preliminary_lwoff_precipitation_response.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "# from sklearn.neighbors import KernelDensity\n",
    "import colorcet as cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/distributed/node.py:155: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 52839 instead\n",
      "  http_address[\"port\"], self.http_server.port\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:52840</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:52839/status' target='_blank'>http://127.0.0.1:52839/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>2</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>20.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:52840' processes=2 threads=4, memory=20.00 GB>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from dask.distributed import Client\n",
    "# client = Client(n_workers=2, threads_per_worker=2, memory_limit='10GB')\n",
    "# client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloc = Path(\"/Volumes/Glyph6TB/CMIP6\")\n",
    "models = [\"CESM2\", \"IPSL-CM6A-LR\", \"CNRM-CM6-1\", 'MRI-ESM2-0']\n",
    "experiments = [\"amip\", \"amip-lwoff\", \"aqua-control\", \"aqua-control-lwoff\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign colors by model:\n",
    "clrs = dict()\n",
    "for i,m in enumerate(models):\n",
    "    clrs[m] = f\"C{i}\"\n",
    "\n",
    "def select_color(m):\n",
    "    for i in models:\n",
    "        if i in m:\n",
    "            return clrs[i]\n",
    "    print(f\"No color selected for {m}\")\n",
    "    return \"gray\"\n",
    "    \n",
    "# and then assign same colors to other keys\n",
    "clrs2 = dict()\n",
    "for m in models:\n",
    "    for ex in experiments:\n",
    "        clrs2[f\"{m}-{ex}\"] = clrs[m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amip / CESM2 : 10 members.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amip / IPSL-CM6A-LR : 11 members.\n",
      "amip / CNRM-CM6-1 : 1 members.\n",
      "amip / MRI-ESM2-0 : 3 members.\n",
      "amip-lwoff / CESM2 : 1 members.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amip-lwoff / IPSL-CM6A-LR : 1 members.\n",
      "amip-lwoff / CNRM-CM6-1 : 1 members.\n",
      "amip-lwoff / MRI-ESM2-0 : 1 members.\n",
      "aqua-control / CESM2 : 1 members.\n",
      "aqua-control / IPSL-CM6A-LR : 1 members.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aqua-control / CNRM-CM6-1 : 1 members.\n",
      "aqua-control-lwoff / CESM2 : 1 members.\n",
      "aqua-control-lwoff / IPSL-CM6A-LR : 1 members.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aqua-control-lwoff / CNRM-CM6-1 : 1 members.\n"
     ]
    }
   ],
   "source": [
    "# make a second structure to hold the amip runs\n",
    "# - Experiment\n",
    "#   |_ Model\n",
    "#     |_ Member\n",
    "#        |_dataset\n",
    "# there are multiple ensemble members, so need to account for them\n",
    "#pr_day_CESM2_amip_r4i1p1f1_gn_19700101-19791231.n\n",
    "\n",
    "#\n",
    "# K is the dict of datasets\n",
    "#\n",
    "K = dict()\n",
    "for ex in experiments:\n",
    "    K[ex] = dict()\n",
    "    for m in models:\n",
    "        fils = sorted(list(dataloc.glob(f\"pr_day_{m}_{ex}_*.nc\")))\n",
    "        # only add an entry if there is something:\n",
    "        if len(fils) == 0:\n",
    "            continue\n",
    "        K[ex][m] = dict()\n",
    "        # search for files:\n",
    "        # find out if there are multiple ensemble members:\n",
