pybootcamp-part2/01-numpy.ipynb

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   "source": [
    "# Analysing Star Data"
   ]
  },
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   "source": [
    "<section class=\"objectives panel panel-warning\">\n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-certificate\"></span>Learning Objectives</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    "> * Explain what a library is, and what libraries are used for.\n",
    "* Load a Python library and use the things it contains. \n",
    "* Read tabular data from a file into a program. \n",
    "* Assign values to variables.\n",
    "* Select individual values and subsections from data. \n",
    "* Perform operations on arrays of data. \n",
    "* Display simple graphs. \n",
    "\n",
    "---\n",
    "\n",
    "Words are useful, but what’s more useful are the sentences and stories we build with them. Similarly, while a lot of powerful tools are built into languages like Python, even more live in the libraries they are used to build.\n",
    "\n",
    "In order to load our star data, we need to import a library called NumPy. In general you should use this library if you want to do fancy things with numbers, especially if you have matrices or arrays. We can load NumPy using:"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing a library is like getting a piece of lab equipment out of a storage locker and setting it up on the bench. Once you’ve loaded the library, we can ask the library to read our data file for us: "
   ]
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       "array([[ 18.82835767,  18.68449456,  18.36846526, ...,  22.74609287,\n",
       "         21.59114633,  22.94081404],\n",
       "       [ 18.44871905,  19.02446975,  20.04453884, ...,  23.23142209,\n",
       "         21.62410045,  23.72676083],\n",
       "       [ 17.59471749,  17.86504252,  19.04176145, ...,  21.65369823,\n",
       "         22.58501188,  22.75590757],\n",
       "       ..., \n",
       "       [ 19.14663683,  17.5576204 ,  17.7209417 , ...,  23.08328494,\n",
       "         23.2540362 ,  22.54150028],\n",
       "       [ 17.91820205,  17.51246092,  19.22285053, ...,  22.40570422,\n",
       "         22.20877154,  23.69307697],\n",
       "       [ 18.99298186,  18.17059033,  20.43678823, ...,  23.82297922,\n",
       "         22.80203104,  23.13107049]])"
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   "source": [
    "numpy.loadtxt(fname='data/star_data_01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression `numpy.loadtxt(...)` is a [function call](reference.html#function-call)\n",
    "that asks Python to run the function `loadtxt` that belongs to the `numpy` library.\n",
    "This [dotted notation](reference.html#dotted-notation) is used everywhere in Python\n",
    "to refer to the parts of things as `thing.component`.\n",
    "\n",
    "`numpy.loadtxt` has two [parameters](reference.html#parameter): the name of the file we want to read, and the [delimiter](reference.html#delimiter) that separates values on a line. These both need to be character strings (or [strings](reference.html#string) for short), so we put them in quotes.\n",
    "\n",
    "When we are finished typing and press <kbd>Shift</kbd>+<kbd>Enter</kbd>, the notebook runs our command. Since we haven't told it to do anything else with the function's output, the notebook displays it.\n",
    "In this case, that output is the data we just loaded. By default, only a few rows and columns are shown (with `...` to omit elements when displaying big arrays).\n",
    "\n",
    "Our call to `numpy.loadtxt` read our file, but didn't save the data in memory. To do that,\n",
    "we need to [assign](reference.html#assignment) the array to a [variable](reference.html#variable). A variable is just a name for a value, such as `x`, `current_temperature`, or `subject_id`.\n",
    "\n",
    "\n",
    "## Variables\n",
    "\n",
    "Python's variables must begin with a letter and are [case sensitive](reference.html#case-sensitive). We can create a new variable by assigning a value to it using `=`.\n",
    "As an illustration, let's step back and instead of considering a table of data, consider the simplest \"collection\" of data, a single value. The line below assigns the value `55` to a variable `weight_kg`:"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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    "weight_kg = 55"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This statement says to the computer \"put the value of `55` inside the box labelled `weight_kg`\". This is useful because we can use it later in calculations. For example: once a variable has a value, we can print it to the screen: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and do arithmetic with it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in pounds 121.00000000000001\n"
