gp1_wrf_MANUSCRIPT_figure_4_precip_time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:03.110841Z",
     "start_time": "2018-08-16T18:51:59.786234Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import xarray\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import glob\n",
    "import netCDF4\n",
    "import pandas\n",
    "import wrf\n",
    "import string\n",
    "import seaborn\n",
    "import matplotlib.colors\n",
    "import cmocean\n",
    "import matplotlib.cm\n",
    "\n",
    "import metpy\n",
    "import metpy.plots\n",
    "import metpy.calc\n",
    "import metpy.units\n",
    "\n",
    "import cartopy\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set up wet and dry regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:03.126954Z",
     "start_time": "2018-08-16T18:52:03.119838Z"
    }
   },
   "outputs": [],
   "source": [
    "dry_lon_lo, dry_lon_hi, dry_lat_lo, dry_lat_hi = -60,-50,-10,10\n",
    "wet_lon_lo, wet_lon_hi, wet_lat_lo, wet_lat_hi = -80,-75,-10,10\n",
    "\n",
    "dry_lon_lo_degE = 360+dry_lon_lo\n",
    "dry_lon_hi_degE = 360+dry_lon_hi\n",
    "\n",
    "wet_lon_lo_degE = 360+wet_lon_lo\n",
    "wet_lon_hi_degE = 360+wet_lon_hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:03.224642Z",
     "start_time": "2018-08-16T18:52:03.135079Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_lo_ama = dry_lon_lo\n",
    "lon_hi_ama = dry_lon_hi\n",
    "lat_lo_ama = dry_lat_lo\n",
    "lat_hi_ama = dry_lat_hi\n",
    "\n",
    "lon_lo_and = wet_lon_lo\n",
    "lon_hi_and = wet_lon_hi\n",
    "lat_lo_and = wet_lat_lo\n",
    "lat_hi_and = wet_lat_hi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open control data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:06.021408Z",
     "start_time": "2018-08-16T18:52:05.214884Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:13.729726Z",
     "start_time": "2018-08-16T18:52:06.029824Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:52:48.312739Z",
     "start_time": "2018-08-16T18:52:13.738347Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_400 = cont_mfds['RAINC'].values\n",
    "rainnc_400 = cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.109445Z",
     "start_time": "2018-08-16T18:52:48.321237Z"
    }
   },
   "outputs": [],
   "source": [
    "time = cont_mfds['Times'].values\n",
    "xlat = cont_mfds['XLAT'].values[0,:,:]\n",
    "xlong = cont_mfds['XLONG'].values[0,:,:]\n",
    "landmask = cont_mfds['LANDMASK'].values[0,:,:]\n",
    "landmask_midpoints = landmask[1:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.171385Z",
     "start_time": "2018-08-16T18:53:16.117816Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt = cont_mfds['HGT'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get time info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.238561Z",
     "start_time": "2018-08-16T18:53:16.179911Z"
    }
   },
   "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": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.314739Z",
     "start_time": "2018-08-16T18:53:16.246901Z"
    }
   },
   "outputs": [],
   "source": [
    "time_datetimes_shifted_ama = time_datetimes + datetime.timedelta(hours=-4)\n",
    "time_datetimes_shifted_and = time_datetimes + datetime.timedelta(hours=-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get land mask info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.427208Z",
     "start_time": "2018-08-16T18:53:16.323054Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask_indices = numpy.copy(landmask)\n",
    "landmask_indices[landmask==1]=1\n",
    "landmask_indices[landmask!=1]=0\n",
    "landmask_indices = numpy.array(landmask_indices, dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.552810Z",
     "start_time": "2018-08-16T18:53:16.435896Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_indices_ama = (xlong>=lon_lo_ama)&(xlong<=lon_hi_ama)\n",
    "lat_indices_ama = (xlat>=lat_lo_ama)&(xlat<=lat_hi_ama)\n",
    "latlon_indices_ama = (lon_indices_ama)&(lat_indices_ama)&(landmask_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.678082Z",
     "start_time": "2018-08-16T18:53:16.561346Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_indices_and = (xlong>=lon_lo_and)&(xlong<=lon_hi_and)\n",
    "lat_indices_and = (xlat>=lat_lo_and)&(xlat<=lat_hi_and)\n",
    "latlon_indices_and = (lon_indices_and)&(lat_indices_and)&(landmask_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:16.811105Z",
     "start_time": "2018-08-16T18:53:16.686471Z"
    }
   },
   "outputs": [],
   "source": [
    "latlon_indices_and_high = (lon_indices_and)&(lat_indices_and)&(landmask_indices)&(hgt>1500.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open phys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:53:24.443506Z",
     "start_time": "2018-08-16T18:53:16.819424Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]\n",
    "\n",
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:04.866346Z",
     "start_time": "2018-08-16T18:53:29.750866Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_1000 = phys_mfds['RAINC'].values\n",
    "rainnc_1000 = phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# rain diffs for hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:04.944331Z",
     "start_time": "2018-08-16T18:54:04.876071Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_400 = rainc_400+rainnc_400\n",
    "raintot_1000 = rainc_1000+rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:05.155855Z",
     "start_time": "2018-08-16T18:54:04.952713Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_400 = numpy.zeros((raintot_400.shape))*numpy.nan\n",
    "raintot_diffs_1000 = numpy.zeros((raintot_1000.shape))*numpy.nan\n",
    "\n",
    "raintot_diffs_400[:-1,:,:] = numpy.diff(raintot_400, axis=0)\n",
    "raintot_diffs_1000[:-1,:,:] = numpy.diff(raintot_1000, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# raintot diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:05.186173Z",
     "start_time": "2018-08-16T18:54:05.164517Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: Mean of empty slice\n",
      "  after removing the cwd from sys.path.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "raintot_diffs_400_latlon_subset_ama = raintot_diffs_400[:,latlon_indices_ama]\n",
    "raintot_diffs_1000_latlon_subset_ama = raintot_diffs_1000[:,latlon_indices_ama]\n",
    "\n",
    "raintot_diffs_400_latlon_mean_ama = numpy.nanmean(raintot_diffs_400_latlon_subset_ama, axis=1)\n",
    "raintot_diffs_1000_latlon_mean_ama = numpy.nanmean(raintot_diffs_1000_latlon_subset_ama, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:05.287361Z",
     "start_time": "2018-08-16T18:54:05.194435Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: Mean of empty slice\n",
      "  after removing the cwd from sys.path.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: Mean of empty slice\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/export/home/blangenb/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: Mean of empty slice\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "raintot_diffs_400_latlon_subset_and = raintot_diffs_400[:,latlon_indices_and]\n",
