0a-Intro_Python.ipynb

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    "A short intro to Python\n",
    "=======================\n",
    "\n",
    "`\n",
    "Author : Alexandre Gramfort\n",
    "`\n",
    "\n",
    "based on the work of J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is Python?\n",
    "---------------\n",
    "\n",
    "[Python](http://www.python.org/) is a modern, general-purpose, object-oriented, high-level programming language.\n",
    "\n",
    "General characteristics of Python:\n",
    "\n",
    "* **clean and simple language:** Easy-to-read and intuitive code, easy-to-learn minimalistic syntax, maintainability scales well with size of projects\n",
    "* **expressive language:** Fewer lines of code, fewer bugs, easier to maintain.\n",
    "\n",
    "Technical details:\n",
    "\n",
    "* **dynamically typed:** No need to define the type of variables, function arguments or return types.\n",
    "* **automatic memory management:** No need to explicitly allocate and deallocate memory for variables and data arrays. No memory leak bugs. \n",
    "* **interpreted:** No need to compile the code. The Python interpreter reads and executes the python code directly.\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* **Ease of programming**: minimize development time, debug and code maintainance.\n",
    "* **Encourage good programming practices**:\n",
    " * Modular, *relatively* good system for packaging and re-use of code.\n",
    " * Documentation tightly integrated with the code.\n",
    " * Forces code indentation\n",
    "* A **large standard library**, and a **large collection of add-on packages**.\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* Since Python is an **interpreted** and **dynamically typed** programming language, the execution of python can be code slow compared to compiled statically typed programming languages, such as C and Fortran. \n",
    "* Somewhat decentralized, with different environment, packages and documentation spread out at different places. Can make it harder to get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What makes python suitable for scientific computing?\n",
    "----------------------------------------------------\n",
    "\n",
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/optimizing-what.png\" width=\"600\">\n",
    "\n",
    "* Python has a strong position in scientific computing: \n",
    "    * Large community of users, easy to find help and documentation.\n",
    "\n",
    "* Extensive ecosystem of scientific libraries and environments\n",
    "    * numpy: http://numpy.scipy.org - Numerical Python\n",
    "    * scipy: http://www.scipy.org -  Scientific Python\n",
    "    * matplotlib: http://www.matplotlib.org - graphics library\n",
    "\n",
    "* Great performance due to close integration with time-tested and highly optimized codes written in C and Fortran:\n",
    "    * blas, altas blas, lapack, arpack, Intel MKL, ...\n",
    "\n",
    "* Good support for \n",
    "    * Parallel processing with processes and threads (have a look at http://pythonhosted.org/joblib/)\n",
    "    * Interprocess communication (MPI)\n",
    "    * GPU computing (OpenCL and CUDA)\n",
    "\n",
    "* Readily available and suitable for use on high-performance computing clusters. \n",
    "\n",
    "* No license costs, no unnecessary use of research budget."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python interpreter\n",
    "\n",
    "The standard way to use Python is via the Python interpreter. It is a program that reads and executes the python code in files passed to it as arguments. At the command prompt, the command ``python`` is used to invoke the Python interpreter.\n",
    "\n",
    "For example, to run a file ``my_program.py`` that contains python code from the command prompt, use::\n",
    "\n",
    "    $ python my_program.py\n",
    "\n",
    "We can also start the interpreter by simply typing ``python`` at the command line, and interactively type python code into the interpreter. \n",
    "\n",
    "<!-- <img src=\"files/images/python-screenshot.jpg\" width=\"600\"> -->\n",
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/python-screenshot.jpg\" width=\"600\">\n",
    "\n",
    "**Works but not good enough** when developing scientific applications...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPython\n",
    "\n",
    "IPython is a powerful and user friendly interactive shell.\n",
    "\n",
    "<!-- <img src=\"files/images/ipython-screenshot.jpg\" width=\"600\"> -->\n",
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/ipython-screenshot.jpg\" width=\"600\">\n",
    "\n",
    "Some of the many useful features of IPython includes:\n",
    "\n",
    "* Command history, which can be browsed with the up and down arrows on the keyboard.\n",
    "* Tab auto-completion.\n",
    "* In-line editing of code.\n",
    "* Object introspection, and automatic extract of documentation strings from python objects like classes and functions.\n",
    "* Good interaction with operating system shell.\n",
    "* Support for multiple parallel back-end processes, that can run on computing clusters or cloud services like Amazon EE2.\n",
    "\n",
    "WATCH : https://www.youtube.com/watch?v=2G5YTlheCbw\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython notebook (jupyter)\n",
    "--------------------------\n",
    "\n",
    "[IPython notebook](<http://ipython.org/ipython-doc/dev/interactive/htmlnotebook.html/) is an HTML-based notebook environment for Python, similar to Mathematica or Maple.\n",
    "\n",
    "<!-- <img src=\"files/images/ipython-notebook-screenshot.jpg\" width=\"800\"> -->\n",
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/ipython-notebook-screenshot.jpg\" width=\"800\">\n",
    "\n",
    "To start a new IPython Jupyter notebook session, run the following command:\n",
    "\n",
    "    $ jupyter notebook\n",
    "\n",
    "from a directory where you want the notebooks to be stored. This will open a new browser window (or a new tab in an existing window) with an index page where existing notebooks are shown and from which new notebooks can be created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spyder\n",
