gp1_figure7_moisture_precip_hovmoller_geopot_coords.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:25:14.617158Z",
     "start_time": "2018-08-16T00:25:12.048605Z"
    }
   },
   "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 pandas\n",
    "import glob\n",
    "import netCDF4\n",
    "import matplotlib.dates\n",
    "import matplotlib.gridspec\n",
    "\n",
    "import matplotlib.ticker\n",
    "import cartopy.mpl.gridliner #import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "import mpl_toolkits\n",
    "import cartopy\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:25:14.631775Z",
     "start_time": "2018-08-16T00:25:14.625990Z"
    }
   },
   "outputs": [],
   "source": [
    "run_info = 'FMAASO'\n",
    "skip_time=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:25:15.092987Z",
     "start_time": "2018-08-16T00:25:14.639728Z"
    }
   },
   "outputs": [],
   "source": [
    "geo_em_ncfile = xarray.open_dataset('/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/geo_em.d01_amazon_50km.nc')\n",
    "HGT_M = geo_em_ncfile.variables['HGT_M'].data[0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import FMAASO 400 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:25:16.429280Z",
     "start_time": "2018-08-16T00:25:15.101405Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400/ens_mean_wrfout_files'\n",
    "file_list = glob.glob(root_dir+'/'+'wrfout*.nc')\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:25:24.138557Z",
     "start_time": "2018-08-16T00:25:16.437827Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:28:10.181571Z",
     "start_time": "2018-08-16T00:25:24.147139Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = numpy.load('DATA_ptot_400.npy')\n",
    "\n",
    "wa_400 = numpy.load('DATA_wa_400.npy')\n",
    "\n",
    "ua_400 = numpy.load('DATA_ua_400.npy')\n",
    "\n",
    "geopot_400 = numpy.load('DATA_geopot_400.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:28:10.197787Z",
     "start_time": "2018-08-16T00:28:10.189899Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_400 = cont_mfds['RAINC']\n",
    "rainnc_400 = cont_mfds['RAINNC']\n",
    "qvapor_400 = cont_mfds['QVAPOR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:28:48.589042Z",
     "start_time": "2018-08-16T00:28:10.207138Z"
    }
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "# import FMAASO 1000 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:28:49.759686Z",
     "start_time": "2018-08-16T00:28:48.597384Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000/ens_mean_wrfout_files'\n",
    "file_list = glob.glob(root_dir+'/'+'wrfout*.nc')\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:28:58.063693Z",
     "start_time": "2018-08-16T00:28:49.768132Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:31:42.187274Z",
     "start_time": "2018-08-16T00:28:58.072282Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = numpy.load('DATA_ptot_400.npy')\n",
    "\n",
    "wa_1000 = numpy.load('DATA_wa_1000.npy')\n",
    "\n",
    "ua_1000 = numpy.load('DATA_ua_1000.npy')\n",
    "\n",
    "geopot_1000 = numpy.load('DATA_geopot_1000.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:31:42.195576Z",
     "start_time": "2018-08-16T00:31:42.189625Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_1000 = phys_mfds['RAINC']\n",
    "rainnc_1000 = phys_mfds['RAINNC']\n",
    "qvapor_1000 = phys_mfds['QVAPOR']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get time stamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:31:42.325157Z",
     "start_time": "2018-08-16T00:31:42.209484Z"
    }
   },
   "outputs": [],
   "source": [
    "time_datetimes = pandas.to_datetime([i.decode('utf-8') for i in time], format = '%Y-%m-%d_%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:31:42.433225Z",
     "start_time": "2018-08-16T00:31:42.330316Z"
    }
   },
   "outputs": [],
   "source": [
    "lat_hi = 10.\n",
    "lat_lo = -10.\n",
    "# subset between lat_hi and lat_lo\n",
    "lat_indices = (xlat<=lat_hi)&(xlat>=lat_lo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# qvapor diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:32:35.160012Z",
     "start_time": "2018-08-16T00:31:42.435955Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_diffs = qvapor_1000 - qvapor_400"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "qvapor_diffs_landmask = numpy.copy(qvapor_diffs)\n",
    "qvapor_diffs_landmask[:,:,landmask==0]=numpy.nan\n",
    "\n",
    "qvapor_400_landmask = numpy.copy(qvapor_400)\n",
    "qvapor_400_landmask[:,:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:32:35.191613Z",
     "start_time": "2018-08-16T00:32:35.162382Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_diffs_lat_subset = qvapor_diffs[:,:,lat_indices[:,0],:]\n",
    "qvapor_400_lat_subset = qvapor_400[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:36:36.719190Z",
     "start_time": "2018-08-16T00:32:35.194028Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_diffs_lat_mean = numpy.nanmean(qvapor_diffs_lat_subset, axis=2)\n",
    "qvapor_400_lat_mean = numpy.nanmean(qvapor_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ptot diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:36:38.181031Z",
     "start_time": "2018-08-16T00:36:36.727655Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_lat_subset = ptot_400[:,:,lat_indices[:,0],:]\n",
    "ptot_400_lat_mean = numpy.nanmean(ptot_400_lat_subset, axis=2)\n",
    "ptot_400_lat_time_mean = numpy.nanmean(ptot_400_lat_mean, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# raintot diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:36:42.870085Z",