    "raintot_diffs_1000_latlon_subset_and = raintot_diffs_1000[:,latlon_indices_and]\n",
    "\n",
    "raintot_diffs_400_latlon_mean_and = numpy.nanmean(raintot_diffs_400_latlon_subset_and, axis=1)\n",
    "raintot_diffs_1000_latlon_mean_and = numpy.nanmean(raintot_diffs_1000_latlon_subset_and, axis=1)\n",
    "\n",
    "raintot_diffs_400_latlon_subset_and_high = raintot_diffs_400[:,latlon_indices_and_high]\n",
    "raintot_diffs_1000_latlon_subset_and_high = raintot_diffs_1000[:,latlon_indices_and_high]\n",
    "\n",
    "raintot_diffs_400_latlon_mean_and_high = numpy.nanmean(raintot_diffs_400_latlon_subset_and_high, axis=1)\n",
    "raintot_diffs_1000_latlon_mean_and_high = numpy.nanmean(raintot_diffs_1000_latlon_subset_and_high, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# time periods:\n",
    "* Amazon:  8am to 8pm (AMtoPM) - daytime\n",
    "* Amazon:  8pm to 8am (PMtoAM) - overnight\n",
    "\n",
    "#### EDIT (2018/08/16):  USE 8am to 8pm for BOTH regions\n",
    "\n",
    "```\n",
    "IGNORE THIS:\n",
    "* Andes:  10am to 6pm (AMtoPM) - daytime\n",
    "* Andes:  6pm to 10am (PMtoAM) - overnight\n",
    "```"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:05.370591Z",
     "start_time": "2018-08-16T18:54:05.295601Z"
    }
   },
   "source": [
    "time_indices_AMtoPM_ama = [t.hour in range(8,20) for t in time_datetimes_shifted_ama]\n",
    "time_indices_PMtoAM_ama = [(t.hour in range(20,24)) or (t.hour in range(0,8)) for t in time_datetimes_shifted_ama]\n",
    "\n",
    "time_indices_AMtoPM_and = [t.hour in range(10,18) for t in time_datetimes_shifted_and]\n",
    "time_indices_PMtoAM_and = [(t.hour in range(18,24)) or (t.hour in range(0,10)) for t in time_datetimes_shifted_and]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:13:47.745446Z",
     "start_time": "2018-08-16T19:13:47.731774Z"
    }
   },
   "outputs": [],
   "source": [
    "time_indices_AMtoPM_ama = [t.hour in range(8,20) for t in time_datetimes_shifted_ama]\n",
    "time_indices_PMtoAM_ama = [(t.hour in range(20,24)) or (t.hour in range(0,8)) for t in time_datetimes_shifted_ama]\n",
    "\n",
    "time_indices_AMtoPM_and = [t.hour in range(8,20) for t in time_datetimes_shifted_and]\n",
    "time_indices_PMtoAM_and = [(t.hour in range(20,24)) or (t.hour in range(0,8)) for t in time_datetimes_shifted_and]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate difference percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:11:01.908291Z",
     "start_time": "2018-08-16T19:11:01.887704Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_400_AMtoPM_ama = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_ama[time_indices_AMtoPM_ama])\n",
    "raintot_diffs_400_PMtoAM_ama = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_ama[time_indices_PMtoAM_ama])\n",
    "\n",
    "raintot_diffs_1000_AMtoPM_ama = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_ama[time_indices_AMtoPM_ama])\n",
    "raintot_diffs_1000_PMtoAM_ama = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_ama[time_indices_PMtoAM_ama])\n",
    "\n",
    "raintot_diffs_400_timemean_ama = numpy.nanmean(raintot_diffs_400_latlon_subset_ama)\n",
    "raintot_diffs_1000_timemean_ama = numpy.nanmean(raintot_diffs_1000_latlon_subset_ama)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:03:50.166163Z",
     "start_time": "2018-08-16T19:03:50.150382Z"
    }
   },
   "source": [
    "raintot_diffs_400_AMtoPM_and = (8/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and[time_indices_AMtoPM_and])\n",
    "raintot_diffs_400_PMtoAM_and = (16/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_1000_AMtoPM_and = (8/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and[time_indices_AMtoPM_and])\n",
    "raintot_diffs_1000_PMtoAM_and = (16/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_400_timemean_and = numpy.nanmean(raintot_diffs_400_latlon_subset_and)\n",
    "raintot_diffs_1000_timemean_and = numpy.nanmean(raintot_diffs_1000_latlon_subset_and)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:11:02.730732Z",
     "start_time": "2018-08-16T19:11:02.717323Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_400_AMtoPM_and = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and[time_indices_AMtoPM_and])\n",
    "raintot_diffs_400_PMtoAM_and = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_1000_AMtoPM_and = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and[time_indices_AMtoPM_and])\n",
    "raintot_diffs_1000_PMtoAM_and = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_400_timemean_and = numpy.nanmean(raintot_diffs_400_latlon_subset_and)\n",
    "raintot_diffs_1000_timemean_and = numpy.nanmean(raintot_diffs_1000_latlon_subset_and)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:15:25.747091Z",
     "start_time": "2018-08-16T19:15:25.733470Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_diffs_400_AMtoPM_and_high = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and_high[time_indices_AMtoPM_and])\n",
    "raintot_diffs_400_PMtoAM_and_high = (12/24)*numpy.nanmean(raintot_diffs_400_latlon_subset_and_high[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_1000_AMtoPM_and_high = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and_high[time_indices_AMtoPM_and])\n",
    "raintot_diffs_1000_PMtoAM_and_high = (12/24)*numpy.nanmean(raintot_diffs_1000_latlon_subset_and_high[time_indices_PMtoAM_and])\n",
    "\n",
    "raintot_diffs_400_timemean_and_high = numpy.nanmean(raintot_diffs_400_latlon_subset_and_high)\n",
    "raintot_diffs_1000_timemean_and_high = numpy.nanmean(raintot_diffs_1000_latlon_subset_and_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:06.053098Z",
     "start_time": "2018-08-16T18:54:05.945756Z"
    }
   },
   "outputs": [],
   "source": [
    "and_total_diff = raintot_diffs_1000_timemean_and-raintot_diffs_400_timemean_and\n",
    "and_AMtoPM_diff = raintot_diffs_1000_AMtoPM_and-raintot_diffs_400_AMtoPM_and\n",
    "and_PMtoAM_diff = raintot_diffs_1000_PMtoAM_and-raintot_diffs_400_PMtoAM_and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:22.570531Z",
     "start_time": "2018-08-16T19:04:22.561723Z"
    }
   },
   "outputs": [],
   "source": [
    "and_high_total_diff = raintot_diffs_1000_timemean_and_high-raintot_diffs_400_timemean_and_high\n",
    "and_high_AMtoPM_diff = raintot_diffs_1000_AMtoPM_and_high-raintot_diffs_400_AMtoPM_and_high\n",
    "and_high_PMtoAM_diff = raintot_diffs_1000_PMtoAM_and_high-raintot_diffs_400_PMtoAM_and_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:22.855607Z",
     "start_time": "2018-08-16T19:04:22.851394Z"
    }
   },
   "outputs": [],
   "source": [
    "ama_total_diff = raintot_diffs_1000_timemean_ama-raintot_diffs_400_timemean_ama\n",
    "ama_AMtoPM_diff = raintot_diffs_1000_AMtoPM_ama-raintot_diffs_400_AMtoPM_ama\n",
    "ama_PMtoAM_diff = (raintot_diffs_1000_PMtoAM_ama-raintot_diffs_400_PMtoAM_ama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:23.487380Z",
     "start_time": "2018-08-16T19:04:23.482900Z"
    }
   },
   "outputs": [],
   "source": [
    "orange_cmap = seaborn.color_palette('Oranges', 6)\n",