    "------\n",
    "\n",
    "[Spyder](http://code.google.com/p/spyderlib/) is a MATLAB-like IDE for scientific computing with python. It has the many advantages of a traditional IDE environment, for example that everything from code editing, execution and debugging is carried out in a single environment, and work on different calculations can be organized as projects in the IDE environment.\n",
    "\n",
    "<!-- <img src=\"files/images/spyder-screenshot.jpg\" width=\"800\"> -->\n",
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/spyder-screenshot.jpg\" width=\"800\">\n",
    "\n",
    "Some advantages of Spyder:\n",
    "\n",
    "* Powerful code editor, with syntax high-lighting, dynamic code introspection and integration with the python debugger.\n",
    "* Variable explorer, IPython command prompt.\n",
    "* Integrated documentation and help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyCharm\n",
    "-------\n",
    "\n",
    "[PyCharm](http://www.jetbrains.com/pycharm/) is a powerful IDE dedicated to Python (free since version 3).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/jpeg": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='images/pycharm-screenshot.jpg', width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions of Python\n",
    "\n",
    "There are still currently two versions of python: Python 2 and Python 3. Python 3 is now the standard.\n",
    "\n",
    "To see which version of Python you have, run\n",
    "    \n",
    "    $ python --version\n",
    "    Python 2.7.15\n",
    "\n",
    "    $ python3 --version\n",
    "    Python 3.6.7\n",
    "\n",
    "Several versions of Python can be installed in parallel, as shown above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "*Anaconda* (recommended)\n",
    "\n",
    "See http://www.continuum.io/downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python program files\n",
    "\n",
    "* Python code is usually stored in text files with the file ending \"`.py`\":\n",
    "\n",
    "        myprogram.py\n",
    "\n",
    "* Every line in a Python program file is assumed to be a Python statement, or part thereof. \n",
    "\n",
    "    * The only exception is comment lines, which start with the character `#` (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.\n",
    "\n",
    "\n",
    "* To run our Python program from the command line we use:\n",
    "\n",
    "        $ python myprogram.py\n",
    "\n",
    "* On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is concerned):\n",
    "\n",
    "        #!/usr/bin/env python\n",
    "\n",
    "  If we do, and if we additionally set the file script to be executable, we can run the program like this:\n",
    "\n",
    "        $ ./myprogram.py\n",
    "\n",
    "#### Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mscripts/hello-world-in-french.py\u001b[m\u001b[m* \u001b[31mscripts/hello-world.py\u001b[m\u001b[m*\r\n"
     ]
    }
   ],
   "source": [
    "ls scripts/hello-world*.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env python\r\n",
      "\r\n",
      "print(\"Hello world!\")\r\n"
     ]
    }
   ],
   "source": [
    "cat scripts/hello-world.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world!\r\n"
     ]
    }
   ],
   "source": [
    "!python scripts/hello-world.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Character encoding\n",
    "\n",
    "The standard character encoding is ASCII, but we can use any other encoding, for example UTF-8. To specify that UTF-8 is used we include the special line\n",
    "\n",
    "    # -*- coding: UTF-8 -*-\n",
    "\n",
    "at the top of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env python\r\n",
      "# -*- coding: UTF-8 -*-\r\n",
      "\r\n",
      "print(\"Bonjour à tout le monde!\")\r\n"
     ]
    }
   ],
   "source": [
    "cat scripts/hello-world-in-french.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonjour à tout le monde!\r\n"
     ]
    }
   ],
   "source": [
    "!python scripts/hello-world-in-french.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other than these two *optional* lines in the beginning of a python code file, no additional code is required for for initializing a program. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython notebooks\n",
    "\n",
    "This document is not a .py file but an IPython notebook stored as a file in the [JSON](http://en.wikipedia.org/wiki/JSON) format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Playing with numbers\n",
    "--------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6 * 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 4  # assign value 4 to variable a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)  # what is the type of a?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable names in Python can contain alphanumerical characters `a-z`, `A-Z`, `0-9` and some special characters such as `_`. Normal variable names must start with a letter. \n",
    "\n",
    "By convension, variable names start with a lower-case letter, and Class names start with a capital letter. \n",
    "\n",
    "In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:\n",
    "\n",
    "    and, as, assert, break, class, continue, def, del, elif, else, except, \n",
    "    exec, finally, for, from, global, if, import, in, is, lambda, not, or,\n",
    "    pass, print, raise, return, try, while, with, yield\n",
    "\n",
    "Note: Be aware of the keyword **lambda**, which could easily be a natural variable name in a scientific program. But being a keyword, it cannot be used as a variable name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-13-d7c31ab15148>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-d7c31ab15148>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    int a = 1;  # in C\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "int a = 1;  # in C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "c = 2.1\n",