     "start_time": "2018-08-16T00:36:38.189400Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400 = rainc_400+rainnc_400\n",
    "raintot_1000 = rainc_1000+rainnc_1000\n",
    "\n",
    "raintot_diffs = raintot_1000-raintot_400\n",
    "\n",
    "raintot_diffs_lat_subset = raintot_diffs[:,lat_indices[:,0],:]\n",
    "raintot_400_lat_subset = raintot_400[:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:36:42.890538Z",
     "start_time": "2018-08-16T00:36:42.878645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241, 45, 289)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raintot_diffs_lat_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:42.034533Z",
     "start_time": "2018-08-16T00:36:42.898680Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: Mean of empty slice\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "raintot_hourly_diffs_lat_subset = numpy.zeros((raintot_diffs_lat_subset.shape))*numpy.nan\n",
    "raintot_hourly_diffs_lat_subset[1:,:,:] = numpy.diff(raintot_diffs_lat_subset, axis=0)\n",
    "raintot_hourly_diffs_lat_mean = numpy.nanmean(raintot_hourly_diffs_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# geopot 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:42.356542Z",
     "start_time": "2018-08-16T00:37:42.042706Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_lat_subset = geopot_400[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:42.374549Z",
     "start_time": "2018-08-16T00:37:42.364884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241, 29, 45, 289)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geopot_400_lat_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:43.612923Z",
     "start_time": "2018-08-16T00:37:42.382707Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_lat_mean = numpy.nanmean(geopot_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:43.643666Z",
     "start_time": "2018-08-16T00:37:43.621265Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_lat_time_mean = numpy.nanmean(geopot_400_lat_mean, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open mse from npy files"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:20.825374Z",
     "start_time": "2018-03-15T21:05:18.725380Z"
    }
   },
   "source": [
    "mse_400 = numpy.load('./DATA_mse_400.npy')\n",
    "mse_1000 = numpy.load('./DATA_mse_1000.npy')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:21.871982Z",
     "start_time": "2018-03-15T21:05:21.023404Z"
    }
   },
   "source": [
    "mse_diffs = mse_1000 - mse_400"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mse_diffs_landmask = numpy.copy(mse_diffs)\n",
    "mse_diffs_landmask[:,:,landmask==0]=numpy.nan\n",
    "\n",
    "mse_400_landmask = numpy.copy(mse_400)\n",
    "mse_400_landmask[:,:,landmask==0]=numpy.nan\n",
    "\n",
    "mse_1000_landmask = numpy.copy(mse_1000)\n",
    "mse_1000_landmask[:,:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:22.711254Z",
     "start_time": "2018-03-15T21:05:21.920598Z"
    }
   },
   "source": [
    "#mse_diffs_lat_subset = mse_diffs_landmask[:,:,lat_indices[:,0],:]\n",
    "#mse_400_lat_subset = mse_400_landmask[:,:,lat_indices[:,0],:]\n",
    "#mse_1000_lat_subset = mse_1000_landmask[:,:,lat_indices[:,0],:]\n",
    "\n",
    "mse_diffs_lat_subset = mse_diffs[:,:,lat_indices[:,0],:]\n",
    "mse_400_lat_subset = mse_400[:,:,lat_indices[:,0],:]\n",
    "mse_1000_lat_subset = mse_1000[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:23.991004Z",
     "start_time": "2018-03-15T21:05:22.713145Z"
    }
   },
   "source": [
    "mse_diffs_lat_mean = numpy.nanmean(mse_diffs_lat_subset, axis=2)\n",
    "mse_400_lat_mean = numpy.nanmean(mse_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ua differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:44.990826Z",
     "start_time": "2018-08-16T00:37:43.652237Z"
    }
   },
   "outputs": [],
   "source": [
    "ua_diffs = ua_1000-ua_400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:45.635988Z",
     "start_time": "2018-08-16T00:37:44.999378Z"
    }
   },
   "outputs": [],
   "source": [
    "ua_diffs_lat_subset = ua_diffs[:,:,lat_indices[:,0],:]\n",
    "ua_400_lat_subset = ua_400[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:47.882927Z",
     "start_time": "2018-08-16T00:37:45.644524Z"
    }
   },
   "outputs": [],
   "source": [
    "ua_diffs_lat_mean = numpy.nanmean(ua_diffs_lat_subset, axis=2)\n",
    "ua_400_lat_mean = numpy.nanmean(ua_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wa differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:48.898040Z",
     "start_time": "2018-08-16T00:37:47.891330Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs = wa_1000-wa_400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:49.545922Z",
     "start_time": "2018-08-16T00:37:48.906745Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_lat_subset = wa_diffs[:,:,lat_indices[:,0],:]\n",
    "wa_400_lat_subset = wa_400[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:51.814449Z",
     "start_time": "2018-08-16T00:37:49.554465Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_lat_mean = numpy.nanmean(wa_diffs_lat_subset, axis=2)\n",
    "wa_400_lat_mean = numpy.nanmean(wa_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PBLH differences"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:30.081930Z",
     "start_time": "2018-03-15T21:05:29.996894Z"
    }
   },
   "source": [
    "pblh_diffs = pblh_1000 - pblh_400"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:30.824349Z",
     "start_time": "2018-03-15T21:05:30.083988Z"
    }
   },
   "source": [
    "pblh_diffs_landmask = numpy.copy(pblh_diffs)\n",
    "pblh_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "pblh_diffs_landmask[pblh_diffs_landmask==9.96921e36]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:31.121738Z",