    "blue_cmap = seaborn.color_palette('Blues', 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:23.983000Z",
     "start_time": "2018-08-16T19:04:23.972963Z"
    }
   },
   "outputs": [],
   "source": [
    "class Handler(object):\n",
    "    def __init__(self, color1, color2):\n",
    "        self.color1=color1\n",
    "        self.color2=color2\n",
    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
    "        width, height = handlebox.width, handlebox.height\n",
    "        patch = mp.Rectangle([x0, y0], width, height, facecolor=self.color1,\n",
    "                                   edgecolor='None', transform=handlebox.get_transform())\n",
    "        patch2 = mp.Rectangle([x0+width/2., y0], width/2., height, facecolor=self.color2,\n",
    "                                   edgecolor='None', transform=handlebox.get_transform())\n",
    "        handlebox.add_artist(patch)\n",
    "        handlebox.add_artist(patch2)\n",
    "        return patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:25.094492Z",
     "start_time": "2018-08-16T19:04:24.800812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2af3e78f6e80>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+dLxGIjIMGAbQunXrCty8McYrvDzg80LgKhEZGDFto4hcq6rvFk1Q1fGEr42lp6dXygV9Y9yS+0AHt0M4QuLj66p8m54d8Kmq/SPfi4gCqaq6ozK3a4zxHhvwaYzxvPL0qC4ChovIY0AmEeOnVLVzRQV2OFWV0luZw9106lS3QzjCNLcDMFGnvAM+jTGmypQ5UanqgsoIxBhjjsUe7mCM8TxLVMYYz7NEZYzxPG8+qsMDbm71H7dDOEI/twMwxiXWozLGeJ4lKmOM51miMsZ4niUqY4zn2cV0Y6KcNGhF7IDHiWnZFc3+kYK3H6Hwhy+I+/VEYlLPKtk4xkfeXy8ipt05xF52LxrMp+DNURRu+AwA39kD0Z3rKNy01IU9OTZLVMZEubhB/yS0ai75k27D17Uvcdc/SeCJ3uS/OKREO995vyKmxWnowT34+9xN4JkBSEId4m56hrwnLwV/HDEtTqPgi9dc2pNjs0RlTBST5p2hVgOCc58GIPTVm+juTSAxoIWH2jVsTWzvoQSeuuKY6/KfO5jg569WeszlYYnKmCgW06ILumsTsdf+BV/HC9DsreS/PaZEkgKI7TuC4GcvQ2A/AMGPniJh+LTiUz/iaiHJbdBPJrqwF6Wzi+nGRDFJakBM+x4UrvuEwBPnE1w6nfgbnwH/oafLScPWxKSeSXDRy8XTQsveJvBEb/L+9nMKN3yG/7xbCH46GV/Pm4kfOY+4weMgrjIL+JaNJSpjooz/4jtIeGwVCY+tAn8sunMNoa/fgWAeoc9fAV8s0qjdofY9BhP86k0oOMYzWZIaIEn1QQT/Gb8k76krKdz6Df4LhlXRHpXOEpUxUSY492kCD3ci8HAndNdGiIkt2UAEgoHin33dMwh9+/4x1+fv9SuCn0wkpmlHQhsWQf5BQitnE9O80upglpklKmOiWGjNx0jdxvjSrwV/PL4eN6F7fnQSGCBN00Bi0G0rjr6Cuk1AfOje7RTuXIfv1N6QWA/f6VdQuHNt1e1IKSxRGRPNAvvIe3EI/nMGkvDQ5/g6X0L+lN8Xz45JTadw67fHXNzfawjBT14EQDPXE/rmfRJGzHGuac1/vtLDP1H2rZ8xUU63fkPeuP5HnRda9DKhiIvohwvOfKLk+/nPEpz/bIXGVxGsR2WM8TxLVMYYz7NEZYzxPEtUxhjP83SiEpFbRGSjiOSIyAIR6eh2TMaYqufZRCUiHYBxwG+BFOADwHu3dUepRy8/lQ4pSSWmNa4dx1MZJQf5xfqEO3q34aXB3Xj+ui5c0TkFgNpxPh7q055Jg07nrvNT8YWfY+2PEW4/r3WV7IOpOTybqICLgXmq+r6q5gJ/A7qJSEOX44pq3ZrXYViPVnRqWrt4WlJsDBd3SOYPF7VDDmt/Q/fm1I7zM3zaCv780QauP6M5zevGc9VpjVmXdZBbX/uW/FAhF5ySDECfjo2Ys2ZXFe6RqQm8nKjeAn4f8f5M4CCwz51wqodOTWoTEyPkFoSKp9WO93Nq41rsyS0o0VaAizsk8+LiLeTkhdiUncuomWvYFwhyREYD4v0xtKqfwLpdByt5L0xN49lEpao7VXUjgIj0B94F7lfVYGQ7ERkmIktEZElWVpYLkUaX15Zt57lPN7M/cOjXmJmTz3OfbuadFTtLtG1SN54YEXq2bcDz13Vh3IDTaNswiZz8EDNWZNIhJYn/DOxKnC+GBet3c3mnFN5fZcfAVDxPj0wXkUbABJze1FBVfefwNqo6HhgPkJ6erlUbYfVWN95PUpyPhkmx/H76d7RNTuKhPqewKTuXjT/lMvajDcVti9odyA8xpm8HGtWK4+Ul21i8aY+Le2CqC8/2qESkFvAJsBtIO1qSMlVj6rLt5IeUNZkHWL5tH50jrm8VuapzY2Z8l8lN6S2YvXYXD763hhu6NycpzudCxKa68WyiAgYD21X116qa43YwNVFmTh4AvphDF6REID9YsnpkvQQ/8f4YMnPySU1O5ItNe9gXCLIpO5dmdeOrNGZTPXn51O904AIRCR42vb2qbnIjoJpmT26Q73bsZ/CZzZm4eCttGibSqWltJny+tUS7q7s4vSmALdkBerVryNfb9tE+OYmd+/LcCJ2bW/3Hle2Wpp/bAUQpz/aoVPUOVY1RVf9hL0tSVegf83+gTryf56/vym96tuap+RvJjvh2sFGtOIKFWjzt/5Zs47K0FP56TRpvfL2dnPzQsVZtzAnzco/KVKLh0747Ytp3O3K4682VJabtyQ3yl7nfH3M9uw7k89pX24vfZ+bkc9+7qysuUGPwcI/KGGOKWKIyxnienfoZ42E3nTrV7RCOMM2FbVqPyhjjeZaojDGeZ4nKGON5lqiMMZ5nicoY43n2rZ8xUa5vpxT6n96UeH8Mq3bm8Oynm9iTGyQxNobf9WrDGS3rkZMX5MM1u5i+fAcAvzq7JRec0pBtewI8tWAjuw7kA3Drua14Zek2DhYUHm+TVc56VMZEsVb1E7j+Z834y5wNDJv6LbkFIW5KbwHAgG7NiPXF8JvXv+VPs9bTNy2Frs3q0LVZHVrUi+f211cwZ+0ubujeDIA2DRLJzMnzXJICS1TGRLXOTWuzdOte1u06SCBYyMINP9GqQWLxvHdW7CQnL8S2vQFWZ+bQqn7CMdfVt1MKH3i08KGd+hkTxWav2cWctbsBqB3vo3f7hqzJdKoiPfL+WgpCTi3Jtg0T6di4Fu98u5N1uw6S3roez13XpfjUr2PjWvyw+yD5IW/WnrREZUwUCymgyiWnJnP7eW3ICxby6Kx1AMVJ59ErTqVTk9qsycxhy54AABMXb2Xi4kPleq79WVNeXLyV357XmtOb1+XjDT/x6lc/Vvn+HIud+hkTZQZ0a8rUW85g6i1nMKBbUwBmr93NoJeWMWXJNkb+vH3x48sARs1cy7Cp3xIoKGTQmc2PWF+35nX4bkcOvds1IFSo/H76d6Q2TKRrszpVtUulskRlTJSZtnwH109exvWTl1Go0Lud8wS5/JAya3UWteN9JMb5+OPlHagd75SC/ulgAZ/9kE3j2kdWXO3VriELN/xEasMkvty8l2ChsnTLXtomJ1bpfh2PJSpjoti+QAEZpzehce04Yn3CL7o0YXN2Ljl5IfwxQkbXpiT4Y0iuFcuFHZJZsX1/ieXPaVOfLzfvQYEte3LpkVqfBH8MZ7epz5bsgDs7dRR2jcqYKDZ33W5a1k9g7JUdifPHsDYzh7/N/QGApxdu4jc9W/PCwK4czA+xYMNPzFyVWbysAOmt6jHuE6do7py1u+lyfh2ev74rH2/YzbJt3nmEpiUqY6JYocKkL7Yx6YttR8zbvi+PMR+sO+ayCsVJCiBYqDw5/4fKCPOk2amfMcbzLFEZYzzPEpUxxvMsURljPM/TiUpELhORdSJyQEReFxHvjEAzxlQZzyYqEWkAvAqMAFrifJv6qKtBGWNc4dlEBfwC+EpV31LVbOAJYKDLMRljXODlRHU6sCzi/QqgiYjUdikeY4xLRNWbZR1E5AVgk6o+FjGtAGijqj9GTBsGDAu/7QisqdJAS9cI2OV2EBWguuxHeVSXfffifrRR1ZTSGnl5ZPoeoPiuSBGJw4k3O7KRqo4HxldtaCdORJaoarrbcZys6rIf5VFd9j2a98PLp36rgS4R7zsB61Q116V4jDEu8XKiegvoLSK9w98A/hl4xeWYjDEu8GyiUtXdwC3AS8AWIBN43NWgysezp6VlVF32ozyqy75H7X549mJ6TSAik4DVqvqE27EAiMgYoKmq3u52LNHAjl/V8WyPyhhjiliiKgcRuUpEVovIPhGZJiJJ4dt9vhWRvSIyQ0RahduOEZEJ4Wl7RGS+iCSLyEjgZmBs+OeqjP9CEVkuIv8Qkf0islRETg/PbiQic0UkICKfiUiKiPQVka0Ry9cWkTwRaS8iF4nIyvB65ohI66rcl/Kw4xeFx09V7VWGF5AK7AbOBGoDc4FHcIZNXAzUBcYCn4TbjwFygYvC7T8BHgnPmwTc78I+XIhTN+0uIBa4F1gJ/AkIRMQ6B3gYiMMZLtItvPwvgeXhnzcCfYF44G/A624fIzt+1e/4WY+q7AYC76jqUlXNAX4HFALvqupcVd2H88d9hog0DS/znqrOC7efDbRyI/DD/AT8S1ULgL8DLYB2wIcRsc4FUlU1H5gJXBle9krgvxHrugxoAjwA3FZF8ZeXHb8oPH6WqMquNfB90RtVXQU0B9ZHTCsA9gKNw5MiRwMH8MZA260a/khV1UKcXkZjYHtEmxCHYn0TuCL88+Uc+kO/IrzcV+HXhZUa9cmz4xeFx88SVdntxvn0AUBEeuB8IrWMmFYP53aFzVUe3Ykr7hWISDzQFNiBc0pxNO/j9DIuBgKq+q2I1AVOUdXBOL+TvwITKjfsk2bHLwqPnyWqsvsvcJ2IdBWRZOAfwD+Ba0WkZ/im6THATFXdU8q6gjj/IdzQQERuFZEEYDROj+L7YzUOn0rMwznNmF40GZgiIn1w/pbigKxKjfrk2fGLwuNniaqMVHUZ8CDwHrAW+Bz4N3An8DLOge4EDD+B1X0IDBeRe0TkAhEJVk7UR7UBOB+nh/Fz4LoTWOZNoBvwOoCq7se5Ifw5nFOl3+IM0iX8TZnnPp3t+EXn8bMBnzWQiFwIPKeqaWVcrgfwoqp2qpTAzAmpicfPelTmhIhTBnoIMNHtWEzZRfvx88K3FyY6LMEZizPC7UBMuUT18bNTP2OM59mpnzHG8yxRGWM8zxKVMcbzLFF5jIgsFJFtIlLmYxO+0/+5yojLHOlkjpUpG/sFe4iItAHScEYMn+9yOOY47FhVLUtU3nIjzqjh14EboLj20Bci8k8RyQrXUTo7PC9JRN4QkVwRWQ6cWrSicM2kaeEaSivC93gVzXtcRHaLyA4RecJ6BOVyrGO1UkTGicgBEVksIr8I17nKEpHioQEiMlRENonIQRFZVFRPKlwTKhh+hUREw0kRERkRXiZTRP4Vvn2GcI2s0eG6VNki8ny1O6Zu15mx16EXsAo4Bzgb59aIWA7VHhoJJODUDJoXbv9HnLIjyUA6Tk2l58LzZuDUVaoFZITn1QqvbzVQD+dG1rXAFW7ve7S9SjlWd+DUg1qMc2NzO5zbcvLDv/e6wEGc21niwsd0+lG28TrwdvjnG8LbbIdzA/GHwNjwvPk4z7Nsi1NvKxu4wO3fUUW+bMCnR4hIOhCjqovD7/cClwIHcO4/+6uqqojMwCl8BjAIGKLOgzB2i8grgE9EmuCcjlyjqiHgTRG5C7gE556uJkAfnBpF3XFurjUnqJRjtQcYFz5Wy4FPVfX7cLt9OL/7zcBZOImnJc7vP/mwbdwK9MRJZuDcg/fXiHX9EafSwUPh+f9W1R/C85bjjZpZFaZ6dQ+j201AqojsEpFdOIXQbgjP+0nDH52UrIfUkpKlSIqeIN0G5xP9gDglaQM4f/RNVXU+cDfOTbg7gSlAg8rZpWrreMcqM+JYFQL7Dlu26P/cA8AmnKcsnRbZQEQ64lQ5uCn8IQTOMV0f0SyLQ/WywJs1syqMJSoPEBE/TuXJS4GfhV+XAdcQ8bToo9iJUwiuSNHPmUCWqiYUvcLrnCoi3YCvVPV8nE9dBf5QkftTnZ3AsTqRWz1uwjlFa6uqF+JUcihafxzwKk71znkRy2QRUTML6IBTRrhGsETlDZcCe1V1gapuDb/m41z7qHOc5aYBo0SkYfh05FoAVd0IbBSR20UkXkQuARbiXOM6C5ggIs1w/lMl4NEaRB5V3mMVyQ/4gITwRfThQKyICM6zKwM4NbEivQGMEJFW4VP7+3F6YzWCJSpvuJGSNayLvAs8f5zlxuD8B9kEjAMmR8wbCAzAqa39b5zTiB04f9zfAStwPpGzwvPNiSnvsYr0Ms61wkzgWZwe7ak49dvvwblIH4j49u+CcLvZODcXrwG+BZ4+if2IKnZTsjHG86xHZYzxPEtUxhjPs0RljPE8S1TGGM+zRGWM8TxLVMYYz7NEZYzxPEtUxhjPs0RljPG8/wcakNtlMS7euQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 306x234 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "fig = mp.figure(figsize=(4.25,3.25))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "\n",