    "        members = set([f.name.split(\"_\")[4] for f in fils])\n",
    "        print(f\"{ex} / {m} : {len(members)} members.\")\n",
    "        for b in members:\n",
    "            mfils = sorted(list(dataloc.glob(f\"pr_day_{m}_{ex}_{b}_*.nc\")))\n",
    "            if len(mfils) == 1:\n",
    "                K[ex][m][b] = xr.open_dataset(mfils[0])\n",
    "            elif len(mfils) > 1:\n",
    "                # print(f\"There are {len(mfils)} files for this member.\")\n",
    "                K[ex][m][b] = xr.open_mfdataset(mfils, combine='by_coords')\n",
    "            else:\n",
    "                print(f\"Sorry, failed to find files for {m}, {ex}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2 Identified 10 members.\n",
      "CESM2 : check member r1i1p1f1\n",
      "CESM2 : check member r3i1p1f1\n",
      "CESM2 : check member r5i1p1f1\n",
      "CESM2 : check member r6i1p1f1\n",
      "CESM2 : check member r10i1p1f1\n",
      "CESM2 : check member r8i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2 : check member r4i1p1f1\n",
      "CESM2 : check member r9i1p1f1\n",
      "CESM2 : check member r2i1p1f1\n",
      "CESM2 : check member r7i1p1f1\n",
      "IPSL-CM6A-LR Identified 21 members.\n",
      "IPSL-CM6A-LR : check member r6i1p1f1\n",
      "IPSL-CM6A-LR : check member r15i1p1f1\n",
      "IPSL-CM6A-LR : check member r13i1p1f1\n",
      "IPSL-CM6A-LR : check member r8i1p1f1\n",
      "IPSL-CM6A-LR : check member r17i1p1f1\n",
      "IPSL-CM6A-LR : check member r18i1p1f1\n",
      "IPSL-CM6A-LR : check member r2i1p1f1\n",
      "IPSL-CM6A-LR : check member r20i1p1f1\n",
      "IPSL-CM6A-LR : check member r5i1p1f1\n",
      "IPSL-CM6A-LR : check member r19i1p1f1\n",
      "IPSL-CM6A-LR : check member r11i1p1f1\n",
      "IPSL-CM6A-LR : check member r12i1p1f1\n",
      "IPSL-CM6A-LR : check member r16i1p1f1\n",
      "IPSL-CM6A-LR : check member r1i1p1f1\n",
      "IPSL-CM6A-LR : check member r3i1p1f1\n",
      "IPSL-CM6A-LR : check member r10i1p1f1\n",
      "IPSL-CM6A-LR : check member r4i1p1f1\n",
      "IPSL-CM6A-LR : check member r14i1p1f1\n",
      "IPSL-CM6A-LR : check member r9i1p1f1\n",
      "IPSL-CM6A-LR : check member r21i1p1f1\n",
      "IPSL-CM6A-LR : check member r7i1p1f1\n",
      "CNRM-CM6-1 Identified 1 members.\n",
      "CNRM-CM6-1 : check member r1i1p1f2\n",
      "MRI-ESM2-0 Identified 3 members.\n",
      "MRI-ESM2-0 : check member r1i1p1f1\n",
      "MRI-ESM2-0 : check member r2i1p1f1\n",
      "MRI-ESM2-0 : check member r3i1p1f1\n",
      "CESM2 Identified 1 members.\n",
      "CESM2 : check member r1i1p1f1\n",
      "IPSL-CM6A-LR Identified 1 members.\n",
      "IPSL-CM6A-LR : check member r1i1p1f1\n",
      "CNRM-CM6-1 Identified 1 members.\n",
      "CNRM-CM6-1 : check member r1i1p1f2\n",
      "MRI-ESM2-0 Identified 1 members.\n",
      "MRI-ESM2-0 : check member r1i1p1f1\n",
      "CESM2 Identified 0 members.\n",
      "IPSL-CM6A-LR Identified 0 members.\n",
      "CNRM-CM6-1 Identified 0 members.\n",
      "MRI-ESM2-0 Identified 0 members.\n",
      "CESM2 Identified 0 members.\n",
      "IPSL-CM6A-LR Identified 0 members.\n",
      "CNRM-CM6-1 Identified 0 members.\n",
      "MRI-ESM2-0 Identified 0 members.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# LAND - dict of land percentage\n",
    "#\n",
    "LAND = dict()\n",
    "vname = \"sftlf\"\n",
    "table = \"fx\"\n",
    "for ex in experiments:\n",
    "    LAND[ex] = dict()\n",
    "    for m in models:\n",
    "        LAND[ex][m] = dict()\n",
    "        # search for files:\n",