     "start_time": "2018-03-15T21:05:30.826380Z"
    }
   },
   "source": [
    "pblh_400_landmask = numpy.copy(pblh_400)\n",
    "pblh_400_landmask[:,landmask==0]=numpy.nan\n",
    "#pblh_400_landmask[pblh_400_landmask==9.96921e36]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:31.260173Z",
     "start_time": "2018-03-15T21:05:31.123600Z"
    }
   },
   "source": [
    "pblh_1000_landmask = numpy.copy(pblh_1000)\n",
    "pblh_1000_landmask[:,landmask==0]=numpy.nan\n",
    "#pblh_1000_landmask[pblh_1000_landmask==9.96921e36]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:31.441694Z",
     "start_time": "2018-03-15T21:05:31.262038Z"
    }
   },
   "source": [
    "pblh_diffs_lat_subset = pblh_diffs_landmask[:,lat_indices[:,0],:]\n",
    "pblh_400_lat_subset = pblh_400_landmask[:,lat_indices[:,0],:]\n",
    "pblh_1000_lat_subset = pblh_1000_landmask[:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-15T21:05:31.639845Z",
     "start_time": "2018-03-15T21:05:31.444275Z"
    }
   },
   "source": [
    "pblh_diffs_lat_mean = numpy.nanmean(pblh_diffs_lat_subset, axis=1)\n",
    "pblh_400_lat_mean = numpy.nanmean(pblh_400_lat_subset, axis=1)\n",
    "pblh_1000_lat_mean = numpy.nanmean(pblh_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hövmoller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pick out based on geopotential height (1-3km)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T06:26:06.587198Z",
     "start_time": "2018-05-10T06:26:06.536254Z"
    }
   },
   "source": [
    "qvapor_diffs_lat_mean_1_3km = numpy.copy(qvapor_diffs_lat_mean)\n",
    "qvapor_diffs_lat_mean_1_3km[(geopot_400_lat_mean<10000.)|(geopot_400_lat_mean>30000.)] = numpy.nan\n",
    "qvapor_diffs_lat_mean_1_3km = numpy.nanmean(qvapor_diffs_lat_mean_1_3km, axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T06:11:09.706690Z",
     "start_time": "2018-05-10T06:11:09.657568Z"
    }
   },
   "source": [
    "qvapor_diffs_lat_mean_0_1km = numpy.copy(qvapor_diffs_lat_mean)\n",
    "qvapor_diffs_lat_mean_0_1km[(geopot_400_lat_mean>10000.)] = numpy.nan\n",
    "qvapor_diffs_lat_mean_0_1km = numpy.nanmean(qvapor_diffs_lat_mean_1_3km, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pick out qvapor based on pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:51.865517Z",
     "start_time": "2018-08-16T00:37:51.822852Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_diffs_lat_mean_850_700hPa = numpy.copy(qvapor_diffs_lat_mean)\n",
    "qvapor_diffs_lat_mean_850_700hPa[(ptot_400_lat_mean>90000.)|(ptot_400_lat_mean<70000.)] = numpy.nan\n",
    "qvapor_diffs_lat_mean_850_700hPa = numpy.nanmean(qvapor_diffs_lat_mean_850_700hPa, axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T06:11:09.706690Z",
     "start_time": "2018-05-10T06:11:09.657568Z"
    }
   },
   "source": [
    "qvapor_diffs_lat_mean_850_700hPa = numpy.copy(qvapor_diffs_lat_mean)\n",
    "qvapor_diffs_lat_mean_850_700hPa[(geopot_400_lat_mean>10000.)] = numpy.nan\n",
    "qvapor_diffs_lat_mean_850_700hPa = numpy.nanmean(qvapor_diffs_lat_mean_1_3km, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pick out wa based on pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:51.989383Z",
     "start_time": "2018-08-16T00:37:51.874173Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_lat_mean_700_200hPa = numpy.copy(wa_diffs_lat_mean)\n",
    "#wa_diffs_lat_mean_700_200hPa[(ptot_400_lat_mean>50000.)] = numpy.nan\n",
    "wa_diffs_lat_mean_700_200hPa = numpy.nanmean(wa_diffs_lat_mean_700_200hPa, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:52.464078Z",
     "start_time": "2018-08-16T00:37:51.997936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b8e06a81b70>"
      ]
     },
     "execution_count": 35,
     "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": [
    "wa_diffs_lat_mean.shape\n",
    "mp.contourf(numpy.mean(wa_diffs_lat_mean[48:72,:,:],axis=0)*86400/1000, levels=numpy.arange(-.2,.21,.05), extend='both', cmap='RdBu')\n",
    "mp.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate mean wind in the layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:52.478475Z",
     "start_time": "2018-08-16T00:37:52.472425Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_left = 285.\n",
    "lon_right = 315.\n",
    "xlong_indices_ua_calc = (xlong[0,:]+360. >= lon_left) & (xlong[0,:]+360 <= lon_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:52.651435Z",
     "start_time": "2018-08-16T00:37:52.486642Z"
    }
   },
   "outputs": [],
   "source": [
    "ua_400_lat_mean_850_700hPa = numpy.copy(ua_400_lat_mean)\n",
    "ua_400_lat_mean_850_700hPa[(ptot_400_lat_mean>90000.)|(ptot_400_lat_mean<70000.)] = numpy.nan\n",
    "ua_400_lat_mean_850_700hPa = numpy.nanmean(ua_400_lat_mean_850_700hPa, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:52.748819Z",
     "start_time": "2018-08-16T00:37:52.660157Z"
    }
   },
   "outputs": [],
   "source": [
    "ua_400_mean = numpy.nanmean(ua_400_lat_mean_850_700hPa[:,xlong_indices_ua_calc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# format xticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:52.868890Z",
     "start_time": "2018-08-16T00:37:52.757232Z"
    }
   },
   "outputs": [],
   "source": [
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate Andes height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T00:37:53.026946Z",
     "start_time": "2018-08-16T00:37:52.877403Z"
    }
   },
   "outputs": [],
   "source": [
    "HGT_M_lat_subset = HGT_M[lat_indices[:,0],:]\n",
    "HGT_M_lat_subset_maxvals = numpy.max(HGT_M_lat_subset, axis=0)\n",
    "HGT_M_lat_subset_equator = HGT_M[round(HGT_M_lat_subset.shape[0]/2),:]\n",