    "loc1=1\n",
    "loc2=2\n",
    "loc3=3.25\n",
    "loc4=4.25\n",
    "barwidth = .75\n",
    "\n",
    "ax.set_xlim(loc1-.75,loc4+.75)\n",
    "ax.bar(loc1,raintot_diffs_400_PMtoAM_and*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc1,raintot_diffs_400_AMtoPM_and*24.,bottom=raintot_diffs_400_PMtoAM_and*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "\n",
    "ax.bar(loc2,raintot_diffs_1000_PMtoAM_and*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_AMtoPM_and*24.,bottom=raintot_diffs_1000_PMtoAM_and*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([loc1,loc2,loc3,loc4])\n",
    "\n",
    "ax.bar(loc3,raintot_diffs_400_PMtoAM_ama*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc3,raintot_diffs_400_AMtoPM_ama*24.,bottom=raintot_diffs_400_PMtoAM_ama*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "\n",
    "ax.bar(loc4,raintot_diffs_1000_PMtoAM_ama*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_AMtoPM_ama*24.,bottom=raintot_diffs_1000_PMtoAM_ama*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "\n",
    "ax.set_yticks([0,2,4,6,8,10])\n",
    "ax.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax.text(s='cont.',x=loc1,y=-.25,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='phys.',x=loc2,y=-.25,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='cont.',x=loc3,y=-.25,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='phys.',x=loc4,y=-.25,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='Andes',x=.5*(loc1+loc2),y=-1.25,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='Amazon',x=.5*(loc3+loc4),y=-1.25,fontsize=fontsize,ha='center',va='top')\n",
    "\n",
    "\n",
    "# andes night\n",
    "fraction = and_PMtoAM_diff/and_total_diff\n",
    "y0 = 0\n",
    "y1 = raintot_diffs_1000_PMtoAM_and*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc2,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='1')\n",
    "# andes day\n",
    "fraction = and_AMtoPM_diff/and_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_and*24\n",
    "y1 = (raintot_diffs_1000_PMtoAM_and+raintot_diffs_1000_AMtoPM_and)*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc2,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='1')\n",
    "\n",
    "\n",
    "# amazon night\n",
    "fraction = -ama_PMtoAM_diff/ama_total_diff\n",
    "y0 = 0\n",
    "y1 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc4,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='1')\n",
    "# amazon day\n",
    "fraction = -ama_AMtoPM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "y1 = (raintot_diffs_1000_AMtoPM_ama+raintot_diffs_1000_PMtoAM_ama)*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc4,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='1')\n",
    "\n",
    "\n",
    "handles = [mp.Rectangle((0,0),1,1) for i  in range(2)]\n",
    "colors = [[blue_cmap[-1],blue_cmap[-3]],[orange_cmap[-1],orange_cmap[-3]]]\n",
    "hmap = dict(zip(handles, [Handler(color_list[0],color_list[1]) for color_list in colors] ))\n",
    "\n",
    "labels = ['night', 'day']\n",
    "ax.legend(handles=handles, labels=labels, handler_map=hmap, fontsize=fontsize,\\\n",
    "          loc='upper right', bbox_to_anchor=(.95,.95), borderaxespad=0, labelspacing=0.5, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, handletextpad=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# big panel plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:17:14.986875Z",
     "start_time": "2018-08-16T19:17:10.470326Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(4,2,width_ratios=[1,2])\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,1])\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_400_latlon_mean_and,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_1000_latlon_mean_and,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "ax1.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax1.yaxis.set_label_coords(1.1,0.5)\n",
    "ax1.yaxis.tick_right()\n",
    "ax1.set_xticks(numpy.arange(0,241,24))\n",
    "ax1.set_xlim(0,240)\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax1.xaxis.grid(which='minor')\n",
    "ax1.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "#ax1.set_ylim(-.2,ax1.get_ylim()[1])\n",
    "ax1.set_ylim(ax1.get_ylim()[0],1.3)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1,1], sharex=ax1)\n",
    "ax2.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, (raintot_diffs_1000_latlon_mean_and-raintot_diffs_400_latlon_mean_and),\\\n",
    "       color='0.5',lw=1,label='diff.',zorder=3)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "ax2.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax2.yaxis.set_label_coords(1.1,0.5)\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.set_xticks(numpy.arange(0,241,24))\n",
    "#ax2.set_yticks([-.05,0,.05])\n",
    "ax2.set_xlim(0,240)\n",
    "ax2.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax2.set_xticks(numpy.arange(18,241,24), minor=True)\n",
    "ax2.xaxis.grid(which='minor')\n",
    "ax2.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax2.set_ylim(-.15,.15)\n",
    "\n",
    "ax3 = fig.add_subplot(gs[2,1], sharex=ax1)\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_400_latlon_mean_ama,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "ax3.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax3.yaxis.set_label_coords(1.1,0.5)\n",
    "ax3.yaxis.tick_right()\n",
    "ax3.set_xticks(numpy.arange(0,241,24))\n",
    "ax3.set_xlim(0,240)\n",
    "ax3.xaxis.grid(which='minor')\n",
    "ax3.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "handles, labels = ax3.get_legend_handles_labels()\n",
    "ax3.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=2, bbox_to_anchor=(.99,.95), bbox_transform=ax3.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax3.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(gs[3,1], sharex=ax1)\n",
    "ax4.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, (raintot_diffs_1000_latlon_mean_ama-raintot_diffs_400_latlon_mean_ama),\\\n",
    "       color='0.5',lw=1,label='diff.')\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "ax4.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax4.yaxis.set_label_coords(1.1,0.5)\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.set_xticks(numpy.arange(0,241,24))\n",
    "ax4.set_xlim(0,240)\n",
    "handles, labels = ax4.get_legend_handles_labels()\n",
    "ax4.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.05), bbox_transform=ax4.transAxes, \\\n",
    "          loc='lower right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax4.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax4.xaxis.grid(which='minor')\n",
    "ax4.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax4.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax4.set_ylim(ax4.get_ylim()[0],0.13)\n",
    "ax4.set_xlabel('hour', fontsize=fontsize)\n",
    "#ax4.set_xticklabels(ax4.get_xticks())\n",
