gp1_latmean_and_maps_WRF50km_WRF2km_CESM_precip_change_comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:02.901904Z",
     "start_time": "2018-07-03T02:06:41.464857Z"
    }
   },
   "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 netCDF4\n",
    "import cftime\n",
    "import seaborn\n",
    "import cmocean\n",
    "import matplotlib.gridspec\n",
    "import scipy.interpolate\n",
    "\n",
    "import glob\n",
    "import pandas\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": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-18T20:10:54.341537Z",
     "start_time": "2018-06-18T20:10:54.029323Z"
    }
   },
   "source": [
    "geo_em_ncfile = xarray.open_dataset('/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/geo_em.d01_amazon_50km.nc')\n",
    "hgt = geo_em_ncfile.variables['HGT'].data[0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 30day runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import cam 400 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:03.846572Z",
     "start_time": "2018-07-03T02:07:02.992323Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_fileroot = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.30days.cam0400.clm0400.ens_10members/ens_mean_cesm_files/'\n",
    "cont_filename = 'B_1850_CAM5.30days.cam0400.clm0400.ens.cam.h0.0011-03-20-0000_0011-04-20-0000_ens_mean.nc'\n",
    "cont_cam_ds = xarray.open_dataset(cont_fileroot+cont_filename, decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:04.903517Z",
     "start_time": "2018-07-03T02:07:03.881423Z"
    }
   },
   "outputs": [],
   "source": [
    "topo_filename = 'USGS-gtopo30_1.9x2.5_remap_c050602.nc'\n",
    "topo_fileroot = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.30days.cam0400.clm0400.ens_10members/'\n",
    "topo_ds = xarray.open_dataset(topo_fileroot+topo_filename, decode_times=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate latitude information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:05.530121Z",
     "start_time": "2018-07-03T02:07:04.934373Z"
    }
   },
   "outputs": [],
   "source": [
    "lat_lo_open = -35.\n",
    "lat_hi_open = 35.\n",
    "lat_lo = -10.\n",
    "lat_hi = 10.\n",
    "lon_lo = 270.\n",
    "lon_hi = 330."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:12.062871Z",
     "start_time": "2018-07-03T02:07:05.556487Z"
    }
   },
   "outputs": [],
   "source": [
    "lat = cont_cam_ds['lat'].sel(lat=slice(lat_lo_open,lat_hi_open)).values\n",
    "lon = cont_cam_ds['lon'].sel(lon=slice(lon_lo,lon_hi)).values\n",
    "\n",
    "lat_indices = (lat>=lat_lo)&(lat<=lat_hi)\n",
    "lat_indices_open = (lat>=lat_lo_open)&(lat<=lat_hi_open)\n",
    "lon_indices = (lon>=lon_lo)&(lon<=lon_hi)\n",
    "\n",
    "lat_subset = lat[lat_indices]\n",
    "lon_subset = lon[lon_indices]\n",
    "\n",
    "lat_subset_open = lat[lat_indices_open]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_subset_open = lat[lat_indices_open]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get land fraction info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:32.925556Z",
     "start_time": "2018-07-03T02:07:12.098419Z"
    }
   },
   "outputs": [],
   "source": [
    "LANDFRAC = cont_cam_ds['LANDFRAC'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values[0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get 400 fields for cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:33.470784Z",
     "start_time": "2018-07-03T02:07:32.960424Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_400_30d = cont_cam_ds['PRECC'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECL_400_30d = cont_cam_ds['PRECL'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import cam 1000 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:34.215703Z",
     "start_time": "2018-07-03T02:07:33.506422Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_fileroot = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.30days.cam0400.clm1000.ens_10members/ens_mean_cesm_files/'\n",
    "phys_filename = 'B_1850_CAM5.30days.cam0400.clm1000.ens.cam.h0.0011-03-20-0000_0011-04-20-0000_ens_mean.nc'\n",
    "phys_cam_ds = xarray.open_dataset(phys_fileroot+phys_filename, decode_times=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get 1000 fields for cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:34.755951Z",
     "start_time": "2018-07-03T02:07:34.251422Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_1000_30d = phys_cam_ds['PRECC'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECL_1000_30d = phys_cam_ds['PRECL'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "convert time information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:34.907103Z",
     "start_time": "2018-07-03T02:07:34.791426Z"
    }
   },
   "outputs": [],
   "source": [
    "time_400 = cont_cam_ds['time']\n",
    "time_converted = netCDF4.num2date(time_400.values, time_400.units, time_400.calendar)\n",
    "\n",
    "time_manual = []\n",
    "for t_idx in range(time_converted.size):\n",
    "    year=time_converted[t_idx].year\n",
    "    month=time_converted[t_idx].month\n",
    "    day=time_converted[t_idx].day\n",
    "    hour=time_converted[t_idx].hour\n",
    "    minute=time_converted[t_idx].minute\n",
    "    hour_round = round(hour+minute/60)\n",
    "    time_manual.append(cftime.DatetimeNoLeap(year,month,day,hour_round))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "specify timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:35.247601Z",
     "start_time": "2018-07-03T02:07:34.942420Z"
    }
   },
   "outputs": [],
   "source": [
    "skip_time = 24\n",
    "#end_time = 745"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate PRECT diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:35.866786Z",
     "start_time": "2018-07-03T02:07:35.274419Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_400_30d = PRECC_400_30d+PRECL_400_30d\n",
    "PRECT_1000_30d = PRECC_1000_30d+PRECL_1000_30d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:36.293586Z",
     "start_time": "2018-07-03T02:07:35.902428Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_1000_timemean_10day = numpy.mean(PRECT_1000_30d[skip_time:240,:,:], axis=0)\n",
    "PRECT_1000_timemean_10day_landmask = numpy.copy(PRECT_1000_timemean_10day)\n",
    "PRECT_1000_timemean_10day_landmask[LANDFRAC==0] = numpy.nan\n",
    "\n",
    "PRECT_400_timemean_10day = numpy.nanmean(PRECT_400_30d[skip_time:240,:,:], axis=0)\n",
    "PRECT_400_timemean_10day_landmask = numpy.copy(PRECT_400_timemean_10day)\n",
    "PRECT_400_timemean_10day_landmask[LANDFRAC==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:36.506551Z",
     "start_time": "2018-07-03T02:07:36.331464Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_1000_timemean_30day = numpy.mean(PRECT_1000_30d[skip_time:,:,:], axis=0)\n",
    "PRECT_1000_timemean_30day_landmask = numpy.copy(PRECT_1000_timemean_30day)\n",
    "PRECT_1000_timemean_30day_landmask[LANDFRAC==0] = numpy.nan\n",
    "\n",
    "PRECT_400_timemean_30day = numpy.nanmean(PRECT_400_30d[skip_time:,:,:], axis=0)\n",
    "PRECT_400_timemean_30day_landmask = numpy.copy(PRECT_400_timemean_30day)\n",