    "print(type(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5\n",
      "0.5\n",
      "<class 'complex'>\n"
     ]
    }
   ],
   "source": [
    "a = 1.5 + 0.5j\n",
    "print(a.real)\n",
    "print(a.imag)\n",
    "print(type(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 < 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = (3 > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 * 3.  # int x float -> float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 10  # exponent (^ in Matlab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 % 3  # modulo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WARNING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 // 2  # !!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / 2.  # OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / float(2)  # OK too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-b531fc1091c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'cos' is not defined"
     ]
    }
   ],
   "source": [
    "cos(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules\n",
    "\n",
    "Most of the functionality in Python is provided by *modules*.\n",
    "\n",
    "The Python Standard Library is a large collection of modules to access the operating system, do file I/O, string management, network communication, and much more.\n",
    "\n",
    "### References\n",
    " \n",
    " * The Python Language Reference: http://docs.python.org/2/reference/index.html\n",
    " * The Python Standard Library: http://docs.python.org/2/library/\n",
    "\n",
    "To use a module in a python program it first has to be imported. A module can be imported using the `import` statement. For example, to import the module `math`, which contains many standard mathematical functions, we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This includes the whole module and makes it available for use later in the program. For example, we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "print(math.cos(2 * math.pi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix \"`math.`\" every time we use something from the `math` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from math import *\n",
    "\n",
    "x = cos(2 * pi)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the `import math` pattern. This would elminate potentially confusing problems with name space collisions.\n",
    "\n",
    "As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character `*`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from math import cos, pi\n",
    "\n",
    "x = cos(2 * pi)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at what a module contains, and its documentation\n",
    "\n",
    "Once a module is imported, we can list the symbols it provides using the `dir` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc']\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "print(dir(math))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using the funciton **`help`** we can get a description of each function (most functions have docstrings). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function log in module math:\n",
      "\n",
      "log(...)\n",
      "    log(x[, base])\n",
      "    \n",
      "    Return the logarithm of x to the given base.\n",
      "    If the base not specified, returns the natural logarithm (base e) of x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### or with IPython use **\"?\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "math.exp?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "math.log?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3219280948873626"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.ceil(2.7)  # round to the next integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the `help` function directly on modules: Try\n",
    "\n",
    "    help(math) \n",
    "\n",
    "Some very useful modules form the Python standard library are `os`, `sys`, `math`, `shutil`, `re`, `subprocess`, `multiprocessing`, `threading`. \n",
    "\n",
    "A complete lists of standard modules for Python 2 and Python 3 are available at http://docs.python.org/2/library/ and http://docs.python.org/3/library/, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXCERCISE : Calculate the next power of 2 of a number n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 1234\n",
    "# new_pow2 = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More on Types\n",
    "\n",
    "Test if variables are of certain types:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the `isinstance` method for testing types of variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1.1\n",
    "isinstance(x, float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Type casting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "print(x, type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "y = float(x)  # cast as float\n",
    "print(y, type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't convert complex to float",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-b54c8aa39d7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can't convert complex to float"
     ]
    }
   ],
   "source": [
    "z = 1j\n",
    "float(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complex variables **cannot be cast** to floats or integers:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operators and comparisons\n",
    "\n",
    "Most operators and comparisons in Python work as one would expect:\n",
    "\n",
    "* Arithmetic operators `+`, `-`, `*`, `/`, `//` (integer division), '**' power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 2 3 1 4\n"
     ]
    }
   ],
   "source": [
    "print(1*2, 1+1, 1*3, 3//2, 2**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 // 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3 < 2) or (4 == 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Comparison operators `>`, `<`, `>=` (greater or equal), `<=` (less or equal), `==` equality, `is` identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 > 1, 2 < 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 > 2, 2 < 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 >= 2, 2 <= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equality\n",
