gp1_calculations_for_SENSITIVITY_RUNS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:26:27.792023Z",
     "start_time": "2018-08-20T23:26:18.306429Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import xarray\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import matplotlib.colors\n",
    "import scipy.stats\n",
    "import itertools\n",
    "import string\n",
    "import matplotlib.patches\n",
    "import glob\n",
    "import matplotlib.gridspec\n",
    "import scipy.ndimage\n",
    "\n",
    "import wrf\n",
    "import netCDF4\n",
    "import cmocean\n",
    "\n",
    "import pandas\n",
    "\n",
    "import matplotlib.ticker\n",
    "import cartopy.mpl.gridliner #import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "import mpl_toolkits\n",
    "import cartopy\n",
    "import cartopy.io.shapereader\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:26:27.810618Z",
     "start_time": "2018-08-20T23:26:27.803454Z"
    }
   },
   "outputs": [],
   "source": [
    "run_info = 'FMAASO'\n",
    "skip_time=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# opening mean fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## control data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:26:28.094186Z",
     "start_time": "2018-08-20T23:26:27.821118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001\t\t\t001_MYNN25_scheme\t  001_UW_scheme\r\n",
      "001_ACM2_scheme\t\t001_MYNN30_scheme\t  001_UW_scheme_10days\r\n",
      "001_ACM2_scheme_10days\t001_MYNN30_scheme_10days  001_with_extras\r\n",
      "001_MYJ_scheme\t\t001_TEMF_scheme\t\t  001_with_xkzs\r\n",
      "001_MYJ_scheme_10days\t001_TEMF_scheme_10days\t  001_with_xkzs_and_fxpbls\r\n"
     ]
    }
   ],
   "source": [
    "!ls /beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:26:28.552997Z",
     "start_time": "2018-08-20T23:26:28.100776Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_with_extras'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:08.414400Z",
     "start_time": "2018-08-20T23:26:28.563905Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = wrf.getvar(ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_ptot_400_sensitivity.npy', ptot_400)\n",
    "\n",
    "geopot_400 = wrf.getvar(ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_geopot_400_sensitivity.npy', geopot_400)\n",
    "\n",
    "tk_400 = wrf.getvar(ds_list, 'tk', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_tk_400_sensitivity.npy', tk_400)\n",
    "\n",
    "ua_400 = wrf.getvar(ds_list, 'ua', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_ua_400_sensitivity.npy', ua_400)\n",
    "\n",
    "va_400 = wrf.getvar(ds_list, 'va', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_va_400_sensitivity.npy', va_400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:10.246786Z",
     "start_time": "2018-08-20T23:28:08.425367Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = numpy.load('DATA_ptot_400_sensitivity.npy')\n",
    "geopot_400 = numpy.load('DATA_geopot_400_sensitivity.npy')\n",
    "tk_400 = numpy.load('DATA_tk_400_sensitivity.npy')\n",
    "ua_400 = numpy.load('DATA_ua_400_sensitivity.npy')\n",
    "va_400 = numpy.load('DATA_va_400_sensitivity.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:11.614017Z",
     "start_time": "2018-08-20T23:28:10.257776Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:14.809565Z",
     "start_time": "2018-08-20T23:28:11.625059Z"
    }
   },
   "outputs": [],
   "source": [
    "time = cont_mfds['Times'].values\n",
    "xlat = cont_mfds['XLAT'].values[0,:,:]\n",
    "xlong = cont_mfds['XLONG'].values[0,:,:]\n",
    "landmask = cont_mfds['LANDMASK'].values[0,:,:]\n",
    "landmask_midpoints = landmask[1:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:57.334264Z",
     "start_time": "2018-08-20T23:28:14.820890Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_400 = cont_mfds['QVAPOR'].values\n",
    "u_400 = cont_mfds['U'].values\n",
    "v_400 = cont_mfds['V'].values\n",
    "w_400 = cont_mfds['W'].values\n",
    "t_400 = cont_mfds['T'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:59.480908Z",
     "start_time": "2018-08-20T23:28:57.345205Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh_400 = cont_mfds['PBLH'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:28:59.501583Z",
     "start_time": "2018-08-20T23:28:59.492504Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_400 = cont_mfds['RAINC']\n",
    "rainnc_400 = cont_mfds['RAINNC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:30:51.969408Z",
     "start_time": "2018-08-20T23:28:59.512673Z"
    }
   },
   "outputs": [],
   "source": [
    "rqvcuten_400 = cont_mfds['RQVCUTEN'].values\n",
    "rucuten_400 = cont_mfds['RUCUTEN'].values\n",
    "rvcuten_400 = cont_mfds['RVCUTEN'].values\n",
    "rthcuten_400 = cont_mfds['RTHCUTEN'].values\n",
    "\n",
    "rqvblten_400 = cont_mfds['RQVBLTEN'].values\n",
    "rublten_400 = cont_mfds['RUBLTEN'].values\n",
    "rvblten_400 = cont_mfds['RVBLTEN'].values\n",
    "rthblten_400 = cont_mfds['RTHBLTEN'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:31:49.991379Z",
     "start_time": "2018-08-20T23:30:51.980578Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzq3d_400 = cont_mfds['XKZQ3D'].values\n",
    "xkzh3d_400 = cont_mfds['XKZH3D'].values\n",
    "xkzm3d_400 = cont_mfds['XKZM3D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:31:54.395299Z",
     "start_time": "2018-08-20T23:31:50.002893Z"
    }
   },
   "outputs": [],
   "source": [
    "hfxpbl2d_400 = cont_mfds['HFXPBL2D'].values\n",
    "qfxpbl2d_400 = cont_mfds['QFXPBL2D'].values\n",
    "ufxpbl2d_400 = cont_mfds['UFXPBL2D'].values\n",
    "vfxpbl2d_400 = cont_mfds['VFXPBL2D'].values\n",
    "bfxpbl2d_400 = cont_mfds['BFXPBL2D'].values\n",
    "we2d_400 = cont_mfds['WE2D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:31:56.280293Z",
     "start_time": "2018-08-20T23:31:54.406808Z"
    }
   },
   "outputs": [],
   "source": [
    "deltaysu_400 = cont_mfds['DELTA_YSU'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:31:58.150670Z",
     "start_time": "2018-08-20T23:31:56.291299Z"
    }
   },
   "outputs": [],
   "source": [
    "qfx_400 = cont_mfds['QFX'].values\n",
    "hfx_400 = cont_mfds['HFX'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:31:59.571823Z",
     "start_time": "2018-08-20T23:31:58.161363Z"
    }
   },
   "outputs": [],
   "source": [
    "hgamq2d_400 = cont_mfds['HGAMQ2D'].values\n",
    "hgamt2d_400 = cont_mfds['HGAMT2D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:32:15.722999Z",
     "start_time": "2018-08-20T23:31:59.582817Z"
    }
   },
   "outputs": [],
   "source": [
    "ri3d_400 = cont_mfds['RI3D'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## phys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:32:15.977779Z",
     "start_time": "2018-08-20T23:32:15.734092Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_with_extras'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:33:42.425353Z",
     "start_time": "2018-08-20T23:32:15.988840Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_1000 = wrf.getvar(ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_ptot_1000_sensitivity.npy', ptot_1000)\n",
    "\n",
    "geopot_1000 = wrf.getvar(ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_geopot_1000_sensitivity.npy', geopot_1000)\n",
    "\n",
    "tk_1000 = wrf.getvar(ds_list, 'tk', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_tk_1000_sensitivity.npy', tk_1000)\n",
    "\n",
    "ua_1000 = wrf.getvar(ds_list, 'ua', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_ua_1000_sensitivity.npy', ua_1000)\n",
    "\n",
    "va_1000 = wrf.getvar(ds_list, 'va', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_va_1000_sensitivity.npy', va_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:33:44.488714Z",
     "start_time": "2018-08-20T23:33:42.436547Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_1000 = numpy.load('DATA_ptot_1000_sensitivity.npy')\n",
    "geopot_1000 = numpy.load('DATA_geopot_1000_sensitivity.npy')\n",
    "tk_1000 = numpy.load('DATA_tk_1000_sensitivity.npy')\n",
    "ua_1000 = numpy.load('DATA_ua_1000_sensitivity.npy')\n",
    "va_1000 = numpy.load('DATA_va_1000_sensitivity.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:33:45.899812Z",
     "start_time": "2018-08-20T23:33:44.500590Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:34:24.031439Z",
     "start_time": "2018-08-20T23:33:45.910958Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_1000 = phys_mfds['QVAPOR'].values\n",
    "u_1000 = phys_mfds['U'].values\n",
    "v_1000 = phys_mfds['V'].values\n",
    "w_1000 = phys_mfds['W'].values\n",