    "PRECT_400_timemean_30day_landmask[LANDFRAC==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get topography info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:36.752286Z",
     "start_time": "2018-07-03T02:07:36.538187Z"
    }
   },
   "outputs": [],
   "source": [
    "topo_data = topo_ds['PHIS'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values/9.80665"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:07:37.003644Z",
     "start_time": "2018-07-03T02:07:36.788423Z"
    }
   },
   "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": [
    "topo_data_landmask = numpy.copy(topo_data)\n",
    "topo_data_landmask[LANDFRAC==0] = numpy.nan\n",
    "topo_data_latmean = numpy.nanmean(topo_data_landmask, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10year simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "400 clm runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:11:56.361385Z",
     "start_time": "2018-07-03T02:07:37.033425Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.10years.cam0400.clm0400/'\n",
    "cont_cam_mfds = xarray.open_mfdataset(data_dir + 'atm/hist/*.nc', decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:14:07.698257Z",
     "start_time": "2018-07-03T02:11:56.392624Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_400_10y = cont_cam_mfds['PRECC'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECL_400_10y = cont_cam_mfds['PRECL'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECT_400_10y = PRECC_400_10y+PRECL_400_10y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1000 clm runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:18:49.596191Z",
     "start_time": "2018-07-03T02:14:07.734426Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range\n",
      "  enable_cftimeindex)\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range\n",
      "  return self.func(self.array[key])\n"
     ]
    }
   ],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.10years.cam0400.clm1000/'\n",
    "phys_cam_mfds = xarray.open_mfdataset(data_dir + 'atm/hist/*.nc')#, decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:03.567533Z",
     "start_time": "2018-07-03T02:18:49.638427Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_1000_10y = phys_cam_mfds['PRECC'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECL_1000_10y = phys_cam_mfds['PRECL'].sel(lat=slice(lat_lo_open,lat_hi_open),lon=slice(lon_lo,lon_hi)).values\n",
    "PRECT_1000_10y = PRECC_1000_10y+PRECL_1000_10y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "take time averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:03.655129Z",
     "start_time": "2018-07-03T02:21:03.598627Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_400_timemean_10year = numpy.mean(PRECT_400_10y, axis=0)\n",
    "PRECT_1000_timemean_10year = numpy.mean(PRECT_1000_10y, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:04.044364Z",
     "start_time": "2018-07-03T02:21:03.690462Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_400_timemean_10year_landmask = numpy.copy(PRECT_400_timemean_10year)\n",
    "PRECT_1000_timemean_10year_landmask = numpy.copy(PRECT_1000_timemean_10year)\n",
    "\n",
    "PRECT_400_timemean_10year_landmask[LANDFRAC==0] = numpy.nan\n",
    "PRECT_1000_timemean_10year_landmask[LANDFRAC==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate all differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:04.203192Z",
     "start_time": "2018-07-03T02:21:04.065738Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_diffs_10day = PRECT_1000_timemean_10day_landmask-PRECT_400_timemean_10day_landmask\n",
    "PRECT_diffs_30day = PRECT_1000_timemean_30day_landmask-PRECT_400_timemean_30day_landmask\n",
    "PRECT_diffs_10year = PRECT_1000_timemean_10year_landmask-PRECT_400_timemean_10year_landmask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wrf 50km runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 400 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:05.314911Z",
     "start_time": "2018-07-03T02:21:04.235421Z"
    }
   },
   "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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:28.085745Z",
     "start_time": "2018-07-03T02:21:05.350425Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:31.401290Z",
     "start_time": "2018-07-03T02:21:28.121421Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_400 = cont_mfds['RAINC'].values\n",
    "rainnc_400 = cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:35.955455Z",
     "start_time": "2018-07-03T02:21:31.433844Z"
    }
   },
   "outputs": [],
   "source": [
    "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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:36.023748Z",
     "start_time": "2018-07-03T02:21:35.987005Z"
    }
   },
   "outputs": [],
   "source": [
    "xlat_indices = (xlat>=-10)&(xlat<=10.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.284687Z",
     "start_time": "2018-07-03T02:07:03.800Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt_50km = cont_mfds['HGT'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.324340Z",
     "start_time": "2018-07-03T02:07:03.933Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt_50km_landmask = numpy.copy(hgt_50km)\n",
    "hgt_50km_landmask[landmask==0] = numpy.nan\n",
    "\n",
    "hgt_50km_landmask_lat_subset = hgt_50km_landmask[xlat_indices[:,0],:]\n",
    "hgt_50km_latmean = numpy.nanmean(hgt_50km_landmask_lat_subset, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1000 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.370572Z",
     "start_time": "2018-07-03T02:07:04.501Z"
    }
   },
   "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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.389118Z",
     "start_time": "2018-07-03T02:07:04.832Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.439341Z",
     "start_time": "2018-07-03T02:07:05.060Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_1000 = phys_mfds['RAINC'].values\n",
    "rainnc_1000 = phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate WRF differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T02:21:39.469345Z",
     "start_time": "2018-07-03T02:07:05.444Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400 = rainc_400+rainnc_400\n",
    "raintot_1000 = rainc_1000+rainnc_1000\n",
    "\n",
    "raintot_400_diff = numpy.zeros((raintot_400.shape))*numpy.nan\n",
    "raintot_400_diff[:-1,:,:] = numpy.diff(raintot_400, axis=0)#*(24./3) # convert to mm/day\n",
    "\n",
    "raintot_1000_diff = numpy.zeros((raintot_1000.shape))*numpy.nan\n",
    "raintot_1000_diff[:-1,:,:] = numpy.diff(raintot_1000, axis=0)#*(24./3) # convert to mm/day\n",
    "\n",
    "# monthly mean\n",
    "raintot_diffs_time_mean = numpy.nanmean(raintot_1000_diff[skip_time:,:,:] - raintot_400_diff[skip_time:,:,:], axis=0)\n",
    "raintot_diffs_time_mean_landmask = numpy.copy(raintot_diffs_time_mean)\n",
    "raintot_diffs_time_mean_landmask[landmask==0] = numpy.nan\n",
    "\n",
    "raintot_400_time_mean = numpy.nanmean(raintot_400_diff[skip_time:,:,:], axis=0)\n",
    "raintot_400_time_mean_landmask = numpy.copy(raintot_400_time_mean)\n",
    "raintot_400_time_mean_landmask[landmask==0] = numpy.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "day1_start_hour = 10 #6am = 10:00 because of the time offset\n",