    "HGT_M_lat_subset_minvals = numpy.min(HGT_M_lat_subset, axis=0)\n",
    "HGT_M_lat_subset_mean = numpy.mean(HGT_M_lat_subset, axis=0)\n",
    "HGT_M_lat_subset_median = numpy.median(HGT_M_lat_subset, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot manuscript figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T01:28:22.814649Z",
     "start_time": "2018-08-16T01:28:22.804913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(0, 14400)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.timedelta(hours=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T01:31:38.089980Z",
     "start_time": "2018-08-16T01:31:38.080092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_datetimes[-1].day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-08-18T01:03:04.923Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "fig = mp.figure(figsize=(8.5,6))\n",
    "\n",
    "#ax1=fig.add_subplot(131)\n",
    "\n",
    "levels=numpy.arange(-0.3,0.310,0.05)\n",
    "levels_precip = numpy.arange(-0.125,0.1251,0.0125)\n",
    "\n",
    "ax2=fig.add_subplot(121)\n",
    "contour_plot = ax2.contourf(xlong[0,:]+360.,time_datetimes-datetime.timedelta(hours=4),\\\n",
    "                           #qvapor_diffs_lat_mean_1_3km*1000.,\\\n",
    "                           qvapor_diffs_lat_mean_850_700hPa*1000.,\\\n",
    "                           cmap='PuOr',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax2.contour(xlong[0,:]+360.,time_datetimes-datetime.timedelta(hours=4),\\\n",
    "                           #qvapor_diffs_lat_mean_1_3km*1000.,\\\n",
    "                           qvapor_diffs_lat_mean_850_700hPa*1000.,\\\n",
    "                           levels=[-0.1,0.1],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "\n",
    "ax2.text(s='(a) $\\Delta$ moisture (900–700 hPa layer)', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax2.set_xticks([280,300,320])\n",
    "ax2.set_xticklabels(xticks_format)\n",
    "ax2.set_xlim(275.,325.)\n",
    "ax2.set_ylim(datetime.datetime(1979,1,1,4,0,0),datetime.datetime(1979,1,10))\n",
    "ax2.set_yticks(time_datetimes[[t.hour==0 and t.day<11 for t in time_datetimes]])\n",
    "ax2.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "ax3 = fig.add_subplot(122)\n",
    "contour_rain = ax3.contourf(xlong[0,:]+360.,time_datetimes-datetime.timedelta(hours=4),\\\n",
    "                           raintot_hourly_diffs_lat_mean,\\\n",
    "                           levels=levels_precip,\\\n",
    "                           cmap='BrBG', extend='both')\n",
    "ax3.text(s='(b) $\\Delta$ hourly precipitation', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax3.set_xticks([280,300,320])\n",
    "ax3.set_xticklabels(xticks_format)\n",
    "ax3.set_xlim(275.,325.)\n",
    "ax3.set_ylim(datetime.datetime(1979,1,1,4,0,0),datetime.datetime(1979,1,10))\n",
    "ax3.set_yticks(time_datetimes[[t.hour==0 and t.day<11 for t in time_datetimes]])\n",
    "ax3.set_yticklabels([])\n",
    "ax3.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# ax1_pos = ax1.get_position()\n",
    "ax2_pos = ax2.get_position()\n",
    "ax3_pos = ax3.get_position()\n",
    "\n",
    "cbar1_ax = fig.add_axes([ax2_pos.x0,ax2_pos.y0-0.1,ax2_pos.width,0.03])\n",
    "cbar1 = mp.colorbar(contour_plot, cax=cbar1_ax, orientation='horizontal')\n",
    "cbar1_ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_ticks([-0.3,-0.15,0,0.15,0.3])\n",
    "#cbar1_ax.axvline(cbar1_ax.get_xticks()[2],lw=0.5,c='0.1',ls='--')\n",
    "#cbar1_ax.axvline(cbar1_ax.get_xticks()[4],lw=0.5,c='0.1',ls='--')\n",
    "cbar1.set_label('g kg$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar1.add_lines(contour_line, ls=':')\n",
    "\n",
    "cbar2_ax = fig.add_axes([ax3_pos.x0,ax3_pos.y0-0.1,ax3_pos.width,0.03])\n",
    "cbar2 = mp.colorbar(contour_rain, cax=cbar2_ax, orientation='horizontal')\n",
    "cbar2_ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_ticks([-0.1,0,0.1])\n",
    "cbar2.set_label('mm hour$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax2_orog = fig.add_axes([ax2_pos.x0,ax2_pos.y0,ax2_pos.width,ax2_pos.height*0.2])\n",
    "ax2_orog.axis('off')\n",
    "ax2_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax2_orog.set_xlim(ax2.get_xlim())#xlong.min()+55.+360.,xlong.max()+360.)\n",
    "\n",
    "ax3_orog = fig.add_axes([ax3_pos.x0,ax3_pos.y0,ax3_pos.width,ax3_pos.height*0.2])\n",
    "ax3_orog.axis('off')\n",
    "ax3_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax3_orog.set_xlim(ax2.get_xlim())\n",
    "\n",
    "#ax1.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "#ax1.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "\n",
    "speed1 = 2.3 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed1\n",
    "slope1 = dt/-dx # sec/meter\n",
    "intercept1 = 10*dt\n",
    "xvals1 = numpy.linspace(ax2.get_xlim()[0],ax2.get_xlim()[1],100)\n",
    "xvals1_m = xvals1*50000.\n",
    "yvals1 = slope1*(xvals1_m-ax2.get_xlim()[0]*50000) + intercept1\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals1_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals1]\n",
    "\n",
    "speed2 = 6#-ua_400_mean #5.1 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed2\n",
    "slope2 = dt/-dx # sec/meter\n",
    "intercept2 = 7.*dt\n",
    "xvals2 = numpy.linspace(ax2.get_xlim()[0],ax2.get_xlim()[1],100)\n",
    "xvals2_m = xvals2*50000.\n",
    "yvals2 = slope2*(xvals2_m-ax2.get_xlim()[0]*50000) + intercept2\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals2_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals2]\n",
    "\n",
    "#ax2.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "ax2.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "plot_ratio = ax2.get_data_ratio()\n",
