, a distribution of points covering a surface. For the 2D domain, we will need:\n",
    "- One 2D array containing the X coordinate of the points.\n",
    "- One 2D array containing the Y coordinate of the points.\n",
    "- One 2D array containing the function value at the points.\n",
    "\n",
    "The three matrices must have the exact same dimensions, because each cell of them represents a particular point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We can create the X and Y matrices by hand, or use a function designed to make ir easy:\n",
    "\n",
    "#we create two 1D arrays of the desired lengths:\n",
    "x_1d = np.linspace(0, 5, 5)\n",
    "y_1d = np.linspace(-2, 4, 7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_1d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_1d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#And we use the meshgrid function to create the X and Y matrices!\n",
    "X, Y = np.????(x_1d, y_1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that with the meshgrid function we can only create rectangular grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Using Numpy arrays, calculating the function value at the points is easy!\n",
    "Z = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Let's plot it!\n",
    "plt.????(X, Y, Z)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try a little more resolution..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 7\n",
    "x_1d = np.linspace(0, 5, n)\n",
    "y_1d = np.linspace(-2, 4, n)\n",
    "X, Y = np.???(x_1d, y_1d)\n",
    "Z = funcion(X,Y)\n",
    "plt.contour(X, Y, Z)     #How about adding a new parameter? np.linspace(-2, 2, 6)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The countourf function is simmilar, but it colours also between the lines. In both functions, we can manually adjust the number of lines/zones we want to differentiate on the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 7\n",
    "plt.contourf(X, Y, Z, np.linspace(-2, 2, n),cmap=plt.cm.Spectral) #With cmap, a color map is specified\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#We can even combine them!\n",
    "n = 100\n",
    "plt.contourf(X, Y, Z, np.linspace(-2, 2, n),cmap=plt.cm.Spectral)\n",
    "plt.colorbar()\n",
    "cs = plt.contour(X, Y, Z, np.linspace(-2, 2, 9), colors='k') #Colors = 'k'?? Why would you do that??\n",
    "plt.clabel(cs) #Vaya, qué hará esto??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions can be enormously useful when you want to visualize something.\n",
    "\n",
    "And remember!\n",
    "\n",
    "## Always visualize data!\n",
    "\n",
    "Let's try it with **Real** data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_vector = np.loadtxt('data/ligo_tiempos.txt')\n",
    "frequency_vector =  np.loadtxt('data/ligo_frecuencias.txt')\n",
    "intensity_matrix =  np.loadtxt('data/ligo_datos.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time and frequency vectors contain the values at which the instrument was reading, and the intensity matrix, the postprocessed strength measured for each frequency at each time.\n",
    "\n",
    "We need again to create the 2D arrays of coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frequency_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_2D, freq_2D = np.????(time_vector, frequency_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,6)) #We can manually adjust the sice of the picture\n",
    "plt.???Pintarrelleno???(time_2D, freq_2D,intensity_matrix,np.linspace(0, 0.02313, 200),cmap='bone')\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! What is that? Let's zoom into it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.???Pintarrelleno???(time_2D, freq_2D,intensity_matrix,np.linspace(0, 0.02313, 200),cmap = plt.cm.Spectral)\n",
    "plt.colorbar()\n",
    "plt.???Pintarallasdenivel???(time_2D, freq_2D,intensity_matrix,np.linspace(0, 0.02313, 9), colors='k')\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "\n",
    "plt.axis([9.9, 10.05, 0, 300])#HERE HAPPENS THE ZOOM MAGIC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPython Widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IPython Widgets are interactive tools to use in the notebook. They are fun and very useful to quickly understand how different parameters affect a certain function.\n",
    "\n",
    "This is based on a section of the PyConEs 14 talk by Kiko Correoso \"Hacking the notebook\": http://nbviewer.jupyter.org/github/kikocorreoso/PyConES14_talk-Hacking_the_Notebook/blob/master/notebooks/Using%20Interact.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Lets define a extremely simple function:\n",
    "def ejemplo(x):\n",
    "    print(x*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "???(???, x =10) #Try changing the value of x to True, 'Hello' or ['hello', 'world']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We can control the slider values with more precission:\n",
    "???(???, x = (9, 10, 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want a dropdown menu that passes non-string values to the Python function, you can pass a dictionary. The keys in the dictionary are used for the names in the dropdown menu UI and the values are the arguments that are passed to the underlying Python function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "???(???, x={'Puerta A': , 'Puerta B': , 'Puerta C':})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have some fun! We talked before about frequencys and waves. Have you ever learn about AM and FM modulation? It's the process used to send radio communications!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe13b5b6400>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ny8A6POKc5JV8vC1SrqEThyGvfQus6f85J4mLUCQjEBQUwgCUYMieCfpgDSjxEKQBL4Pd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\nFHMvD8WoiKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1400c8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1, 7, 1000)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.subplot(211)#This allows us to display multiple sub-plots, and where to put them\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.grid(False)\n",
    "plt.title(\"Audio signal: modulator\")\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(x, np.sin(50 * x))\n",
    "plt.grid(False)\n",
