1_1_baby_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A crash course on Python programming\n",
    "* In this class, we'll introduce the Python language from scratch.\n",
    "* No previous experience with Python is required.\n",
    "* The class is meant to be interactive and disruptive, so you're more than welcome to interrupt me at any time to say / ask something, or crack a joke.\n",
    "\n",
    "Content\n",
    "=======\n",
    "* Basic material like printing, strings, dictionaries, casting, functions, modules, globbling, plotting (histograms, scatter plots, colorbars, error bars, etc.) will be covered.\n",
    "* The later classes (tomorrow ?) will be more specialized. We'll see how to do brain science using Python. We'll focus on the [nilearn](http://nilearn.github.io) package (and talk a bit about nipy and nipype if necessary).\n",
    "\n",
    "----------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing\n",
    "========"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, World!\n"
     ]
    }
   ],
   "source": [
    "# The irreducible Hello-World example, present in any Turning-incomplete language <== this is a comment, not exec'ed\n",
    "print(\"Hello, World!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aix-la-Chapelle is another name for Aachen.\n"
     ]
    }
   ],
   "source": [
    "print(\"Aix-la-Chapelle is another name for Aachen.\")  # this is a comment too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(1 + 1)  # more on arithmetic latter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings and other goodies\n",
    "========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Sponge Bob!\n"
     ]
    }
   ],
   "source": [
    "template = \"Hello, %s!\"  # more on variables latter\n",
    "print(template % \"Sponge Bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Sponge Bob. 1 + 2 = 3.\n"
     ]
    }
   ],
   "source": [
    "template = \"Hello, %s. %i + %i = %i.\"\n",
    "print(template % (\"Sponge Bob\", 1, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie\n",
      "jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie\n",
      "\n"
     ]
    }
   ],
   "source": [
    "garbage = \"\"\"\n",
    "jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie\n",
    "jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie jhjehehjeh iueiueiueiueieuieie\n",
    "\"\"\"  # another way to do strings: using triple quotes \"\"\"\n",
    "print(garbage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting and joining strings\n",
    "-----------------------------\n",
    "You can split a string w.r.t to some delimiter. You can also join a list of strings along a given delimiter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, I'm Mr. Bumble!\n",
      "['Hello,', \"I'm\", 'Mr.', 'Bumble!']\n"
     ]
    }
   ],
   "source": [
    "raw = \"Hello, I'm Mr. Bumble!\"\n",
    "print(raw)\n",
    "parts = raw.split(\" \")\n",
    "print(parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, I'm Mr. Bumble!\n"
     ]
    }
   ],
   "source": [
    "print(\" \".join(parts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/disk/func.nii\n"
     ]
    }
   ],
   "source": [
    "dirname = \"/disk\"\n",
    "basename = \"func.nii\"\n",
    "fullpath = \"/\".join([dirname, basename])  # XXX the proper way for splitting and join paths is using the os module as we'll see later\n",
    "print(fullpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'disk', 'func.nii']\n"
     ]
    }
   ],
   "source": [
    "print(fullpath.split(\"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interlude: Getting user input interactively\n",
    "===========================================\n",
    "Often, it happens that we want our script to interactively get (text) input from the use, and then take some action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your name, please: Mr. Bumble\n",
      "Hello, Mr. Bumble!\n"
     ]
    }
   ],
   "source": [
    "name = raw_input(\"Your name, please: \")  # in Python 3, raplace with input(\"Your name, please:\")\n",
    "print(\"Hello, %s!\" % name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerics\n",
    "========\n",
    "Example: Computing simple interests\n",
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interest rate = 0.100000 principal = 500.000000 euros, time = 3.000000. Therefore new amount = 665.500000 euros.\n"
     ]
    }
   ],
   "source": [
    "# the following are variables\n",
    "interest_rate = .1 # a fraction\n",
    "principal = 500  # euros\n",
    "time = 3# years\n",
    "amount = principal * (1 + interest_rate)** time  # euros\n",
    "print(\"Interest rate = %f principal = %f euros, time = %f. Therefore new amount = %f euros.\" % (\n",