    "day1_end_hour = 24 #8pm = 24:00 because of time offset\n",
    "raintot_diffs_time_mean_day1 = numpy.nanmean(raintot_1000_diff[day1_start_hour:day1_end_hour+1,:,:] - raintot_400_diff[day1_start_hour:day1_end_hour+1,:,:], axis=0)\n",
    "raintot_diffs_time_mean_day1_landmask = numpy.copy(raintot_diffs_time_mean_day1)\n",
    "raintot_diffs_time_mean_day1_landmask[landmask==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import WRF 2km days 2-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:13:11.606454Z",
     "start_time": "2018-07-03T04:12:37.649850Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/nested_FMAASO_10days/400/001/'\n",
    "cont_mfds_2km = xarray.open_mfdataset(data_dir + 'wrfout_d03_1979-01-0[2,3,4,5,6,7,8,9]*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:13:59.539246Z",
     "start_time": "2018-07-03T04:13:23.201435Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/nested_FMAASO_10days/1000/001/'\n",
    "phys_mfds_2km = xarray.open_mfdataset(data_dir + 'wrfout_d03_1979-01-0[2,3,4,5,6,7,8,9]*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:14:13.590447Z",
     "start_time": "2018-07-03T04:14:13.546453Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400_2km = cont_mfds_2km['RAINNC']\n",
    "raintot_1000_2km = phys_mfds_2km['RAINNC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:16:30.230990Z",
     "start_time": "2018-07-03T04:14:27.466443Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400_2km_diffs = raintot_400_2km.diff(dim='Time').values\n",
    "raintot_1000_2km_diffs = raintot_1000_2km.diff(dim='Time').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:17:07.700095Z",
     "start_time": "2018-07-03T04:16:45.418440Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400_2km_time_mean = numpy.nanmean(raintot_400_2km_diffs[skip_time:,:,:], axis=0)\n",
    "raintot_1000_2km_time_mean = numpy.nanmean(raintot_1000_2km_diffs[skip_time:,:,:], axis=0)\n",
    "\n",
    "raintot_diffs_2km_time_mean = raintot_1000_2km_time_mean-raintot_400_2km_time_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T04:18:49.264458Z",
     "start_time": "2018-07-03T04:17:21.772438Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask_2km = cont_mfds_2km['LANDMASK'].values[0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-18T20:31:40.117649Z",
     "start_time": "2018-06-18T20:31:40.101081Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_2km_time_mean_landmask = numpy.copy(raintot_diffs_2km_time_mean)\n",
    "raintot_diffs_2km_time_mean_landmask[landmask_2km==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'landmask_2m' 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-59-316870758877>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlandmask_2m\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'landmask_2m' is not defined"
     ]
    }
   ],
   "source": [
    "landmask_2m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 2000)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlat_2km.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:08:42.413185Z",
     "start_time": "2018-06-19T06:07:42.797464Z"
    }
   },
   "outputs": [],
   "source": [
    "xlat_2km = cont_mfds_2km['XLAT'].values[0,:,:]\n",
    "xlong_2km = cont_mfds_2km['XLONG'].values[0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:32:11.690769Z",
     "start_time": "2018-06-21T15:32:11.271385Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt_2km = cont_mfds_2km['HGT'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:33:05.055949Z",
     "start_time": "2018-06-21T15:33:05.024929Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt_2km_landmask = numpy.copy(hgt_2km)\n",
    "hgt_2km_landmask[landmask_2km==0] = numpy.nan\n",
    "hgt_2km_latmean = numpy.nanmean(hgt_2km_landmask, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import WRF 2km day 1"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:13:30.990430Z",
     "start_time": "2018-06-12T17:13:23.169426Z"
    }
   },
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/nested_FMAASO_10days/400/001/'\n",
    "cont_mfds_2km_day1 = xarray.open_mfdataset(data_dir + 'wrfout_d03_1979-01-01*')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:14:01.431700Z",
     "start_time": "2018-06-12T17:13:53.705612Z"
    }
   },
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/nested_FMAASO_10days/1000/001/'\n",
    "phys_mfds_2km_day1 = xarray.open_mfdataset(data_dir + 'wrfout_d03_1979-01-01*')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:14:26.476781Z",
     "start_time": "2018-06-12T17:14:26.434436Z"
    }
   },
   "source": [
    "raintot_400_2km_day1 = cont_mfds_2km_day1['RAINNC']\n",
    "raintot_1000_2km_day1 = phys_mfds_2km_day1['RAINNC']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:14:52.558781Z",
     "start_time": "2018-06-12T17:14:52.439418Z"
    }
   },
   "source": [
    "raintot_400_2km_day1_diffs = raintot_400_2km_day1.diff(dim='Time')#.values\n",
    "raintot_1000_2km_day1_diffs = raintot_400_2km_day1.diff(dim='Time')#.values"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:20:51.926434Z",
     "start_time": "2018-06-12T17:20:51.868990Z"
    }
   },
   "source": [
    "raintot_400_2km_day1_diffs_time_mean = raintot_400_2km_day1_diffs.mean(dim='Time')\n",
    "raintot_1000_2km_day1_diffs_time_mean = raintot_400_2km_day1_diffs.mean(dim='Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate lat means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:18:23.195860Z",
     "start_time": "2018-06-19T06:18:23.170484Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_2km_latmean = numpy.nanmean(raintot_diffs_2km_time_mean, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:22:54.976644Z",
     "start_time": "2018-06-21T15:22:54.966691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179, 289)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raintot_diffs_time_mean_landmask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:23:10.782364Z",
     "start_time": "2018-06-21T15:23:10.748995Z"
    }
   },
   "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:6: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "raintot_diffs_latmean = numpy.nanmean(raintot_diffs_time_mean_landmask[(xlat[:,0]>=-10)&(xlat[:,0]<=10),:], axis=0)\n",
    "raintot_diffs_latmean_day1 = numpy.nanmean(raintot_diffs_time_mean_day1_landmask[(xlat[:,0]>=-10)&(xlat[:,0]<=10),:], axis=0)\n",
    "raintot_diffs_2km_latmean = numpy.nanmean(raintot_diffs_2km_time_mean, axis=0)\n",
    "PRECT_diffs_10day_latmean = numpy.nanmean(PRECT_diffs_10day[lat_indices,:], axis=0)\n",
    "PRECT_diffs_30day_latmean = numpy.nanmean(PRECT_diffs_30day[lat_indices,:], axis=0)\n",
    "PRECT_diffs_10year_latmean = numpy.nanmean(PRECT_diffs_10year[lat_indices,:], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate stdevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:18:27.313316Z",
     "start_time": "2018-06-19T06:18:27.227292Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1434: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  keepdims=keepdims)\n"
     ]
    }
   ],
   "source": [
    "raintot_diffs_latstd = numpy.nanstd(raintot_diffs_time_mean_landmask[(xlat[:,0]>=-10)&(xlat[:,0]<=10),:], axis=0, ddof=1)\n",