    "ax2.set_ylabel('simulation day', fontsize=fontsize)\n",
    "\n",
    "# slope_rotation1 = numpy.degrees(numpy.arctan(slope1/plot_ratio))\n",
    "# ax2.text(s='$-\\,$'+str(speed1)+' m s$^{\\,-1}$',x=0.69,y=0.2,transform=ax2.transAxes,\\\n",
    "#          ha='center',\\\n",
    "#          va='center',\\\n",
    "#          fontsize=fontsize,\\\n",
    "#          rotation=slope_rotation1+4)\n",
    "\n",
    "slope_rotation2 = numpy.degrees(numpy.arctan(slope2/plot_ratio))\n",
    "ax2.text(s='$-\\,$'+'{:.1f}'.format(speed2)+' m s$^{\\,-1}$',x=0.7,y=0.41,transform=ax2.transAxes,\\\n",
    "         ha='center',\\\n",
    "         va='center',\\\n",
    "         fontsize=fontsize,\\\n",
    "         rotation=slope_rotation2+4)\n",
    "\n",
    "fig.savefig('figures/manuscript_fig7_qvapor_hovmoller_with_rain_diffs.png',dpi=600,bbox_inches='tight',transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T23:30:48.320269Z",
     "start_time": "2018-05-10T23:30:35.774988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "fig = mp.figure(figsize=(7.5,5))\n",
    "\n",
    "#ax1=fig.add_subplot(131)\n",
    "\n",
    "levels=numpy.arange(-0.3,0.310,0.05)\n",
    "levels_precip = numpy.arange(-3,3.1,0.5)\n",
    "levels_wa = numpy.arange(-.5,.51,0.1)\n",
    "\n",
    "ax1=fig.add_subplot(131)\n",
    "contour_plot = ax1.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           #qvapor_diffs_lat_mean_1_3km*1000.,\\\n",
    "                           qvapor_diffs_lat_mean_850_700hPa*1000.,\\\n",
    "                           cmap='PuOr',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax1.contour(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           #qvapor_diffs_lat_mean_1_3km*1000.,\\\n",
    "                           qvapor_diffs_lat_mean_850_700hPa*1000.,\\\n",
    "                           levels=[-0.1,0.1],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "\n",
    "ax1.text(s='(a) $\\Delta$ moist. (900–700 hPa)', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax1.set_xticks([280,300,320])\n",
    "ax1.set_xticklabels(xticks_format)\n",
    "ax1.set_xlim(275.,325.)\n",
    "ax1.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax1.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax1.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "contour_rain = ax2.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           raintot_hourly_diffs_lat_mean*24.,\\\n",
    "                           levels=levels_precip,\\\n",
    "                           cmap='BrBG', extend='both')\n",
    "ax2.text(s='(b) $\\Delta$ hourly precipitation', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax2.set_xticks([280,300,320])\n",
    "ax2.set_xticklabels(xticks_format)\n",
    "ax2.set_xlim(275.,325.)\n",
    "ax2.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax2.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax2.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "contour_wa = ax3.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           wa_diffs_lat_mean_700_200hPa*86400./1000.,\\\n",
    "                           levels=levels_wa,\\\n",
    "                           cmap='RdBu', extend='both')\n",
    "ax3.text(s='(c) $\\Delta$ w (700–200 hPa)', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax3.set_xticks([280,300,320])\n",
    "ax3.set_xticklabels(xticks_format)\n",
    "ax3.set_xlim(275.,325.)\n",
    "ax3.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax3.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax3.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ax1_pos = ax1.get_position()\n",
    "ax1_pos = ax1.get_position()\n",
    "ax2_pos = ax2.get_position()\n",
    "ax3_pos = ax3.get_position()\n",
    "\n",
    "cbar1_ax = fig.add_axes([ax1_pos.x0,ax1_pos.y0-0.12,ax1_pos.width,0.03])\n",
    "cbar1 = mp.colorbar(contour_plot, cax=cbar1_ax, orientation='horizontal')\n",
    "cbar1_ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_ticks([-0.3,-0.15,0,0.15,0.3])\n",
    "cbar1.set_label('g kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "cbar2_ax = fig.add_axes([ax2_pos.x0,ax2_pos.y0-0.12,ax2_pos.width,0.03])\n",
    "cbar2 = mp.colorbar(contour_rain, cax=cbar2_ax, orientation='horizontal')\n",
    "cbar2_ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_ticks([-3,-2,-1,0,1,2,3])\n",
    "cbar2.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "cbar3_ax = fig.add_axes([ax3_pos.x0,ax3_pos.y0-0.12,ax3_pos.width,0.03])\n",
    "cbar3 = mp.colorbar(contour_wa, cax=cbar3_ax, orientation='horizontal')\n",
    "cbar3_ax.tick_params(labelsize=fontsize)\n",
    "#cbar3.set_ticks([-3,-2,-1,0,1,2,3])\n",
    "cbar3.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax1_orog = fig.add_axes([ax1_pos.x0,ax1_pos.y0,ax1_pos.width,ax1_pos.height*0.2])\n",
    "ax1_orog.axis('off')\n",
    "ax1_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax1_orog.set_xlim(ax1.get_xlim())#xlong.min()+55.+360.,xlong.max()+360.)\n",
    "\n",
    "ax2_orog = fig.add_axes([ax2_pos.x0,ax2_pos.y0,ax2_pos.width,ax2_pos.height*0.2])\n",
    "ax2_orog.axis('off')\n",
    "ax2_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax2_orog.set_xlim(ax1.get_xlim())\n",
    "\n",
    "\n",
    "ax1.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "\n",
    "ax1.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "\n",
    "speed1 = 2.3 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed1\n",
    "slope1 = dt/-dx # sec/meter\n",
    "intercept1 = 10*dt\n",
    "xvals1 = numpy.linspace(ax1.get_xlim()[0],ax1.get_xlim()[1],100)\n",
    "xvals1_m = xvals1*50000.\n",