    "\n",
    "################################################################################\n",
    "################################################################################\n",
    "ax = fig.add_subplot(gs[:,0])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "loc1=1\n",
    "loc2=2\n",
    "loc3=3.25\n",
    "loc4=4.25\n",
    "barwidth = .85\n",
    "\n",
    "ax.set_xlim(loc1-.75,loc4+.75)\n",
    "ax.bar(loc1,raintot_diffs_400_PMtoAM_and*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc1,raintot_diffs_400_AMtoPM_and*24.,bottom=raintot_diffs_400_PMtoAM_and*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_PMtoAM_and*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_AMtoPM_and*24.,bottom=raintot_diffs_1000_PMtoAM_and*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([loc1,loc2,loc3,loc4])\n",
    "ax.bar(loc3,raintot_diffs_400_PMtoAM_ama*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc3,raintot_diffs_400_AMtoPM_ama*24.,bottom=raintot_diffs_400_PMtoAM_ama*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_PMtoAM_ama*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_AMtoPM_ama*24.,bottom=raintot_diffs_1000_PMtoAM_ama*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_yticks([0,2,4,6,8,10])\n",
    "ax.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_ylim(0,10)\n",
    "\n",
    "ax.set_xticklabels(['cont.','phys.','cont.','phys.'])\n",
    "ax.text(s='Andes',x=.5*(loc1+loc2),y=-1,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='Amazon',x=.5*(loc3+loc4),y=-1,fontsize=fontsize,ha='center',va='top')\n",
    "# andes night\n",
    "fraction = and_PMtoAM_diff/and_total_diff\n",
    "y0 = 0\n",
    "y1 = raintot_diffs_1000_PMtoAM_and*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc2,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# andes day\n",
    "fraction = and_AMtoPM_diff/and_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_and*24\n",
    "y1 = (raintot_diffs_1000_PMtoAM_and+raintot_diffs_1000_AMtoPM_and)*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc2,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon night\n",
    "fraction = -ama_PMtoAM_diff/ama_total_diff\n",
    "y0 = 0\n",
    "y1 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc4,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon day\n",
    "fraction = -ama_AMtoPM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "y1 = (raintot_diffs_1000_AMtoPM_ama+raintot_diffs_1000_PMtoAM_ama)*24\n",
    "ax.text(s='{:.0f}'.format(fraction*100.)+'%',x=loc4,y=0.5*(y0+y1),ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "\n",
    "handles = [mp.Rectangle((0,0),1,1) for i  in range(2)]\n",
    "colors = [[blue_cmap[-1],blue_cmap[-3]],[orange_cmap[-1],orange_cmap[-3]]]\n",
    "hmap = dict(zip(handles, [Handler(color_list[0],color_list[1]) for color_list in colors] ))\n",
    "labels = ['night', 'day']\n",
    "ax.legend(handles=handles, labels=labels, handler_map=hmap, fontsize=fontsize,\\\n",
    "          loc='upper right', bbox_to_anchor=(.95,.95), borderaxespad=0, labelspacing=0.5, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, handletextpad=0.5)\n",
    "\n",
    "mp.setp(ax1.get_xticklabels(), visible=False)\n",
    "mp.setp(ax2.get_xticklabels(), visible=False)\n",
    "mp.setp(ax3.get_xticklabels(), visible=False)\n",
    "\n",
    "fig.tight_layout()\n",
    "gs.update(hspace=0.,wspace=0.05)\n",
    "\n",
    "ax1.text(s='Andes', x=285, y=ax1.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "ax3.text(s='Amazon', x=285, y=ax3.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "\n",
    "ax.text(s='(a) Mean regional precip.', x=0, y=1.02, fontsize=fontsize, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax1.text(s='(b)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(c)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(d)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(e)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.savefig('precip_change_and_percentages_daynight.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# also do for precip over mountainous areas ONLY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:04:31.113939Z",
     "start_time": "2018-08-16T19:04:31.105815Z"
    }
   },
   "outputs": [],
   "source": [
    "mp.rcParams.update({'mathtext.default': 'regular'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:25:33.609541Z",
     "start_time": "2018-08-16T19:25:28.973182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(4,2,width_ratios=[1,2.5])\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,1])\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, raintot_diffs_400_latlon_mean_and_high,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, raintot_diffs_1000_latlon_mean_and_high,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "# ax1.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax1.yaxis.set_label_coords(1.1,0.5)\n",
    "ax1.yaxis.tick_right()\n",
    "ax1.set_xticks(numpy.arange(0,241,24))\n",
    "ax1.set_xlim(0,240)\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax1.xaxis.grid(which='minor')\n",
    "ax1.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax1.set_ylim(-.3,ax1.get_ylim()[1])\n",
    "#ax1.set_ylim(-.5,ax1.get_ylim()[1])\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1,1], sharex=ax1)\n",
    "ax2.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, (raintot_diffs_1000_latlon_mean_and_high-raintot_diffs_400_latlon_mean_and_high),\\\n",
    "       color='0.5',lw=1,label='diff.',zorder=3)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "# ax2.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax2.yaxis.set_label_coords(1.1,0.5)\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.set_xticks(numpy.arange(0,241,24))\n",
    "#ax2.set_yticks([-.05,0,.05])\n",
    "ax2.set_xlim(0,240)\n",
    "ax2.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax2.set_xticks(numpy.arange(18,241,24), minor=True)\n",
    "ax2.xaxis.grid(which='minor')\n",
    "ax2.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax2.set_ylim(ax2.get_ylim()[0],.23)\n",
    "\n",
    "ax3 = fig.add_subplot(gs[2,1], sharex=ax1)\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_400_latlon_mean_ama,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "# ax3.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax3.yaxis.set_label_coords(1.1,0.5)\n",
    "ax3.yaxis.tick_right()\n",
    "ax3.set_xticks(numpy.arange(0,241,24))\n",
    "ax3.set_xlim(0,240)\n",
    "ax3.xaxis.grid(which='minor')\n",
    "ax3.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "handles, labels = ax3.get_legend_handles_labels()\n",
    "ax3.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=2, bbox_to_anchor=(.99,.95), bbox_transform=ax3.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax3.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(gs[3,1], sharex=ax1)\n",
    "ax4.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, (raintot_diffs_1000_latlon_mean_ama-raintot_diffs_400_latlon_mean_ama),\\\n",