    "        fils = sorted(list(dataloc.glob(f\"{vname}_{table}_{m}_{ex}_*.nc\")))\n",
    "        # find out if there are multiple ensemble members:\n",
    "        members = set([f.name.split(\"_\")[4] for f in fils])\n",
    "        print(f\"{m} Identified {len(members)} members.\")\n",
    "        for b in members:\n",
    "            print(f\"{m} : check member {b}\")\n",
    "            mfils = sorted(list(dataloc.glob(f\"{vname}_{table}_{m}_{ex}_{b}_*.nc\")))\n",
    "            if len(mfils) == 1:\n",
    "                LAND[ex][m][b] = xr.open_dataset(mfils[0])\n",
    "            elif len(mfils) > 1:\n",
    "                print(f\"There are {len(mfils)} files for this member.\")\n",
    "                LAND[ex][m][b] = xr.open_mfdataset(mfils, combine='by_coords')\n",
    "            else:\n",
    "                print(f\"Sorry, failed to find files for {m}, {ex}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r1i1p1f1\n",
      "working on amip - CESM2 - r3i1p1f1\n",
      "working on amip - CESM2 - r5i1p1f1\n",
      "working on amip - CESM2 - r6i1p1f1\n",
      "working on amip - CESM2 - r10i1p1f1\n",
      "working on amip - CESM2 - r8i1p1f1\n",
      "working on amip - CESM2 - r4i1p1f1\n",
      "working on amip - CESM2 - r9i1p1f1\n",
      "working on amip - CESM2 - r2i1p1f1\n",
      "working on amip - CESM2 - r7i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r3i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r5i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r6i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r10i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r8i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r4i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r9i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r11i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r2i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r7i1p1f1\n",
      "working on amip - CNRM-CM6-1 - r1i1p1f2\n",
      "working on amip - MRI-ESM2-0 - r1i1p1f1\n",
      "working on amip - MRI-ESM2-0 - r2i1p1f1\n",
      "working on amip - MRI-ESM2-0 - r3i1p1f1\n",
      "working on amip-lwoff - CESM2 - r1i1p1f1\n",
      "working on amip-lwoff - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on amip-lwoff - CNRM-CM6-1 - r1i1p1f2\n",
      "working on amip-lwoff - MRI-ESM2-0 - r1i1p1f1\n",
      "working on aqua-control - CESM2 - r1i1p1f1\n",
      "working on aqua-control - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on aqua-control - CNRM-CM6-1 - r1i1p1f2\n",
      "working on aqua-control-lwoff - CESM2 - r1i1p1f1\n",
      "working on aqua-control-lwoff - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on aqua-control-lwoff - CNRM-CM6-1 - r1i1p1f2\n"
     ]
    }
   ],
   "source": [
    "# zonal average for each simulation\n",
    "ZM = dict()\n",
    "for ex in K:\n",
    "    ZM[ex] = dict()\n",
    "    for m in K[ex]:\n",
    "        ZM[ex][m] = dict()\n",
    "        for b in K[ex][m]:\n",
    "            print(f\"working on {ex} - {m} - {b}\")\n",
    "            ZM[ex][m][b] = (K[ex][m][b]['pr']*86400.).mean(dim=('time','lon')).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 538.583x325.984 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the zonal means:\n",
    "# quarter page is smallest 95 mm x 115 mm = \n",
    "# full page Full page = 190 mm x 230 mm\n",
    "def mm2inches(x):\n",
    "    return x * 0.0393701\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(mm2inches(190), mm2inches(115)), ncols=2, constrained_layout=True, sharey=True)\n",