     ]
    }
   ],
   "source": [
    "print('weight in pounds', 2.2*weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change a variable’s value by assigning it a new one: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms is now: 57.5\n"
     ]
    }
   ],
   "source": [
    "weight_kg = 57.5\n",
    "print ('weight in kilograms is now:', weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the example above shows, we can print several things at once by separating them with commas.\n",
    "\n",
    "We should imagine the variable as a box to store something in the computer's memory. The name of the variable is the label attached to the box, like a sticky note.\n",
    "\n",
    "<img src='images/python-sticky-note-variables-01.svg'/>\n",
    "<div style=\"text-align: center;\">Figure: Variables as sticky note labels on boxes</div>\n",
    "\n",
    "This means that assigning a value to one variable does *not* change the values of other variables. For example, let’s store the subject’s weight in pounds in a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms: 57.5 and in pounds: 126.50000000000001\n"
     ]
    }
   ],
   "source": [
    "weight_lb = 2.2 * weight_kg\n",
    "print ('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='images/python-sticky-note-variables-02.svg'/>\n",
    "<div style=\"text-align: center;\">Figure: Creating another variable</div>\n",
    "\n",
    "and then change `weight_kg`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms is now: 100.0 and weight in pounds is still: 126.50000000000001\n"
     ]
    }
   ],
   "source": [
    "weight_kg = 100.0\n",
    "print ('weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='images/python-sticky-note-variables-03.svg'/>\n",
    "<div style=\"text-align: center;\">Figure: Updating a variable</div>\n",
    "    \n",
    "Since `weight_lb` doesn’t “remember” where its value came from, it isn’t \n",
    "automatically updated when `weight_kg` changes. This is different from the way \n",
    "spreadsheets work.\n",
    "\n",
    "The statement `weight_kg=100.0` is not a mathematical equation. It's an instruction to the computer which means \"take the value on the RHS (`100.0`), and store it in a variable with the name on the LHS (`weight_kg`)\". The RHS is calculated first, then the result is put in the variable named on the left. Therefore, the following statement makes perfect sense in Python!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms is now: 95.0\n"
     ]
    }
   ],
   "source": [
    "weight_kg = weight_kg - 5\n",
    "print ('weight in kilograms is now:', weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as we can assign a single value to a variable, we can also assign an array of values to a variable using the same syntax. Let’s re-run `numpy.loadtxt` and save its result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(fname='data/star_data_01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This statement doesn’t produce any output because assignment doesn’t display anything. If we want to check that our data has been loaded, we can print the variable’s value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18.82835767  18.68449456  18.36846526 ...,  22.74609287  21.59114633\n",
      "   22.94081404]\n",
      " [ 18.44871905  19.02446975  20.04453884 ...,  23.23142209  21.62410045\n",
      "   23.72676083]\n",
      " [ 17.59471749  17.86504252  19.04176145 ...,  21.65369823  22.58501188\n",
      "   22.75590757]\n",
      " ..., \n",
      " [ 19.14663683  17.5576204   17.7209417  ...,  23.08328494  23.2540362\n",
      "   22.54150028]\n",
      " [ 17.91820205  17.51246092  19.22285053 ...,  22.40570422  22.20877154\n",
      "   23.69307697]\n",
      " [ 18.99298186  18.17059033  20.43678823 ...,  23.82297922  22.80203104\n",
      "   23.13107049]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data is in memory, we can start doing things with it. First,\n",
    "let's ask what [type](reference.html#type) of thing `data` refers to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print( type(data) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output tells us that `data` currently refers to an N-dimensional array created by the NumPy library. These data corresponds to star's brightness over time. The rows are the individual stars and the columns are their brightness measurements.\n",
    "\n",
    "We can see what its [shape](reference.html#shape) is like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 101)\n"
     ]
    }
   ],
   "source": [
    "print( data.shape )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that `data` has 20 rows and 101 columns. \n",
    "\n",
    "When we created the\n",