    "1 == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# objects identical?\n",
    "a = b = 'hi'\n",
    "\n",
    "a is b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound types: Strings, List and dictionaries\n",
    "\n",
    "### Strings\n",
    "\n",
    "Strings are the variable type that is used for storing text messages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "s = \"Hello world\"\n",
    "# or\n",
    "s = 'Hello world'\n",
    "print(type(s))\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of the string: the number of characters\n",
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello test\n"
     ]
    }
   ],
   "source": [
    "# replace a substring in a string with somethign else\n",
    "s2 = s.replace(\"world\", \"test\")\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can index a character in a string using `[]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heads up MATLAB users:** Indexing start at 0!\n",
    "\n",
    "We can use extract a part of a string using the syntax `[start:stop]`, which extracts characters between index `start` and `stop`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "Hello\n"
     ]
    }
   ],
   "source": [
    "start, stop = 0, 5\n",
    "print(s[start:stop])\n",
    "print(s[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(len(s[start:stop]))\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s) - 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we omit either (or both) of `start` or `stop` from `[start:stop]`, the default is the beginning and the end of the string, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'world'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[6:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define the step size using the syntax `[start:stop:step]` (the default value for `step` is 1, as we saw above):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello world'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hlowrd'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This technique is called *slicing*. Read more about the syntax here: http://docs.python.org/release/2.7.3/library/functions.html?highlight=slice#slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has a very rich set of functions for text processing. See for example http://docs.python.org/2/library/string.html for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE : Given a string s defined below. Make it lower case and replace any occurance of 'o' with 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hella warld!'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"HeLlO wOrLd!\"\n",
    "# TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world!hello world!'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('-' * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String formatting examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str1 str2 str3\n"
     ]
    }
   ],
   "source": [
    "print(\"str1\", \"str2\", \"str3\")  # The print statement concatenates strings with a space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str1 1.0 False (-0-1j)\n"
     ]
    }
   ],
   "source": [
    "print(\"str1\", 1.0, False, -1j)  # The print statements converts all arguments to strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str1str2str3\n"
     ]
    }
   ],
   "source": [
    "print(\"str1\" + \"str2\" + \"str3\") # strings added with + are concatenated without space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "str1str1str1\n"
     ]
    }
   ],
   "source": [
    "print(\"str1\" * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 1.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"value = %f\" % 1.0)       # we can use C-style string formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 1.00\n"
     ]
    }
   ],
   "source": [
    "print(\"value = %1.2f\" % 1.0)       # we can use C-style string formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject003\n"
     ]
    }
   ],
   "source": [
    "subject_nb = 3\n",
    "print(\"subject%03d\" % subject_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value1 = 3.14. value2 = 1\n"
     ]
    }
   ],
   "source": [
    "# this formatting creates a string\n",
    "s = \"value1 = %.2f. value2 = %d\" % (3.1415, 1.5)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value1 = 3.1415. value2 = 1.5\n"
     ]
    }
   ],
   "source": [
    "# this formatting creates a string\n",
    "s = \"value1 = %s. value2 = %s\" % (3.1415, 1.5)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value1 = 3.1415, value2 = 1.5\n"
     ]
    }
   ],
   "source": [
    "# alternative, more intuitive way of formatting a string \n",
    "s = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List\n",
    "\n",
    "Lists are very similar to strings, except that each element can be of any type.\n",
    "\n",
    "The syntax for creating lists in Python is `[...]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4]\n",
    "\n",
    "print(type(l))\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the same slicing techniques to manipulate lists as we could use on strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "[2, 3]\n",
      "[1, 3]\n"
     ]
    }
   ],
   "source": [
    "print(l)\n",
    "print(l[1:3])\n",
    "print(l[::2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heads up MATLAB users:** Indexing starts at 0!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elements in a list do not all have to be of the same type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'a', 1.0, (1-1j)]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 'a', 1.0, 1-1j]\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python lists can be inhomogeneous and arbitrarily nested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, [2, [3, [4, [5]]]]]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested_list = [1, [2, [3, [4, [5]]]]]\n",