    "t_1000 = phys_mfds['T'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:34:25.578912Z",
     "start_time": "2018-08-20T23:34:24.042513Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh_1000 = phys_mfds['PBLH'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:34:25.598127Z",
     "start_time": "2018-08-20T23:34:25.589895Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_1000 = phys_mfds['RAINC']\n",
    "rainnc_1000 = phys_mfds['RAINNC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:00.750609Z",
     "start_time": "2018-08-20T23:34:25.609473Z"
    }
   },
   "outputs": [],
   "source": [
    "rqvcuten_1000 = phys_mfds['RQVCUTEN'].values\n",
    "rucuten_1000 = phys_mfds['RUCUTEN'].values\n",
    "rvcuten_1000 = phys_mfds['RVCUTEN'].values\n",
    "rthcuten_1000 = phys_mfds['RTHCUTEN'].values\n",
    "\n",
    "rqvblten_1000 = phys_mfds['RQVBLTEN'].values\n",
    "rublten_1000 = phys_mfds['RUBLTEN'].values\n",
    "rvblten_1000 = phys_mfds['RVBLTEN'].values\n",
    "rthblten_1000 = phys_mfds['RTHBLTEN'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:48.193038Z",
     "start_time": "2018-08-20T23:36:00.762217Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzq3d_1000 = phys_mfds['XKZQ3D'].values\n",
    "xkzh3d_1000 = phys_mfds['XKZH3D'].values\n",
    "xkzm3d_1000 = phys_mfds['XKZM3D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:52.514787Z",
     "start_time": "2018-08-20T23:36:48.203943Z"
    }
   },
   "outputs": [],
   "source": [
    "hfxpbl2d_1000 = phys_mfds['HFXPBL2D'].values\n",
    "qfxpbl2d_1000 = phys_mfds['QFXPBL2D'].values\n",
    "ufxpbl2d_1000 = phys_mfds['UFXPBL2D'].values\n",
    "vfxpbl2d_1000 = phys_mfds['VFXPBL2D'].values\n",
    "bfxpbl2d_1000 = phys_mfds['BFXPBL2D'].values\n",
    "we2d_1000 = phys_mfds['WE2D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:53.775282Z",
     "start_time": "2018-08-20T23:36:52.525613Z"
    }
   },
   "outputs": [],
   "source": [
    "deltaysu_1000 = phys_mfds['DELTA_YSU'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:55.697643Z",
     "start_time": "2018-08-20T23:36:53.786207Z"
    }
   },
   "outputs": [],
   "source": [
    "qfx_1000 = phys_mfds['QFX'].values\n",
    "hfx_1000 = phys_mfds['HFX'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:36:56.841473Z",
     "start_time": "2018-08-20T23:36:55.708780Z"
    }
   },
   "outputs": [],
   "source": [
    "hgamq2d_1000 = phys_mfds['HGAMQ2D'].values\n",
    "hgamt2d_1000 = phys_mfds['HGAMT2D'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:15.710105Z",
     "start_time": "2018-08-20T23:36:56.852592Z"
    }
   },
   "outputs": [],
   "source": [
    "ri3d_1000 = phys_mfds['RI3D'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define dx, dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:15.728686Z",
     "start_time": "2018-08-20T23:37:15.721746Z"
    }
   },
   "outputs": [],
   "source": [
    "dx = 50000. # 50,000 m or 50 km\n",
    "dy = 50000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:16.084460Z",
     "start_time": "2018-08-20T23:37:15.739753Z"
    }
   },
   "outputs": [],
   "source": [
    "L_v = 2264705.0 # J/kg\n",
    "c_p = 1005.7 # J/kgK\n",
    "g = 9.80665 # m/s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get landmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:16.345471Z",
     "start_time": "2018-08-20T23:37:16.089479Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask = cont_mfds['LANDMASK'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:16.930140Z",
     "start_time": "2018-08-20T23:37:16.357315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2abaa6c37e48>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.contourf(landmask);mp.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:19.461256Z",
     "start_time": "2018-08-20T23:37:16.941275Z"
    }
   },
   "outputs": [],
   "source": [
    "mse_400 = geopot_400 + L_v*qvapor_400 + c_p*tk_400\n",
    "mse_1000 = geopot_1000 + L_v*qvapor_1000 + c_p*tk_1000\n",
    "\n",
    "numpy.save('DATA_mse_400_sensitivity.npy', mse_400)\n",
    "numpy.save('DATA_mse_1000_sensitivity.npy', mse_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:20.296764Z",
     "start_time": "2018-08-20T23:37:19.472240Z"
    }
   },
   "outputs": [],
   "source": [
    "mse_400 = numpy.load('DATA_mse_400_sensitivity.npy')\n",
    "mse_1000 = numpy.load('DATA_mse_1000_sensitivity.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set up time/lev numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:20.319132Z",
     "start_time": "2018-08-20T23:37:20.311922Z"
    }
   },
   "outputs": [],
   "source": [
    "ntime = 25\n",
    "nlev = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate dtheta/dt for full, cu, and pbl schemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:21.063636Z",
     "start_time": "2018-08-20T23:37:20.329424Z"
    }
   },
   "outputs": [],
   "source": [
    "dthetadt_400 = numpy.zeros(t_400.shape)*numpy.nan\n",
    "dthetadt_1000 = numpy.zeros(t_1000.shape)*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:22.406267Z",
     "start_time": "2018-08-20T23:37:21.074551Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dthetadt_400[t,:,:,:] = (t_400[t+1,:,:,:]-t_400[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dthetadt_1000[t,:,:,:] = (t_1000[t+1,:,:,:]-t_1000[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dqvapor/dt for full, cu, and pbl schemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:22.916734Z",
     "start_time": "2018-08-20T23:37:22.417053Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_400 = numpy.zeros(qvapor_400.shape)*numpy.nan\n",
    "dqdt_1000 = numpy.zeros(qvapor_1000.shape)*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:24.263836Z",
     "start_time": "2018-08-20T23:37:22.927672Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dqdt_400[t,:,:,:] = (qvapor_400[t+1,:,:,:]-qvapor_400[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dqdt_1000[t,:,:,:] = (qvapor_1000[t+1,:,:,:]-qvapor_1000[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# duadt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:24.780407Z",
     "start_time": "2018-08-20T23:37:24.274769Z"
    }
   },
   "outputs": [],
   "source": [
    "duadt_400 = numpy.zeros(ua_400.shape)*numpy.nan\n",
    "duadt_1000 = numpy.zeros(ua_1000.shape)*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:26.141010Z",
     "start_time": "2018-08-20T23:37:24.791530Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    duadt_400[t,:,:,:] = (ua_400[t+1,:,:,:]-ua_400[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    duadt_1000[t,:,:,:] = (ua_1000[t+1,:,:,:]-ua_1000[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dvadt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:26.648398Z",
     "start_time": "2018-08-20T23:37:26.151454Z"
    }
   },
   "outputs": [],
   "source": [
    "dvadt_400 = numpy.zeros(va_400.shape)*numpy.nan\n",
    "dvadt_1000 = numpy.zeros(va_1000.shape)*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:27.991543Z",
     "start_time": "2018-08-20T23:37:26.659064Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dvadt_400[t,:,:,:] = (va_400[t+1,:,:,:]-va_400[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dvadt_1000[t,:,:,:] = (va_1000[t+1,:,:,:]-va_1000[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dmse/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:28.505405Z",
     "start_time": "2018-08-20T23:37:28.003130Z"
    }
   },
   "outputs": [],
   "source": [
    "dmsedt_400 = numpy.zeros((ntime,nlev,xlat.shape[0],xlat.shape[1]))*numpy.nan\n",
    "dmsedt_1000 = numpy.zeros((ntime,nlev,xlat.shape[0],xlat.shape[1]))*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:29.884898Z",
     "start_time": "2018-08-20T23:37:28.516431Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dmsedt_400[t,:,:,:] = (mse_400[t+1,:,:,:]-mse_400[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dmsedt_1000[t,:,:,:] = (mse_1000[t+1,:,:,:]-mse_1000[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate lat lon stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:29.904843Z",
     "start_time": "2018-08-20T23:37:29.895961Z"
    }
   },
   "outputs": [],
   "source": [
    "dry_lon_lo, dry_lon_hi, dry_lat_lo, dry_lat_hi = -60,-50,-10,10\n",
    "wet_lon_lo, wet_lon_hi, wet_lat_lo, wet_lat_hi = -80,-75,-10,10\n",
    "\n",
    "dry_lon_lo_degE = 360+dry_lon_lo\n",
    "dry_lon_hi_degE = 360+dry_lon_hi\n",
    "\n",
    "wet_lon_lo_degE = 360+wet_lon_lo\n",
    "wet_lon_hi_degE = 360+wet_lon_hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:30.378444Z",
     "start_time": "2018-08-20T23:37:29.916462Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_lo = dry_lon_lo\n",