    "       color='0.5',lw=1,label='diff.')\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "# ax4.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax4.yaxis.set_label_coords(1.1,0.5)\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.set_xticks(numpy.arange(0,241,24))\n",
    "ax4.set_xlim(0,240)\n",
    "handles, labels = ax4.get_legend_handles_labels()\n",
    "ax4.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.05), bbox_transform=ax4.transAxes, \\\n",
    "          loc='lower right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax4.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax4.xaxis.grid(which='minor')\n",
    "ax4.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax4.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax4.set_ylim(ax4.get_ylim()[0],0.13)\n",
    "ax4.set_xlabel('hour', fontsize=fontsize)\n",
    "#ax4.set_xticklabels(ax4.get_xticks())\n",
    "\n",
    "################################################################################\n",
    "################################################################################\n",
    "\n",
    "ax = fig.add_subplot(gs[:,0])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "loc1=1\n",
    "loc2=2\n",
    "loc3=3.25\n",
    "loc4=4.25\n",
    "barwidth = .9\n",
    "\n",
    "ax.set_xlim(loc1-.75,loc4+.75)\n",
    "ax.bar(loc1,raintot_diffs_400_PMtoAM_and_high*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc1,raintot_diffs_400_AMtoPM_and_high*24.,bottom=raintot_diffs_400_PMtoAM_and_high*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_PMtoAM_and_high*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_AMtoPM_and_high*24.,bottom=raintot_diffs_1000_PMtoAM_and_high*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([loc1,loc2,loc3,loc4])\n",
    "ax.bar(loc3,raintot_diffs_400_PMtoAM_ama*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc3,raintot_diffs_400_AMtoPM_ama*24.,bottom=raintot_diffs_400_PMtoAM_ama*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_PMtoAM_ama*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_AMtoPM_ama*24.,bottom=raintot_diffs_1000_PMtoAM_ama*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_yticks([0,5,10,15])\n",
    "ax.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_ylim(0,15)\n",
    "\n",
    "ax.set_xticklabels(['cont.','phys.','cont.','phys.'])\n",
    "ax.text(s='Andes',x=.5*(loc1+loc2),y=-1.75,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='Amazon',x=.5*(loc3+loc4),y=-1.75,fontsize=fontsize,ha='center',va='top')\n",
    "# PHYS RESPONSE\n",
    "# andes night\n",
    "fraction = raintot_diffs_1000_PMtoAM_and_high/(raintot_diffs_1000_AMtoPM_and_high+raintot_diffs_1000_PMtoAM_and_high)\n",
    "y0 = raintot_diffs_1000_PMtoAM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc2,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# andes day\n",
    "fraction = raintot_diffs_1000_AMtoPM_and_high/(raintot_diffs_1000_AMtoPM_and_high+raintot_diffs_1000_PMtoAM_and_high)\n",
    "y0 = raintot_diffs_1000_PMtoAM_and_high*24\n",
    "y1 = raintot_diffs_1000_AMtoPM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc2,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon night\n",
    "fraction = raintot_diffs_1000_PMtoAM_ama/(raintot_diffs_1000_AMtoPM_ama+raintot_diffs_1000_PMtoAM_ama)\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc4,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon day\n",
    "fraction = raintot_diffs_1000_AMtoPM_ama/(raintot_diffs_1000_AMtoPM_ama+raintot_diffs_1000_PMtoAM_ama)\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "y1 = raintot_diffs_1000_AMtoPM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc4,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "\n",
    "# CONTROL\n",
    "# andes night\n",
    "fraction = raintot_diffs_400_PMtoAM_and_high/(raintot_diffs_400_AMtoPM_and_high+raintot_diffs_400_PMtoAM_and_high)\n",
    "y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc1,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.8')\n",
    "# andes day\n",
    "fraction = raintot_diffs_400_AMtoPM_and_high/(raintot_diffs_400_AMtoPM_and_high+raintot_diffs_400_PMtoAM_and_high)\n",
    "y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "y1 = raintot_diffs_400_AMtoPM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc1,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.8')\n",
    "# amazon night\n",
    "fraction = raintot_diffs_400_PMtoAM_ama/(raintot_diffs_400_AMtoPM_ama+raintot_diffs_400_PMtoAM_ama)\n",
    "y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc3,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.8')\n",
    "# amazon day\n",
    "fraction = raintot_diffs_400_AMtoPM_ama/(raintot_diffs_400_AMtoPM_ama+raintot_diffs_400_PMtoAM_ama)\n",
    "y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "y1 = raintot_diffs_400_AMtoPM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc3,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.8')\n",
    "\n",
    "# fraction for change\n",
    "# fraction = and_high_total_diff/raintot_diffs_400_timemean_and_high\n",
    "# y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "# ax.text(s='${:.0f}$'.format(fraction*100.)+'%\\ninc.',x=loc1,y=y0,ha='center',va='center',fontsize=fontsize,color='.9')\n",
    "\n",
    "# fraction = -ama_total_diff/raintot_diffs_400_timemean_ama\n",
    "# y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "# ax.text(s='${:.0f}$'.format(fraction*100.)+'%\\ndec.',x=loc3,y=y0,ha='center',va='center',fontsize=fontsize,color='.9')\n",
    "\n",
    "handles = [mp.Rectangle((0,0),1,1) for i  in range(2)]\n",
    "colors = [[blue_cmap[-1],blue_cmap[-3]],[orange_cmap[-1],orange_cmap[-3]]]\n",
    "hmap = dict(zip(handles, [Handler(color_list[0],color_list[1]) for color_list in colors] ))\n",
    "labels = ['night', 'day']\n",
    "ax.legend(handles=handles, labels=labels, handler_map=hmap, fontsize=fontsize,\\\n",
    "          loc='upper right', bbox_to_anchor=(.95,.95), borderaxespad=0, labelspacing=0.5, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, handletextpad=0.5)\n",
    "\n",
    "mp.setp(ax1.get_xticklabels(), visible=False)\n",
    "mp.setp(ax2.get_xticklabels(), visible=False)\n",
    "mp.setp(ax3.get_xticklabels(), visible=False)\n",
    "\n",
    "#================================================================================\n",
    "fig.tight_layout()\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "fig.text(s='mm hour$^{-1}$', x=ax1pos.x1+0.05, y=ax4pos.y0+(ax1pos.y1-ax4pos.y0)*0.5,\\\n",
    "         fontsize=fontsize, rotation=-90, transform=fig.transFigure, va='center', ha='left')\n",
    "\n",
    "gs.update(hspace=0.,wspace=0.05)\n",
    "\n",
    "ax1.text(s='Andes', x=275, y=ax1.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "ax3.text(s='Amazon', x=275, y=ax3.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "\n",
    "ax.text(s='(a) Mean regional precip.', x=0, y=1.02, fontsize=fontsize, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax1.text(s='(b)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(c)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(d)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(e)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.savefig('figures/precip_change_and_high_percentages_daynight_high_andes_figure_4.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T19:07:31.598831Z",
