Intro_to_Pyton_for_Astronomers.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## A quick introduction to Python for Astronomers\n",
    "\n",
    "Written for the Practical Introduction to Research Class at UT Austin in Fall 2015 \n",
    "\n",
    "Updated for TAURUS sumemr research program in June 2016\n",
    "\n",
    "Emma Yu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Backgroud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a few weeks working with IDL, many of you are already IDL ninjas. Now let's have a look at another language in which a lot of Astronomy packages are now available - [Python](https://www.python.org/).\n",
    "\n",
    "Python is an object-orientied, dynamic, general-purpose language. Unlike C and Fortran, it's an [interpreted language](https://en.wikipedia.org/wiki/Interpreted_language), which means the code is interpreted at runtime, and one does not need to compile the code ahead of time. Python is an free and [open source software](https://en.wikipedia.org/wiki/Free_and_open-source_software), which means one does not need to pay anything to use it, and everyone is welcome to contribute and to make the software better! \n",
    "\n",
    "The language was first develoted by [Guido van Rossum](https://www.python.org/~guido/), and the first version appearred in [1991](https://en.wikipedia.org/wiki/Python_(programming_language)). There are a few popular distributions available for easy installation and package management. I personally recomend [Anoconda](https://store.continuum.io/cshop/anaconda/) - with which one can install and manage libraraies all in one place and easily create different working environments. \n",
    "\n",
    "This tutorial is written in [Jupyter notebook](http://jupyter.org), which allows one to write programs in a browser, and save all the instructions, code, and plots in the same notebook. It's great for writing a tutorial like this, but may not be optimal for writing a large project. \n",
    "\n",
    "This tutorial is based on Python 2.7 because at the time when this tutorial was first written, many Astronomy packages were not available in Python 3 yet. For a comparison between the two versions, please see this [wiki article](https://wiki.python.org/moin/Python2orPython3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. A few practical differences between Python and IDL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Similarities:\n",
    "- the data type is dynamic - it can be changed in the code.\n",
    "- index starts from 0.\n",
    "- Note that \"copy\" only generates a new view and does not actually copy the data!!!!\n",
    "\n",
    "#### Differences:\n",
    "\n",
    "- Python uses indentation to denote the structure of the code. \n",
    "- Things are organized as objects in Python.\n",
    "- The basic Python does not usually support what we want to do. One needs to load several packages for example to manipulate multi-dimensional arrays.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3. Array basics\n",
    "\n",
    "For the purposes of this tutorial, we will mostly use the [Numpy](http://www.numpy.org/) package to manipulate array-based data.\n",
    "\n",
    "First, let load the package by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy supports the array data type just as the arrays we've previously seen in IDL. Similar to 'findgen', one can generate an array with indics by: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(15)\n",
    "print a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"a\" is a numpy array object. Numpy array provides a range of a lot of default precedures called \"methods\" that can be applied to numpy array objects. One can use \"dir()\" to find out what methods are avialible.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_wrap__', '__class__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__delslice__', '__div__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', '__hex__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__', '__ilshift__', '__imod__', '__imul__', '__index__', '__init__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__long__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__nonzero__', '__oct__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdiv__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setslice__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']\n"
     ]
    }
   ],
   "source": [
    "print type(a)\n",
    "print dir(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to use the reshape method to reshape the array!\n",
    "In general, one can apply a method to an object by using \"arrayname dot nameofthemethod\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]]\n"
     ]
    }
   ],
   "source": [
    "# For example, to rearrange it\n",
    "a = a.reshape(3,5)\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find out the shape\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int64'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find out the type of data contained in the array\n",
    "a.dtype.name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've seen in the examples, \"#\" denotes comments in Python, and the line after the # sign will not be executed.\n",
    "\n",
    "One can also create arrays by specifying the elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 6 2 6]\n",
      " [5 7 2 8]]\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[4,6,2,6],[5,7,2,8]])\n",