    "lon_hi = dry_lon_hi\n",
    "lat_lo = dry_lat_lo\n",
    "lat_hi = dry_lat_hi\n",
    "\n",
    "# lon_lo = wet_lon_lo\n",
    "# lon_hi = wet_lon_hi\n",
    "# lat_lo = wet_lat_lo\n",
    "# lat_hi = wet_lat_hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:30.776618Z",
     "start_time": "2018-08-20T23:37:30.389151Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask_indices = numpy.copy(landmask)\n",
    "landmask_indices[landmask==1]=1\n",
    "landmask_indices[landmask!=1]=0\n",
    "landmask_indices = numpy.array(landmask_indices, dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:31.020759Z",
     "start_time": "2018-08-20T23:37:30.782288Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_indices = (xlong>=lon_lo)&(xlong<=lon_hi)\n",
    "lat_indices = (xlat>=lat_lo)&(xlat<=lat_hi)\n",
    "latlon_indices = (lon_indices)&(lat_indices)&(landmask_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# take latlon means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:31.489020Z",
     "start_time": "2018-08-20T23:37:31.024285Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "duadt_400_latlon_mean = numpy.nanmean(duadt_400[:,:,latlon_indices], axis=2)\n",
    "duadt_1000_latlon_mean = numpy.nanmean(duadt_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:31.830673Z",
     "start_time": "2018-08-20T23:37:31.499928Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dvadt_400_latlon_mean = numpy.nanmean(dvadt_400[:,:,latlon_indices], axis=2)\n",
    "dvadt_1000_latlon_mean = numpy.nanmean(dvadt_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:32.142453Z",
     "start_time": "2018-08-20T23:37:31.841724Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: Mean of empty slice\n",
      "  after removing the cwd from sys.path.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: Mean of empty slice\n",
      "  import sys\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dthetadt_400_latlon_mean = numpy.nanmean(dthetadt_400[:,:,latlon_indices], axis=2)\n",
    "dthetadt_1000_latlon_mean = numpy.nanmean(dthetadt_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "dqdt_400_latlon_mean = numpy.nanmean(dqdt_400[:,:,latlon_indices], axis=2)\n",
    "dqdt_1000_latlon_mean = numpy.nanmean(dqdt_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "dmsedt_400_latlon_mean = numpy.nanmean(dmsedt_400[:,:,latlon_indices], axis=2)\n",
    "dmsedt_1000_latlon_mean = numpy.nanmean(dmsedt_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pblh, deltaysu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:32.282280Z",
     "start_time": "2018-08-20T23:37:32.152799Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh_400_latlon_mean = numpy.nanmean(pblh_400[:,latlon_indices], axis=1)\n",
    "pblh_1000_latlon_mean = numpy.nanmean(pblh_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:32.526020Z",
     "start_time": "2018-08-20T23:37:32.293311Z"
    }
   },
   "outputs": [],
   "source": [
    "deltaysu_400_latlon_mean = numpy.nanmean(deltaysu_400[:,latlon_indices], axis=1)\n",
    "deltaysu_1000_latlon_mean = numpy.nanmean(deltaysu_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tendencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:32.781556Z",
     "start_time": "2018-08-20T23:37:32.537538Z"
    }
   },
   "outputs": [],
   "source": [
    "rthcuten_400_latlon_mean = numpy.nanmean(rthcuten_400[:,:,latlon_indices], axis=2)\n",
    "rthcuten_1000_latlon_mean = numpy.nanmean(rthcuten_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "rqvcuten_400_latlon_mean = numpy.nanmean(rqvcuten_400[:,:,latlon_indices], axis=2)\n",
    "rqvcuten_1000_latlon_mean = numpy.nanmean(rqvcuten_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:33.026250Z",
     "start_time": "2018-08-20T23:37:32.792302Z"
    }
   },
   "outputs": [],
   "source": [
    "rthblten_400_latlon_mean = numpy.nanmean(rthblten_400[:,:,latlon_indices], axis=2)\n",
    "rthblten_1000_latlon_mean = numpy.nanmean(rthblten_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "rqvblten_400_latlon_mean = numpy.nanmean(rqvblten_400[:,:,latlon_indices], axis=2)\n",
    "rqvblten_1000_latlon_mean = numpy.nanmean(rqvblten_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:33.200610Z",
     "start_time": "2018-08-20T23:37:33.036545Z"
    }
   },
   "outputs": [],
   "source": [
    "rublten_400_latlon_mean = numpy.nanmean(rublten_400[:,:,latlon_indices], axis=2)\n",
    "rublten_1000_latlon_mean = numpy.nanmean(rublten_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "rvblten_400_latlon_mean = numpy.nanmean(rvblten_400[:,:,latlon_indices], axis=2)\n",
    "rvblten_1000_latlon_mean = numpy.nanmean(rvblten_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:33.358048Z",
     "start_time": "2018-08-20T23:37:33.211628Z"
    }
   },
   "outputs": [],
   "source": [
    "rucuten_400_latlon_mean = numpy.nanmean(rucuten_400[:,:,latlon_indices], axis=2)\n",
    "rucuten_1000_latlon_mean = numpy.nanmean(rucuten_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "rvcuten_400_latlon_mean = numpy.nanmean(rvcuten_400[:,:,latlon_indices], axis=2)\n",
    "rvcuten_1000_latlon_mean = numpy.nanmean(rvcuten_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "adding in xkz terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:33.777964Z",
     "start_time": "2018-08-20T23:37:33.368661Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzq3d_400_latlon_mean = numpy.nanmean(xkzq3d_400[:,:,latlon_indices], axis=2)\n",
    "xkzq3d_1000_latlon_mean = numpy.nanmean(xkzq3d_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:34.024534Z",
     "start_time": "2018-08-20T23:37:33.789168Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzh3d_400_latlon_mean = numpy.nanmean(xkzh3d_400[:,:,latlon_indices], axis=2)\n",
    "xkzh3d_1000_latlon_mean = numpy.nanmean(xkzh3d_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:34.426163Z",
     "start_time": "2018-08-20T23:37:34.034080Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzm3d_400_latlon_mean = numpy.nanmean(xkzm3d_400[:,:,latlon_indices], axis=2)\n",
    "xkzm3d_1000_latlon_mean = numpy.nanmean(xkzm3d_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t (theta) for dtheta/dz info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:34.684776Z",
     "start_time": "2018-08-20T23:37:34.437354Z"
    }
   },
   "outputs": [],
   "source": [
    "t_400_latlon_mean = numpy.nanmean(t_400[:,:,latlon_indices], axis=2)\n",
    "t_1000_latlon_mean = numpy.nanmean(t_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ri3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:35.335342Z",
     "start_time": "2018-08-20T23:37:34.695759Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: Mean of empty slice\n",
      "  after removing the cwd from sys.path.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "ri3d_400[ri3d_400==999] = numpy.nan\n",
    "ri3d_1000[ri3d_1000==999] = numpy.nan\n",
    "\n",
    "ri3d_400_latlon_mean = numpy.nanmean(ri3d_400[:,:,latlon_indices], axis=2)\n",
    "ri3d_1000_latlon_mean = numpy.nanmean(ri3d_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "adding in fxpbl2d terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:35.356676Z",
     "start_time": "2018-08-20T23:37:35.345852Z"
    }
   },
   "outputs": [],
   "source": [
    "hfxpbl2d_400_latlon_mean = numpy.nanmean(hfxpbl2d_400[:,latlon_indices], axis=1)\n",
    "hfxpbl2d_1000_latlon_mean = numpy.nanmean(hfxpbl2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:35.638158Z",
     "start_time": "2018-08-20T23:37:35.367843Z"
    }
   },
   "outputs": [],
   "source": [
    "qfxpbl2d_400_latlon_mean = numpy.nanmean(qfxpbl2d_400[:,latlon_indices], axis=1)\n",
    "qfxpbl2d_1000_latlon_mean = numpy.nanmean(qfxpbl2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:35.881926Z",
     "start_time": "2018-08-20T23:37:35.648788Z"
    }
   },
   "outputs": [],
   "source": [
    "ufxpbl2d_400_latlon_mean = numpy.nanmean(ufxpbl2d_400[:,latlon_indices], axis=1)\n",
    "ufxpbl2d_1000_latlon_mean = numpy.nanmean(ufxpbl2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:36.183410Z",
     "start_time": "2018-08-20T23:37:35.892802Z"
    }
   },
   "outputs": [],
   "source": [
    "vfxpbl2d_400_latlon_mean = numpy.nanmean(vfxpbl2d_400[:,latlon_indices], axis=1)\n",
    "vfxpbl2d_1000_latlon_mean = numpy.nanmean(vfxpbl2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:36.354236Z",
     "start_time": "2018-08-20T23:37:36.194410Z"
    }
   },
   "outputs": [],
   "source": [
    "bfxpbl2d_400_latlon_mean = numpy.nanmean(bfxpbl2d_400[:,latlon_indices], axis=1)\n",