    "nested_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists play a very important role in Python, and are for example used in loops and other flow control structures (discussed below). There are number of convenient functions for generating lists of various types, for example the `range` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(1, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = 10\n",
    "stop = 30\n",
    "step = 2\n",
    "list(range(start, stop, step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(-10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 'e', 'l', 'l', 'o']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert a string to a list by type casting:\n",
    "s2 = list(\"hello\")\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['e', 'h', 'l', 'l', 'o']\n"
     ]
    }
   ],
   "source": [
    "# sorting lists (inplace)\n",
    "s2.sort()\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['h', 'e', 'l', 'l', 'o']\n",
      "['e', 'h', 'l', 'l', 'o']\n"
     ]
    }
   ],
   "source": [
    "# to return a sorted copy use \"sorted\"\n",
    "s2 = list(\"hello\")\n",
    "s3 = sorted(s2)\n",
    "print(s2)\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding, inserting, modifying, and removing elements from lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'd', 'd']\n"
     ]
    }
   ],
   "source": [
    "# create a new empty list\n",
    "l = []\n",
    "\n",
    "# add an elements using `append`\n",
    "l.append(\"A\")  # same as : l += [\"A\"]\n",
    "l.append(\"d\")\n",
    "l.append(\"d\")\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can modify lists by assigning new values to elements in the list. In technical jargon, lists are *mutable*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'p', 'p']\n"
     ]
    }
   ],
   "source": [
    "l[1] = \"p\"\n",
    "l[2] = \"p\"\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'd', 'd']\n"
     ]
    }
   ],
   "source": [
    "l[1:3] = [\"d\", \"d\"]\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert at element a specific index using `insert`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'n', 's', 'e', 'r', 't', 'A', 'd', 'd']\n"
     ]
    }
   ],
   "source": [
    "l.insert(0, \"i\")\n",
    "l.insert(1, \"n\")\n",
    "l.insert(2, \"s\")\n",
    "l.insert(3, \"e\")\n",
    "l.insert(4, \"r\")\n",
    "l.insert(5, \"t\")\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove first element with specific value using 'remove'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'n', 's', 'e', 'r', 't', 'd', 'd']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.remove(\"A\")\n",
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove an element at a specific location using `del`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'n', 's', 'e', 'r', 't']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del l[7]\n",
    "del l[6]\n",
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List concatenation with '+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'n', 's', 'e', 'r', 't', 'i', 'n', 's', 'e', 'r', 't']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l + l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `help(list)` for more details, or read the online documentation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuples\n",
    "\n",
    "Tuples are like lists, except that they cannot be modified once created, that is they are *immutable*. \n",
    "\n",
    "In Python, tuples are created using the syntax `(..., ..., ...)`, or even `..., ...`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 20) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "point = (10, 20)\n",
    "\n",
    "print(point, type(point))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 20) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "point = 10, 20\n",
    "\n",
    "print(point, type(point))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can unpack a tuple by assigning it to a comma-separated list of variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = 10\n",
      "y = 20\n"
     ]
    }
   ],
   "source": [
    "x, y = point\n",
    "\n",
    "print(\"x =\", x)\n",
    "print(\"y =\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to assign a new value to an element in a tuple we get an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-9734b1daa940>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "point[0] = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries\n",
    "\n",
    "Dictionaries are also like lists, except that each element is a key-value pair. The syntax for lists are `{key1 : value1, ...}`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'parameter1': 1.0, 'parameter2': 2.0, 'parameter3': 3.0}\n"
     ]
    }
   ],
   "source": [
    "params = {\"parameter1\" : 1.0,\n",
    "          \"parameter2\" : 2.0,\n",
    "          \"parameter3\" : 3.0,}\n",
    "\n",
    "# or equivalent\n",
    "\n",
    "params = dict(parameter1=1.0, parameter2=2.0, parameter3=3.0)\n",
    "\n",
    "print(type(params))\n",
    "print(params)  # what do you observed about the order of keys?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter1 = 1.0\n",
      "parameter2 = 2.0\n",
      "parameter3 = 3.0\n"
     ]
    }
   ],
   "source": [
    "print(\"parameter1 =\", params[\"parameter1\"])\n",
    "print(\"parameter2 =\", params[\"parameter2\"])\n",
    "print(\"parameter3 =\", params[\"parameter3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter1 = A\n",
      "parameter2 = B\n",
      "parameter3 = 3.0\n",
      "parameter4 = D\n"
     ]
    }
   ],
   "source": [
    "params[\"parameter1\"] = \"A\"\n",
    "params[\"parameter2\"] = \"B\"\n",
    "\n",
    "# add a new entry\n",
    "params[\"parameter4\"] = \"D\"\n",
    "\n",
    "print(\"parameter1 =\", params[\"parameter1\"])\n",
    "print(\"parameter2 =\", params[\"parameter2\"])\n",
    "print(\"parameter3 =\", params[\"parameter3\"])\n",
