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   "source": [
    "# PySeison - Tutorial 0: First steps into Python and Ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Launching Ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From your shell, ***cd*** to your python workspace/folder and type:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "$ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This command will launch Ipython with the *pylab* option on. This option will provide an interactive environment, to generate plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Importing libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing libraries permits to extend python default functionality by calling exterior code packages. There are several ways to [import libraries in python](http://effbot.org/zone/import-confusion.htm) with different implications. In this tutorial, two different *import* statements will be use.\n",
    "\n",
    "Let us import a very useful library, *numpy*. [*numpy*](http://www.numpy.org/) has very similar capability to *matlab* and is therefore widely use in the scientific community."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the entire package has been imported and renamed *np*. This format of *import* format is good-practise since it allows to keep track of the package and their attached functions and avoid confusions. for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function numpy.core.fromnumeric.round_>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is different from:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function round>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Ipython useful shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a non-exhaustive list of useful shortcuts available in Ipython:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Tab:** *Tab* completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type ***object_name.Tab*** to view the object’s attributes (see the [readline section](http://ipython.org/ipython-doc/2/interactive/reference.html#readline) for more). Besides Python objects and keywords, tab completion also works on file and directory names.\n",
    "\n",
    "- **Up & down arrows:** IPython stores both the commands you enter, and the results it produces. You can easily go through previous commands with the up- and down-arrow keys, or access your history in more sophisticated ways. One can also use up- and down-arrow keys for auto-completion purposes based on one's command history.\n",
    "\n",
    "- **?:** *?* permits a quick access to the documentation of any object or function. Typing ***object_name?*** will unravel the documentation related to the object. Sometimes, one has to type ***q*** to quit the documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Numpy vs. Matlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite their capability similitudes, there are few major differences between *numpy* and *matlab*. Here follows probably the most important ones:\n",
    "- ***[ ] vs. ( ):*** if M is a matrix, i th and j th element of M would be respectively called **M[i,j]** in *numpy* and **M(j,i)** in *matlab*.\n",
    "- ***Row-major vs. Column-major:*** as one may has notice the previous point, **the order of row and column indices are inverse** between *python* and *matlab*. Consequently, be extra cautious when translating *matlab* scripts to *python* and similarly when optimisizing double for loops.\n",
    "- ***0 to N-1 vs. 1 to N:*** Additionally to the index order, the number attributed to first element of a matrix is different between *python* and *matlab*. **In *matlab*, the number attributed to first element of a matrix is one whereas, in *python* it is zero**, for instance: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of M:  (10,)\n",
      "First element=  1.0\n",
      "Last element=  1.0\n"
     ]
    }
   ],
   "source": [
    "N = 10\n",
    "M = np.ones((N)) #define a 1D array of N elements\n",
    "print \"Shape of M: \", M.shape\n",
    "print \"First element= \", M[0]\n",
    "print \"Last element= \", M[9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ***Indentation: *** Python uses whitespace indentation, rather than curly braces or keywords, to delimit blocks; this feature is also termed the off-side rule. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block...this features will be illustrated later on with exercises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, *numpy* and *matlab* have more similitudes than differences, so much that [*matlab* users](http://mathesaurus.sourceforge.net/matlab-numpy.html) will rapidly make their heads around *numpy* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. PEP convetions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PEP stands for Python Enhancement Proposals. They are Best-practises type documents for Python programming. It is recommended to follow as much as possible PEP 20, PEP 8 and PEP 257 (i.e.http://legacy.python.org/dev/peps/) \n"
   ]
  }
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