    "bfxpbl2d_1000_latlon_mean = numpy.nanmean(bfxpbl2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:36.737151Z",
     "start_time": "2018-08-20T23:37:36.365737Z"
    }
   },
   "outputs": [],
   "source": [
    "we2d_400_latlon_mean = numpy.nanmean(we2d_400[:,latlon_indices], axis=1)\n",
    "we2d_1000_latlon_mean = numpy.nanmean(we2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hgam and qfx terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:37.168367Z",
     "start_time": "2018-08-20T23:37:36.748087Z"
    }
   },
   "outputs": [],
   "source": [
    "hgamq2d_400_latlon_mean = numpy.nanmean(hgamq2d_400[:,latlon_indices], axis=1)\n",
    "hgamq2d_1000_latlon_mean = numpy.nanmean(hgamq2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:37.614443Z",
     "start_time": "2018-08-20T23:37:37.179339Z"
    }
   },
   "outputs": [],
   "source": [
    "hgamt2d_400_latlon_mean = numpy.nanmean(hgamt2d_400[:,latlon_indices], axis=1)\n",
    "hgamt2d_1000_latlon_mean = numpy.nanmean(hgamt2d_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:37.913530Z",
     "start_time": "2018-08-20T23:37:37.625030Z"
    }
   },
   "outputs": [],
   "source": [
    "qfx_400_latlon_mean = numpy.nanmean(qfx_400[:,latlon_indices], axis=1)\n",
    "qfx_1000_latlon_mean = numpy.nanmean(qfx_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:38.213503Z",
     "start_time": "2018-08-20T23:37:37.922773Z"
    }
   },
   "outputs": [],
   "source": [
    "hfx_400_latlon_mean = numpy.nanmean(hfx_400[:,latlon_indices], axis=1)\n",
    "hfx_1000_latlon_mean = numpy.nanmean(hfx_1000[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:38.362618Z",
     "start_time": "2018-08-20T23:37:38.223470Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_diffs_latlon_mean = dqdt_1000_latlon_mean-dqdt_400_latlon_mean\n",
    "dthetadt_diffs_latlon_mean = dthetadt_1000_latlon_mean-dthetadt_400_latlon_mean\n",
    "dmsedt_diffs_latlon_mean = dmsedt_1000_latlon_mean-dmsedt_400_latlon_mean\n",
    "duadt_diffs_latlon_mean = duadt_1000_latlon_mean-duadt_400_latlon_mean\n",
    "\n",
    "rthblten_diffs_latlon_mean = rthblten_1000_latlon_mean-rthblten_400_latlon_mean\n",
    "rqvblten_diffs_latlon_mean = rqvblten_1000_latlon_mean-rqvblten_400_latlon_mean\n",
    "\n",
    "rthcuten_diffs_latlon_mean = rthcuten_1000_latlon_mean-rthcuten_400_latlon_mean\n",
    "rqvcuten_diffs_latlon_mean = rqvcuten_1000_latlon_mean-rqvcuten_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:38.825575Z",
     "start_time": "2018-08-20T23:37:38.369420Z"
    }
   },
   "outputs": [],
   "source": [
    "rucuten_diffs_latlon_mean = rucuten_1000_latlon_mean-rucuten_400_latlon_mean\n",
    "rvcuten_diffs_latlon_mean = rvcuten_1000_latlon_mean-rvcuten_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:39.192725Z",
     "start_time": "2018-08-20T23:37:38.828931Z"
    }
   },
   "outputs": [],
   "source": [
    "rublten_diffs_latlon_mean = rublten_1000_latlon_mean-rublten_400_latlon_mean\n",
    "rvblten_diffs_latlon_mean = rvblten_1000_latlon_mean-rvblten_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:39.548675Z",
     "start_time": "2018-08-20T23:37:39.196609Z"
    }
   },
   "outputs": [],
   "source": [
    "xkzq3d_diffs_latlon_mean = xkzq3d_1000_latlon_mean - xkzq3d_400_latlon_mean\n",
    "xkzh3d_diffs_latlon_mean = xkzh3d_1000_latlon_mean - xkzh3d_400_latlon_mean\n",
    "xkzm3d_diffs_latlon_mean = xkzm3d_1000_latlon_mean - xkzm3d_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:39.816380Z",
     "start_time": "2018-08-20T23:37:39.551816Z"
    }
   },
   "outputs": [],
   "source": [
    "bfxpbl2d_diffs_latlon_mean = bfxpbl2d_1000_latlon_mean - bfxpbl2d_400_latlon_mean\n",
    "hfxpbl2d_diffs_latlon_mean = hfxpbl2d_1000_latlon_mean - hfxpbl2d_400_latlon_mean\n",
    "qfxpbl2d_diffs_latlon_mean = qfxpbl2d_1000_latlon_mean - qfxpbl2d_400_latlon_mean\n",
    "ufxpbl2d_diffs_latlon_mean = ufxpbl2d_1000_latlon_mean - ufxpbl2d_400_latlon_mean\n",
    "vfxpbl2d_diffs_latlon_mean = vfxpbl2d_1000_latlon_mean - vfxpbl2d_400_latlon_mean\n",
    "we2d_diffs_latlon_mean = we2d_1000_latlon_mean - we2d_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:40.220826Z",
     "start_time": "2018-08-20T23:37:39.819184Z"
    }
   },
   "outputs": [],
   "source": [
    "hgamq2d_diffs_latlon_mean = hgamq2d_1000_latlon_mean - hgamq2d_400_latlon_mean\n",
    "hgamt2d_diffs_latlon_mean = hgamt2d_1000_latlon_mean - hgamt2d_400_latlon_mean\n",
    "\n",
    "qfx_diffs_latlon_mean = qfx_1000_latlon_mean - qfx_400_latlon_mean\n",
    "hfx_diffs_latlon_mean = hfx_1000_latlon_mean - hfx_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:40.521332Z",
     "start_time": "2018-08-20T23:37:40.223714Z"
    }
   },
   "outputs": [],
   "source": [
    "t_diffs_latlon_mean = t_1000_latlon_mean - t_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:40.708064Z",
     "start_time": "2018-08-20T23:37:40.524232Z"
    }
   },
   "outputs": [],
   "source": [
    "ri3d_diffs_latlon_mean = ri3d_1000_latlon_mean - ri3d_400_latlon_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plots for fxpbl2d stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\overline{(w'\\theta')_h}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:44.112621Z",
     "start_time": "2018-08-20T23:37:40.711257Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mp.figure(figsize=(4.25,4))\n",
    "\n",
    "fontsize=12\n",
    "\n",
    "xvals = numpy.arange(ntime)\n",
    "color_list = [matplotlib.cm.magma(i) for i in numpy.linspace(0.,0.8,4)]\n",
    "\n",
    "# bfx, hfx, qfx, ufx, vfx, we\n",
    "labels = [\\\n",
    "r\"$\\overline{(w \\prime \\theta_v \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime \\theta \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime q \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime u \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime v \\prime)}_h$\", \\\n",
    "r\"$w_e$\", \\\n",
    "]\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.plot(xvals-4, bfxpbl2d_400_latlon_mean, label=labels[0], color=color_list[0])\n",
    "ax1.plot(xvals-4, hfxpbl2d_400_latlon_mean, label=labels[1], color=color_list[1])\n",
    "#ax1.plot(xvals-4, qfxpbl2d_400_latlon_mean, label='qfxpbl', color=color_list[2])\n",
    "ax1.plot(xvals-4, ufxpbl2d_400_latlon_mean, label=labels[3], color=color_list[2])\n",
    "ax1.plot(xvals-4, vfxpbl2d_400_latlon_mean, label=labels[4], color=color_list[3])\n",
    "#ax1.plot(xvals-4, we2d_400_latlon_mean, label=labels[5], color=color_list[5])\n",
    "ax1.set_xlim(0,20)\n",
    "ax1.set_xticks([i for i in range(0,21,4)])\n",
    "ax1.set_xticklabels([])\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "ax1.text(x=0,y=1.02,s='(a) control', ha='left',va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.plot(xvals-4, bfxpbl2d_diffs_latlon_mean, label='bfxpbl', color=color_list[0])\n",
    "ax2.plot(xvals-4, hfxpbl2d_diffs_latlon_mean, label='hfxpbl', color=color_list[1])\n",
    "#ax2.plot(xvals-4, qfxpbl2d_diffs_latlon_mean, label='qfxpbl', color=color_list[2])\n",
    "ax2.plot(xvals-4, ufxpbl2d_diffs_latlon_mean, label='ufxpbl', color=color_list[2])\n",
    "ax2.plot(xvals-4, vfxpbl2d_diffs_latlon_mean, label='vfxpbl', color=color_list[3])\n",
    "#ax2.plot(xvals-4, we2d_diffs_latlon_mean, label='w$_e$', color=color_list[5])\n",
    "ax2.set_xlim(0,20)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(0,21,4)])\n",
    "ax2.set_xlabel('hour', fontsize=fontsize)\n",
    "ax2.text(x=0,y=1.02,s='(b) change', ha='left',va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2.5)\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(1.03,1.0), bbox_transform=ax1.transAxes, \\\n",
    "          loc='upper left', borderaxespad=0, labelspacing=0.333, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0., columnspacing=0.5, \\\n",
    "          handletextpad=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('figures/entrainment_output.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:45.050069Z",
     "start_time": "2018-08-20T23:37:44.115865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mp.figure(figsize=(4.25,5))\n",
    "\n",
    "fontsize=12\n",
    "xvals = numpy.arange(ntime)\n",
    "\n",
    "ax = fig.add_subplot(411)\n",
    "ax.plot(xvals-4, hgamq2d_diffs_latlon_mean*1000.)\n",
    "ax.set_xlim(0,20)\n",
    "ax.set_xticks([i for i in range(0,21,4)])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.text(s=r'$\\Delta \\,\\, \\gamma_q$', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "ax = fig.add_subplot(412)\n",
    "ax.plot(xvals-4, qfx_diffs_latlon_mean*2264.705*1000.)\n",
    "ax.set_xlim(0,20)\n",