    "print(\"parameter4 =\", params[\"parameter4\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"parameter1\" in params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'parameter5'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-dc5723e59a2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"parameter5\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'parameter5'"
     ]
    }
   ],
   "source": [
    "params[\"parameter5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'parameter1': 'A', 'parameter2': 'B', 'parameter4': 'D'}\n"
     ]
    }
   ],
   "source": [
    "# delete a key\n",
    "del params[\"parameter3\"]\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_runs_per_subjects = {'subj1': {'age': 34, 'n_runs': 4}, 'subj2': {'age': 32, 'n_runs': 3}}\n",
    "n_runs_per_subjects['subj1']['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{1: 'subj1', 2: 'subj2'}[1][-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional statements: if, elif, else\n",
    "\n",
    "The Python syntax for conditional execution of code use the keywords `if`, `elif` (else if), `else`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statement1 and statement2 are False\n"
     ]
    }
   ],
   "source": [
    "statement1 = False\n",
    "statement2 = False\n",
    "\n",
    "if statement1:  # PAY ATTENTION TO THE :\n",
    "    print(\"statement1 is True\")  # INDENTATION IS MANDATORY\n",
    "elif statement2:\n",
    "    print(\"statement2 is True\")\n",
    "else:\n",
    "    print(\"statement1 and statement2 are False\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, the extent of a code block is defined by the indentation level (ideally four white spaces). This means that we have to **be careful to indent our code correctly**, or else we will get syntax errors. \n",
    "\n",
    "**Examples:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "both statement1 and statement2 are True\n"
     ]
    }
   ],
   "source": [
    "statement1 = statement2 = True\n",
    "\n",
    "if statement1:\n",
    "    if statement2:\n",
    "        print(\"both statement1 and statement2 are True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-111-4a18b89c04b2>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-111-4a18b89c04b2>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    print(\"both statement1 and statement2 are True\") # this line is not properly indented\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "# Bad indentation!\n",
    "if statement1:\n",
    "    if statement2:\n",
    "    print(\"both statement1 and statement2 are True\") # this line is not properly indented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "statement1 = False \n",
    "\n",
    "if statement1:\n",
    "    print(\"printed if statement1 is True\")\n",
    "    \n",
    "    print(\"still inside the if block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now outside the if block\n"
     ]
    }
   ],
   "source": [
    "if statement1:\n",
    "    print(\"printed if statement1 is True\")\n",
    "    \n",
    "print(\"now outside the if block\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loops\n",
    "\n",
    "In Python, loops can be programmed in a number of different ways. The most common is the `for` loop, which is used together with iterable objects, such as lists. The basic syntax is:\n",
    "\n",
    "\n",
    "**`for` loops**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for x in [1, 2, 3]:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `for` loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the `for` loop. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for x in range(4): # by default range start at 0\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: `range(4)` does not include 4 !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3\n",
      "-2\n",
      "-1\n",
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for x in range(-3,3):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scientific\n",
      "computing\n",
      "with\n",
      "python\n"
     ]
    }
   ],
   "source": [
    "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n",
    "    print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do something for subject subj1\n",
      "do something for subject subj2\n",
      "do something for subject subj3\n"
     ]
    }
   ],
   "source": [
    "for subj in [\"subj1\", \"subj2\", \"subj3\"]:\n",
    "    print('do something for subject %s' % subj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do something for subject s\n",
      "do something for subject u\n",
      "do something for subject b\n",
      "do something for subject j\n",
      "do something for subject 1\n"
     ]
    }
   ],
   "source": [
    "for subj in \"subj1\":\n",
    "    print('do something for subject %s' % subj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H\n",
      "e\n",
      "l\n",
      "l\n",
      "o\n",
      " \n",
      "w\n",
      "o\n",
      "r\n",
      "l\n",
      "d\n",
      "!\n"
     ]
    }
   ],
   "source": [
    "for x in 'Hello world!':\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To iterate over key-value pairs of a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter1  =  A\n",
      "parameter2  =  B\n",
      "parameter4  =  D\n"
     ]
    }
   ],
   "source": [
    "for key, value in params.items():\n",
    "    print(key, \" = \", value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_runs_per_subjects = {'subj1': 4, 'subj2': 3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 4 runs for subject subj1\n",
      "There is 3 runs for subject subj2\n"
     ]
    }
   ],
   "source": [
    "for subj, n_runs in n_runs_per_subjects.items():\n",
    "    print(\"There is %d runs for subject %s\" % (n_runs, subj))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the `enumerate` function for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -3\n",
      "1 -2\n",
      "2 -1\n",
      "3 0\n",
      "4 1\n",
      "5 2\n"
     ]
    }
   ],
   "source": [
    "for idx, x in enumerate(range(-3,3)):\n",
    "    print(idx, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE :\n",
    "\n",