    "raintot_diffs_day1_latstd = numpy.nanstd(raintot_diffs_time_mean_day1_landmask[(xlat[:,0]>=-10)&(xlat[:,0]<=10),:], axis=0, ddof=1)\n",
    "raintot_diffs_2km_latstd = numpy.nanstd(raintot_diffs_2km_time_mean, axis=0, ddof=1)\n",
    "PRECT_diffs_10day_latstd = numpy.nanstd(PRECT_diffs_10day[lat_indices,:], axis=0, ddof=1)\n",
    "PRECT_diffs_30day_latstd = numpy.nanstd(PRECT_diffs_30day[lat_indices,:], axis=0, ddof=1)\n",
    "PRECT_diffs_10year_latstd = numpy.nanstd(PRECT_diffs_10year[lat_indices,:], axis=0, ddof=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate stderr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:18:33.207432Z",
     "start_time": "2018-06-19T06:18:33.199373Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_latstderr = raintot_diffs_latstd/numpy.sqrt(xlat[:,0].size)\n",
    "raintot_diffs_day1_latstderr = raintot_diffs_day1_latstd/numpy.sqrt(xlat[:,0].size)\n",
    "raintot_diffs_2km_latstderr = raintot_diffs_2km_latstd/numpy.sqrt(xlat_2km[:,0].size)\n",
    "PRECT_diffs_10day_latstderr = PRECT_diffs_10day_latstd/numpy.sqrt(lat_subset.size)\n",
    "PRECT_diffs_30day_latstderr = PRECT_diffs_30day_latstd/numpy.sqrt(lat_subset.size)\n",
    "PRECT_diffs_10year_latstderr = PRECT_diffs_10year_latstd/numpy.sqrt(lat_subset.size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_subset.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:18:35.128726Z",
     "start_time": "2018-06-19T06:18:35.115315Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_latmean_smooth = pandas.Series(raintot_diffs_latmean).rolling(window=3, center=True, min_periods=0).mean()\n",
    "raintot_diffs_latmean_smooth_day1 = pandas.Series(raintot_diffs_latmean_day1).rolling(window=3, center=True, min_periods=0).mean()\n",
    "\n",
    "raintot_diffs_2km_latmean_smooth = pandas.Series(raintot_diffs_2km_latmean).rolling(window=75, center=True, min_periods=0).mean()\n",
    "PRECT_diffs_10day_latmean_smooth = pandas.Series(PRECT_diffs_10day_latmean).rolling(window=3, center=True, min_periods=0).mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:18:37.047500Z",
     "start_time": "2018-06-19T06:18:37.038338Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_latstd_smooth = pandas.Series(raintot_diffs_latstd).rolling(window=3, center=True, min_periods=0).std(ddof=1)\n",
    "raintot_diffs_2km_latstd_smooth = pandas.Series(raintot_diffs_2km_latstd).rolling(window=75, center=True, min_periods=0).std(ddof=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## no error bars"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T22:14:14.440850Z",
     "start_time": "2018-06-21T22:14:11.129378Z"
    }
   },
   "source": [
    "colors = [cmocean.cm.thermal(0.3),\\\n",
    "          cmocean.cm.thermal(0.4),\\\n",
    "          cmocean.cm.thermal(0.5),\\\n",
    "          cmocean.cm.thermal(0.6),\\\n",
    "          cmocean.cm.thermal(0.7)]\n",
    "\n",
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,5.5))\n",
    "\n",
    "ymax = 1920.6472595214843\n",
    "#ax2topo.get_ylim()\n",
    "##################################################\n",
    "# xvals = xlong[0,:]+360.\n",
    "# yvals = raintot_diffs_latmean_smooth*24.\n",
    "# f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "# xvals_interp = numpy.linspace(277.5, xvals.max(), 1000)\n",
    "# yvals_interp = f(xvals_interp)\n",
    "ax1 = fig.add_subplot(511)\n",
    "ax1.plot(xlong[0,:]+360.,\\\n",
    "         raintot_diffs_latmean_smooth*24., \\\n",
    "         color=colors[0],\\\n",
    "         label='days 2-10')\n",
    "\n",
    "ax1.plot(xlong[0,:]+360.,\\\n",
    "         raintot_diffs_latmean_smooth_day1*24., \\\n",
    "         color=colors[0],\\\n",
    "         label='day 1', ls='--')\n",
    "\n",
    "ax1topo = ax1.twinx()\n",
    "ax1topo.fill_between(xlong[0,:]+360., \\\n",
    "                     numpy.zeros(hgt_50km_latmean.size), \\\n",
    "                     hgt_50km_latmean, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "y0,y1 = ax1topo.get_ylim()\n",
    "ax1topo.set_ylim(0,ymax)\n",
    "ax1topo.axis('off')\n",
    "\n",
    "# ax1.fill_between(xlong[0,:]+360., \\\n",
    "#                 (raintot_diffs_latmean_smooth-raintot_diffs_latstd_smooth)*24.,\\\n",
    "#                 (raintot_diffs_latmean_smooth+raintot_diffs_latstd_smooth)*24.,\\\n",
    "#                 facecolor=colors[0], alpha=0.333)\n",
    "\n",
    "\n",
    "##################################################\n",
    "xvals = xlong_2km[0,:]+360.\n",
    "yvals = raintot_diffs_2km_latmean_smooth*24.\n",
    "f = scipy.interpolate.interp1d(xvals, yvals, kind='cubic')\n",
    "xvals_interp = numpy.linspace(xvals.min(), xvals.max(), 1000)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax2 = fig.add_subplot(512)\n",
    "ax2.plot(xvals_interp,\\\n",
    "        yvals_interp, \\\n",
    "        color=colors[1],\\\n",
    "        label='WRF 2-km 10d')\n",
    "\n",
    "ax2topo = ax2.twinx()\n",
    "ax2topo.fill_between(xlong_2km[0,:]+360., \\\n",
    "                     numpy.zeros(hgt_2km_latmean.size), \\\n",
    "                     hgt_2km_latmean, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax2topo.set_ylim(0,ymax)\n",
    "ax2topo.axis('off')\n",
    "\n",
    "# yvals = PRECT_diffs_10day_latstderr\n",
    "# yvals_interp_error = f(xvals_interp)\n",
    "# f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "# ax2.fill_between(xlong_2km[0,:]+360., \\\n",
    "#                 (raintot_diffs_2km_latmean_smooth-raintot_diffs_2km_latstd_smooth)*24.,\\\n",
    "#                 (raintot_diffs_2km_latmean_smooth+raintot_diffs_2km_latstd_smooth)*24.,\\\n",
    "#                 facecolor=colors[1], alpha=0.333)\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_10day_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax3 = fig.add_subplot(513)\n",
    "ax3.plot(xvals_interp,\\\n",
    "        yvals_interp*86400.*1000., \\\n",
    "        color=colors[2],\\\n",
    "        label='CESM 2$\\degree$ 10d')\n",
    "\n",
    "ax3topo = ax3.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax3topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax3topo.set_ylim(0,ymax)\n",
    "ax3topo.axis('off')\n",
    "\n",
    "# ax3topo.fill_between(lon_subset, \\\n",
    "#                      numpy.zeros(topo_data_latmean.size), \\\n",
    "#                      topo_data_latmean, \\\n",
    "#                      facecolor='0', alpha=0.25)\n",
    "\n",
    "# yvals = PRECT_diffs_10day_latstderr\n",
    "# f = scipy.interpolate.interp1d(xvals, yvals, kind='cubic')\n",
    "# yvals_interp_error = f(xvals_interp)\n",
    "# ax3.fill_between(xvals_interp, \\\n",
    "#                 (yvals_interp+yvals_interp_error)*86400.*1000.,\\\n",
    "#                 (yvals_interp-yvals_interp_error)*86400.*1000.,\\\n",
    "#                 facecolor=colors[1], alpha=0.333)\n",
    "\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_30day_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax4 = fig.add_subplot(514)\n",
    "ax4.plot(xvals_interp,\\\n",
    "        yvals_interp*86400.*1000., \\\n",
    "        color=colors[3],\\\n",
    "        label='CESM 2$\\degree$ 30d')\n",
    "\n",
