1D-plotting-example-KSMITH.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "from scipy.io import loadmat as loadmat #this is the scipy module that loads .mat files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mpl.rc('font',size=12,weight='bold') #set default font size and weight for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "CMIPmat = loadmat('area_average_trends_19572005.mat')  # load .mat file for CMIP5 model Antarctic surface air temperature time series and trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_all = CMIPmat['AA_avg_all'] #Area-average Antarctic (AA) surface air temperature for 40 CMIP5 from 1957-2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<f8')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA_avg_all.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 49)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA_avg_all.shape #40 CMIP5 models x 49 years (1957-2005) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 244.33431817,  244.07134916,  244.46510697, ...,  243.77483375,\n",
       "         245.00686083,  244.52615183],\n",
       "       [ 243.18314806,  242.74833561,  243.96775206, ...,  243.86073636,\n",
       "         243.89626713,  243.43070348],\n",
       "       [ 237.97451499,  237.03086765,  237.20415361, ...,  237.81353024,\n",
       "         238.78635798,  238.39412982],\n",
       "       ..., \n",
       "       [ 245.08394012,  245.68797715,  246.82771724, ...,  245.96992463,\n",
       "         246.1119029 ,  246.43898527],\n",
       "       [ 239.46379616,  240.05438813,  240.86210841, ...,  241.27807241,\n",
       "         240.93540255,  240.2181941 ],\n",
       "       [ 240.84977552,  241.28208411,  241.74169855, ...,  241.65104609,\n",
       "         241.40601168,  241.5335413 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA_avg_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_all_E = CMIPmat['AA_avg_all_E'] #East AA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_all_W = CMIPmat['AA_avg_all_W'] #West AA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_mmm = AA_avg_all.mean(axis=0) #average over 40 CMIP5 models (mmm = multi-model mean) \n",
    "E_AA_avg_mmm = AA_avg_all_E.mean(axis=0)\n",
    "W_AA_avg_mmm = AA_avg_all_W.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_std = AA_avg_all.std(axis=0) #standard deviation across 40 CMIP5 models\n",
    "E_AA_avg_std = AA_avg_all_E.std(axis=0)\n",
    "W_AA_avg_std = AA_avg_all_W.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "AA_avg_min = np.amin(AA_avg_all,axis=0) #minimum of 40 CMIP5 models\n",
    "E_AA_avg_min = np.amin(AA_avg_all_E,axis=0)\n",
    "W_AA_avg_min = np.amin(AA_avg_all_W,axis=0)\n",
    "AA_avg_max = np.amax(AA_avg_all,axis=0) #maximum of 40 CMIP5 models\n",
    "E_AA_avg_max = np.amax(AA_avg_all_E,axis=0)\n",
    "W_AA_avg_max = np.amax(AA_avg_all_W,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA_avg_min.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M10mat = loadmat('M10_ANN_19602005_AA_trends.mat')  # load .mat file for Monaghan et al. (2010) Antarctic surface air temperature time series reconstruction and trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_M10 = M10mat['AA_avg'] #area-averaged AA SAT \n",
    "E_AA_avg_M10 = M10mat['E_AA_avg'] #area-averaged East AA SAT \n",
    "W_AA_avg_M10 = M10mat['W_AA_avg'] #area-averaged West AA SAT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ERAImat = loadmat('ERAI_19792005_AA_trends.mat')  # load .mat file for ERA-Interim Antarctic surface air temperature time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AA_avg_ERAI = np.mean(ERAImat['AA_avg']) #area-averaged and climatological average (average over year 1979-2005) of ERAI AA SAT \n",
    "E_AA_avg_ERAI = np.mean(ERAImat['E_AA_avg']) #area-averaged and climatological average (average over year 1979-2005) of ERAI East AA SAT \n",
    "W_AA_avg_ERAI = np.mean(ERAImat['W_AA_avg']) #area-averaged and climatological average (average over year 1979-2005) of ERAI West AA SAT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year = np.arange(1,len(AA_avg_mmm)+1,1) #create a variable called year with values 1-49, i.e. the same length as the number of year in the CMIP5 time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year2 = year[3:49] #create a shorter subset of variable 'year' for the M10 time series\n",