    "* Given a string e.g. \"HelLO world!!\", compute the number of occurence of each letter (capital or not).\n",
    "* HINT : Iterate over lower case letters and use a dictionnary to count\n",
    "* HINT : str object has a lower method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = \"HelLO world!!\"\n",
    "# ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE :\n",
    "\n",
    "Compute an approximation of $\\pi$ using Wallis' formula:\n",
    "\n",
    "<!-- <img src=\"files/images/spyder-screenshot.jpg\" width=\"800\"> -->\n",
    "<img src=\"http://scipy-lectures.github.io/_images/math/31913b3982be13ed2063b0ffccbcab9cf4931fdb.png\" width=\"200\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List comprehensions: Creating lists using `for` loops**:\n",
    "\n",
    "A convenient and compact way to initialize lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16]\n"
     ]
    }
   ],
   "source": [
    "l1 = [x**2 for x in range(0,5) if x % 2 == 0]\n",
    "\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`while` loops**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "\n",
    "while i < 5:\n",
    "    print(i)\n",
    "    \n",
    "    i = i + 1\n",
    "    \n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `print \"done\"` statement is not part of the `while` loop body because of the difference in indentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "A function in Python is defined using the keyword `def`, followed by a function name, a signature within parenthises `()`, and a colon `:`. The following code, with one additional level of indentation, is the function body."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def func0():\n",
    "    print(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n",
      "test\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "func0()\n",
    "out = func0()\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, but highly recommended, we can define a so called \"docstring\", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def func1(s):\n",
    "    \"\"\"\n",
    "    Print a string 's' and tell how many characters it has    \n",
    "    \"\"\"\n",
    "    print(s, \"has\", len(s), \"characters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function func1 in module __main__:\n",
      "\n",
      "func1(s)\n",
      "    Print a string 's' and tell how many characters it has\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(func1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "func1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test has 4 characters\n"
     ]
    }
   ],
   "source": [
    "func1(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] has 3 characters\n"
     ]
    }
   ],
   "source": [
    "func1([1, 2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions that returns a value use the `return` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    \"\"\"\n",
    "    Return the square of x.\n",
    "    \"\"\"\n",
    "    return x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can return multiple values from a function using tuples (see above):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def powers(x):\n",
    "    \"\"\"\n",
    "    Return a few powers of x.\n",
    "    \"\"\"\n",
    "    return x ** 2, x ** 3, x ** 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 27, 81)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powers(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(powers(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    }
   ],
   "source": [
    "x2, x3, x4 = powers(3)\n",
    "print(x3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE : Write the factorial function fact(n) = n * (n-1) * (n-2) ... 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "def fact(n):\n",
    "    # TODO\n",
    "    return None\n",
    "\n",
    "print(fact(5))\n",
    "print(math.factorial(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default argument and keyword arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def myfunc(x, p=2, debug=False):  # debug has a default value = False\n",
    "    if debug:\n",
    "        print(\"evaluating myfunc for x =\", x, \"using exponent p =\", p)\n",
    "    return x**p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we don't provide a value of the `debug` argument when calling the the function `myfunc` it defaults to the value provided in the function definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfunc(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating myfunc for x = 5 using exponent p = 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfunc(5, debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *keyword* arguments : If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating myfunc for x = 7 using exponent p = 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "343"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfunc(p=3, debug=True, x=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE : implement Quicksort\n",
    "\n",
    "The [wikipedia page](http://en.wikipedia.org/wiki/Quicksort) describes the quicksort algorithm in pseudo-code:\n",
    "\n",
    "    function quicksort('array')\n",
    "       if length('array') <= 1\n",
    "            return 'array'\n",
    "       select and remove a pivot value 'pivot' from 'array'\n",
    "       create empty lists 'less' and 'greater'\n",
    "       for each 'x' in 'array'\n",
    "           if 'x' <= 'pivot' then append 'x' to 'less'\n",
    "           else append 'x' to 'greater'\n",
    "       return concatenate(quicksort('less'), 'pivot', quicksort('greater'))\n",
    "\n",
    "Convert this pseudo-code in valid Python code.\n",
    "\n",
    "**Hints**:\n",
    "\n",
    "- the length of a list is obtained with `len(l)`\n",
    "\n",
    "- two lists can be concatenated with `l1 + l2`\n",
    "\n",
    "- `l.pop()` removes the last element from a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes\n",
    "\n",
    "Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object. \n",
    "\n",