    "    interest_rate, principal, time, amount))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interest rate = 0.10, principal = 500.00, time = 3.000000 years. Therefore new amount = 665.50.\n"
     ]
    }
   ],
   "source": [
    "print(\"Interest rate = %.2f, principal = %.2f, time = %f years. Therefore new amount = %.2f.\" % (\n",
    "    interest_rate, principal, time, amount))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting strings to numbers\n",
    "-----------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n"
     ]
    }
   ],
   "source": [
    "age = \"40\"\n",
    "print(int(age) + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries\n",
    "============\n",
    "A dictionary is a data structure for storing key-value pairs. For example, a person might have a name \"Semga\", age 45, weight 95, etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semga is 45 and weighs at 95.\n"
     ]
    }
   ],
   "source": [
    "person = {\"name\": \"Semga\", \"age\": 45, \"weight\": 95}\n",
    "print(\"%s is %i and weighs at %i.\" % (person[\"name\"], person[\"age\"], person[\"weight\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Another way to do dictionaries...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semga is 45 and weighs at 95.\n"
     ]
    }
   ],
   "source": [
    "person = dict(name=\"Semga\", age=45, weight=95)\n",
    "print(\"%s is %i and weighs at %i.\" % (person[\"name\"], person[\"age\"], person[\"weight\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries have keys..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'name', 'weight']\n"
     ]
    }
   ],
   "source": [
    "print(person.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and values.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[45, 'Semga', 95]\n"
     ]
    }
   ],
   "source": [
    "print(person.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the value associate with a dictionary key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semga (aka ghost rider) is 46 and weighs at 95.\n"
     ]
    }
   ],
   "source": [
    "person[\"age\"] += 1\n",
    "person[\"name\"] = \"Semga (aka ghost rider)\"\n",
    "print(\"%s is %i and weighs at %i.\" % (person[\"name\"], person[\"age\"], person[\"weight\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can delete stuff from a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'age': 45, 'name': 'Semga', 'weight': 95}\n",
      "Deleting age\n",
      "{'name': 'Semga', 'weight': 95}\n",
      "Deleting weight\n",
      "{'name': 'Semga'}\n"
     ]
    }
   ],
   "source": [
    "person = dict(name=\"Semga\", age=45, weight=95)\n",
    "print(person)\n",
    "print(\"Deleting age\")\n",
    "del person[\"age\"]\n",
    "print(person)\n",
    "print(\"Deleting weight\")\n",
    "weight = person.pop(\"weight\")\n",
    "print(person)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditionals\n",
    "============\n",
    "Conditionals are control \"constructs\" that let you execute branches of a code, under specific conditions.\n",
    "The *if* conditional\n",
    "--------------------\n",
    "Execute a code segment if a given condition is satisfied, otherwise skip it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 1 - 1 + 1\n",
    "if a < b:\n",
    "    print(\"a < b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *if-else* conditional\n",
    "-------------------------\n",
    "Execute a code segment if a given condition is satisfied; otherwise do something else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a >= b\n"
     ]
    }
   ],
   "source": [
    "if a < b:\n",
    "    print(\"a < b\")\n",
    "else:\n",
    "    print(\"a >= b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *if-elif-else* conditional\n",
    "------------------------------\n",
    "Execude a code segment if a given condition is satisfied; otherwise execute another code segment if its associate conditon is satisfied, and so on and so forth. Otherwise, execute some default code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a == b\n"
     ]
    }
   ],
   "source": [
    "if a < b:\n",
    "    print(\"a < b\")\n",
    "elif a > b:\n",
    "    print(\"a > b\")\n",
    "else:\n",
    "    print(\"a == b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loops\n",
    "=====\n",
    "Looks make you run some code in a repeated manner. Automation starts here!\n",
    "\n",
    "*for* loops\n",
    "-----------\n",
    "Do something about a collection of things..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Bobga! Welcome abord!\n",
      "Hello, Alex! Welcome abord!\n",