     "start_time": "2018-08-16T19:07:27.145557Z"
    }
   },
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,4))\n",
    "\n",
    "gs = matplotlib.gridspec.GridSpec(4,2,width_ratios=[1,2.5])\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,1])\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, raintot_diffs_400_latlon_mean_and_high,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax1.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, raintot_diffs_1000_latlon_mean_and_high,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "# ax1.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax1.yaxis.set_label_coords(1.1,0.5)\n",
    "ax1.yaxis.tick_right()\n",
    "ax1.set_xticks(numpy.arange(0,241,24))\n",
    "ax1.set_xlim(0,240)\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax1.xaxis.grid(which='minor')\n",
    "ax1.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax1.set_ylim(-.3,ax1.get_ylim()[1])\n",
    "#ax1.set_ylim(-.5,ax1.get_ylim()[1])\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1,1], sharex=ax1)\n",
    "ax2.plot(numpy.arange(raintot_diffs_400_latlon_mean_and_high.size)-5, (raintot_diffs_1000_latlon_mean_and_high-raintot_diffs_400_latlon_mean_and_high),\\\n",
    "       color='0.5',lw=1,label='diff.',zorder=3)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "# ax2.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax2.yaxis.set_label_coords(1.1,0.5)\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.set_xticks(numpy.arange(0,241,24))\n",
    "#ax2.set_yticks([-.05,0,.05])\n",
    "ax2.set_xlim(0,240)\n",
    "ax2.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax2.set_xticks(numpy.arange(18,241,24), minor=True)\n",
    "ax2.xaxis.grid(which='minor')\n",
    "ax2.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax2.set_ylim(ax2.get_ylim()[0],.23)\n",
    "\n",
    "ax3 = fig.add_subplot(gs[2,1], sharex=ax1)\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_400_latlon_mean_ama,\\\n",
    "       color='0.1',lw=1,label='cont.')\n",
    "ax3.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama,\\\n",
    "       color='firebrick',lw=1,label='phys.')\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "# ax3.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax3.yaxis.set_label_coords(1.1,0.5)\n",
    "ax3.yaxis.tick_right()\n",
    "ax3.set_xticks(numpy.arange(0,241,24))\n",
    "ax3.set_xlim(0,240)\n",
    "ax3.xaxis.grid(which='minor')\n",
    "ax3.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "handles, labels = ax3.get_legend_handles_labels()\n",
    "ax3.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=2, bbox_to_anchor=(.99,.95), bbox_transform=ax3.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax3.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(gs[3,1], sharex=ax1)\n",
    "ax4.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, (raintot_diffs_1000_latlon_mean_ama-raintot_diffs_400_latlon_mean_ama),\\\n",
    "       color='0.5',lw=1,label='diff.')\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "# ax4.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize, rotation=-90)\n",
    "ax4.yaxis.set_label_coords(1.1,0.5)\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.set_xticks(numpy.arange(0,241,24))\n",
    "ax4.set_xlim(0,240)\n",
    "handles, labels = ax4.get_legend_handles_labels()\n",
    "ax4.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.05), bbox_transform=ax4.transAxes, \\\n",
    "          loc='lower right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "ax4.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "ax4.xaxis.grid(which='minor')\n",
    "ax4.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax4.axhline(0, lw=1, ls='--', c='0.1')\n",
    "ax4.set_ylim(ax4.get_ylim()[0],0.13)\n",
    "ax4.set_xlabel('hour', fontsize=fontsize)\n",
    "#ax4.set_xticklabels(ax4.get_xticks())\n",
    "\n",
    "################################################################################\n",
    "################################################################################\n",
    "\n",
    "ax = fig.add_subplot(gs[:,0])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "loc1=1\n",
    "loc2=2\n",
    "loc3=3.25\n",
    "loc4=4.25\n",
    "barwidth = .9\n",
    "\n",
    "ax.set_xlim(loc1-.75,loc4+.75)\n",
    "ax.bar(loc1,raintot_diffs_400_PMtoAM_and_high*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc1,raintot_diffs_400_AMtoPM_and_high*24.,bottom=raintot_diffs_400_PMtoAM_and_high*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_PMtoAM_and_high*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc2,raintot_diffs_1000_AMtoPM_and_high*24.,bottom=raintot_diffs_1000_PMtoAM_and_high*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([loc1,loc2,loc3,loc4])\n",
    "ax.bar(loc3,raintot_diffs_400_PMtoAM_ama*24.,fc=blue_cmap[-1],width=barwidth)\n",
    "ax.bar(loc3,raintot_diffs_400_AMtoPM_ama*24.,bottom=raintot_diffs_400_PMtoAM_ama*24.,fc=orange_cmap[-1],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_PMtoAM_ama*24.,fc=blue_cmap[-3],width=barwidth)\n",
    "ax.bar(loc4,raintot_diffs_1000_AMtoPM_ama*24.,bottom=raintot_diffs_1000_PMtoAM_ama*24.,fc=orange_cmap[-3],width=barwidth)\n",
    "ax.set_yticks([0,5,10,15])\n",
    "ax.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_ylim(0,15)\n",
    "\n",
    "ax.set_xticklabels(['cont.','phys.','cont.','phys.'])\n",
    "ax.text(s='Andes',x=.5*(loc1+loc2),y=-1.75,fontsize=fontsize,ha='center',va='top')\n",
    "ax.text(s='Amazon',x=.5*(loc3+loc4),y=-1.75,fontsize=fontsize,ha='center',va='top')\n",
    "# PHYS RESPONSE\n",
    "# andes night\n",
    "fraction = and_high_PMtoAM_diff/and_high_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc2,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# andes day\n",
    "fraction = and_high_AMtoPM_diff/and_high_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_and_high*24\n",
    "y1 = raintot_diffs_1000_AMtoPM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc2,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon night\n",
    "fraction = ama_PMtoAM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc4,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon day\n",
    "fraction = ama_AMtoPM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_1000_PMtoAM_ama*24\n",
    "y1 = raintot_diffs_1000_AMtoPM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc4,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "\n",
    "# CONTROL\n",
    "# andes night\n",
    "fraction = and_high_PMtoAM_diff/and_high_total_diff\n",
    "y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc1,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# andes day\n",
    "fraction = and_high_AMtoPM_diff/and_high_total_diff\n",
    "y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "y1 = raintot_diffs_400_AMtoPM_and_high*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc1,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon night\n",