    "for ex in ZM:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "#             print(f\"working on {ex} - {m} - {b}\")\n",
    "            xlat = ZM[ex][m][b]['lat']\n",
    "            xzm = ZM[ex][m][b]\n",
    "            c = select_color(m)\n",
    "            if 'lwoff' in ex:\n",
    "                ls = 'dashed'\n",
    "            else:\n",
    "                ls = 'solid'\n",
    "            if max(xzm) < 1:\n",
    "                print(f\"WARNING: {ex} - {m} - {b}\")\n",
    "            if 'aqua' in ex:\n",
    "                a = ax[1]\n",
    "            else:\n",
    "                a = ax[0]\n",
    "            a.plot(xlat, xzm, color=c, linestyle=ls)\n",
    "ax[0].set_ylabel(\"Precipitation (mm/day)\")\n",
    "ax[0].set_xlabel(\"Latitude\")\n",
    "ax[0].set_title(\"AMIP\", loc='left')\n",
    "ax[1].set_title(\"AQUA\", loc='left')\n",
    "[a.set_xlim([-90,90]) for a in ax]\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 460.8x316.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## First, get the time mean of pr and plot them\n",
    "\n",
    "with plt.style.context('seaborn-paper'):    \n",
    "    fig, ax = plt.subplots()\n",
    "    for ex in H:\n",
    "        if \"lwoff\" in ex:\n",
    "            linsty = \"dashed\"\n",
    "        else:\n",
    "            linsty = \"solid\"\n",
    "        for m in H[ex]:\n",
    "            datmp = (H[ex][m]['pr']*86400.).compute()\n",
    "            ax.plot(datmp['lat'], datmp.mean(dim=('time','lon')), linestyle=linsty, label=f\"{m}-{ex}\", color=clrs[m])\n",
    "    fig.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TOO SLOW -- Can make this more easily than this.\n",
    "# with plt.style.context('seaborn-paper'):    \n",
    "#     fig, ax = plt.subplots()\n",
    "#     for ex in K:\n",
    "#         if \"lwoff\" in ex:\n",
    "#             linsty = \"dashed\"\n",
    "#         else:\n",
    "#             linsty = \"solid\"\n",
    "#         for m in K[ex]:\n",
    "#             print(f\"working on {ex} - {m}\")\n",
    "#             # join members\n",
    "#             da_list = [(K[ex][m][b]['pr']*86400.).compute().mean(dim=('time','lon')) for b in K[ex][m]]\n",
    "#             datmp = sum(da_list) / len(da_list)\n",
    "#             ax.plot(datmp['lat'], datmp, linestyle=linsty, label=f\"{m}-{ex}\", color=clrs[m])\n",
    "#     fig.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# now let's focus on the rainbelts, 20S-20N\n",
    "# and take a look at the KDE\n",
    "# kde = dict()\n",
    "# for ex in H:\n",
    "#     for m in H[ex]:\n",
    "#         k = \"-\".join([m,ex])\n",
    "#         print(k)\n",
    "#         datmp = (H[ex][m]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "#         kde[k] = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(datmp.values.flatten().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip-r1i1p1f1\n",
      "CESM2-amip-r3i1p1f1\n",
      "CESM2-amip-r5i1p1f1\n",
      "CESM2-amip-r6i1p1f1\n",
      "CESM2-amip-r10i1p1f1\n",
      "CESM2-amip-r8i1p1f1\n",
      "CESM2-amip-r4i1p1f1\n",
      "CESM2-amip-r9i1p1f1\n",
      "CESM2-amip-r2i1p1f1\n",
      "CESM2-amip-r7i1p1f1\n",
      "IPSL-CM6A-LR-amip-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-r3i1p1f1\n",
      "IPSL-CM6A-LR-amip-r5i1p1f1\n",
      "IPSL-CM6A-LR-amip-r6i1p1f1\n",
      "IPSL-CM6A-LR-amip-r10i1p1f1\n",
      "IPSL-CM6A-LR-amip-r8i1p1f1\n",
      "IPSL-CM6A-LR-amip-r4i1p1f1\n",
