{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_lcl =  951.68202846\n",
      "lcl_height =  550.0\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'file_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-6168e1681824>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# Removes the file lists which are not yet written to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mfile_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'file_list' is not defined"
     ]
    }
   ],
   "source": [
    "from skewPy import SkewT\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns; sns.set()\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_style({'xtick.minor.size': '1.0'})\n",
    "\n",
    "plt.close('all')\n",
    "\n",
    "#S1 = SkewT.Sounding(filename='/Volumes/cruiseshare/Soundings/Palau/91408_KororPalau/201808.txt', fmt='PALAU', date_to_read = 0, init_plot = 1)\n",
    "S1 = SkewT.Sounding(filename='/Volumes/cruiseshare/Soundings/Yap/201808.txt', fmt='PALAU', date_to_read = 0, init_plot = 1)\n",
    "\n",
    "# Main folder where the reading is done\n",
    "read_folder = '/Volumes/cruiseshare/Soundings/91408_KororPalau/'\n",
    "file_list = sorted(glob.glob(read_folder + '2*.txt'))\n",
    "print(file_list)\n",
    "\n",
    "#\n",
    "#S1 = SkewT.Sounding(filename=file_list[-1], fmt='PISTON_EDT', init_plot=1)\n",
    "\n",
    "# Removes the file lists which are not yet written to\n",
    "file_list = file_list[0:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 1 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-9a565a367046>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mntimes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;31m#number of days\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mS1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mntimes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mntimes\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mS1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'temp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mntimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/zanemartin/Desktop/PISTON/csu_sdg_code_zm/skewPy/SkewT.py\u001b[0m in \u001b[0;36mreadfile\u001b[0;34m(self, fname)\u001b[0m\n\u001b[1;32m   1225\u001b[0m                 \u001b[0mlcounter\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1227\u001b[0;31m                     \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdat_col\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlhi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mrhi\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)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1228\u001b[0m                     \u001b[0mldata\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1229\u001b[0m                     \u001b[0mdata_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 1 with size 0"
     ]
    }
   ],
   "source": [
    "ntimes = 0 #number of days\n",
    "for file_name in file_list:\n",
    "    S1.readfile(file_name)\n",
    "    ntimes = ntimes + S1.data['temp'].shape[1]\n",
    "print(ntimes)\n",
    "#S1.fig.legend()\n",
    "\n",
    "# For reading in the data_str from the dictionary\n",
    "def read_all():\n",
    "    data_out = list()\n",
    "    for file_name in file_list:\n",
    "        S1 = SkewT.Sounding(data={}, fmt='PALAU', date_to_read = 0, init_plot=0)\n",
    "        S1.readfile(file_name)\n",
    "        data_out.append(S1.data)\n",
    "    return(data_out)\n",
    "#S1.fig.legend()\n",
    "\n",
    "\n",
    "# Interpolates a single variable data_in from A SINGLE .TXT FILE onto goal_hghts\n",
    "def interp_all(data_str, goal_hghts, data_in):\n",
    "    data_out = np.empty([len(goal_hghts), len(data_in)])\n",
    "    for i in range(len(data_in)):\n",
    "        data_out[:,i] = np.interp(goal_hghts, data_in[i]['hght'], data_in[i][data_str])\n",
    "    return(data_out)\n",
    "\n",
    "# Interpolates a data_set from full monthly data formed from many files \n",
    "def interp_all_monthly(data_set_in, goal_hghts, data_str):\n",
    "    data_out = np.empty([len(goal_hghts), ntimes])\n",
    "    idx_start = 0\n",
    "    for i in range(len(data_set_in)):\n",
    "        idx_end = idx_start + data_set_in[i]['hght'].shape[1]\n",
    "        #print(data_out[:, idx_start:idx_end].shape)\n",
    "        #print('goal hghts', goal_hghts)\n",
    "        for j in range(idx_end - idx_start):\n",
    "            hght_mask = np.where(data_set_in[i]['hght'][:,j].mask == False)\n",
    "            data_mask = np.where(data_set_in[i][data_str][:,j].mask == False)\n",
    "            #print('actual hghts', data_set_in[i]['hght'][:,j][hght_mask])\n",
    "            #print('actual hghts shape', data_set_in[i]['hght'][:,j][hght_mask].shape)\n",
    "            #print('hght_mask shape ', hght_mask[0].shape)\n",
    "            #print('data_mask shape ', data_mask[0].shape)\n",
    "            data_out[:, idx_start+j] = np.interp(goal_hghts, \\\n",
    "                                       data_set_in[i]['hght'][:,j][hght_mask], \\\n",
    "                                       data_set_in[i][data_str][:,j][hght_mask], left=np.nan, right=np.nan)\n",
    "            #print(data_out[0, idx_start+j])\n",
    "            data_out[np.where(data_out == -999)] = np.nan\n",
    "        idx_start = idx_end\n",