    "ax4topo = ax4.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax4topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax4topo.set_ylim(0,ymax)\n",
    "ax4topo.axis('off')\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_10year_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax5 = fig.add_subplot(515)\n",
    "ax5.plot(xvals_interp,\\\n",
    "       yvals_interp*86400.*1000., \\\n",
    "       color=colors[4],\\\n",
    "       label='CESM 2$\\degree$ 10yr')\n",
    "\n",
    "ax5topo = ax5.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax5topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax5topo.set_ylim(0,ymax)\n",
    "ax5topo.axis('off')\n",
    "\n",
    "##################################################\n",
    "\n",
    "ax_list = [ax1,ax2,ax3,ax4,ax5]\n",
    "label_list = ['(a) WRF 50-km',\\\n",
    "              '(b) WRF 2-km 10d', \\\n",
    "              '(c) CESM 2$\\degree$ 10d', \\\n",
    "              '(d) CESM 2$\\degree$ 30d', \\\n",
    "              '(e) CESM 2$\\degree$ 10y']\n",
    "\n",
    "fig.tight_layout()#h_pad=0)\n",
    "fig.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "map_lat_lo = -10.\n",
    "map_lat_hi = 10.\n",
    "map_lon_lo = 278\n",
    "map_lon_hi = 325.5\n",
    "\n",
    "ax1.zorder=1\n",
    "ax2.zorder=1\n",
    "ax3.zorder=1\n",
    "ax4.zorder=1\n",
    "ax5.zorder=1\n",
    "\n",
    "ax1.set_facecolor('None')\n",
    "ax2.set_facecolor('None')\n",
    "ax3.set_facecolor('None')\n",
    "ax4.set_facecolor('None')\n",
    "ax5.set_facecolor('None')\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in ax_list]\n",
    "[ax.set_xticklabels(['','','']) for ax in ax_list]\n",
    "[ax.axhline(0, lw=1, ls='--', c='0.1') for ax in ax_list]\n",
    "#[ax.set_ylim(-1.8,1.8) for ax in ax_list]\n",
    "[ax.set_yticks([-1.,0,1.]) for ax in ax_list]\n",
    "[ax_list[i].yaxis.tick_right() for i in [1,3]]\n",
    "[ax.set_xlim(map_lon_lo, map_lon_hi) for ax in ax_list]\n",
    "[ax.xaxis.grid() for ax in ax_list]\n",
    "[ax.set_xticks([280,290,300.,310,320.]) for ax in ax_list]\n",
    "\n",
    "[ax_list[i].text(s=label_list[i], x=0.98, y=0.87,\\\n",
    "                 ha='right', va='top', \\\n",
    "                 fontsize=fontsize, \\\n",
    "                 color=colors[i], \\\n",
    "                 transform=ax_list[i].transAxes) for i in range(5)]\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "ax5pos = ax5.get_position()\n",
    "fig.text(s='mm day$^{\\,-1}$', \\\n",
    "         x=0, y=ax5pos.y0+0.5*(ax1pos.y1-ax5pos.y0), \\\n",
    "         rotation=90, \\\n",
    "         transform=fig.transFigure, \\\n",
    "         va='center', \\\n",
    "         ha='right', \\\n",
    "         fontsize=fontsize)\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(0.275,0.98), \\\n",
    "          bbox_transform=ax1.transAxes, loc='upper left', borderaxespad=0, ncol=1, labelspacing=0.19, \\\n",
    "          fancybox=False, edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "          handletextpad=0.5, handlelength=1.1)\n",
    "\n",
    "ax5.set_xlabel('longitude', fontsize=fontsize)\n",
    "ax5.set_xticklabels(['280$\\degree$E','','300$\\degree$E','','320$\\degree$E'])\n",
    "\n",
    "fig.savefig('figures/manuscript_fig4_precip_change_latmean_plots.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## yes error bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x396 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [cmocean.cm.thermal(0.3),\\\n",
    "          cmocean.cm.thermal(0.4),\\\n",
    "          cmocean.cm.thermal(0.5),\\\n",
    "          cmocean.cm.thermal(0.6),\\\n",
    "          cmocean.cm.thermal(0.7)]\n",
    "\n",
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,5.5))\n",
    "\n",
    "ymax = 1920.6472595214843\n",
    "##################################################\n",
    "ax1 = fig.add_subplot(511)\n",
    "ax1.plot(xlong[0,:]+360.,\\\n",
    "         raintot_diffs_latmean_smooth*24., \\\n",
    "         color=colors[0],\\\n",
    "         label='days 2-10')\n",
    "\n",
    "ax1.plot(xlong[0,:]+360.,\\\n",
    "         raintot_diffs_latmean_smooth_day1*24., \\\n",
    "         color=colors[0],\\\n",
    "         label='day 1', ls='--')\n",
    "\n",
    "ax1topo = ax1.twinx()\n",
    "ax1topo.fill_between(xlong[0,:]+360., \\\n",
    "                     numpy.zeros(hgt_50km_latmean.size), \\\n",
    "                     hgt_50km_latmean, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "y0,y1 = ax1topo.get_ylim()\n",
    "ax1topo.set_ylim(0,ymax)\n",
    "ax1topo.axis('off')\n",
    "\n",
    "ax1.fill_between(xlong[0,:]+360., \\\n",
    "                (raintot_diffs_latmean_smooth-1.96*raintot_diffs_latstderr)*24.,\\\n",
    "                (raintot_diffs_latmean_smooth+1.96*raintot_diffs_latstderr)*24.,\\\n",
    "                facecolor=colors[0], alpha=0.333)\n",
    "\n",
    "\n",
    "ax1.fill_between(xlong[0,:]+360., \\\n",
    "                (raintot_diffs_latmean_smooth_day1-1.96*raintot_diffs_day1_latstderr)*24.,\\\n",
    "                (raintot_diffs_latmean_smooth_day1+1.96*raintot_diffs_day1_latstderr)*24.,\\\n",
    "                facecolor=colors[0], alpha=0.333)\n",
    "\n",
    "##################################################\n",
    "xvals = xlong_2km[0,:]+360.\n",
    "yvals = raintot_diffs_2km_latmean_smooth*24.\n",
    "f = scipy.interpolate.interp1d(xvals, yvals, kind='cubic')\n",
    "xvals_interp = numpy.linspace(xvals.min(), xvals.max(), 1000)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax2 = fig.add_subplot(512)\n",
    "ax2.plot(xvals_interp,\\\n",
    "        yvals_interp, \\\n",
    "        color=colors[1],\\\n",
    "        label='WRF 2-km 10d')\n",
    "\n",
    "ax2topo = ax2.twinx()\n",
    "ax2topo.fill_between(xlong_2km[0,:]+360., \\\n",
    "                     numpy.zeros(hgt_2km_latmean.size), \\\n",
    "                     hgt_2km_latmean, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax2topo.set_ylim(0,ymax)\n",
    "ax2topo.axis('off')\n",
    "\n",
    "yvals = raintot_diffs_2km_latstderr*24.\n",
    "f = scipy.interpolate.interp1d(xvals, yvals, kind='cubic')\n",
    "yvals_interp_error = f(xvals_interp)\n",
    "ax2.fill_between(xvals_interp, \\\n",
    "                (yvals_interp-1.96*yvals_interp_error),\\\n",
    "                (yvals_interp+1.96*yvals_interp_error),\\\n",
    "                facecolor=colors[1], alpha=0.333)\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_10day_latmean*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax3 = fig.add_subplot(513)\n",
    "ax3.plot(xvals_interp,\\\n",
    "        yvals_interp, \\\n",
    "        color=colors[2],\\\n",
    "        label='CESM 2$\\degree$ 10d')\n",
    "\n",
    "yvals = PRECT_diffs_10day_latstderr*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate(limit_direction='both').values, kind='cubic')\n",
    "yvals_interp_error = f(xvals_interp)\n",
    "ax3.fill_between(xvals_interp, \\\n",
    "                (yvals_interp-1.96*yvals_interp_error),\\\n",
    "                (yvals_interp+1.96*yvals_interp_error),\\\n",
    "                facecolor=colors[2], alpha=0.333)\n",
    "\n",
    "ax3topo = ax3.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax3topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax3topo.set_ylim(0,ymax)\n",