      "IPSL-CM6A-LR-amip-r9i1p1f1\n",
      "IPSL-CM6A-LR-amip-r11i1p1f1\n",
      "IPSL-CM6A-LR-amip-r2i1p1f1\n",
      "IPSL-CM6A-LR-amip-r7i1p1f1\n",
      "CNRM-CM6-1-amip-r1i1p1f2\n",
      "MRI-ESM2-0-amip-r1i1p1f1\n",
      "MRI-ESM2-0-amip-r2i1p1f1\n",
      "MRI-ESM2-0-amip-r3i1p1f1\n",
      "CESM2-amip-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-amip-lwoff-r1i1p1f2\n",
      "MRI-ESM2-0-amip-lwoff-r1i1p1f1\n",
      "CESM2-aqua-control-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-r1i1p1f2\n",
      "CESM2-aqua-control-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2\n",
      "CPU times: user 13min 46s, sys: 2min 15s, total: 16min 1s\n",
      "Wall time: 17min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# well, kde is super slow, what about just old fashioned histogram\n",
    "B = np.arange(0, 101, 1)\n",
    "ahist = dict()\n",
    "aquan = dict()\n",
    "quantiles = [0.5, 0.75, 0.9, 0.95, 0.99, 0.999]\n",
    "for ex in K:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "            k = \"-\".join([m,ex,b])\n",
    "            print(k)\n",
    "            datmp = (K[ex][m][b]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "            arr = datmp.values.flatten()\n",
    "            weights = np.cos(np.radians(datmp['lat'])).broadcast_like(datmp) \n",
    "            ahist[k] = np.histogram(arr, bins=B, weights=weights.values.flatten(), density=True)\n",
    "            aquan[k] = np.quantile(arr, quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CESM2-amip-r1i1p1f1': array([ 1.29379094,  4.64119828, 10.67339802, 16.91421127, 35.51686516,\n",
       "        65.00972488]),\n",
       " 'CESM2-amip-r3i1p1f1': array([ 1.28605473,  4.63834715, 10.68369675, 16.9515481 , 35.69089527,\n",
       "        65.57940376]),\n",
       " 'CESM2-amip-r5i1p1f1': array([ 1.29007936,  4.63948977, 10.68024826, 16.93764114, 35.61595192,\n",
       "        65.39424133]),\n",
       " 'CESM2-amip-r6i1p1f1': array([ 1.29276884,  4.64790392, 10.69873238, 16.96941948, 35.68247604,\n",
       "        65.31494347]),\n",
       " 'CESM2-amip-r10i1p1f1': array([ 1.29074764,  4.64044201, 10.69307327, 16.96402168, 35.70678368,\n",
       "        65.24638741]),\n",
       " 'CESM2-amip-r8i1p1f1': array([ 1.28995389,  4.64428806, 10.68810558, 16.94162941, 35.55892639,\n",
       "        65.05521561]),\n",
       " 'CESM2-amip-r4i1p1f1': array([ 1.28641093,  4.63465738, 10.67182541, 16.94267702, 35.66520691,\n",
       "        65.37205208]),\n",
       " 'CESM2-amip-r9i1p1f1': array([ 1.2882427 ,  4.63939857, 10.66386604, 16.91576385, 35.59289665,\n",
       "        65.10003662]),\n",
       " 'CESM2-amip-r2i1p1f1': array([ 1.29074585,  4.64563608, 10.69264221, 16.96277428, 35.61753082,\n",
       "        65.11352177]),\n",
       " 'CESM2-amip-r7i1p1f1': array([ 1.28714204,  4.63841438, 10.67845249, 16.94042015, 35.57369232,\n",
       "        65.09966446]),\n",
       " 'IPSL-CM6A-LR-amip-r1i1p1f1': array([  2.25321281,   5.81226873,   9.15155029,  12.10170374,\n",
       "         39.52292332, 120.34479806]),\n",
       " 'IPSL-CM6A-LR-amip-r3i1p1f1': array([  2.31690979,   5.8100996 ,   9.12356377,  12.05335093,\n",
       "         38.82891941, 117.2848745 ]),\n",
       " 'IPSL-CM6A-LR-amip-r5i1p1f1': array([  2.32847142,   5.8147707 ,   9.13224125,  12.07844663,\n",
       "         39.14315796, 117.73062065]),\n",
       " 'IPSL-CM6A-LR-amip-r6i1p1f1': array([  2.31272829,   5.80251312,   9.12604141,  12.06841373,\n",
