plot_vert_heatmapProfiles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "date = 20170714  # Format:  YYYYMMDD\n",
    "plotTG = True  # True if trace gas, otherwise aerosol extinction assumed\n",
    "#whatisit = 'AerExt'   # What is being plotted (for save name)\n",
    "whatisit = 'NO2'\n",
    "#whatisit = 'HCHO'\n",
    "\n",
    "#root = 'C:\\\\Sciatran2\\\\AEROSOL_RETRIEVAL_v-1-5\\\\Campaign\\\\'\n",
    "folder = 'BM_aer_retr_Julyfireperiod\\\\'+str(date)+'\\\\profiles\\\\NO2_profiles\\\\'\n",
    "\n",
    "root = 'C:\\\\Sciatran2\\\\TRACEGAS_RETRIEVAL_v-1-4\\\\Campaign\\\\'\n",
    "#folder = 'BM_aer_retr1_HCHO\\\\20170221\\\\profiles\\\\'\n",
    "\n",
    "total_path = root+folder\n",
    "\n",
    "retrieved_ = pd.DataFrame()\n",
    "height_ = pd.DataFrame()\n",
    "times = []\n",
    "\n",
    "allFiles = glob.glob(total_path + \"/*.dat\")\n",
    "for file in allFiles:\n",
    "    name = file[-29:]\n",
    "    year = name[10:14]\n",
    "    month = name[14:16]\n",
    "    day = name[16:18]\n",
    "    hour = name[19:21]\n",
    "    minute = name[21:23]\n",
    "    sec = name[23:25]\n",
    "\n",
    "    date = day+'/'+month+'/'+year\n",
    "    time = hour+':'+minute\n",
    "    #print(date+' '+time)\n",
    "    f = pd.read_csv(file, header=0, delim_whitespace=True)\n",
    "    #retrieved_['0'] = f['z']\n",
    "    #retrieved['z'] = f['z']\n",
    "    if plotTG == True:\n",
    "        retrieved_[str(time)] = f['retr_vmr']\n",
    "    else:\n",
    "        retrieved_[str(time)] = f['retrieved']\n",
    "    times.append(date+' '+time)\n",
    "    \n",
    "retrieved = retrieved_.transpose()\n",
    "retrieved = retrieved.reset_index()\n",
    "Retrieved_ = retrieved.set_index(retrieved['index'])\n",
    "Retrieved_ = Retrieved_.drop(['index'], axis=1)\n",
    "Retrieved = Retrieved_.transpose()\n",
    "Retrieved['z'] = f['z']\n",
    "data = Retrieved.set_index(['z'])\n",
    "data = data.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YM3sh8NN8+y2pA/fuvEEREQmrVNKP8a0B5jXuseFzoLHb5gFPpWLc/Q/uvgj4KvCFZNeb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\npaFBSURESkODkoiIlIYGJRERKY3/DxjbClY56UxrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbef4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "sns.heatmap(data[:], ax=ax, cmap ='RdYlBu_r', vmin=0, vmax=0.005)\n",
    "ax.invert_yaxis()\n",
    "ax.invert_xaxis()\n",
    "#ax.set_xticklabels(data_['DateTime'].dt.strftime('%hh:%mm:%ss'))\n",
    "#ax.xaxis.set_major_locator(dates.HourLocator())\n",
    "for label in ax.xaxis.get_ticklabels()[::2]:\n",
    "    label.set_visible(False)\n",
    "for label in ax.yaxis.get_ticklabels()[::2]:\n",
    "    label.set_visible(False)\n",
    "    \n",
    "fig.savefig(root+folder+whatisit+'_plot.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0xbecf780>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7mrvJKY2fPbIkzlS5rc/Kw66aTtKKThd4uQM+q+ryMxP44s3zuO/RHf3K8tMd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\nqeqyuZEsE4ZN96abGa2yDMDMVpWNeTEhzQwNZ9k7neG502sNzvl928e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+QThrKSVifAK6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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbecf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(data, cmap='RdYlBu_r')\n",
    "plt.colorbar\n",
    "plt.contour(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "#root = 'C:\\\\Sciatran2\\\\AEROSOL_RETRIEVAL_v-1-5\\\\Campaign\\\\'\n",
    "#folder = 'BM_aer_retr1\\\\20170221\\\\profiles\\\\'\n",
    "root = 'C:\\\\Sciatran2\\\\TRACEGAS_RETRIEVAL_v-1-4\\\\Campaign\\\\'\n",
    "folder = 'BM_aer_retr1\\\\20170221\\\\profiles\\\\'\n",
    "\n",
    "total_path = root+folder\n",
    "\n",
    "retrieved_ = pd.DataFrame()\n",
    "height_ = pd.DataFrame()\n",
    "times = []\n",
    "\n",
    "allFiles = glob.glob(total_path + \"/*.dat\")\n",
    "for file in allFiles:\n",
    "    name = file[-29:]\n",
    "    year = name[10:14]\n",
    "    month = name[14:16]\n",
    "    day = name[16:18]\n",
    "    hour = name[19:21]\n",
    "    minute = name[21:23]\n",
    "    sec = name[23:25]\n",
    "\n",
    "    date = day+'/'+month+'/'+year\n",
    "    time = hour+':'+minute+':'+sec\n",
    "    #print(date+' '+time)\n",
    "    f = pd.read_csv(file, header=0, delim_whitespace=True)\n",
    "    #retrieved_['0'] = f['z']\n",
    "    #retrieved['z'] = f['z']\n",
    "    retrieved_[str(time)] = f['retr_vmr']\n",
    "    times.append(date+' '+time)\n",
    "height_['z'] = f['z']\n",
    "\n",
    "y = height_.as_matrix()\n",
    "x = times\n",
    "z = retrieved_.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '21/02/2017 06:08:30'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-6e1cd8698fc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mcontourf\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2872\u001b[0m                       mplDeprecation)\n\u001b[0;32m   2873\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2874\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2875\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2876\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1895\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1896\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1897\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mcontourf\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   5833\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5834\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'filled'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5835\u001b[1;33m         \u001b[0mcontours\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcontour\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuadContourSet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5836\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5837\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontours\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m    862\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'transform'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 864\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    865\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m_process_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1427\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_corner_mask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'contour.corner_mask'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1429\u001b[1;33m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_contour_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1431\u001b[0m             \u001b[0m_mask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetmask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m_contour_args\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1506\u001b[0m             \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1507\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mNargs\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1508\u001b[1;33m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_xyz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1509\u001b[0m             \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1510\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m_check_xyz\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1535\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1537\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1538\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1539\u001b[0m         \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    529\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m     \"\"\"\n\u001b[1;32m--> 531\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '21/02/2017 06:08:30'"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e11668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.contourf(x,y,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}