    "variable `data` to store our star data, we didn't just create the array, we also created information about the array, called [members](reference.html#member) or attributes. This extra information describes `data` in the same way an adjective describes a noun.\n",
    "\n",
    "`data.shape` is an attribute  of `data` which described the dimensions of `data`. We use the same dotted notation for the attributes of variables that we use for the functions in libraries because they have the same part-and-whole relationship.\n",
    "\n",
    "If we want to get a single number from the array, we must provide an [index](reference.html#index) in square brackets, just as we do in math:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first value in data: 18.8283576684\n"
     ]
    }
   ],
   "source": [
    "print('first value in data:', data[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "middle value in data: 22.9296967381\n"
     ]
    }
   ],
   "source": [
    "print('middle value in data:', data[10,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression `data[10, 50]` may not surprise you if you've done some programming before,\n",
    "but `data[0, 0]` might. Programming languages like Fortran and MATLAB start counting at 1,\n",
    "because that's what human beings have done for thousands of years. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that's simpler for computers to do.\n",
    "\n",
    "As a result, if we have an M$\\times$N array in Python, its indices go from 0 to M-1 on the first axis and 0 to N-1 on the second. It takes a bit of getting used to, but one way to remember the rule is that the index is how many steps we have to take from the start to get the item we want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"panel panel-info\">\n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-thumb-tack\"></span>In the corner</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    "> What may also surprise you is that when Python displays an array,\n",
    "> it shows the element with index `[0, 0]` in the upper left corner\n",
    "> rather than the lower left.\n",
    "> This is consistent with the way mathematicians draw matrices,\n",
    "> but different from the Cartesian coordinates.\n",
    "> The indices are (row, column) instead of (column, row) for the same reason,\n",
    "> which can be confusing when plotting data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An index like `[10, 50]` selects a single element of an array,\n",
    "but we can select whole sections as well.\n",
    "For example,\n",
    "we can select the first five measurements (columns) of brightness\n",
    "for the first four stars (rows) like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18.82835767  18.68449456  18.36846526  19.58984702  20.03332339]\n",
      " [ 18.44871905  19.02446975  20.04453884  18.37560557  20.13318031]\n",
      " [ 17.59471749  17.86504252  19.04176145  18.6676958   19.09431148]\n",
      " [ 18.1584409   19.6630396   19.33900909  19.53610008  19.49240261]]\n"
     ]
    }
   ],
   "source": [
    "print( data[0:4, 0:5] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The [slice](reference.html#slice) `0:4` means, \"Start at index 0 and go up to, but not including, index 4.\" Again, the up-to-but-not-including takes a bit of getting used to, but the rule is that the difference between the upper and lower bounds is the number of values in the slice.\n",
    "\n",
    "We don't have to start slices at 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18.54685036  17.38553589  18.62617314  18.97304937  18.46415131]\n",
      " [ 18.67357342  18.41288864  18.79151865  18.18382694  19.81400528]\n",
      " [ 17.85769513  17.60871584  17.3801041   19.27209263  19.8817563 ]\n",
      " [ 18.93911752  18.38229775  18.65671745  19.50607296  18.65884615]\n",
      " [ 18.01124928  17.54423449  19.16271396  18.79464109  19.12882441]]\n"
     ]
    }
   ],
   "source": [
    "print (data[5:10, 0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also don't have to include the upper and lower bound on the slice. If we don't include the lower bound, Python uses 0 by default;\n",
    "if we don't include the upper, the slice runs to the end of the axis,\n",
    "and if we don't include either (i.e., if we just use ':' on its own),\n",
    "the slice includes everything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small is:\n",
      "[[ 22.74609287  21.59114633  22.94081404]\n",
      " [ 23.23142209  21.62410045  23.72676083]\n",
      " [ 21.65369823  22.58501188  22.75590757]]\n"
     ]
    }
   ],
   "source": [
    "small = data[:3, 98:]\n",
    "print ('small is:')\n",
    "print (small)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arrays also know how to perform common mathematical operations on their values. The simplest operations with data are arithmetic: add, subtract, multiply, and divide. When you do such operations on arrays, the operation is done on each individual element of the array.\n",