    "print b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to IDL, a function to the array can be broadcasted to every element. So unlike in C and Fortran, one doesn't have to loop through every element. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8 12  4 12]\n",
      " [10 14  4 16]]\n"
     ]
    }
   ],
   "source": [
    "print b*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16 36  4 36]\n",
      " [25 49  4 64]]\n"
     ]
    }
   ],
   "source": [
    "print b**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To slice the array or access individual elements, one can select by location just as in IDL, and the index start from 0 as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 6]\n"
     ]
    }
   ],
   "source": [
    "print b[0,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\":\" means everything, just like \"*\" in IDL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 6 2 6]\n"
     ]
    }
   ],
   "source": [
    "print b[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Control Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- The if statement in Python looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is positive!\n"
     ]
    }
   ],
   "source": [
    "x = 3\n",
    "\n",
    "if x < 0:\n",
    "    print 'x is negative!'\n",
    "elif x == 0:\n",
    "    print 'x is zero!'\n",
    "else:\n",
    "    print 'x is positive!'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note that the begining and end of the statement is marked by indentation, and there is the \":\" after each control statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- A for loop looks like this in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat 3\n",
      "window 6\n",
      "defenestrate 12\n"
     ]
    }
   ],
   "source": [
    "words = ['cat', 'window', 'defenestrate']\n",
    "for w in words:\n",
    "    print w, len(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. User defined functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1: define a function to print out the user's name and age in a nice format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def printinfo( name, age ):\n",
    "    \"This prints a passed info into this function\"\n",
    "    print \"Name: \", name\n",
    "    print \"Age: \", age\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can call printinfo function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  Eva\n",
      "Age:  22\n"
     ]
    }
   ],
   "source": [
    "printinfo( \"Eva\", 22 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can specify variable names, and put them in arbitrary order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  Tom\n",
      "Age:  18\n"
     ]
    }
   ],
   "source": [
    "printinfo( age= 18, name=\"Tom\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: Apply the same function to all array elements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def addminus(a,b):\n",
    "    if a > b:\n",
    "        return a - b\n",
    "    else:\n",
    "        return a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "A function can be very useful when we want to apply the same procedure again and again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=  0 , b=  1 , result=  1\n",
      "a=  3 , b=  3 , result=  6\n",
      "a=  6 , b=  5 , result=  1\n",
      "a=  9 , b=  7 , result=  2\n"
     ]
    }
   ],
   "source": [
    "a = [0,3,6,9]\n",
    "b = [1,3,5,7]\n",
    "\n",
    "for i in range(0,4):\n",
    "    print 'a= ', a[i], ', b= ', b[i], ', result= ', addminus(a[i],b[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Basic plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1: A simple scatter plot of the data above: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1083ecd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(a,b,'o')\n",
    "plt.xlabel('a')\n",
    "plt.ylabel('b')\n",
    "plt.title('A simple scatter plot')\n",
    "plt.axis([-2,12,0,10]) #set the range of axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: A histogram of samples generated from a Standard Normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XL54+bGt9OE0Pt4xKnl7DU1NTtSx/Wmvns3DAcK/5O+epanll5y87vTW8dZbP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107278f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate data from a standard normal distribution\n",
    "x = np.random.randn(10000)\n",
    "\n",
    "plt.hist(x, 50, facecolor='green') # number of bins = 50 \n",
    "plt.xlabel('x')\n",
    "plt.ylabel('count')\n",
    "plt.title('A histogram of the standard normal distribution')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 7. Reading FITS data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Astronomers work with data in FITS format a lot. \n",
    "#### Now let's load the \"Astropy\" package to look at some data we've seen early on in the IDL tutorial!\n",
    "\n",
    "\n",
    "#### This part of the tutorial is modified from [Jacob Hummel's talk](http://www.as.utexas.edu/~jsilverman/gsps/hummel_gsps_f14_ipython_notebook/hummel_gsps_f14_ipython_notebook.html]) on iPython notebook at Graduate Student and Postdoc Seminar \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ngc720j.fits\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    PRIMARY     PrimaryHDU     106   (494, 494)   int16   \n"
     ]
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "\n",
    "hdu_list = fits.open('ngc720j.fits')\n",
    "hdu_list.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# The data in read in as a numpy array: \n",