    "ax.set_xticks([i for i in range(0,21,4)])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.text(s=r'$\\Delta \\,\\, \\overline{(w \\prime q \\prime)}_0$', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "ax = fig.add_subplot(413)\n",
    "ax.plot(xvals-4, hgamt2d_diffs_latlon_mean)\n",
    "ax.set_xlim(0,20)\n",
    "ax.set_xticks([i for i in range(0,21,4)])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.text(s=r'$\\Delta \\,\\, \\gamma_\\theta$', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "ax = fig.add_subplot(414)\n",
    "ax.plot(xvals-4, hfx_diffs_latlon_mean)\n",
    "ax.set_xlim(0,20)\n",
    "ax.set_xticks([i for i in range(0,21,4)])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.text(s=r'$\\Delta \\,\\, \\overline{(w \\prime \\theta \\prime)}_0$', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:47.635253Z",
     "start_time": "2018-08-20T23:37:45.061968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mp.figure(figsize=(4.25,4))\n",
    "\n",
    "fontsize=12\n",
    "\n",
    "xvals = numpy.arange(ntime)\n",
    "color_list = [matplotlib.cm.magma(i) for i in numpy.linspace(0.,0.8,6)]\n",
    "\n",
    "# bfx, hfx, qfx, ufx, vfx, we\n",
    "labels = [\\\n",
    "r\"$\\overline{(w \\prime \\theta_v \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime \\theta \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime q \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime u \\prime)}_h$\", \\\n",
    "r\"$\\overline{(w \\prime v \\prime)}_h$\", \\\n",
    "r\"$w_e$\", \\\n",
    "]\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.plot(xvals-4, bfxpbl2d_400_latlon_mean, label=labels[0], color=color_list[0])\n",
    "ax1.plot(xvals-4, hfxpbl2d_400_latlon_mean, label=labels[1], color=color_list[1])\n",
    "ax1.plot(xvals-4, qfxpbl2d_400_latlon_mean*1000., label=labels[2], color=color_list[2])\n",
    "ax1.plot(xvals-4, ufxpbl2d_400_latlon_mean, label=labels[3], color=color_list[3])\n",
    "ax1.plot(xvals-4, vfxpbl2d_400_latlon_mean, label=labels[4], color=color_list[4])\n",
    "ax1.plot(xvals-4, we2d_400_latlon_mean, label=labels[5], color=color_list[5])\n",
    "ax1.set_xlim(0,20)\n",
    "ax1.set_xticks([i for i in range(0,21,4)])\n",
    "ax1.set_xticklabels([])\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "ax1.text(x=0,y=1.02,s='(a) control', ha='left',va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.plot(xvals-4, bfxpbl2d_diffs_latlon_mean, label='bfxpbl', color=color_list[0])\n",
    "ax2.plot(xvals-4, hfxpbl2d_diffs_latlon_mean, label='hfxpbl', color=color_list[1])\n",
    "ax2.plot(xvals-4, qfxpbl2d_diffs_latlon_mean*1000., label='qfxpbl', color=color_list[2])\n",
    "ax2.plot(xvals-4, ufxpbl2d_diffs_latlon_mean, label='ufxpbl', color=color_list[3])\n",
    "ax2.plot(xvals-4, vfxpbl2d_diffs_latlon_mean, label='vfxpbl', color=color_list[4])\n",
    "ax2.plot(xvals-4, we2d_diffs_latlon_mean, label='w$_e$', color=color_list[5])\n",
    "ax2.set_xlim(0,20)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(0,21,4)])\n",
    "ax2.set_xlabel('hour', fontsize=fontsize)\n",
    "ax2.text(x=0,y=1.02,s='(b) change', ha='left',va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2.5)\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(1.03,1.0), bbox_transform=ax1.transAxes, \\\n",
    "          loc='upper left', borderaxespad=0, labelspacing=0.333, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0., columnspacing=0.5, \\\n",
    "          handletextpad=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('figures/entrainment_output.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:47.686451Z",
     "start_time": "2018-08-20T23:37:47.646445Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_latlon_time_mean = numpy.nanmean(ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "geopot_400_latlon_time_mean = numpy.nanmean(geopot_400[:,:,latlon_indices], axis=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:47.797674Z",
     "start_time": "2018-08-20T23:37:47.697519Z"
    }
   },
   "outputs": [],
   "source": [
    "xx, pp = numpy.meshgrid(numpy.arange(ntime), ptot_400_latlon_time_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:47.994125Z",
     "start_time": "2018-08-20T23:37:47.808597Z"
    }
   },
   "outputs": [],
   "source": [
    "xx_geopot, gg = numpy.meshgrid(numpy.arange(ntime), geopot_400_latlon_time_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for dqdt"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dqdt_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rqvcuten_diffs_latlon_mean.T*1000.*86400., \\\n",
    "                             cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "#cbar3.set_ticks([-.01,0,.01])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_day1_ysu_and_tiedtke.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-20T23:37:51.112752Z",
     "start_time": "2018-08-20T23:37:48.005265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,4))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "contour_plot1 = ax1.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             dqdt_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "\n",
    "ax1.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "#ax1.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000.+deltaysu_1000_latlon_mean/1000., c='firebrick')\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "#ax1.set_ylim(1013.25,700)\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "#ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "#ax2.set_ylim(1013.25,700)\n",
    "ax2.set_ylim(0,3.25)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "#ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "# ax3 = fig.add_subplot(313)\n",
    "# ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "# contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "#                              rqvcuten_diffs_latlon_mean.T*1000.*86400., \\\n",
    "#                              cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "\n",
    "# ax3.set_ylim(1013.25,700)\n",
    "# ax3.set_xlim(6,18)\n",
    "# ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "# ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "# ax3.set_xticks([i for i in range(6,19,2)])\n",
    "# ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "# ax3.text(s='(c) $\\Delta$ dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax2.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.0333, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_day1_ysu_zcoords.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:30:05.369680Z",
     "start_time": "2018-08-21T01:30:02.721484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.25,0.251,0.05)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dqdt_400_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rqvblten_400_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "# ax3 = fig.add_subplot(313)\n",
    "# ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "# contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "#                              rqvcuten_400_latlon_mean.T*1000.*86400., \\\n",
    "#                              cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "# ax3.set_ylim(1013.25,700)\n",
    "# ax3.set_xlim(6,18)\n",
    "# ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "# ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "# ax3.set_xticks([i for i in range(6,19,2)])\n",
    "# ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "#ax3.text(s='(c) dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax2.get_position()\n",
    "cbar_ax = fig.add_axes([ax1pos.x1+0.05, ax2pos.y0, 0.0333, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_day1_background_control_ysu.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:30:13.303085Z",
     "start_time": "2018-08-21T01:30:08.701294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "levels = numpy.arange(0,401,25)\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.25,0.251,0.05)\n",
    "contour_plot1 = ax1.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             dqdt_400_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot3 = ax3.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             rqvblten_400_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "\n",
    "ax1.text(s='(a) dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax3.text(s='(b) dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot4 = ax4.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "\n",
    "ax4.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax2.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax4.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2, w_pad=8)\n",