    "ax3topo.axis('off')\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_30day_latmean*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax4 = fig.add_subplot(514)\n",
    "ax4.plot(xvals_interp,\\\n",
    "        yvals_interp, \\\n",
    "        color=colors[3],\\\n",
    "        label='CESM 2$\\degree$ 30d')\n",
    "\n",
    "yvals = PRECT_diffs_30day_latstderr*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate(limit_direction='both').values, kind='cubic')\n",
    "yvals_interp_error = f(xvals_interp)\n",
    "ax4.fill_between(xvals_interp, \\\n",
    "                (yvals_interp-1.96*yvals_interp_error),\\\n",
    "                (yvals_interp+1.96*yvals_interp_error),\\\n",
    "                facecolor=colors[3], alpha=0.333)\n",
    "\n",
    "ax4topo = ax4.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax4topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax4topo.set_ylim(0,ymax)\n",
    "ax4topo.axis('off')\n",
    "\n",
    "##################################################\n",
    "xvals = lon_subset\n",
    "yvals = PRECT_diffs_10year_latmean*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "xvals_interp = numpy.linspace(277.5, 325., 100)\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax5 = fig.add_subplot(515)\n",
    "ax5.plot(xvals_interp,\\\n",
    "       yvals_interp, \\\n",
    "       color=colors[4],\\\n",
    "       label='CESM 2$\\degree$ 10yr')\n",
    "\n",
    "yvals = PRECT_diffs_30day_latstderr*86400.*1000.\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate(limit_direction='both').values, kind='cubic')\n",
    "yvals_interp_error = f(xvals_interp)\n",
    "ax5.fill_between(xvals_interp, \\\n",
    "                (yvals_interp-1.96*yvals_interp_error),\\\n",
    "                (yvals_interp+1.96*yvals_interp_error),\\\n",
    "                facecolor=colors[4], alpha=0.333)\n",
    "\n",
    "ax5topo = ax5.twinx()\n",
    "yvals = topo_data_latmean\n",
    "f = scipy.interpolate.interp1d(xvals, pandas.Series(yvals).interpolate().values, kind='cubic')\n",
    "yvals_interp = f(xvals_interp)\n",
    "ax5topo.fill_between(xvals_interp, \\\n",
    "                     numpy.zeros(yvals_interp.size), \\\n",
    "                     yvals_interp, \\\n",
    "                     facecolor='0', alpha=0.25)\n",
    "ax5topo.set_ylim(0,ymax)\n",
    "ax5topo.axis('off')\n",
    "\n",
    "##################################################\n",
    "\n",
    "ax_list = [ax1,ax2,ax3,ax4,ax5]\n",
    "label_list = ['(a) WRF 50-km',\\\n",
    "              '(b) WRF 2-km 10d', \\\n",
    "              '(c) CESM 2$\\degree$ 10d', \\\n",
    "              '(d) CESM 2$\\degree$ 30d', \\\n",
    "              '(e) CESM 2$\\degree$ 10y']\n",
    "\n",
    "fig.tight_layout()#h_pad=0)\n",
    "fig.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "map_lat_lo = -10.\n",
    "map_lat_hi = 10.\n",
    "map_lon_lo = 278\n",
    "map_lon_hi = 325.5\n",
    "\n",
    "ax1.zorder=1\n",
    "ax2.zorder=1\n",
    "ax3.zorder=1\n",
    "ax4.zorder=1\n",
    "ax5.zorder=1\n",
    "\n",
    "ax1.set_facecolor('None')\n",
    "ax2.set_facecolor('None')\n",
    "ax3.set_facecolor('None')\n",
    "ax4.set_facecolor('None')\n",
    "ax5.set_facecolor('None')\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in ax_list]\n",
    "[ax.set_xticklabels(['','','']) for ax in ax_list]\n",
    "[ax.axhline(0, lw=1, ls='--', c='0.1') for ax in ax_list]\n",
    "#[ax.set_ylim(-1.8,1.8) for ax in ax_list]\n",
    "[ax.set_yticks([-1.,0,1.]) for ax in ax_list]\n",
    "[ax_list[i].yaxis.tick_right() for i in [1,3]]\n",
    "[ax.set_xlim(map_lon_lo, map_lon_hi) for ax in ax_list]\n",
    "[ax.xaxis.grid() for ax in ax_list]\n",
    "[ax.set_xticks([280,290,300.,310,320.]) for ax in ax_list]\n",
    "\n",
    "[ax_list[i].text(s=label_list[i], x=0.98, y=0.87,\\\n",
    "                 ha='right', va='top', \\\n",
    "                 fontsize=fontsize, \\\n",
    "                 color=colors[i], \\\n",
    "                 transform=ax_list[i].transAxes) for i in range(5)]\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "ax5pos = ax5.get_position()\n",
    "fig.text(s='mm day$^{\\,-1}$', \\\n",
    "         x=0, y=ax5pos.y0+0.5*(ax1pos.y1-ax5pos.y0), \\\n",
    "         rotation=90, \\\n",
    "         transform=fig.transFigure, \\\n",
    "         va='center', \\\n",
    "         ha='right', \\\n",
    "         fontsize=fontsize)\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(0.275,0.98), \\\n",
    "          bbox_transform=ax1.transAxes, loc='upper left', borderaxespad=0, ncol=1, labelspacing=0.19, \\\n",
    "          fancybox=False, edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "          handletextpad=0.5, handlelength=1.2)\n",
    "\n",
    "ax5.set_xlabel('longitude', fontsize=fontsize)\n",
    "ax5.set_xticklabels(['280$\\degree$E','','300$\\degree$E','','320$\\degree$E'])\n",
    "\n",
    "fig.savefig('figures/manuscript_fig4_precip_change_latmean_plots_with_error_bars.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot WRF 50km and 2km map plus meridional mean precip decrease below"
   ]
  },
  {
   "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": []
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:19:17.826998Z",
     "start_time": "2018-06-19T06:19:17.818174Z"
    }
   },
   "outputs": [],
   "source": [
    "xlat_indices = (xlat[:,0]>=-10.) & (xlat[:,0]<=10.)\n",
    "xlong_indices = (xlong[0,:]>=280-360.)&(xlong[0,:]<=320-260.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:19:21.457449Z",
     "start_time": "2018-06-19T06:19:19.875447Z"
    }
   },
   "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_diffs_landmask = raintot_1000_diff-raintot_400_diff\n",
    "raintot_diffs_landmask[:,landmask==0] = numpy.nan\n",
    "raintot_diffs_latmean = numpy.nanmean(raintot_diffs_landmask[:,xlat_indices,:], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:19:23.387733Z",
     "start_time": "2018-06-19T06:19:23.376664Z"
    }
   },
   "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": [
    "raintot_diffs_latmean_day1 = numpy.nanmean(raintot_diffs_latmean[:24,:], axis=0)\n",
    "raintot_diffs_latmean_days2to10 = numpy.nanmean(raintot_diffs_latmean[24:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:19:25.343746Z",
     "start_time": "2018-06-19T06:19:25.330133Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_latmean_day1 = pandas.Series(raintot_diffs_latmean_day1).rolling(window=3, \\\n",
    "                                                                               center=True, \\\n",
    "                                                                               min_periods=1).mean()\n",
    "\n",
    "raintot_diffs_latmean_days2to10 = pandas.Series(raintot_diffs_latmean_days2to10).rolling(window=3, \\\n",
    "                                                                               center=True, \\\n",
    "                                                                               min_periods=1).mean()\n",
    "\n",
    "raintot_diffs_latmean_std_day1 = pandas.Series(raintot_diffs_latmean_day1).rolling(window=10, \\\n",