    "\n",
    "Thus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doubledata = data * 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "will create a new array `doubledata` whose elements have the value of two times the value of the corresponding elements in `data`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original:\n",
      "[[ 22.74609287  21.59114633  22.94081404]\n",
      " [ 23.23142209  21.62410045  23.72676083]\n",
      " [ 21.65369823  22.58501188  22.75590757]]\n",
      "doubledata:\n",
      "[[ 45.49218574  43.18229266  45.88162809]\n",
      " [ 46.46284418  43.2482009   47.45352166]\n",
      " [ 43.30739646  45.17002377  45.51181515]]\n"
     ]
    }
   ],
   "source": [
    "print ('original:')\n",
    "print (data[:3,98:])\n",
    "print ('doubledata:')\n",
    "print (doubledata[:3,98:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If, instead of taking an array and doing arithmetic with a single value (as above) you did the arithmetic operation with another array of the same shape, the operation will be done on corresponding elements of the two arrays.\n",
    "\n",
    "Thus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tripledata = data + doubledata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "will give you an array where `tripledata[0,0]` will equal `doubledata[0,0]` plus `data[0,0]`, and so on for all other elements of the arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tripledata:\n",
      "[[ 68.2382786   64.77343899  68.82244213]\n",
      " [ 69.69426627  64.87230134  71.18028248]\n",
      " [ 64.96109469  67.75503565  68.26772272]]\n"
     ]
    }
   ],
   "source": [
    "print ('tripledata:')\n",
    "print (tripledata[:3,98:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, we want to do more than add, subtract, multiply, and divide values of data.\n",
    "Arrays also know how to do more complex operations on their values.\n",
    "If we want to find the average brightness for all stars at all times,\n",
    "for example, we can just ask the array for its mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.5002003719\n"
     ]
    }
   ],
   "source": [
    "print (data.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`mean` is a [method](reference.html#method) of the array, i.e., a function that belongs to it in the same way that the member `shape` does. If variables are nouns, methods are verbs: they are what the thing in question knows how to do. We need empty parentheses for `data.mean()`,\n",
    "even when we're not passing in any parameters, to tell Python to go and do something for us. `data.shape` doesn't need `()` because it is just a description but `data.mean()` requires the `()` because it is an action. This will become clearer when we cover functions later.\n",
    "\n",
    "Numpy arrays have lots of useful methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum brightness: 26.9747210312\n",
      "minimum brightness: 16.4477700722\n",
      "standard deviation: 1.71095670561\n"
     ]
    }
   ],
   "source": [
    "print ('maximum brightness:', data.max())\n",
    "print ('minimum brightness:', data.min())\n",
    "print ('standard deviation:', data.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When analyzing data, though, we often want to look at partial statistics, such as the maximum brightness per star or the average brightness per day. One way to do this is to create a new temporary array of the data we want, then ask it to do the calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum brightness for star 0: 26.0072998375\n"
     ]
    }
   ],
   "source": [
    "star_0 = data[0,:] # 0 on the first axis (row), everything on the second (column)\n",
    "print ('maximum brightness for star 0:', star_0.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don’t actually need to store the row in a variable of its own. Instead, we can combine the selection and the method call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean brightness for star 2: 22.4261580993\n"
     ]
    }
   ],
   "source": [
    "print ('mean brightness for star 2:', data[2,:].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we need the maximum brightness for all stars (as in the next diagram on the left), or the average at each time (as in the diagram on the right)? As the diagram below shows, we want to perform the operation across an axis: \n",
    "\n",
    "<img src='images/numpy_apply_axis.svg'/>\n",
    "<div style=\"text-align: center;\">Figure: Operations across axes</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To support this, most array methods allow us to specify the axis we want to work on. If we ask for the average across axis 0 (rows in our 2D example), we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 18.09248632  18.35365907  18.85192129  18.71415503  19.37229196\n",