    "year3 = year[22:49] #create an even shorter subset of variable 'year' for the ERAI time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "AA_avg_ERAI_clim = np.full((27,1),AA_avg_ERAI) #create arrays with dimension (27,1) but with the same value in each cell\n",
    "E_AA_avg_ERAI_clim = np.full((27,1),E_AA_avg_ERAI)\n",
    "W_AA_avg_ERAI_clim = np.full((27,1),W_AA_avg_ERAI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v9ikUPYGSNcePjsocPc8raErQWCnlnuPZPoWiu1Hy5tjTETmjxjWK3kR3KlURNGHzDZpg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2lqGqGtbW0JFUBz8zDXXPMEjz9+LQBvfON315xQAVRXZ9mz54U1b18K3d19HD7cyw9/eDP33PNO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\nqRIRERERESmCkioREREREZEi/P8lhddLDNqQdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106cb44e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#big plot example\n",
    "fig = plt.figure(figsize=(12,24)) #define figure size\n",
    "ax1 = plt.subplot2grid((6,12),(0,0),colspan=4) #define location of subplots\n",
    "ax2 = plt.subplot2grid((6,12),(0,4),colspan=4)\n",
    "ax3 = plt.subplot2grid((6,12),(0,8),colspan=4)\n",
    "\n",
    "#subplot 1\n",
    "ax1.fill_between(year,AA_avg_min,AA_avg_max,facecolor = 'lightgray',edgecolor = 'lightgray') #fill_between shades the area between two curves, here min and max\n",
    "ax1.fill_between(year,AA_avg_mmm - AA_avg_std,AA_avg_mmm + AA_avg_std,facecolor = 'gray',edgecolor = 'gray') #same as above but for the mmm +/- 1 sigma\n",
    "ax1.plot(year, AA_avg_mmm, 'k',linewidth=2) #simple line plot in black\n",
    "ax1.plot(year2, AA_avg_M10.T, 'b',linewidth=2) #simple line plot in blue (note x value is differnt than above)\n",
    "ax1.plot(year3, AA_avg_ERAI_clim,'r',linewidth=2) #simple line plot in red (note x values is again differnt than above)\n",
    "ax1.axis([3,49,220,265]) #set x- and y-axis limits; only want to plot from 1960 onwards\n",
    "x = np.arange(3,50,10) #create a vector to define tick spacing for x-axis\n",
    "ax1.set_xticks(x) #add ticks to x-axis\n",
    "ax1.set_xticklabels([i+1957 for i in x]) #tick labels\n",
    "ax1.set_ylabel('SAT (K)',fontsize=12,fontweight='bold') #ylabel\n",
    "ax1.set_xlabel('Year',fontsize=12,fontweight='bold') #xlabel\n",
    "ax1.set_title('(a) Total Antartic',fontsize=12,fontweight='bold') #title\n",
    "\n",
    "#subplot 2\n",
    "ax2.plot([],[],color = 'lightgray',linewidth=10,label = 'CMIP5 Range') #create an empty plot to use for figure legend (this is necessary because fill_between shading cannot be included in a legend)\n",
    "ax2.plot([],[],color = 'gray',linewidth=10,label = 'CMIP5 1$\\sigma$') #create an empty plot to use for figure legend\n",
    "ax2.fill_between(year,E_AA_avg_min,E_AA_avg_max,facecolor = 'lightgray',edgecolor = 'lightgray')\n",
    "ax2.fill_between(year,E_AA_avg_mmm - E_AA_avg_std,E_AA_avg_mmm + E_AA_avg_std,facecolor = 'gray',edgecolor = 'gray')\n",
    "ax2.plot(year, E_AA_avg_mmm, 'k',label = 'CMIP5 MMM',linewidth=2) #labels are communicated to the legend function\n",
    "ax2.plot(year2, E_AA_avg_M10.T, 'b',label = 'M10',linewidth=2)\n",
    "ax2.plot(year3, E_AA_avg_ERAI_clim,'r',label = 'ERAI',linewidth=2)\n",
    "ax2.axis([4,49,220,265])\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels([i+1957 for i in x])\n",
    "ax2.set_ylabel('SAT (K)',fontsize=12,fontweight='bold')\n",
    "ax2.set_xlabel('Year',fontsize=12,fontweight='bold')\n",
    "ax2.set_title('(b) East Antartic',fontsize=12,fontweight='bold')\n",
    "ax2.legend(loc='upper center') #adds legend to subplot\n",
    "\n",
    "#subplot 3\n",
    "ax3.fill_between(year,W_AA_avg_min,W_AA_avg_max,facecolor = 'lightgray',edgecolor = 'lightgray')\n",
    "ax3.fill_between(year,W_AA_avg_mmm - W_AA_avg_std,W_AA_avg_mmm + W_AA_avg_std,facecolor = 'gray',edgecolor = 'gray')\n",
    "ax3.plot(year, W_AA_avg_mmm, 'k',linewidth=2)\n",
    "ax3.plot(year2, W_AA_avg_M10.T, 'b',linewidth=2)\n",
    "ax3.plot(year3, W_AA_avg_ERAI_clim,'r',linewidth=2)\n",
    "ax3.axis([4,49,220,265])\n",
    "ax3.set_xticks(x)\n",
    "ax3.set_xticklabels([i+1957 for i in x])\n",
    "ax3.set_ylabel('SAT (K)',fontsize=12,fontweight='bold')\n",
    "ax3.set_xlabel('Year',fontsize=12,fontweight='bold')\n",
    "ax3.set_title('(c) West Antartic',fontsize=12,fontweight='bold')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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