       "         38.93237362, 117.37837208]),\n",
       " 'IPSL-CM6A-LR-amip-r10i1p1f1': array([  2.31607676,   5.8105669 ,   9.13322353,  12.07450604,\n",
       "         38.97804356, 117.54625393]),\n",
       " 'IPSL-CM6A-LR-amip-r8i1p1f1': array([  2.31588554,   5.80338717,   9.12266874,  12.06313133,\n",
       "         38.87317295, 117.42086514]),\n",
       " 'IPSL-CM6A-LR-amip-r4i1p1f1': array([  2.30575144,   5.80137062,   9.1312809 ,  12.07567954,\n",
       "         39.01091976, 117.56419266]),\n",
       " 'IPSL-CM6A-LR-amip-r9i1p1f1': array([  2.3067286 ,   5.80375016,   9.12477732,  12.07122517,\n",
       "         39.08681755, 117.44665333]),\n",
       " 'IPSL-CM6A-LR-amip-r11i1p1f1': array([  2.30248404,   5.79755878,   9.11508179,  12.06002188,\n",
       "         38.9645071 , 117.67249519]),\n",
       " 'IPSL-CM6A-LR-amip-r2i1p1f1': array([  2.32337356,   5.80704463,   9.12081528,  12.05037975,\n",
       "         38.79820404, 117.7290715 ]),\n",
       " 'IPSL-CM6A-LR-amip-r7i1p1f1': array([  2.31384063,   5.8032527 ,   9.11964512,  12.05940104,\n",
       "         38.99704399, 117.5741708 ]),\n",
       " 'CNRM-CM6-1-amip-r1i1p1f2': array([ 0.37936381,  3.43249971, 13.27853861, 21.95052042, 46.12204124,\n",
       "        90.27406081]),\n",
       " 'MRI-ESM2-0-amip-r1i1p1f1': array([ 0.68612546,  4.85005236, 13.06488705, 19.60054979, 36.47022186,\n",
       "        64.70590402]),\n",
       " 'MRI-ESM2-0-amip-r2i1p1f1': array([ 0.68653008,  4.86585104, 13.0656147 , 19.59161768, 36.42718666,\n",
       "        64.5173043 ]),\n",
       " 'MRI-ESM2-0-amip-r3i1p1f1': array([ 0.68712074,  4.845451  , 13.05173874, 19.58128166, 36.46121677,\n",
       "        64.71756302]),\n",
       " 'CESM2-amip-lwoff-r1i1p1f1': array([ 2.05596375,  5.50284672, 10.21145439, 14.5665288 , 29.18716612,\n",
       "        58.03717688]),\n",
       " 'IPSL-CM6A-LR-amip-lwoff-r1i1p1f1': array([ 3.98213649,  6.46769857,  9.01206398, 10.97251058, 17.92402773,\n",
       "        46.96216473]),\n",
       " 'CNRM-CM6-1-amip-lwoff-r1i1p1f2': array([ 0.43933785,  4.02422619, 12.87633953, 20.85572205, 42.77420044,\n",
       "        79.52638925]),\n",
       " 'MRI-ESM2-0-amip-lwoff-r1i1p1f1': array([ 1.30552548,  6.3783884 , 12.62359715, 17.20000267, 28.03127861,\n",
       "        45.79294682]),\n",
       " 'CESM2-aqua-control-r1i1p1f1': array([ 1.90977758,  6.36795473, 13.8110218 , 20.98230791, 40.55203304,\n",
       "        70.50351337]),\n",
       " 'IPSL-CM6A-LR-aqua-control-r1i1p1f1': array([ 3.63232529,  6.29714453,  8.60198984, 11.35034389, 42.79119316,\n",
       "        87.81021454]),\n",
       " 'CNRM-CM6-1-aqua-control-r1i1p1f2': array([ 0.97920865,  5.6313746 , 15.20644236, 23.60138826, 45.89860317,\n",
       "        79.45394307]),\n",
       " 'CESM2-aqua-control-lwoff-r1i1p1f1': array([ 3.36055493,  6.40744817, 10.43041992, 13.79606342, 23.73707829,\n",
       "        41.82879345]),\n",
       " 'IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1': array([ 4.74109077,  6.60246384,  8.46514244,  9.71368413, 13.51215558,\n",
       "        31.32345362]),\n",
       " 'CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2': array([ 1.53679395,  5.57398188, 13.74080057, 21.91111126, 45.72547058,\n",
       "        79.1579842 ])}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aquan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'H' 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<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'H' is not defined"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# # well, kde is super slow, what about just old fashioned histogram\n",
