03 - NumPy arrays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "================"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img  height=\"100\" src=\"files/numpy.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-    a powerful N-dimensional array object\n",
    "-    sophisticated (broadcasting) functions\n",
    "-    tools for integrating C/C++ and Fortran code\n",
    "-    useful linear algebra, Fourier transform, and random number capabilities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#allow graphics inline\n",
    "%matplotlib inline \n",
    "import matplotlib.pylab as plt #import plotting library\n",
    "import numpy as np #import numpy library\n",
    "np.set_printoptions(precision=3) # this is just to make the output look better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to use some real data as an example of array manipulations. This will be the AO index downloaded by wget through a system call (you have to be on Linux of course):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!wget www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how data in the file look like (we again use system call for *head* command):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1950    1  -0.60310E-01\r\n",
      " 1950    2   0.62681E+00\r\n",
      " 1950    3  -0.81275E-02\r\n",
      " 1950    4   0.55510E+00\r\n",
      " 1950    5   0.71577E-01\r\n",
      " 1950    6   0.53857E+00\r\n",
      " 1950    7  -0.80248E+00\r\n",
      " 1950    8  -0.85101E+00\r\n",
      " 1950    9   0.35797E+00\r\n",
      " 1950   10  -0.37890E+00\r\n"
     ]
    }
   ],
   "source": [
    "!head monthly.ao.index.b50.current.ascii"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data in to a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ao = np.loadtxt('monthly.ao.index.b50.current.ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.950e+03,   1.000e+00,  -6.031e-02],\n",
       "       [  1.950e+03,   2.000e+00,   6.268e-01],\n",
       "       [  1.950e+03,   3.000e+00,  -8.127e-03],\n",
       "       ..., \n",
       "       [  2.015e+03,   3.000e+00,   1.837e+00],\n",
       "       [  2.015e+03,   4.000e+00,   1.216e+00],\n",
       "       [  2.015e+03,   5.000e+00,   7.628e-01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(785, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it's a *row-major* order. Matlab and Fortran use *column-major* order for arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ao)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy arrays are statically typed, which allow faster operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can't assign value of different type to element of the numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: Year",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-5a47ddfa9232>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mao\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Year'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: Year"
     ]
    }
   ],
   "source": [
    "ao[0,0] = 'Year'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing works similarly to Matlab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.950e+03,   1.000e+00,  -6.031e-02],\n",
       "       [  1.950e+03,   2.000e+00,   6.268e-01],\n",
       "       [  1.950e+03,   3.000e+00,  -8.127e-03],\n",
       "       [  1.950e+03,   4.000e+00,   5.551e-01],\n",
       "       [  1.950e+03,   5.000e+00,   7.158e-02]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[0:5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can look at the data. This is done by matplotlib.pylab module that we have imported in the beggining as `plt`. We will plot only first 780 poins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x106173990>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Z0l8HHOLXOXuL5CvYLEGIXtMqTi8wmpT7gpL7m2q4As0aY0pcrfKXZWKLvfk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1060ada90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ao[:780,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general it is similar to Matlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 12 elements of **second** column (months). Remember that indexing starts with 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[0:12,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First raw:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.950e+03,   1.000e+00,  -6.031e-02])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create mask, selecting all raws where values in second raw (months) equals 10 (October):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = (ao[:,1]==10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we apply this mask and show only first 5 rowd of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.950e+03,   1.000e+01,  -3.789e-01],\n",
       "       [  1.951e+03,   1.000e+01,  -2.129e-01],\n",
       "       [  1.952e+03,   1.000e+01,  -4.372e-01],\n",
       "       [  1.953e+03,   1.000e+01,  -1.945e-01],\n",
       "       [  1.954e+03,   1.000e+01,   5.126e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[mask][:5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't have to create separate variable for mask, but apply it directly. Here instead of first five rows I show five last rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.010e+03,   1.000e+01,  -4.670e-01],\n",
       "       [  2.011e+03,   1.000e+01,   7.997e-01],\n",
       "       [  2.012e+03,   1.000e+01,  -1.514e+00],\n",
       "       [  2.013e+03,   1.000e+01,   2.628e-01],\n",
       "       [  2.014e+03,   1.000e+01,  -1.134e+00]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[ao[:,1]==10][-5:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can combine conditions. In this case we select October-December data (only first 10 elements are shown):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.950e+03,   1.000e+01,  -3.789e-01],\n",
       "       [  1.950e+03,   1.100e+01,  -5.151e-01],\n",
       "       [  1.950e+03,   1.200e+01,  -1.928e+00],\n",
       "       [  1.951e+03,   1.000e+01,  -2.129e-01],\n",
       "       [  1.951e+03,   1.100e+01,  -6.852e-02],\n",
       "       [  1.951e+03,   1.200e+01,   1.987e+00],\n",
       "       [  1.952e+03,   1.000e+01,  -4.372e-01],\n",
       "       [  1.952e+03,   1.100e+01,  -1.891e+00],\n",
       "       [  1.952e+03,   1.200e+01,  -1.827e+00],\n",
       "       [  1.953e+03,   1.000e+01,  -1.945e-01]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[(ao[:,1]>=10)&(ao[:,1]<=12)][0:10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can assighn values to subset of values (*thi expression fixes the problem with very small value at 2015-04*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ao[ao<-10]=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create example array from first 12 values of second column and perform some basic operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = ao[0:12,1]\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
       "        22.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,  180.,\n",
       "        200.,  220.,  240.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,    4.,    9.,   16.,   25.,   36.,   49.,   64.,   81.,\n",
       "        100.,  121.,  144.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months*months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create *ao_values* that will contain onlu data values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ao_values = ao[:,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.2656999999999998"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao_values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4952999999999999"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao_values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.11983340905732483"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao_values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0069283815727728"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao_values.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-94.069226109999988"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao_values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use *np.sum* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-94.069226109999988"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(ao_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can make operations on the subsets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.38995600000000002"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ao[ao[:,1]==1,2]) # January monthly mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result will be the same if we use method on our selected data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.38995600000000002"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ao[ao[:,1]==1,2].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save your data as a text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt('ao_only_values.csv',ao[:, 2], fmt='%.4f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head of resulting file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0603\r\n",
      "0.6268\r\n",
      "-0.0081\r\n",
      "0.5551\r\n",
      "0.0716\r\n",
      "0.5386\r\n",
      "-0.8025\r\n",
      "-0.8510\r\n",
      "0.3580\r\n",
      "-0.3789\r\n"
     ]
    }
   ],
   "source": [
    "!head ao_only_values.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also save it as binary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f=open('ao_only_values.bin', 'w')\n",
    "ao[:,2].tofile(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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