    "In Python a class can contain *attributes* (variables) and *methods* (functions).\n",
    "\n",
    "In python a class are defined almost like a functions, but using the `class` keyword, and the class definition usually contains a number of class method definitions (a function in a class).\n",
    "\n",
    "* Each class method should have an argurment `self` as it first argument. This object is a self-reference.\n",
    "\n",
    "* Some class method names have special meaning, for example:\n",
    "\n",
    " * `__init__`: The name of the method that is invoked when the object is first created.\n",
    " * `__str__` : A method that is invoked when a simple string representation of the class is needed, as for example when printed.\n",
    " * There are many more, see http://docs.python.org/2/reference/datamodel.html#special-method-names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Point(object):\n",
    "    \"\"\"\n",
    "    Simple class for representing a point in a Cartesian coordinate system.\n",
    "    \"\"\"\n",
    "    def __init__(self, x, y):\n",
    "        \"\"\"\n",
    "        Create a new Point at x, y.\n",
    "        \"\"\"\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        \n",
    "    def translate(self, dx, dy):\n",
    "        \"\"\"\n",
    "        Translate the point by dx and dy in the x and y direction.\n",
    "        \"\"\"\n",
    "        self.x += dx\n",
    "        self.y += dy\n",
    "    \n",
    "    def __add__(self, point):\n",
    "        return Point(self.x + point.x, self.y + point.y)\n",
    "\n",
    "    def __str__(self):\n",
    "        return \"Point at [%f, %f]\" % (self.x, self.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a new instance of a class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point at [0.000000, 0.000000]\n"
     ]
    }
   ],
   "source": [
    "p1 = Point(0, 0) # this will invoke the __init__ method in the Point class\n",
    "print(p1)         # this will invode the __str__ method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To invoke a class method in the class instance `p`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point at [0.250000, 1.500000]\n"
     ]
    }
   ],
   "source": [
    "p1.translate(0.25, 1.5)\n",
    "print(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point at [1.000000, 1.000000]\n",
      "Point at [0.000000, 0.000000]\n"
     ]
    }
   ],
   "source": [
    "p1 = Point(0, 0)\n",
    "p2 = Point(1, 1)\n",
    "print(p1 + p2)\n",
    "print(p1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that calling class methods can modifiy the state of the that particular class instance, but does not effect other class instances or any global variables.\n",
    "\n",
    "That is one of the nice things about object-oriented design: code such as functions and related variables are group in separate and independent entities. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exceptions\n",
    "\n",
    "In Python errors are managed with a special language construct called \"Exceptions\". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statements is defined.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate an exception we can use the `raise` statement, which takes an argument that must be an instance of the class `BaseExpection` or a class dervied from it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "description of the error",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-150-c32f93e4dfa0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"description of the error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mException\u001b[0m: description of the error"
     ]
    }
   ],
   "source": [
    "raise Exception(\"description of the error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A typical use of exceptions is to abort functions when some error condition occurs, for example:\n",
    "\n",
    "    def my_function(arguments):\n",
    "    \n",
    "        if not verify(arguments):\n",
    "            raise Expection(\"Invalid arguments\")\n",
    "        \n",
    "        # rest of the code goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the `try` and  `expect` statements:\n",
    "\n",
    "    try:\n",
    "        # normal code goes here\n",
    "    except:\n",
    "        # code for error handling goes here\n",
    "        # this code is not executed unless the code\n",
    "        # above generated an error\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print'. Did you mean print(\"test\")? (<ipython-input-151-e13eabd448ff>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-151-e13eabd448ff>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print \"test\"\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(\"test\")?\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print \"test\"\n",
    "    # generate an error: the variable test is not defined\n",
    "    print(test)\n",
    "except:\n",
    "    print(\"Caught an expection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get information about the error, we can access the `Expection` class instance that describes the exception by using for example:\n",
    "\n",
    "    except Exception as e:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(\"test\")\n",
    "    # generate an error: the variable test is not defined\n",
    "    print(test)\n",
    "except Exception as e:\n",
    "    print(\"Caught an expection:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading\n",
    "\n",
    "* http://www.python.org/dev/peps/pep-0008 - Style guide for Python programming. Highly recommended. \n",
    "* http://www.greenteapress.com/thinkpython/ - A free book on Python programming.\n",
    "* [Python Essential Reference](http://www.amazon.com/Python-Essential-Reference-4th-Edition/dp/0672329786) - A good reference book on Python programming.\n",
    "* http://learnpythonthehardway.org\n",
    "* http://en.wikibooks.org/wiki/Python_Programming\n",
    "* http://scipy-lectures.github.io"
   ]
  }
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