      "Hello, Semga! Welcome abord!\n",
      "Hello, Dopgima! Welcome abord!\n",
      "Hello, Karin! Welcome abord!\n",
      "Hello, Kah! Welcome abord!\n",
      "Hello, Mary! Welcome abord!\n"
     ]
    }
   ],
   "source": [
    "for person in [\"Bobga\", \"Alex\", \"Semga\", \"Dopgima\", \"Karin\", \"Kah\", \"Mary\"]:\n",
    "    print(\"Hello, %s! Welcome abord!\" % person)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You can modify variables in a loop**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] Hello, Bobga! Welcome abord!\n",
      "[2] Hello, Alex! Welcome abord!\n",
      "[3] Hello, Semga! Welcome abord!\n",
      "[4] Hello, Dopgima! Welcome abord!\n",
      "[5] Hello, Karin! Welcome abord!\n",
      "[6] Hello, Kah! Welcome abord!\n",
      "[7] Hello, Mary! Welcome abord!\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "for person in [\"Bobga\", \"Alex\", \"Semga\", \"Dopgima\", \"Karin\", \"Kah\", \"Mary\"]:\n",
    "    print(\"[%i] Hello, %s! Welcome abord!\" % (cnt + 1, person))\n",
    "    cnt += 1  # this means: new cnt = old cnt + 1, i.e increment cnt by 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*while* loops\n",
    "-------------\n",
    "Keep doing something, as long as some condition is being respected / satisfied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] Hello, Bobga! Welcome abord!\n",
      "[2] Hello, Alex! Welcome abord!\n",
      "[3] Hello, Semga! Welcome abord!\n",
      "[4] Hello, Dopgima! Welcome abord!\n",
      "[5] Hello, Karin! Welcome abord!\n",
      "[6] Hello, Kah! Welcome abord!\n",
      "[7] Hello, Mary! Welcome abord!\n"
     ]
    }
   ],
   "source": [
    "persons = [\"Bobga\", \"Alex\", \"Semga\", \"Dopgima\", \"Karin\", \"Kah\", \"Mary\"]\n",
    "cnt = 0\n",
    "while cnt < len(persons):\n",
    "    print(\"[%i] Hello, %s! Welcome abord!\" % (cnt + 1, persons[cnt]))\n",
    "    cnt += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions\n",
    "=======\n",
    "Functions are the building blocks of any software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def addition(a, b):  # The \"def\" tells Python we're defining a function. a and b are arguments of the funciton\n",
    "    output = a + b  # do some computation and store the results\n",
    "    return output  # return the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addition(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function without an argument is perfectly fine..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_banner():\n",
    "    print(\"+\" * 11)\n",
    "    print(\"fMRI rules!\")\n",
    "    print(\"+\" * 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n"
     ]
    }
   ],
   "source": [
    "print_banner()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions can have default arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_banner(times=1):\n",
    "    for _ in range(times):\n",
    "        print(\"+\" * 11)\n",
    "        print(\"fMRI rules!\")\n",
    "        print(\"+\" * 11)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n"
     ]
    }
   ],
   "source": [
    "print_banner()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n",
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n",
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n",
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n",
      "+++++++++++\n",
      "fMRI rules!\n",
      "+++++++++++\n",
      "()\n"
     ]
    }
   ],
   "source": [
    "print_banner(times=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the same function to a list of items\n",
    "-------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(x):\n",
    "    \"\"\"Computes the square of the input.\"\"\"\n",
    "    return x ** 2\n",
    "[square(x) for x in range(10)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map(square, range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "ys = []\n",
    "for x in range(10):\n",
    "    ys.append(square(x))\n",
    "print ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modules\n",
    "=======\n",
    "A module is a **logical unit of software**. This a building block and **implements a specific functionality or group of tightly connected functionalities**. In the same project / package, you can have a module for plotting figures, a module for doing linear algebra, a module for crawling the web, a module for doing i/o stuff (reading files, writing files, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2016, tm_mon=1, tm_mday=14, tm_hour=8, tm_min=20, tm_sec=14, tm_wday=3, tm_yday=14, tm_isdst=0)\n",
      "________________________________________________________________________________\n",
      "Sleeping for a while...\n",