    "fraction = ama_PMtoAM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc3,y=0.5*y0,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "# amazon day\n",
    "fraction = ama_AMtoPM_diff/ama_total_diff\n",
    "y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "y1 = raintot_diffs_400_AMtoPM_ama*24\n",
    "ax.text(s='${:.0f}$'.format(fraction*100.)+'%',x=loc3,y=y0+0.5*y1,ha='center',va='center',fontsize=fontsize,color='0.1')\n",
    "\n",
    "\n",
    "# fraction for change\n",
    "# fraction = and_high_total_diff/raintot_diffs_400_timemean_and_high\n",
    "# y0 = raintot_diffs_400_PMtoAM_and_high*24\n",
    "# ax.text(s='${:.0f}$'.format(fraction*100.)+'%\\ninc.',x=loc1,y=y0,ha='center',va='center',fontsize=fontsize,color='.9')\n",
    "\n",
    "# fraction = -ama_total_diff/raintot_diffs_400_timemean_ama\n",
    "# y0 = raintot_diffs_400_PMtoAM_ama*24\n",
    "# ax.text(s='${:.0f}$'.format(fraction*100.)+'%\\ndec.',x=loc3,y=y0,ha='center',va='center',fontsize=fontsize,color='.9')\n",
    "\n",
    "handles = [mp.Rectangle((0,0),1,1) for i  in range(2)]\n",
    "colors = [[blue_cmap[-1],blue_cmap[-3]],[orange_cmap[-1],orange_cmap[-3]]]\n",
    "hmap = dict(zip(handles, [Handler(color_list[0],color_list[1]) for color_list in colors] ))\n",
    "labels = ['night', 'day']\n",
    "ax.legend(handles=handles, labels=labels, handler_map=hmap, fontsize=fontsize,\\\n",
    "          loc='upper right', bbox_to_anchor=(.95,.95), borderaxespad=0, labelspacing=0.5, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, handletextpad=0.5)\n",
    "\n",
    "mp.setp(ax1.get_xticklabels(), visible=False)\n",
    "mp.setp(ax2.get_xticklabels(), visible=False)\n",
    "mp.setp(ax3.get_xticklabels(), visible=False)\n",
    "\n",
    "#================================================================================\n",
    "fig.tight_layout()\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax4pos = ax4.get_position()\n",
    "fig.text(s='mm hour$^{-1}$', x=ax1pos.x1+0.05, y=ax4pos.y0+(ax1pos.y1-ax4pos.y0)*0.5,\\\n",
    "         fontsize=fontsize, rotation=-90, transform=fig.transFigure, va='center', ha='left')\n",
    "\n",
    "gs.update(hspace=0.,wspace=0.05)\n",
    "\n",
    "ax1.text(s='Andes', x=275, y=ax1.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "ax3.text(s='Amazon', x=275, y=ax3.get_ylim()[0],\\\n",
    "        ha='center',va='center',rotation=-90, fontsize=fontsize)\n",
    "\n",
    "\n",
    "ax.text(s='(a) Mean regional precip.', x=0, y=1.02, fontsize=fontsize, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax1.text(s='(b)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(c)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(d)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(e)', x=0.055, y=0.95, ha='right', va='top', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.savefig('precip_change_and_high_percentages_daynight_high_andes_figure_4.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:35.068566Z",
     "start_time": "2018-08-16T18:54:34.761828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,3))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_400_latlon_mean_ama,\\\n",
    "       color='0.1',lw=2,label='cont.')\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama,\\\n",
    "       color='firebrick',lw=2,label='phys.')\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks(numpy.arange(0,241,24))\n",
    "ax.set_yticks([0,.2,.4,.6,.8,1])\n",
    "ax.set_xlim(0,240)\n",
    "ax.set_ylim(0,1)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.95), bbox_transform=ax.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "\n",
    "ax.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "#ax.set_xticks(numpy.arange(20,241,24), minor=True)\n",
    "#ax.minorticks_off()\n",
    "ax.xaxis.grid(which='minor')\n",
    "\n",
    "ax.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:35.373553Z",
     "start_time": "2018-08-16T18:54:35.077083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2af25a9217b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,3))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama-raintot_diffs_400_latlon_mean_ama,\\\n",
    "       color='0.1',lw=2,label='diff.')\n",
    "# ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_ama.size)-4, raintot_diffs_1000_latlon_mean_ama,\\\n",
    "#        color='firebrick',lw=2,label='phys.')\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks(numpy.arange(0,241,24))\n",
    "#ax.set_yticks([0,.2,.4,.6,.8])\n",
    "ax.set_xlim(0,240)\n",
    "#ax.set_ylim(0,.85)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.95), bbox_transform=ax.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "\n",
    "ax.set_xticks(numpy.arange(15,241,24), minor=True)\n",
    "#ax.minorticks_off()\n",
    "ax.xaxis.grid(which='minor')\n",
    "\n",
    "ax.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n",
    "ax.axhline(0, lw=1, ls='--', c='0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:35.675638Z",
     "start_time": "2018-08-16T18:54:35.382230Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,2))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_400_latlon_mean_and,\\\n",
    "       color='0.1',lw=2,label='cont.',zorder=3)\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_1000_latlon_mean_and,\\\n",
    "       color='firebrick',lw=2,label='phys.')\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks(numpy.arange(0,241,24))\n",
    "ax.set_yticks([0,.2,.4,.6,.8,1])\n",
    "ax.set_xlim(0,240)\n",
    "ax.set_ylim(0.1,1.1)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.95), bbox_transform=ax.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "\n",
    "ax.set_xticks(numpy.arange(10,241,24), minor=True)\n",
    "ax.xaxis.grid(which='minor')\n",
    "\n",
    "ax.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-16T18:54:35.963366Z",
     "start_time": "2018-08-16T18:54:35.684089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,2))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_1000_latlon_mean_and-raintot_diffs_400_latlon_mean_and,\\\n",
    "       color='0.1',lw=2,label='diff.',zorder=3)\n",
    "# ax.plot(numpy.arange(raintot_diffs_400_latlon_mean_and.size)-5, raintot_diffs_1000_latlon_mean_and,\\\n",
    "#        color='firebrick',lw=2,label='phys.')\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylabel('mm h$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax.set_xticks(numpy.arange(0,241,24))\n",
    "#ax.set_yticks([0,.2,.4,.6,.8,1])\n",
    "ax.set_xlim(0,240)\n",
    "#ax.set_ylim(0.1,1.1)\n",
    "ax.axhline(0, lw=1, ls='--', c='0.1')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "          ncol=1, bbox_to_anchor=(.99,.95), bbox_transform=ax.transAxes, \\\n",
    "          loc='upper right', borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "          edgecolor='0', framealpha=0, borderpad=0.25, columnspacing=0.5, \\\n",
    "          handletextpad=0.25, markerscale=1)\n",
    "\n",
    "ax.set_xticks(numpy.arange(18,241,24), minor=True)\n",
    "ax.xaxis.grid(which='minor')\n",
    "\n",
    "ax.tick_params(axis='x',which='minor',bottom=False,labelbottom=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}