    "                                                                                   center=True, \\\n",
    "                                                                                   min_periods=1).std(ddof=1)\n",
    "\n",
    "raintot_diffs_latmean_std_days2to10 = pandas.Series(raintot_diffs_latmean_days2to10).rolling(window=10, \\\n",
    "                                                                                   center=True, \\\n",
    "                                                                                   min_periods=1).std(ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T06:26:33.418614Z",
     "start_time": "2018-06-19T06:26:26.459781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=18\n",
    "\n",
    "fig = mp.figure(figsize=(5,6.5))\n",
    "gs = matplotlib.gridspec.GridSpec(2, 1, width_ratios=[1], height_ratios=[3, 1])\n",
    "\n",
    "map_proj = cartopy.crs.PlateCarree()\n",
    "levels1 = numpy.arange(-2,2.1,0.25)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0], projection=map_proj)\n",
    "contour1 = ax1.contourf(xlong[0,:], xlat[:,0], \\\n",
    "                        raintot_diffs_time_mean_landmask*24., \\\n",
    "                        levels=levels1, cmap=cmocean.cm.balance_r, extend='both')\n",
    "ax1.set_extent([276,327.5,-30,15], crs=map_proj)\n",
    "ax1.coastlines()\n",
    "ax1.set_xticks([280-360.,300-360.,320-360.])\n",
    "ax1.set_xticklabels(['','',''])\n",
    "ax1.set_yticks([-20,-10,0,10])\n",
    "ax1.set_yticklabels(['$20\\degree$S','$10\\degree$S', '$0\\degree$', '$10\\degree$N'])\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "ax1.axhline(0, c='0.1', lw=1)\n",
    "\n",
    "ax1.fill_between(numpy.linspace(276,327.5,2), y1=-10, y2=-40, \\\n",
    "                 alpha=0.333, edgecolor='None', \\\n",
    "                 facecolor='0.1', transform=map_proj)\n",
    "ax1.fill_between(numpy.linspace(276,327.5,2), y1=10, y2=40, \\\n",
    "                 alpha=0.333, edgecolor='None', \\\n",
    "                 facecolor='0.1', transform=map_proj)\n",
    "\n",
    "ax1.text(s='WRF 50-km 10d precip. change', x=0, y=1.02, \\\n",
    "         transform=ax1.transAxes, \\\n",
    "         fontsize=fontsize, ha='left', va='bottom')\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.set_xlim(276,327.5)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "ax2.set_xticks([280,300,320])\n",
    "ax2.set_xticklabels(['$280\\degree$E','$290\\degree$E','$300\\degree$E','$310\\degree$E'])\n",
    "ax2.set_xlabel('longitude', fontsize=fontsize)\n",
    "ax2.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax2.axhline(0, c='0.1', lw=1)\n",
    "\n",
    "ax2.plot(xlong[0,xlong_indices]+360.,\\\n",
    "        raintot_diffs_latmean_days2to10[xlong_indices]*24., \\\n",
    "        c='firebrick', label='days 2–10')\n",
    "\n",
    "ax2.fill_between(xlong[0,xlong_indices]+360., \\\n",
    "                (raintot_diffs_latmean_days2to10[xlong_indices]-raintot_diffs_latmean_std_days2to10[xlong_indices])*24.,\\\n",
    "                (raintot_diffs_latmean_days2to10[xlong_indices]+raintot_diffs_latmean_std_days2to10[xlong_indices])*24.,\\\n",
    "                facecolor='firebrick', alpha=0.333)\n",
    "\n",
    "ax2.plot(xlong[0,xlong_indices]+360.,\\\n",
    "        raintot_diffs_latmean_day1[xlong_indices]*24., \\\n",
    "        c='coral', label='day 1', zorder=5)\n",
    "\n",
    "ax2.fill_between(xlong[0,xlong_indices]+360., \\\n",
    "                (raintot_diffs_latmean_day1[xlong_indices]-raintot_diffs_latmean_std_day1[xlong_indices])*24.,\\\n",
    "                (raintot_diffs_latmean_day1[xlong_indices]+raintot_diffs_latmean_std_day1[xlong_indices])*24.,\\\n",
    "                facecolor='coral', alpha=0.333, zorder=5)\n",
    "\n",
    "ax2.set_ylim(-1.4835328969152064, 1.1870771995510045)\n",
    "\n",
    "fig.tight_layout()\n",
    "h, l=ax2.get_legend_handles_labels()\n",
    "ax2.legend(h, l, fontsize=fontsize, scatterpoints=1, bbox_to_anchor=(1.025,1.), \\\n",
    "            bbox_transform=ax2.transAxes, loc='upper left', borderaxespad=0, labelspacing=0.25, \\\n",
    "            fancybox=False, edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "            handletextpad=0.5, markerscale=1, handlelength=1.25)\n",
    "\n",
    "scale = .85\n",
    "ax1pos = ax1.get_position()\n",
    "cbar_ax = fig.add_axes([ax1pos.x1+0.05, ax1pos.y0+.5*(1-scale)*ax1pos.height, 0.04, ax1pos.height*scale])\n",
    "cbar = mp.colorbar(contour1, cax=cbar_ax)\n",
    "cbar.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-2,-1,0,1,2])\n",
    "\n",
    "fig.savefig('figures/WRF_map_and_meridional_means_2lines.png', dpi=600, bbox_inches='tight', transparent=True)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T16:40:57.207294Z",
     "start_time": "2018-06-12T16:40:40.990434Z"
    }
   },
   "source": [
    "mp.contourf(xlong_2km,\\\n",
    "            xlat_2km,\\\n",
    "            raintot_diffs_2km_time_mean_landmask*24.,\\\n",
    "            levels=bounds,\\\n",
    "            cmap=cmap_custom,\\\n",
    "            extend='both',\\\n",
    "            norm=norm)\n",
    "mp.colorbar()\n",
    "ax = mp.axes()\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# maps of change"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "latmin_d03 = -2.243599\n",
    "latmax_d03 = 12.03965\n",
    "lonmin_d03 = 313.6654510498047\n",
    "lonmax_d03 = 277.7049255371094\n",
    "\n",
    "ax.add_patch(matplotlib.patches.Rectangle(xy=[lonmin_d03, latmin_d03], width=d03_width, height=d03_height,\n",
    "                                facecolor='none',\n",
    "                                edgecolor='gold',\\\n",
    "                                linewidth=3,\\\n",
    "                                transform=map_proj, zorder=2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T17:24:37.284327Z",
     "start_time": "2018-06-12T17:24:37.218591Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap_custom = matplotlib.colors.ListedColormap([cmocean.cm.balance_r(0.1), \\\n",
    "                                                cmocean.cm.balance_r(0.25), \\\n",
    "                                                cmocean.cm.balance_r(0.4), \\\n",
    "                                                cmocean.cm.balance_r(0.55), \\\n",
    "                                                cmocean.cm.balance_r(0.6), \\\n",
    "                                                cmocean.cm.balance_r(0.65)])\n",
    "                                                \n",
    "cmap_custom.set_over(cmocean.cm.balance_r(.8))\n",
    "cmap_custom.set_under(cmocean.cm.balance_r(0))\n",
    "\n",
    "#levels_2km = numpy.array([-10,-5,-0.5,0,0.5,5,10])\n",
    "levels_2km = numpy.arange(-2,2.1,0.25)\n",
    "norm = matplotlib.colors.BoundaryNorm(levels_2km, cmap_custom.N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_subset.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_subset_open.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 25)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRECT_diffs_10day.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132],\n",
       "       [205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132],\n",