      ".zZ.ZzZ.\n",
      "I'm back!\n"
     ]
    }
   ],
   "source": [
    "from time import sleep, localtime  # the time module implements some tightly connected func'ties related to time\n",
    "print(localtime())\n",
    "print(\"_\" * 80)\n",
    "print(\"Sleeping for a while...\")\n",
    "print(\".zZ.ZzZ.\")\n",
    "sleep(5)\n",
    "print(\"I'm back!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way is to import a whole module, and then use individual functions, etc., from it, according to needs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2016, tm_mon=1, tm_mday=14, tm_hour=8, tm_min=20, tm_sec=19, tm_wday=3, tm_yday=14, tm_isdst=0)\n",
      "________________________________________________________________________________\n",
      "Sleeping for a while...\n",
      ".zZ.ZzZ.\n",
      "I'm back!\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "print time.localtime()\n",
    "delay = 2\n",
    "print(\"_\" * 80)\n",
    "print(\"Sleeping for a while...\")\n",
    "print(\".zZ.ZzZ.\")\n",
    "time.sleep(delay)\n",
    "print(\"I'm back!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "OK, let's start some serious business: Globbing files\n",
    "====================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "glob.glob(\"*.py\")  # get all files in the current directory, with the .py extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['baby_python.ipynb', 'nilearn_tutorial.ipynb']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"*.ipynb\")  # get all files in the current directory, with the .ipynb extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_004.img',\n",
       " '/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_008.img',\n",
       " '/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_009.img',\n",
       " '/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_006.img',\n",
       " '/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_005.img',\n",
       " '/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_007.img']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"/home/elvis/nilearn_data/spm_auditory/sub001/fM00223/fM00223_00*.img\")[:6]  # XXX use other path on ur pc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting\n",
    "========"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curves\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xb0ad018c>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zz9jRiEghURNWgevQIWw+NWhQWOpERCQfKIGkxKBB0L49XHtt7EhERAI1YaVIRVPWo49q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0bbc8cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  # the numpy package is the numerical workhorse of python, from a scicomp perspective\n",
    "import matplotlib.pyplot as plt  # import plotting tool\n",
    "x = np.linspace(-1, 2, num=500)\n",
    "y = .5 * x ** 2 - x\n",
    "plt.plot(x, y)\n",
    "plt.title(\"A quadratic curve\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mant curves on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dvJuEpAQ2J20mISmBhMMJbEjcwIETB7igxgW0q9WOtjXb8sAlD9C2ZluiwqP+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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0adcfac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for alpha in [-1, .5, 1]:\n",
    "    y = alpha * x ** 2 - x\n",
    "    plt.plot(x, y,\n",
    "             label=\"alpha = %g\" % alpha  # legend for this particular curve\n",
    "            )\n",
    "plt.legend(loc=\"best\")  # make legend box on figure, and choose an optimal position for it\n",
    "plt.title(\"Quadratic curves\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.grid(\"on\")  # funky stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histograms\n",
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   1.,    0.,    5.,    6.,   17.,   11.,   25.,   43.,   63.,\n",
       "          71.,   92.,  116.,  144.,  160.,  154.,  179.,  171.,  165.,\n",
       "         108.,  109.,  120.,   73.,   70.,   39.,   19.,   18.,    8.,\n",
       "           6.,    3.,    2.,    1.,    1.]),\n",
       " array([-3.32257866, -3.10933821, -2.89609777, -2.68285732, -2.46961688,\n",
       "        -2.25637643, -2.04313599, -1.82989554, -1.6166551 , -1.40341465,\n",
       "        -1.19017421, -0.97693376, -0.76369331, -0.55045287, -0.33721242,\n",
       "        -0.12397198,  0.08926847,  0.30250891,  0.51574936,  0.7289898 ,\n",
       "         0.94223025,  1.15547069,  1.36871114,  1.58195158,  1.79519203,\n",
       "         2.00843247,  2.22167292,  2.43491336,  2.64815381,  2.86139425,\n",
       "         3.0746347 ,  3.28787514,  3.50111559]),\n",