      "  19.52248221  19.67364454  20.29042241  20.4574627   20.31294399\n",
      "  20.57276817  20.81105239  21.22518779  21.33178541  21.27505307\n",
      "  21.18294918  21.55675437  21.40098742  21.51005242  21.79133686\n",
      "  21.71258645  21.95332038  22.1794417   21.76243396  21.90496963\n",
      "  22.30208365  22.00749989  21.8791797   21.86355606  21.85633962\n",
      "  21.89303383  21.44298308  21.86877405  21.85459712  22.06337395\n",
      "  21.57714292  21.49346007  21.31626957  21.40087753  21.2563051\n",
      "  21.21483226  21.50582255  21.25908068  22.17820386  22.26884077\n",
      "  23.01761462  23.31593697  23.78381811  24.08434858  24.18429216\n",
      "  24.04024153  23.82691284  24.10082433  24.13538645  23.97002714\n",
      "  24.02488626  24.34074191  24.338722    24.13667692  24.13243851\n",
      "  24.09342369  24.82766196  24.37718333  24.77509604  24.77981012\n",
      "  24.72882825  24.25050139  24.30764042  24.39156066  24.23850235\n",
      "  24.1826964   24.13769011  24.17456216  23.77952067  23.77599953\n",
      "  23.52540135  23.54859622  23.47995816  23.30139227  23.1124789\n",
      "  23.25623871  22.86721499  22.68945751  22.56457647  22.37963091\n",
      "  22.37735687  22.84335853  22.6586068   22.63715706  22.88578965\n",
      "  22.85114912  23.08154727  23.27159687  23.56745498  23.29833557\n",
      "  23.24320422  23.33254035  22.82910288  23.16761664  22.49790649\n",
      "  22.68461845]\n"
     ]
    }
   ],
   "source": [
    "print (data.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a quick check, we can ask this array what its shape is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101,)\n"
     ]
    }
   ],
   "source": [
    "print  ( data.mean(axis=0).shape )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression `(101,)` tells us we have an N$\\times$1 vector,\n",
    "so this is the average brightness as a function of time for all stars.\n",
    "\n",
    "## Visualising Data\n",
    "\n",
    "The mathematician Richard Hamming once said, \"The purpose of computing is insight, not numbers,\" and the best way to develop insight is often to visualize data. Visualization deserves an entire lecture (or course) of its own, but we can explore a few features of Python's `matplotlib` library here. While there is no \"official\" plotting library, this package is the de facto standard. First, we will import the `pyplot` module from `matplotlib` and use two of its functions to create and display a heat map of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4SpSb2BrT0JYlLgtaI8SrJjbqhlADcpmhCKIDNjTvsnVrQ1LqmjA/1eYLT4qn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\nGTNm3AD8fzyLW9Zl2DwhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111099550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot \n",
    "%matplotlib inline \n",
    "image  = matplotlib.pyplot.imshow(data) \n",
    "matplotlib.pyplot.show(image) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dark regions in this heat map are low values, while light regions show high values. As we can see, most stars in our data show roughly the same trends with time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"panel panel-info\">\n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-thumb-tack\"></span>Some IPython 'magic'</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    "> If you’re using an IPython / Jupyter notebook, you’ll need to execute the following command in order for your matplotlib images to appear in the notebook when ```show()``` is called:\n",
    "\n",
    "> ```python\n",
    "> % matplotlib inline\n",
    "> ```\n",
    "\n",
    "> The % indicates an IPython magic function - a function that is only valid within the notebook environment. Note that you only have to execute this function once per notebook.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the average brightness of a star over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3yNafMHuKdTndkEBfMtJjyC2pp6CswelxvvaJ5l+bT9s3Oh8plZIUhBgUR/oU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\nu/8P2aLBIqWef+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cb6828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ave_brightness = data.mean(axis=0)\n",
    "ave_plot = matplotlib.pyplot.plot(ave_brightness)\n",
    "matplotlib.pyplot.xlabel('Time')\n",
    "matplotlib.pyplot.ylabel('Brightness')\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have put the average across all stars in the variable `ave_brightness`, then asked `matplotlib.pyplot` to create and display a line graph of those values.\n",
    "\n",