    "\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.025, ax3pos.y0, 0.0175, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "cbar3.set_label('g kg$^{\\,-1}$ h$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "\n",
    "ax2pos = ax2.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "cbar_ax4 = fig.add_axes([ax4pos.x1+0.025, ax4pos.y0, 0.0175, ax2pos.y1-ax4pos.y0])\n",
    "cbar4 = fig.colorbar(contour_plot4, cax=cbar_ax4)\n",
    "cbar_ax4.tick_params(labelsize=fontsize)\n",
    "cbar4.set_label('g kg$^{\\,-1}$ h$^{\\,-1}$', fontsize=fontsize)\n",
    "#cbar4.set_ticks([i for i in range(0,501,100)])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_tendency_terms_background_and_percent_change_zcoords.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# for xkz terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "just $K_m$ and $K_h$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:32:46.600501Z",
     "start_time": "2018-08-21T01:32:46.280442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2abc4ec44d30>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.pcolormesh(ri3d_diffs_latlon_mean.T,vmin=-100,vmax=100)\n",
    "mp.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:32:46.897856Z",
     "start_time": "2018-08-21T01:32:46.672237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2abc4eca1e80>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.contourf(t_diffs_latlon_mean.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:32:47.863194Z",
     "start_time": "2018-08-21T01:32:47.569794Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'contour_lines1' 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-98-b3ca1b2250bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#                              colors='0.1', linewidths=[0.5], \\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#                              levels=[1,5])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontour_lines1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minline_spacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%1.0f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m#ax.set_xlim(0,240)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'contour_lines1' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,4))\n",
    "\n",
    "levels = numpy.arange(-.75,.751,0.05)\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             t_diffs_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.balance, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='both')\n",
    "# contour_lines1 = ax1.contour(xx_geopot-4, gg/9.81/1000., \\\n",
    "#                              xkzh3d_400_latlon_mean.T, \\\n",
    "#                              colors='0.1', linewidths=[0.5], \\\n",
    "#                              levels=[1,5])\n",
    "ax1.clabel(contour_lines1, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax1.set_ylim(0,3.25)\n",
    "#ax.set_xlim(0,240)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([0,1,2,3])\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-100,101,10)\n",
    "contour_plot2 = ax2.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             ri3d_diffs_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.balance, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='both')\n",
    "# contour_lines2 = ax2.contour(xx_geopot-4, gg/9.81/1000., \\\n",
    "#                              xkzm3d_400_latlon_mean.T, \\\n",
    "#                              colors='0.1', linewidths=[0.5], \\\n",
    "#                              levels=[1,5])\n",
    "ax2.clabel(contour_lines3, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_xlabel('hour', fontsize=fontsize)\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([0,1,2,3])\n",
    "\n",
    "ax1.text(s=r'(a) $\\Delta$ $\\theta$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s=r'(b) $\\Delta$ bulk Richardson number (Rig)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "\n",
    "ax1.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax2.get_position()\n",
    "cbar_ax1 = fig.add_axes([ax1pos.x1+0.035, ax1pos.y0, 0.025, ax1pos.height])\n",
    "cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "cbar_ax1.tick_params(labelsize=fontsize)\n",
    "cbar1.set_label('$\\degree$C', fontsize=fontsize, labelpad=7)\n",
    "cbar1.set_ticks([-.75,-.5,-.25,0,.25,.5,.75])\n",
    "\n",
    "cbar_ax2 = fig.add_axes([ax2pos.x1+0.035, ax2pos.y0, 0.025, ax2pos.height])\n",
    "cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "cbar_ax2.tick_params(labelsize=fontsize)\n",
    "cbar2.set_label('dimensionless', fontsize=fontsize, labelpad=7)\n",
    "cbar2.set_ticks([i for i in range(-100,101,50)])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_theta_and_rig.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:32:54.024811Z",
     "start_time": "2018-08-21T01:32:48.800640Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:37: RuntimeWarning: invalid value encountered in true_divide\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "levels = numpy.arange(0,401,25)\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "contour_lines1 = ax1.contour(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             colors='0.1', linewidths=[0.5], \\\n",
    "                             levels=[1,5])\n",
    "ax1.clabel(contour_lines1, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax1.set_ylim(0,3.25)\n",
    "#ax.set_xlim(0,240)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([0,1,2,3])\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot3 = ax3.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzm3d_400_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "contour_lines3 = ax3.contour(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzm3d_400_latlon_mean.T, \\\n",
    "                             colors='0.1', linewidths=[0.5], \\\n",
    "                             levels=[1,5])\n",
    "ax3.clabel(contour_lines3, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([0,1,2,3])\n",
    "ax1.text(s='(a) control K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax3.text(s='(c) control K$_{\\,m}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "levels = numpy.arange(0,501,20)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             (xkzh3d_diffs_latlon_mean.T/xkzh3d_400_latlon_mean.T)*100., \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([0,1,2,3])\n",
    "\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot4 = ax4.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             (xkzm3d_diffs_latlon_mean.T/xkzm3d_400_latlon_mean.T)*100., \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_xlabel('hour', fontsize=fontsize)\n",
    "#ax4.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.set_yticks([0,1,2,3])\n",
    "\n",
    "ax2.text(s='(b) $\\Delta$ K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ K$_{\\,m}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax4.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "fig.tight_layout(h_pad=2, w_pad=8)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.025, ax3pos.y0, 0.0175, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "cbar3.set_label('m$^{\\,2}$s$^{\\,-1}$', fontsize=fontsize, labelpad=7)\n",
    "\n",
    "ax2pos = ax2.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "cbar_ax4 = fig.add_axes([ax4pos.x1+0.025, ax4pos.y0, 0.0175, ax2pos.y1-ax4pos.y0])\n",
    "cbar4 = fig.colorbar(contour_plot4, cax=cbar_ax4)\n",
    "cbar_ax4.tick_params(labelsize=fontsize)\n",
    "cbar4.set_label('% change', fontsize=fontsize, labelpad=7)\n",
    "cbar4.set_ticks([i for i in range(0,501,100)])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_xkzm_xkzh_terms_background_and_percent_change_zcoords.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# figure 11 in manuscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:32:58.213956Z",
     "start_time": "2018-08-21T01:32:54.035871Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "levels = numpy.arange(0,401,25)\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "contour_lines1 = ax1.contour(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             colors='0.1', linewidths=[0.5], \\\n",
    "                             levels=[1,5])\n",
    "clabel1 = ax1.clabel(contour_lines1, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([0,1,2,3])\n",
    "\n",
    "levels = numpy.arange(0,501,20)\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             (xkzh3d_diffs_latlon_mean.T/xkzh3d_400_latlon_mean.T)*100., \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([0,1,2,3])\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5', label='PBLH cont.')\n",