    "plt.title(\"Radio signal: carrier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe13b4c5e10>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aasUbBsc3ruE9EBIEFA0gaRgAYhAQn7VSlsJG3DaTYwR+IWwe88jD5k9KELZ2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\nCERPh0d50gnMW7u9KE8uHC/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ebMbZ9UlgvkuYdWJADX3Wzq1OhjBoKmQ93oXaHEIdOdZ0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\nByAQCAQrFOIABAKBYIVCHIBAIBCsUPw/CWNqmV9qpfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe13b61c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Am modulation simply works like this:\n",
    "am_wave = np.sin(50 * x) * (0.5 + 0.5 * np.sin(x))\n",
    "plt.plot(x, am_wave)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to interact with it, we will need to transform it into a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def am_mod (f_carr=50, f_mod=1, depth=0.5): #The default values will be the starting points of the sliders\n",
    "    x = np.???(-1, 7, 1000)\n",
    "    am_wave = np.sin(f_carr * x) * (1- depth/2 + depth/2 * np.sin(f_mod * x))\n",
    "   \n",
    "    plt.???(x, am_wave)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "???(???,\n",
    "        f_carr = (1,100,2),\n",
    "        f_mod = (0.2, 2, 0.1),\n",
    "        depth = (0, 1, 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Other options... \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Other packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Symbolic calculations with SymPy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sympy](./static/sympy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SymPy is a Python package for symbolic math. We will not cover it in depth, but let's take a picure of the basics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Importación\n",
    "from sympy import init_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init_session(use_latex='matplotlib') #We must start calling this function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic unit of this package is the **symbol**. A simbol **object** has name and graphic representation, which can be different:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coef_traccion = symbols('c_T')\n",
    "coef_traccion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "w = symbols('omega')\n",
    "W = symbols('Omega')\n",
    "w, W"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, SymPy takes symbols as complex numbers. That can lead to unexpected results in front of certain operations, like logarithms. We can explicitly signal that a symbol is real when we create it. We can also create several symbols at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x, y, z, t = symbols('x y z t', real=True)\n",
    "x.assumptions0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expressions can be created from symbols:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expr = cos(x)**2 + sin(x)**2\n",
    "expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "simplify(expr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We can substitute pieces of the expression:\n",
    "expr.subs(x, y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We can particularize on a certain value:\n",
    "(sin(x) + 3 * x).subs(x, pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We can evaluate the numerical value with a certain precission:\n",
    "(sin(x) + 3 * x).subs(x, pi).???()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can manipulate the expression in several ways. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expr1 = (x ** 3 + 3 * y + 2) ** 2\n",
    "expr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expr1.expand()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can derivate and integrate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expr = cos(2*x)\n",
    "expr.diff(x, x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expr_xy = y ** 3 * sin(x) ** 2 + x ** 2 * cos(y)\n",
    "expr_xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diff(expr_xy, x, 2, y, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "int2 =  1 / sin(x)\n",
    "integrate(int2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x, a = symbols('x a', real=True)\n",
    "\n",
    "int3 = 1 / (x**2 + a**2)**2\n",
    "integrate(int3, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have ecuations and differential ecuations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a, x, t, C = symbols('a, x, t, C', real=True)\n",
    "ecuacion = Eq(a * exp(x/t), C)\n",
    "ecuacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "???(ecuacion ,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = symbols('x')\n",
    "f = Function('y')\n",
    "ecuacion_dif = Eq(f(x).diff(x,2) + f(x).diff(x) + f(x), cos(x))\n",
    "ecuacion_dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "???(ecuacion_dif, f(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Analysis with pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pandas](./static/pandas_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a package that focus on data structures and data analysis tools. We won't focus a lot on it, but you can check this awesome workshop from Kiko! https://github.com/PyDataMadrid2016/Conference-Info/tree/master/workshops_materials/20160408_1100_Pandas_for_beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Machine Learning with scikit-learn "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![scikit-learn](./static/scikit-learn-logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn is a very complete Python package focusing on machin learning, and data mining and analysis. We will not cover it in depth because it deserves its own workshop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A world of possibilities... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![scikit-learn](./static/cheatsheet-scikit-learn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ¡Gracias por vuetra atención! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![PyData_logo](./static/python-madrid-logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Alguna pregunta?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     },
     "execution_count": 109,
     "metadata": {},
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   ],
   "source": [
    "# Notebook style\n",
    "from IPython.core.display import HTML\n",
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    "HTML(open(css_file, \"r\").read())"
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