    "Since most stars follow the same trend, these changes in brightness can't be intrinsic to the stars themselves. They are probably changes due to clouds absorbing some of the light from all of the stars we see in our data.\n",
    "\n",
    "We could see if any of the stars deviate from this common trend by dividing the brightness of each star the average brightness of all stars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TPl/9GLKfnKQ3dNjgcUZwYmho4RyZ7/h/qZg/p9Oyuh125N09hIxh+8jw9Ezc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\nIyN3gDGZj4yMjNwBxmQ+MjIycgf434rkMf4LAzwVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c62860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processed_data = data / ave_brightness\n",
    "image  = matplotlib.pyplot.imshow(processed_data) \n",
    "matplotlib.pyplot.show(image) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like all of our stars just follow this general trend, since there is nothing but random noise left in the processed data. If any of our stars were varying, it should stand out in this plot. \n",
    "\n",
    "Interestingly, it looks like all of our stars show roughly the same amount of noise - a fact that can be confirmed using `numpy` and an extra plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std. deviation of star 0 is  0.0321183778763\n",
      "Std. deviation of star 1 is  0.0320419928419\n",
      "Std. deviation of star 2 is  0.031276317636\n"
     ]
    },
    {
     "data": {
      "image/png": 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qDBwvxNX5dt/PhFEMHtNcKwuuH7xOj3+TufzJAkCEdAmP5pmzcOfm0Txxsvd3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BAAAg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\nkDBE+AVBEBKGCL8gCELC+P/2KJuiFKKh7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c898dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Std. deviation of star 0 is \", processed_data[0,:].std())\n",
    "print(\"Std. deviation of star 1 is \", processed_data[1,:].std())\n",
    "print(\"Std. deviation of star 2 is \", processed_data[2,:].std())\n",
    "matplotlib.pyplot.plot(processed_data[0,:])\n",
    "matplotlib.pyplot.plot(processed_data[1,:])\n",
    "matplotlib.pyplot.plot(processed_data[2,:])\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"challenge panel panel-success\"> \n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-pencil\"></span>Check your understanding</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    "> What variables refer to what values after each statement in the following program? Check your answer with the person next to you, then run the program in the cell below and see if you were correct.\n",
    "```python\n",
    "mass = 47.5\n",
    "age  = 122\n",
    "mass = mass*2.0\n",
    "age  = age-20\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# INSERT YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"challenge panel panel-success\"> \n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-pencil\"></span>Sorting out references</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    "> What does the following program print out?\n",
    "```python\n",
    "first, second = 'Grace', 'Hopper'\n",
    "third, fourth = second, first\n",
    "print(third, fourth)\n",
    "```\n",
    "Run the code below and see if you were right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# INSERT YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"challenge panel panel-success\"> \n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-pencil\"></span>Slicing strings</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    ">A section of an array is called a [slice](reference.html#slice). We can take slices of character strings as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first three characters: oxy\n",
      "last three characters: gen\n"
     ]
    }
   ],
   "source": [
    "element = 'oxygen'\n",
    "print ('first three characters:', element[0:3])\n",
    "print ('last three characters:', element[3:6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> What is the value of `element[:4]`?\n",
    "> What about `element[4:]`?\n",
    "> Or `element[:]`?\n",
    ">\n",
    "> What is `element[-1]`?\n",
    "> What is `element[-2]`?\n",
    "> Given those answers,\n",
    "> explain what `element[1:-1]` does."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<section class=\"challenge panel panel-success\"> \n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-pencil\"></span>Make your own plot</h2>\n",
    "</div>\n",
    "</section>\n",
    "\n",
    ">Create a plot showing the standard deviation (`numpy.std`) of each star against the star number"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "IPython (Python 3)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
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 },
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