    "yvals1 = slope1*(xvals1_m-ax1.get_xlim()[0]*50000) + intercept1\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals1_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals1]\n",
    "\n",
    "speed2 = 6#-ua_400_mean #5.1 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed2\n",
    "slope2 = dt/-dx # sec/meter\n",
    "intercept2 = 7.*dt\n",
    "xvals2 = numpy.linspace(ax1.get_xlim()[0],ax1.get_xlim()[1],100)\n",
    "xvals2_m = xvals2*50000.\n",
    "yvals2 = slope2*(xvals2_m-ax1.get_xlim()[0]*50000) + intercept2\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals2_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals2]\n",
    "\n",
    "#ax1.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "ax1.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "plot_ratio = ax1.get_data_ratio()\n",
    "ax1.set_ylabel('simulation day', fontsize=fontsize)\n",
    "\n",
    "slope_rotation2 = numpy.degrees(numpy.arctan(slope2/plot_ratio))\n",
    "ax1.text(s='$-\\,$'+'{:.1f}'.format(speed2)+' m s$^{\\,-1}$',x=0.7,y=0.41,transform=ax1.transAxes,\\\n",
    "         ha='center',\\\n",
    "         va='center',\\\n",
    "         fontsize=fontsize,\\\n",
    "         rotation=slope_rotation2+4)\n",
    "\n",
    "fig.savefig('manuscript_fig7alt_qvapor_hovmoller_with_rain_AND_WA_diffs.png',dpi=600,bbox_inches='tight',transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T05:22:08.522063Z",
     "start_time": "2018-08-16T05:22:08.349522Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "add_lines() missing 1 required positional argument: 'CS'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-aac8d4d2c6a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcbar1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: add_lines() missing 1 required positional argument: 'CS'"
     ]
    }
   ],
   "source": [
    "cbar1.add_lines()"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first5days_indices = [t.day in range(1,6) for t in time_datetimes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "fig = mp.figure(figsize=(7.5,4))\n",
    "\n",
    "ax1=fig.add_subplot(131)\n",
    "\n",
    "levels=numpy.arange(-1,1.1,0.125)\n",
    "levels_precip = numpy.arange(-3,3.1,0.5)\n",
    "\n",
    "contour_plot = ax1.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_0_1km_latmean/1000.,\\\n",
    "                           cmap='PuOr_r',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax1.contour(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_0_1km_latmean/1000.,\\\n",
    "                           levels=[-0.25,0.25],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "ax1.set_ylabel('day', fontsize=fontsize)\n",
    "ax1.text(s='(a) $\\Delta$ MSE in $0-1$ km', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_xticks([280,300,320])\n",
    "ax1.set_xticklabels(xticks_format)\n",
    "ax1.set_xlim(275.,325.)\n",
    "ax1.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,6))\n",
    "ax1.set_yticks(time_datetimes[first5days_indices][[t.hour==0 for t in time_datetimes[first5days_indices]]])\n",
    "ax1.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(132)\n",
    "contour_plot = ax2.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_1_3km_latmean/1000.,\\\n",
    "                           cmap='PuOr_r',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax2.contour(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_1_3km_latmean/1000.,\\\n",
    "                           levels=[-0.25,0.25],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "\n",
    "ax2.text(s='(b) $\\Delta$ MSE in $1-3$ km', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax2.set_xticks([280,300,320])\n",
    "ax2.set_xticklabels(xticks_format)\n",
    "ax2.set_xlim(275.,325.)\n",
    "ax2.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,6))\n",
    "ax2.set_yticks(time_datetimes[first5days_indices][[t.hour==0 for t in time_datetimes[first5days_indices]]])\n",
    "ax2.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "contour_rain = ax3.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           raintot_hourly_diffs_lat_subset_latmean*24.,\\\n",
    "                           levels=levels_precip,\\\n",
    "                           cmap='BrBG', extend='both')\n",
    "# contour_rain = ax3.pcolor(xlong[0,:]+360.,time_datetimes,\\\n",
    "#                            raintot_hourly_diffs_lat_subset_latmean*24.,\\\n",
    "#                            vmin=levels_precip.min(),vmax=levels_precip.max(),\\\n",
    "#                            cmap='BrBG',snap=True)\n",
    "ax3.text(s='(c) $\\Delta$ precipitation', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax3.set_xticks([280,300,320])\n",
    "ax3.set_xticklabels(xticks_format)\n",
    "ax3.set_xlim(275.,325.)\n",
    "ax3.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,6))\n",
    "ax3.set_yticks(time_datetimes[first5days_indices][[t.hour==0 for t in time_datetimes[first5days_indices]]])\n",
    "ax3.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax1_pos = ax1.get_position()\n",
    "ax2_pos = ax2.get_position()\n",
    "ax3_pos = ax3.get_position()\n",
    "\n",
    "cbar1_ax = fig.add_axes([ax1_pos.x0,ax1_pos.y0-0.12,ax2_pos.x1-ax1_pos.x0,0.03])\n",
    "cbar1 = mp.colorbar(contour_plot, cax=cbar1_ax, orientation='horizontal')\n",
    "cbar1_ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_ticks([-1.,-.5,-0.25,0,0.25,.5,1])\n",
    "cbar1_ax.axvline(cbar1_ax.get_xticks()[2],lw=0.5,c='0.1',ls='--')\n",
    "cbar1_ax.axvline(cbar1_ax.get_xticks()[4],lw=0.5,c='0.1',ls='--')\n",
    "cbar1.set_label('kJ kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "cbar2_ax = fig.add_axes([ax3_pos.x0,ax3_pos.y0-0.12,ax3_pos.width,0.03])\n",
    "cbar2 = mp.colorbar(contour_rain, cax=cbar2_ax, orientation='horizontal')\n",