    "ax2.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--', label='PBLH phys.')\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-.75,.751,0.05)\n",
    "contour_plot3 = ax3.contourf(xx_geopot-4, gg/9.81/1000., \\\n",
    "                             t_diffs_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.balance, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='both')\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([0,1,2,3])\n",
    "ax3.plot(xx_geopot[0,:]-4, pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax3.plot(xx_geopot[0,:]-4, pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax1.text(s=r'(a) control K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s=r'(b) $\\Delta$ K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s=r'(c) $\\Delta \\theta$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=1.5)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "cbar_ax1 = fig.add_axes([ax1pos.x1+0.04, ax1pos.y0, 0.035, ax1pos.height])\n",
    "cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "cbar_ax1.tick_params(labelsize=fontsize)\n",
    "cbar1.set_label('m$^{\\,2}$s$^{\\,-1}$', fontsize=fontsize, labelpad=7)\n",
    "cbar1.set_ticks([i for i in range(0,401,100)])\n",
    "\n",
    "ax2pos = ax2.get_position()\n",
    "cbar_ax2 = fig.add_axes([ax2pos.x1+0.04, ax2pos.y0, 0.035, ax2pos.height])\n",
    "cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "cbar_ax2.tick_params(labelsize=fontsize)\n",
    "cbar2.set_label('% change', fontsize=fontsize, labelpad=7)\n",
    "cbar2.set_ticks([i for i in range(0,501,100)])\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.04, ax3pos.y0, 0.035, ax3pos.height])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "cbar3.set_label('$\\degree$C', fontsize=fontsize, labelpad=7)\n",
    "cbar3.set_ticks([i for i in numpy.arange(-0.75,0.751,0.25)])\n",
    "\n",
    "handles, labels = ax2.get_legend_handles_labels()\n",
    "legend = ax2.legend(handles, labels, fontsize=fontsize, ncol=1, bbox_to_anchor=(0.05,0.9), \\\n",
    "                   edgecolor='None', facecolor='None', fancybox=False, bbox_transform=ax2.transAxes, \\\n",
    "                   loc='upper left', labelspacing=0, \\\n",
    "                   borderaxespad=0, borderpad=0, handlelength=1.75, \\\n",
    "                   handletextpad=0.5)\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_xkzh_terms_background_and_percent_change_AND_theta_zcoords.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:03.413154Z",
     "start_time": "2018-08-21T01:32:58.224902Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:37: RuntimeWarning: invalid value encountered in true_divide\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "levels = numpy.arange(0,401,25)\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "contour_lines1 = ax1.contour(xx-4, pp/100., \\\n",
    "                             xkzh3d_400_latlon_mean.T, \\\n",
    "                             colors='0.1', linewidths=[0.5], \\\n",
    "                             levels=[1,5])\n",
    "ax1.clabel(contour_lines1, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             xkzm3d_400_latlon_mean.T, \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "contour_lines3 = ax3.contour(xx-4, pp/100., \\\n",
    "                             xkzm3d_400_latlon_mean.T, \\\n",
    "                             colors='0.1', linewidths=[0.5], \\\n",
    "                             levels=[1,5])\n",
    "ax3.clabel(contour_lines3, inline=1, inline_spacing=1, fontsize=fontsize, fmt='%1.0f')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) control K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax3.text(s='(c) control K$_{\\,m}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "levels = numpy.arange(0,501,20)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             (xkzh3d_diffs_latlon_mean.T/xkzh3d_400_latlon_mean.T)*100., \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot4 = ax4.contourf(xx-4, pp/100., \\\n",
    "                             (xkzm3d_diffs_latlon_mean.T/xkzm3d_400_latlon_mean.T)*100., \\\n",
    "                             cmap=cmocean.cm.amp, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='max')\n",
    "ax4.set_ylim(1013.25,700)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_xlabel('hour', fontsize=fontsize)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.set_yticks([700,800,900,1000])\n",
    "ax2.text(s='(b) $\\Delta$ K$_{\\,t}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ K$_{\\,m}$', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2, w_pad=8)\n",
    "\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.025, ax3pos.y0, 0.0175, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "cbar3.set_label('m$^{\\,2}$s$^{\\,-1}$', fontsize=fontsize, labelpad=7)\n",
    "\n",
    "\n",
    "ax2pos = ax2.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "cbar_ax4 = fig.add_axes([ax4pos.x1+0.025, ax4pos.y0, 0.0175, ax2pos.y1-ax4pos.y0])\n",
    "cbar4 = fig.colorbar(contour_plot4, cax=cbar_ax4)\n",
    "cbar_ax4.tick_params(labelsize=fontsize)\n",
    "cbar4.set_label('% change', fontsize=fontsize, labelpad=7)\n",
    "cbar4.set_ticks([i for i in range(0,501,100)])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_xkzm_xkzh_terms_background_and_percent_change.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# draw PBLH and DELTA_YSU on these plots (must convert into height, not pressure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "u winds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:06.852521Z",
     "start_time": "2018-08-21T01:33:03.422939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.2,0.21,0.02)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             duadt_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap=cmocean.cm.curl, \\\n",
    "                             levels=levels,\\\n",
    "                             extend='both')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rublten_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap=cmocean.cm.curl, \\\n",
    "                             levels=levels,\\\n",
    "                             extend='both')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rucuten_diffs_latlon_mean.T*3600.*1e10, \\\n",
    "                             cmap=cmocean.cm.curl, \\\n",
    "                             levels=levels, \\\n",
    "                             extend='both')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) $\\Delta$ du/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ du/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ du/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot1, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "cbar3.set_label('m s$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar3.set_ticks([-.2,-.1,0,.1,.2])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_duadt_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:10.285016Z",
     "start_time": "2018-08-21T01:33:06.863648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dqdt_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rqvcuten_diffs_latlon_mean.T*1000.*86400., \\\n",
    "                             cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "#cbar3.set_ticks([-.01,0,.01])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:13.724502Z",
     "start_time": "2018-08-21T01:33:10.296452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dqdt_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,100)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([200,400,600,800,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,100)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([200,400,600,800,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rqvcuten_diffs_latlon_mean.T*1000.*86400., \\\n",
    "                             cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,100)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([200,400,600,800,1000])\n",
    "ax1.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "#cbar3.set_ticks([-.01,0,.01])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_day1_full_atmosphere.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:18.359120Z",
     "start_time": "2018-08-21T01:33:13.736032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.4,0.41,0.01)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dthetadt_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rthblten_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rthcuten_diffs_latlon_mean.T*86400., \\\n",