    "image_data = hdu_list[0].data\n",
    "print(type(image_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(494, 494)\n"
     ]
    }
   ],
   "source": [
    "# To find out the size of the image\n",
    "print(image_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's close the path once we've read in the data\n",
    "hdu_list.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Min:', 3126)\n",
      "('Max:', 18938)\n",
      "('Mean:', 6002.3003040535004)\n",
      "('Stdev:', 1090.0287982981233)\n"
     ]
    }
   ],
   "source": [
    "# plot some statistics of the plot:\n",
    "print('Min:', np.min(image_data))\n",
    "print('Max:', np.max(image_data))\n",
    "print('Mean:', np.mean(image_data))\n",
    "print('Stdev:', np.std(image_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To have a quick look at the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gt0S+55eD1KBI6Jvidx74nftgtbZy99cSRHfusOrSJNeVUqwSZeFzholpYlng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\n3d369+Gw7dR8PH0PupOweig0OdGJ7B6iGuudKLK3U7uP984g5EQBIRI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ZkzHg2U+uEOKF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\nYwdA+tVvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11167c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_data, cmap='afmhot', origin='lower')\n",
    "plt.title('NGC720 J-band image')\n",
    "plt.colorbar()\n",
    "# save it as a ps file\n",
    "plt.savefig('NGC720J.ps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's plot a histogram of the photon statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.flatiter'>\n"
     ]
    }
   ],
   "source": [
    "print(type(image_data.flat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EJKmXAXEe8d3YksbJgJgBvrBLmkUGhCSplwFxnvFoRNK4GBCSpF6nDYgkH0lyIsnBTt+l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\nev1/K3vZvCm7FwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115744510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NBINS = 1000\n",
    "histogram = plt.hist(image_data.flat, NBINS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11213f350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zone into the inner part of the histogram\n",
    "histogram = plt.hist(image_data.flat, NBINS, range=[4000, 10000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 7. Other tips:\n",
    "\n",
    "- To find help: ?thing_you_want_to_understand\n",
    "- Forget what methods you have: use tab key, and ipython notebook will bring up your options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Putting everything together - Calculating the \"look-back-time\" with redshifts!\n",
    "\n",
    "Following content is modified from a notebook by Jacob Hummel: [https://github.com/hummel/AST376_FALL2014/blob/master/homework/homework1.ipynb]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "\n",
    "A note on how to calculate the look-back-time can be found at [http://www.astro.umd.edu/~miller/teaching/astr422/lecture14.pdf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.integrate import quad\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def integrand(z):\n",
    "    return 1/(1+z) / (0.26*(1+z)**3 + 0.74)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           0.40816327   0.81632653   1.2244898    1.63265306\n",
      "   2.04081633   2.44897959   2.85714286   3.26530612   3.67346939\n",
      "   4.08163265   4.48979592   4.89795918   5.30612245   5.71428571\n",
      "   6.12244898   6.53061224   6.93877551   7.34693878   7.75510204\n",
      "   8.16326531   8.57142857   8.97959184   9.3877551    9.79591837\n",
      "  10.20408163  10.6122449   11.02040816  11.42857143  11.83673469\n",
      "  12.24489796  12.65306122  13.06122449  13.46938776  13.87755102\n",
      "  14.28571429  14.69387755  15.10204082  15.51020408  15.91836735\n",
      "  16.32653061  16.73469388  17.14285714  17.55102041  17.95918367\n",
      "  18.36734694  18.7755102   19.18367347  19.59183673  20.        ]\n"
     ]
    }
   ],
   "source": [
    "# discrete redshift points to calculate\n",
    "# np.linespace generates a seq of n numbers between the beginning and ending points\n",
    "# the default n is 50.\n",
    "\n",
    "zp = np.linspace(0,20) \n",
    "print zp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# crate an empty list to save results\n",
    "# we want it to be a list so that we can easily add elements into the list\n",
    "tp = [] \n",
    "\n",
    "for z in zp:\n",
    "    ans,err = quad(integrand, 0, z)\n",
    "    tp.append(ans * 13.88)\n",
    "    \n",
    "# convert the mutable list to a static array when finished.    \n",
    "# This conversion isn't necessary so long as both zp and tp have same number of points.\n",
    "tp = np.asarray(tp) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b7c6390>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P67n/sa1s29mza9nC+ioO22ch+65oYL8VDSyeN0f1+yKzjJLBDNbTN8jap3bw9ye2s259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\nMIfYd+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b79a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(zp,tp)\n",
    "plt.xlabel('Redshift')\n",
    "plt.ylabel('Lookback Time in Gyr')\n",
    "plt.title('Look back time with redshifts')\n",
    "\n",
    "# To save the plot:\n",
    "#plt.savefig('hw1.png', bbox_inches='tight') #bbox_inches option just tightens up the produced figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}