    "cbar2_ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_ticks([-3,-2,-1,0,1,2,3])\n",
    "#cbar2_ax.axvline(cbar2_ax.get_xticks()[2],lw=0.5,c='0.1',ls='--')\n",
    "#cbar2_ax.axvline(cbar2_ax.get_xticks()[4],lw=0.5,c='0.1',ls='--')\n",
    "cbar2.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax1_orog = fig.add_axes([ax1_pos.x0,ax1_pos.y0,ax1_pos.width,ax1_pos.height*0.2])\n",
    "ax1_orog.axis('off')\n",
    "ax1_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax1_orog.set_xlim(ax1.get_xlim())\n",
    "\n",
    "ax2_orog = fig.add_axes([ax2_pos.x0,ax2_pos.y0,ax2_pos.width,ax2_pos.height*0.2])\n",
    "ax2_orog.axis('off')\n",
    "ax2_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax2_orog.set_xlim(ax2.get_xlim())#xlong.min()+55.+360.,xlong.max()+360.)\n",
    "\n",
    "ax3_orog = fig.add_axes([ax3_pos.x0,ax3_pos.y0,ax3_pos.width,ax3_pos.height*0.2])\n",
    "ax3_orog.axis('off')\n",
    "ax3_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax3_orog.set_xlim(ax1.get_xlim())\n",
    "\n",
    "ax1.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax1.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "\n",
    "speed1 = 2.3 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed1\n",
    "slope1 = dt/-dx # sec/meter\n",
    "intercept1 = 10*dt\n",
    "xvals1 = numpy.linspace(ax1.get_xlim()[0],ax1.get_xlim()[1],100)\n",
    "xvals1_m = xvals1*50000.\n",
    "yvals1 = slope1*(xvals1_m-ax2.get_xlim()[0]*50000) + intercept1\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals1_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals1]\n",
    "\n",
    "speed2 = 5.1 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed2\n",
    "slope2 = dt/-dx # sec/meter\n",
    "intercept2 = 7.*dt\n",
    "xvals2 = numpy.linspace(ax2.get_xlim()[0],ax2.get_xlim()[1],100)\n",
    "xvals2_m = xvals2*50000.\n",
    "yvals2 = slope2*(xvals2_m-ax2.get_xlim()[0]*50000) + intercept2\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals2_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals2]\n",
    "\n",
    "\n",
    "# ax1.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "# ax1.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "# ax2.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "# ax2.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "# ax3.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "# ax3.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "# plot_ratio = ax2.get_data_ratio()\n",
    "\n",
    "# slope_rotation1 = numpy.degrees(numpy.arctan(slope1/plot_ratio))\n",
    "# ax2.text(s='$-\\,$'+str(speed1)+' m s$^{\\,-1}$',x=0.69,y=0.2,transform=ax2.transAxes,\\\n",
    "#          ha='center',\\\n",
    "#          va='center',\\\n",
    "#          fontsize=fontsize,\\\n",
    "#          rotation=slope_rotation1+4)\n",
    "\n",
    "# slope_rotation2 = numpy.degrees(numpy.arctan(slope2/plot_ratio))\n",
    "# ax2.text(s='$-\\,$'+str(speed2)+' m s$^{\\,-1}$',x=0.755,y=0.31,transform=ax2.transAxes,\\\n",
    "#          ha='center',\\\n",
    "#          va='center',\\\n",
    "#          fontsize=fontsize,\\\n",
    "#          rotation=slope_rotation2+4)\n",
    "\n",
    "fig.savefig('figureXX_hovmoller_with_rain_diffs_first5days.png',dpi=600,bbox_inches='tight',transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "fig = mp.figure(figsize=(7.5,4))\n",
    "\n",
    "ax1=fig.add_subplot(131)\n",
    "\n",
    "levels=numpy.arange(-1,1.1,0.125)\n",
    "levels_precip = numpy.arange(-5,5.1,0.5)\n",
    "\n",
    "contour_plot = ax1.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_0_1km_latmean/1000.,\\\n",
    "                           cmap='PuOr_r',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax1.contour(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_0_1km_latmean/1000.,\\\n",
    "                           levels=[-0.25,0.25],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "ax1.set_ylabel('day', fontsize=fontsize)\n",
    "ax1.text(s='(a) $\\Delta$ MSE in $0-1$ km', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_xticks([280,300,320])\n",
    "ax1.set_xticklabels(xticks_format)\n",
    "ax1.set_xlim(275.,325.)\n",
    "ax1.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax1.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax1.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(132)\n",
    "contour_plot = ax2.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_1_3km_latmean/1000.,\\\n",
    "                           cmap='PuOr_r',\\\n",
    "                           levels=levels,\\\n",
    "                           extend='both')\n",
    "contour_line = ax2.contour(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           mse_diffs_lat_subset_1_3km_latmean/1000.,\\\n",
    "                           levels=[-0.25,0.25],\\\n",
    "                           colors='0.1',\\\n",
    "                           linestyles=['--'],\\\n",
    "                           linewidths=[0.5])\n",
    "\n",
    "ax2.text(s='(b) $\\Delta$ MSE in $1-3$ km', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax2.set_xticks([280,300,320])\n",
    "ax2.set_xticklabels(xticks_format)\n",
    "ax2.set_xlim(275.,325.)\n",
    "ax2.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax2.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax2.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "contour_rain = ax3.contourf(xlong[0,:]+360.,time_datetimes,\\\n",
    "                           raintot_diffs_lat_subset_latmean,\\\n",
    "                           levels=levels_precip,\\\n",
    "                           cmap='BrBG', extend='both')\n",
    "# contour_rain = ax3.pcolor(xlong[0,:]+360.,time_datetimes,\\\n",