       " <a list of 32 Patch objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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38bs8PbMcuUuSZsxwl6QOMtwlqYMMd0nqIMNdkjrIcJekDjLcJamDDHdJ6iDDXZI6yHCX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0ae210c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(2000)  # generate 2000 points from a 1D standard gaussian\n",
    "plt.hist(data, bins=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plots\n",
    "-------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xb07cbaac>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UczG7ja+KY0/PHl/HjJLt3UuSm4Ekm0UTWREIRUGuzCVBh0kOpAaw9zV734kxY7aKtlG1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\nIAhCifP/AUb6m2Me+WRKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb08ee1ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000\n",
    "points = np.random.randn(n, 2)  # generate points from a 2D standard gaussian\n",
    "plt.scatter(points[:, 0], points[:, 1],\n",
    "            marker=\"o\",  # shape of the points in the visualization\n",
    "            color=\"green\",\n",
    "            s=50,  # size of the points in the plots\n",
    "           )\n",
    "plt.title(\"%i points from a 2D standard Gaussian\" % n)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error bars\n",
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Aj4EjgAeB7wMnuvuPMmUG6kGUpehoziLlQvvIN25M3pGH2Wtpt/OpT8GhhyZz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb07e418c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-3, 4, num=100)\n",
    "mean_y = 2 * x ** 2 + x - 1\n",
    "noise_amplitude = 7\n",
    "noise = noise_amplitude * np.random.randn(10, len(mean_y))\n",
    "noisy_y = mean_y + noise\n",
    "std_y = noisy_y.std(axis=0)\n",
    "plt.errorbar(x, mean_y, yerr=std_y, linewidth=2)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.grid(\"on\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to do error bars is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GumbGmCnGmIXGmMnGmCbx1r8PP1FCEY9yFV628CpnsT/fMpZhyfkCVeTm2n7i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\naiAURVGUsKiBUBRFUcKiBkJRFEUJy/8DcqdL4T+mBbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0781c4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, mean_y, c=\"b\", linewidth=2)\n",
    "plt.fill_between(x, mean_y - std_y, mean_y + std_y, color=\"r\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.grid(\"on\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Color bars\n",
    "=========="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/elvis/.local/lib/python2.7/site-packages/PIL/Image.py:84: RuntimeWarning: The _imaging extension was built for another  version of Pillow or PIL\n",
      "  warnings.warn(str(v), RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0xb089516c>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aoyuYxBx+QKEY+YFCEX6gUIQfKBThBwpV8/A7oue5ADo7gEfzHgDYhiJGOyqbYjv0/g7/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\noQg/UKj/B3n9LamtBKAkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0799e2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata  # sklearn's fetcher for machine learning datasets\n",
    "mnist = fetch_mldata(\"MNIST original\")  # fetch MNIST \"handwritten digits\" dataset\n",
    "plt.matshow(mnist.data[30020].reshape((28, 28)))  # get a single image reshape it into a square image, then plot it\n",
    "plt.axis(\"off\")  # switch off xtics and labels on axes\n",
    "plt.colorbar()  # add the colorbar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subplots\n",
    "========"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb09778ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 6\n",
    "plt.figure(figsize=(20, 7))\n",
    "for i in range(n_cols):\n",
    "    ax = plt.subplot(\"1%i%i\" % (n_cols, i))  # figure has 1 row, and a number of columns; select the ith column\n",
    "    ax.matshow(mnist.data[30020 + 2 * i].reshape((28, 28)))\n",
    "    ax.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise\n",
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same plots but with 2 rows and 3 columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic statistics\n",
    "================"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing percentiles\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "a = np.arange(10)\n",
    "stats.percentileofscore(a, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the values were aranged in increasing order, what would be the boundar value for the top 5% ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.5499999999999989"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.scoreatpercentile(a, 95)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}