    "                             cmap='PuOr_r', levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s=r'(a) $\\Delta$ d$\\theta$/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s=r'(b) $\\Delta$ d$\\theta$/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s=r'(c) $\\Delta$ d$\\theta$/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('$\\degree$C hour$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar3.set_ticks([-.4,-.3,-.2,-.1,0,.1,.2,.3,.4])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dthetadt_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:22.038287Z",
     "start_time": "2018-08-21T01:33:18.371578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-1,1.1,0.1)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dthetadt_400_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rthblten_400_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rthcuten_400_latlon_mean.T*86400., \\\n",
    "                             cmap='PuOr_r', levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_400_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_400_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s=r'(a) $\\Delta$ d$\\theta$/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s=r'(b) $\\Delta$ d$\\theta$/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s=r'(c) $\\Delta$ d$\\theta$/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('$\\degree$C hour$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar3.set_ticks([-.5,-.4,-.3,-.2,-.1,0,.1,.2,.3,.4,.5])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dthetadt_background_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T01:33:25.550580Z",
     "start_time": "2018-08-21T01:33:22.050142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-1,1.1,0.1)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dthetadt_1000_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line1 = ax1.contour(xx_dmsedt-4, ptot_1000_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_1000_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rthblten_1000_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "# contour_line2 = ax2.contour(xx-4, pp/100., \\\n",
    "#                           dmsedt_1000_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rthcuten_1000_latlon_mean.T*861000., \\\n",
    "                             cmap='PuOr_r', levels=levels, extend='both')\n",
    "# contour_line3 = ax3.contour(xx_dmsedt-4, ptot_1000_dry_latlon_areamean.T/100., \\\n",
    "#                           dmsedt_1000_dry_latlon_areamean.T, \\\n",
    "#                           levels=[0], linewidths=[1], colors='0.1')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s=r'(a) $\\Delta$ d$\\theta$/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s=r'(b) $\\Delta$ d$\\theta$/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s=r'(c) $\\Delta$ d$\\theta$/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "# cbar_ax1 = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0, 0.03, ax1pos.height])\n",
    "# cbar1 = fig.colorbar(contour_plot1, cax=cbar_ax1)\n",
    "# cbar_ax1.tick_params(labelsize=fontsize)\n",
    "# #cbar1.add_lines(contour_line1)\n",
    "# cbar1.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar1.set_ticks([-.1,0,.1])\n",
    "\n",
    "# ax2pos = ax2.get_position()\n",
    "# cbar_ax2 = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.03, ax2pos.height])\n",
    "# cbar2 = fig.colorbar(contour_plot2, cax=cbar_ax2)\n",
    "# cbar_ax2.tick_params(labelsize=fontsize)\n",
    "# cbar2.set_label('J kg$^{\\,-1}$s$^{\\,-1}$', fontsize=fontsize)\n",
    "# #cbar2.add_lines(contour_line2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.05, ax3pos.y0, 0.03, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('$\\degree$C hour$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar3.set_ticks([-.5,-.4,-.3,-.2,-.1,0,.1,.2,.3,.4,.5])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dthetadt_background_phys_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-21T04:19:15.916175Z",
     "start_time": "2018-08-21T04:19:08.340212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 306x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "\n",
    "\n",
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,6))\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(321)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.15,0.151,0.025)\n",
    "contour_plot1 = ax1.contourf(xx-4, pp/100., \\\n",
    "                             dqdt_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax1.set_ylim(1013.25,700)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_yticks([700,800,900,1000])\n",
    "ax1.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "ax2 = fig.add_subplot(323)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(xx-4, pp/100., \\\n",
    "                             rqvblten_diffs_latlon_mean.T*1000.*3600., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax2.set_ylim(1013.25,700)\n",
    "#ax.set_xlim(0,240)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.set_yticks([700,800,900,1000])\n",
    "\n",
    "ax3 = fig.add_subplot(325)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot3 = ax3.contourf(xx-4, pp/100., \\\n",
    "                             rqvcuten_diffs_latlon_mean.T*1000.*86400., \\\n",
    "                             cmap=cmocean.cm.balance_r, levels=levels, extend='both')\n",
    "ax3.set_ylim(1013.25,700)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_xlabel('hour', fontsize=fontsize)\n",
    "ax3.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.set_yticks([700,800,900,1000])\n",
    "ax1.text(s='(a) $\\Delta$ dq/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ dq/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ dq/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "\n",
    "\n",
    "ax12 = fig.add_subplot(322)\n",
    "ax12.tick_params(labelsize=fontsize)\n",
    "levels = numpy.arange(-0.4,0.41,0.01)\n",
    "contour_plot12 = ax12.contourf(xx-4, pp/100., \\\n",
    "                             dthetadt_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax12.set_ylim(1013.25,700)\n",
    "ax12.set_xlim(6,18)\n",
    "ax12.set_xticks([i for i in range(6,19,2)])\n",
    "ax12.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax22 = fig.add_subplot(324)\n",
    "ax22.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot22 = ax22.contourf(xx-4, pp/100., \\\n",
    "                             rthblten_diffs_latlon_mean.T*3600., \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax22.set_ylim(1013.25,700)\n",
    "ax22.set_xlim(6,18)\n",
    "\n",
    "ax22.set_xticks([i for i in range(6,19,2)])\n",
    "ax22.set_yticks([700,800,900,1000])\n",
    "\n",
    "\n",
    "ax32 = fig.add_subplot(326)\n",
    "ax32.tick_params(labelsize=fontsize)\n",
    "\n",
    "contour_plot32 = ax32.contourf(xx-4, pp/100., \\\n",
    "                             rthcuten_diffs_latlon_mean.T*861000., \\\n",
    "                             cmap='PuOr_r', levels=levels, extend='both')\n",
    "ax32.set_ylim(1013.25,700)\n",
    "ax32.set_xlim(6,18)\n",
    "ax32.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax32.set_xticks([i for i in range(6,19,2)])\n",
    "ax32.set_yticks([700,800,900,1000])\n",
    "ax12.text(s=r'(d) $\\Delta$ d$\\theta$/dt (full)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax12.transAxes)\n",
    "ax22.text(s=r'(e) $\\Delta$ d$\\theta$/dt (YSU tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax22.transAxes)\n",
    "ax32.text(s=r'(f) $\\Delta$ d$\\theta$/dt (Tiedtke tendencies)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax32.transAxes)\n",
    "\n",
    "fig.tight_layout(w_pad=10, h_pad=2)\n",
    "\n",
    "ax3pos = ax3.get_position()\n",
    "cbar_ax3 = fig.add_axes([ax3pos.x1+0.025, ax3pos.y0, 0.02, ax1pos.y1-ax3pos.y0])\n",
    "cbar3 = fig.colorbar(contour_plot3, cax=cbar_ax3)\n",
    "cbar_ax3.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar3.set_label('g kg$^{\\,-1}$ hour$^{\\,-1}$', fontsize=fontsize)\n",
    "#cbar3.set_ticks([-.01,0,.01])\n",
    "\n",
    "ax12pos = ax12.get_position()\n",
    "ax32pos = ax32.get_position()\n",
    "cbar_ax32 = fig.add_axes([ax32pos.x1+0.025, ax32pos.y0, 0.02, ax12pos.y1-ax32pos.y0])\n",
    "cbar32 = fig.colorbar(contour_plot32, cax=cbar_ax32)\n",
    "cbar_ax32.tick_params(labelsize=fontsize)\n",
    "#cbar3.add_lines(contour_line3)\n",
    "cbar32.set_label('$\\degree$C hour$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar32.set_ticks([-.5,-.4,-.3,-.2,-.1,0,.1,.2,.3,.4,.5])\n",
    "\n",
    "fig.savefig('figures/sensitivity_analysis_dqdt_and_dthetadt_response_day1.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": []
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