    "#                            raintot_hourly_diffs_lat_subset_latmean*24.,\\\n",
    "#                            vmin=levels_precip.min(),vmax=levels_precip.max(),\\\n",
    "#                            cmap='BrBG',snap=True)\n",
    "ax3.text(s='(c) $\\Delta$ accum. precip.', x=0, y=1.02,\\\n",
    "         fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax3.set_xticks([280,300,320])\n",
    "ax3.set_xticklabels(xticks_format)\n",
    "ax3.set_xlim(275.,325.)\n",
    "ax3.set_ylim(datetime.datetime(1979,1,1),datetime.datetime(1979,1,11))\n",
    "ax3.set_yticks(time_datetimes[[t.hour==0 for t in time_datetimes]])\n",
    "ax3.yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d'))\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax1_pos = ax1.get_position()\n",
    "ax2_pos = ax2.get_position()\n",
    "ax3_pos = ax3.get_position()\n",
    "\n",
    "cbar1_ax = fig.add_axes([ax1_pos.x0,ax1_pos.y0-0.12,ax2_pos.x1-ax1_pos.x0,0.03])\n",
    "cbar1 = mp.colorbar(contour_plot, cax=cbar1_ax, orientation='horizontal')\n",
    "cbar1_ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_ticks([-1.,-.5,-0.25,0,0.25,.5,1])\n",
    "cbar1_ax.axvline(cbar1_ax.get_xticks()[2],lw=0.5,c='0.1',ls='--')\n",
    "cbar1_ax.axvline(cbar1_ax.get_xticks()[4],lw=0.5,c='0.1',ls='--')\n",
    "cbar1.set_label('kJ kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "cbar2_ax = fig.add_axes([ax3_pos.x0,ax3_pos.y0-0.12,ax3_pos.width,0.03])\n",
    "cbar2 = mp.colorbar(contour_rain, cax=cbar2_ax, orientation='horizontal')\n",
    "cbar2_ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_ticks([-5,-2.5,0,2.5,5])\n",
    "#cbar2_ax.axvline(cbar2_ax.get_xticks()[2],lw=0.5,c='0.1',ls='--')\n",
    "#cbar2_ax.axvline(cbar2_ax.get_xticks()[4],lw=0.5,c='0.1',ls='--')\n",
    "cbar2.set_label('mm', fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "ax1_orog = fig.add_axes([ax1_pos.x0,ax1_pos.y0,ax1_pos.width,ax1_pos.height*0.2])\n",
    "ax1_orog.axis('off')\n",
    "ax1_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax1_orog.set_xlim(ax1.get_xlim())\n",
    "\n",
    "ax2_orog = fig.add_axes([ax2_pos.x0,ax2_pos.y0,ax2_pos.width,ax2_pos.height*0.2])\n",
    "ax2_orog.axis('off')\n",
    "ax2_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax2_orog.set_xlim(ax2.get_xlim())#xlong.min()+55.+360.,xlong.max()+360.)\n",
    "\n",
    "ax3_orog = fig.add_axes([ax3_pos.x0,ax3_pos.y0,ax3_pos.width,ax3_pos.height*0.2])\n",
    "ax3_orog.axis('off')\n",
    "ax3_orog.plot(xlong[0,:]+360., HGT_M_lat_subset_mean/1000., lw=1.5, ls='--', c='0.1')#, alpha=0.5)\n",
    "ax3_orog.set_xlim(ax1.get_xlim())\n",
    "\n",
    "ax1.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.yaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax1.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax2.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "ax3.xaxis.grid(True,linestyle='-',c='0.25',lw=0.5)\n",
    "\n",
    "speed1 = 2.3 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed1\n",
    "slope1 = dt/-dx # sec/meter\n",
    "intercept1 = 10*dt\n",
    "xvals1 = numpy.linspace(ax1.get_xlim()[0],ax1.get_xlim()[1],100)\n",
    "xvals1_m = xvals1*50000.\n",
    "yvals1 = slope1*(xvals1_m-ax2.get_xlim()[0]*50000) + intercept1\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals1_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals1]\n",
    "\n",
    "speed2 = 5.1 #6.3 # m/s\n",
    "dt = 86400. # seconds\n",
    "dx = dt*speed2\n",
    "slope2 = dt/-dx # sec/meter\n",
    "intercept2 = 7.*dt\n",
    "xvals2 = numpy.linspace(ax2.get_xlim()[0],ax2.get_xlim()[1],100)\n",
    "xvals2_m = xvals2*50000.\n",
    "yvals2 = slope2*(xvals2_m-ax2.get_xlim()[0]*50000) + intercept2\n",
    "start_date = datetime.datetime(1979,1,1,0)\n",
    "yvals2_datetime = [start_date + datetime.timedelta(seconds=int(t)) for t in yvals2]\n",
    "\n",
    "\n",
    "ax1.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "ax1.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "ax2.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "ax2.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "ax3.plot(xvals1,yvals1_datetime,lw=0.75,ls='--',c='0.1')\n",
    "ax3.plot(xvals2,yvals2_datetime,lw=0.75,ls='--',c='0.1')\n",
    "\n",
    "plot_ratio = ax2.get_data_ratio()\n",
    "\n",
    "slope_rotation1 = numpy.degrees(numpy.arctan(slope1/plot_ratio))\n",
    "ax2.text(s='$-\\,$'+str(speed1)+' m s$^{\\,-1}$',x=0.69,y=0.2,transform=ax2.transAxes,\\\n",
    "         ha='center',\\\n",
    "         va='center',\\\n",
    "         fontsize=fontsize,\\\n",
    "         rotation=slope_rotation1+4)\n",
    "\n",
    "slope_rotation2 = numpy.degrees(numpy.arctan(slope2/plot_ratio))\n",
    "ax2.text(s='$-\\,$'+str(speed2)+' m s$^{\\,-1}$',x=0.755,y=0.31,transform=ax2.transAxes,\\\n",
    "         ha='center',\\\n",
    "         va='center',\\\n",
    "         fontsize=fontsize,\\\n",
    "         rotation=slope_rotation2+4)\n",
    "\n",
    "fig.savefig('figureXX_hovmoller_with_SUMMED_rain_diffs.png',dpi=600,bbox_inches='tight',transparent=True)"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlong_indices_280_310 = (xlong[0,:]+360. >= 280.) & (xlong[0,:]+360 <= 310.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uwind_mean = u_400_lat_mean[:,[0,1,2,3,4,5],:]\n",
    "uwind_mean = numpy.nanmean(uwind_mean[:,:,xlong_indices_280_310])\n",
    "print(uwind_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uwind_mean = u_400_lat_mean[:,[7,8,9,10,11],:]\n",
    "uwind_mean = numpy.nanmean(uwind_mean[:,:,xlong_indices_280_310])\n",
    "print(uwind_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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