       "       [205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132],\n",
       "       ...,\n",
       "       [205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132],\n",
       "       [205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132],\n",
       "       [205.23868, 205.68842, 206.13815, ..., 333.86185, 334.31158,\n",
       "        334.76132]], dtype=float32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlong+360."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. ,\n",
       "       292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. , 312.5,\n",
       "       315. , 317.5, 320. , 322.5, 325. , 327.5, 330. ])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "raintot_diffs_2km_time_mean_interpolated = scipy.interpolate.griddata((xlong_2km.flatten(),xlat_2km.flatten()), \\\n",
    "                                                                     raintot_diffs_2km_time_mean_landmask.flatten(), \\\n",
    "                                                                     (xlong.flatten(),xlat.flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "lonmesh, latmesh = numpy.meshgrid(lon_subset,lat_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "raintot_diffs_2km_time_mean_interpolated = scipy.interpolate.griddata((xlong_2km.flatten()+360.,xlat_2km.flatten()), \\\n",
    "                                                                     raintot_diffs_2km_time_mean_landmask.flatten(), \\\n",
    "                                                                     (lonmesh.flatten(),latmesh.flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51731,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rantot_diffs_2km_time_mean_interpolated.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 120)"
      ]
     },
     "execution_count": 117,
     "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(rantot_diffs_2km_time_mean_interpolated.reshape(xlat.shape)*24, \\\n",
    "              #vmin=-5,vmax=5, \\\n",
    "              #levels = numpy.arange(-2,2.1,1), extend='both', \\\n",
    "              cmap=cmap_custom)  \n",
    "            #cmap=cmocean.cm.balance_r)\n",
    "mp.colorbar()\n",
    "mp.xlim(160,240)\n",
    "mp.ylim(80,120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "turbid = cmocean.cm.turbid_r\n",
    "deep = cmocean.cm.deep\n",
    "turbid_colors = turbid(numpy.linspace(0,1,128))\n",
    "deep_colors = deep(numpy.linspace(0,1,128))\n",
    "turbid_deep_colors = numpy.vstack((turbid_colors,deep_colors))\n",
    "turbid_deep_colors\n",
    "turbid_deep_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('turbid_deep', turbid_deep_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b5a2963a240>"
      ]
     },
     "execution_count": 145,
     "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": [
    "levels_2km = numpy.arange(-5,5.1,1)\n",
    "cmap_2km = matplotlib.cm.PuOr #turbid_deep_cmap\n",
    "norm = matplotlib.colors.BoundaryNorm(levels_2km, cmap_2km.N)\n",
    "mp.pcolormesh(raintot_diffs_2km_time_mean_landmask*24.,\\\n",
    "              cmap=cmap_2km, norm=norm)\n",
    "mp.colorbar(extend='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-12T23:41:25.026395Z",
     "start_time": "2018-06-12T23:40:49.034108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x342 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4.75))\n",
    "\n",
    "map_proj = cartopy.crs.PlateCarree()\n",
    "\n",
    "ax1 = fig.add_subplot(231, projection=map_proj)\n",
    "ax1.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "ax2 = fig.add_subplot(232, projection=map_proj)\n",
    "ax2.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "ax3 = fig.add_subplot(234, projection=map_proj)\n",
    "ax3.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "ax4 = fig.add_subplot(235, projection=map_proj)\n",
    "ax4.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "ax5 = fig.add_subplot(236, projection=map_proj)\n",
    "ax5.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "levels1 = numpy.arange(-2,2.1,0.25)\n",
    "\n",
    "contour1 = ax1.contourf(xlong[0,:], xlat[:,0], raintot_diffs_time_mean_landmask*24., levels=levels1, cmap=turbid_deep_cmap, extend='both')\n",
    "\n",
    "levels_2km = numpy.arange(-5,5.1,1)\n",
    "cmap_2km = matplotlib.cm.PuOr #turbid_deep_cmap\n",
    "norm = matplotlib.colors.BoundaryNorm(levels_2km, cmap_2km.N)\n",
    "# contour2 = ax2.contourf(xlong_2km[0,:], xlat_2km[:,0], raintot_diffs_2km_time_mean_landmask, \\\n",
    "#                         levels=levels_2km, cmap=turbid_deep_cmap, extend='both')\n",
    "contour2 = ax2.pcolormesh(xlong_2km[0,:], xlat_2km[:,0], raintot_diffs_2km_time_mean_landmask*24., \\\n",
    "                          cmap=cmap_2km, norm=norm)\n",
    "\n",
    "contour3 = ax3.contourf(lon_subset, lat_subset_open, PRECT_diffs_10day*86400.*1000, levels=levels1, cmap=turbid_deep_cmap, extend='both')\n",
    "ax3.contour(lon_subset, lat_subset_open, PRECT_diffs_10day, levels=[0], colors='0.1')\n",
    "\n",
    "contour4 = ax4.contourf(lon_subset, lat_subset_open, PRECT_diffs_30day*86400.*1000, levels=levels1, cmap=turbid_deep_cmap, extend='both')\n",
    "ax4.contour(lon_subset, lat_subset_open, PRECT_diffs_30day, levels=[0], colors='0.1')\n",
    "\n",
    "contour5 = ax5.contourf(lon_subset, lat_subset_open, PRECT_diffs_10year*86400.*1000, levels=levels1, cmap=turbid_deep_cmap, extend='both')\n",
    "ax5.contour(lon_subset, lat_subset_open, PRECT_diffs_10year, levels=[0], colors='0.1')\n",
    "\n",
    "ax1.coastlines()\n",
    "ax2.coastlines()\n",
    "ax3.coastlines()\n",
    "ax4.coastlines()\n",
    "ax5.coastlines()\n",
    "\n",
    "ax1.text(s='(a) WRF 50km 2$-$10d mean', x=0, y=1.02, transform=ax1.transAxes, fontsize=fontsize, ha='left', va='bottom')\n",
    "ax2.text(s='(b) WRF 2km 2$-$10d mean', x=0, y=1.02, transform=ax2.transAxes, fontsize=fontsize, ha='left', va='bottom')\n",
    "ax3.text(s='(c) CESM 2$\\degree$ 2$-$10d mean', x=0, y=1.02, transform=ax3.transAxes, fontsize=fontsize, ha='left', va='bottom')\n",
    "ax4.text(s='(d) CESM 2$\\degree$ 30d mean', x=0, y=1.02, transform=ax4.transAxes, fontsize=fontsize, ha='left', va='bottom')\n",
    "ax5.text(s='(e) CESM 2$\\degree$ 10y mean', x=0, y=1.02, transform=ax5.transAxes, fontsize=fontsize, ha='left', va='bottom')\n",
    "\n",
    "fig.tight_layout(h_pad=3)\n",
    "\n",
    "axlpos = ax3.get_position()\n",
    "axrpos = ax5.get_position()\n",
    "\n",
    "cbar_ax = fig.add_axes([axlpos.x0,axlpos.y0-0.1,axrpos.x1-axlpos.x0,0.04])\n",
    "cbar = fig.colorbar(contour4, cax=cbar_ax, orientation='horizontal')\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax2kmpos = ax2.get_position()\n",
    "cbar_ax2 = fig.add_axes([ax2kmpos.x1+0.03,ax2kmpos.y0,0.02,ax2kmpos.height])\n",
    "cbar2 = fig.colorbar(contour2, cax=cbar_ax2, orientation='vertical', extend='both')\n",
    "cbar_ax2.tick_params(labelsize=fontsize)\n",
    "cbar2.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/cesm_10day_30day_10year_mean_precip_change_WITH_WRF_2km.png', dpi=600, bbox_inches='tight', transparent=True)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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