    "    return(data_out)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "palau_full = read_all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "object too deep for desired array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-c2322c3f835a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mutemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sknt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradians\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m270\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'drct'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mvtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sknt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradians\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m270\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'drct'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mutemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hght'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mvtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hght'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mutemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mpalau_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hght'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/zanemartin/anaconda/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36minterp\u001b[0;34m(x, xp, fp, left, right, period)\u001b[0m\n\u001b[1;32m   1672\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcompiled_interp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1673\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1674\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcompiled_interp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1675\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1676\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
     ]
    }
   ],
   "source": [
    "temp = interp_all_monthly(palau_full, z, 'temp')\n",
    "dwpt = interp_all_monthly(palau_full, z, 'dwpt')\n",
    "pres = interp_all_monthly(palau_full, z, 'pres')\n",
    "\n",
    "# Calculates relative humidity; might also read in and compare if in sounding\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([], dtype=int64), array([], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "print(np.where(dwpt == -999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_temp =  [[         nan          nan          nan ...,          nan          nan\n",
      "           nan]\n",
      " [ 28.16470776  28.2         28.37353082 ...,  26.13688294  25.22298056\n",
      "   23.73155853]\n",
      " [ 27.06403557  27.82911073  27.48171791 ...,  25.54950892  25.4\n",
      "   23.68737723]\n",
      " ..., \n",
      " [-71.75246392 -69.96460075 -68.62021681 ..., -67.26578792 -67.38380368\n",
      "  -65.89602932]\n",
      " [-71.71504315 -69.18073788 -68.82524251 ..., -67.44783771 -67.56807264\n",
      "  -65.24543107]\n",
      " [-71.41974522 -68.90082988 -69.0302682  ..., -67.26526055 -67.5837274\n",
      "  -64.59483283]]\n",
      "(300, 1419)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zanemartin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in greater\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(0, 20000, 300)\n",
    "\n",
    "temp = interp_all_monthly(palau_full, z, 'temp')\n",
    "dwpt = interp_all_monthly(palau_full, z, 'dwpt')\n",
    "sknt = interp_all_monthly(palau_full, z, 'sknt')\n",
    "drct = interp_all_monthly(palau_full, z, 'drct')\n",
    "relh = SkewT.RH(temp, dwpt)\n",
    "u = sknt*np.cos(np.radians(270-drct))\n",
    "v = sknt*np.sin(np.radians(270-drct))\n",
    "\n",
    "# Some data management\n",
    "dwpt[np.where(dwpt > 50.)] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "to_plot =  v\n",
    "plt.close('all')\n",
    "plt.contourf(np.arange(to_plot.shape[1]), z, to_plot, np.arange(-25, 25, 5), cmap='RdBu_r', extend='both')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "990.3363116638244"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u[np.where(np.isnan(u) == False)].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.nanmean(temp, 1), z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.nanmean(temp, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(3,1,1)\n",
    "ax2 = fig.add_subplot(3,1,2)\n",
    "ax3 = fig.add_subplot(3,1,3)\n",
    "\n",
    "cf = ax1.contourf(np.arange(0,ndays-1)+1, z, temp, cmap='RdBu_r')\n",
    "plt.colorbar(cf, ax=ax1)\n",
    "ax1.set_title('Temperature (C)')\n",
    "\n",
    "ax2.plot(np.arange(0,len(data_set))+1, np.min(temp, 0))\n",
    "ax2.set_title('Cold point tropopause (C)')\n",
    "\n",
    "melting_level = np.empty(len(data_set))\n",
    "for i in range(len(data_set)):\n",
    "          melting_level[i] = data_set[i]['hght'][np.argmin(abs(data_set[i]['temp']-0))]\n",
    "\n",
    "ax3.plot(np.arange(0,len(data_set))+1, melting_level/1000)\n",
    "ax3.set_title('Melting Level (km)')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.contourf(np.arange(0,len(data_set))+1, z, u, cmap='RdBu_r')\n",
    "plt.title('Zonal wind (knots)')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.contourf(np.arange(0,len(data_set))+1, z, v, cmap='RdBu_r')\n",
    "plt.title('Meridional wind (knots)')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.contourf(np.arange(0,len(data_set))+1, z, relh, np.linspace(50, 100, 25), cmap='Blues')\n",
    "plt.plot(np.arange(0,len(data_set))+1, z[:,-1], color='r')\n",
    "plt.title('Relative humidity (%)')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_list = list()\n",
    "for i in range(15):\n",
    "    test_list.append(i)\n",
    "print(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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