    "# B = np.arange(0, 101, 1)\n",
    "# hist = dict()\n",
    "# quan = dict()\n",
    "# quantiles = [0.5, 0.9, 0.95, 0.99, 0.999]\n",
    "# for ex in H:\n",
    "#     for m in H[ex]:\n",
    "#         k = \"-\".join([m,ex])\n",
    "#         print(k)\n",
    "#         datmp = (K[ex][m]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "#         arr = datmp.values.flatten()\n",
    "#         weights = np.cos(np.radians(datmp['lat'])).broadcast_like(datmp) \n",
    "#         hist[k] = np.histogram(arr, bins=B, weights=weights.values.flatten(), density=True)\n",
    "#         quan[k] = np.quantile(arr, quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = CESM2-amip-r7i1p1f1\n",
      "k = CESM2-amip-r9i1p1f1\n",
      "k = CESM2-amip-r4i1p1f1\n",
      "k = CESM2-amip-r8i1p1f1\n",
      "k = CESM2-amip-r1i1p1f1\n",
      "k = CESM2-amip-r10i1p1f1\n",
      "k = CESM2-amip-r3i1p1f1\n",
      "k = CESM2-amip-r2i1p1f1\n",
      "k = CESM2-amip-r6i1p1f1\n",
      "k = CESM2-amip-r5i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r7i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r9i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r11i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r4i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r8i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r1i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r10i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r3i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r2i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r6i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-r5i1p1f1\n",
      "k = CNRM-CM6-1-amip-r1i1p1f2\n",
      "k = CESM2-amip-lwoff-r1i1p1f1\n",
      "k = IPSL-CM6A-LR-amip-lwoff-r1i1p1f1\n",
      "k = CNRM-CM6-1-amip-lwoff-r1i1p1f2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'CESM1'), Text(0, 0, 'IPSL'), Text(0, 0, 'CNRM')]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = {\"CESM\": 1, \"IPSL\": 2, \"CNRM\": 3}\n",
    "mc = {\"CESM\": \"C0\", \"IPSL\": \"C1\", \"CNRM\": \"C2\"}\n",
    "fig, ax = plt.subplots()\n",
    "for k in aquan:\n",
    "    print(f\"k = {k}\")\n",
    "    xval = mx[k[0:4]]\n",
    "    cval = mc[k[0:4]]\n",
    "    if 'lwoff' in k:\n",
    "        xval += 0.25\n",
    "    ax.scatter(xval*np.ones(len(aquan[k])), aquan[k], color=cval, marker='_')\n",
    "ax.set_ylabel(\"precipitation (mm/day)\")\n",
    "ax.set_xticks([1.125, 2.125, 3.125])\n",
    "ax.set_xticklabels([\"CESM1\", \"IPSL\", \"CNRM\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: RuntimeWarning: divide by zero encountered in log10\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 460.8x316.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bctr = hist['CESM2-aqua-control'][1][0:-1] + 0.5*np.diff(hist['CESM2-aqua-control'][1])\n",
    "with plt.style.context('seaborn-paper'):    \n",
    "    fig, ax = plt.subplots()\n",
    "    for i in hist:\n",
    "        if 'lwoff' in i:\n",
    "            linsty = 'dashed'\n",
    "        else:\n",
    "            linsty = 'solid'\n",
    "        ax.plot(bctr, np.log10(hist[i][0]), color=clrs2[i], linestyle=linsty, label=i)\n",
    "        nearest_idx = np.abs(bctr - quan[i][-1]).argmin()\n",
    "        ax.plot(quan[i][-1], np.log10(hist[i][0][nearest_idx]), marker='o', color=clrs2[i], markersize=9)\n",
    "    ax.legend(loc='lower left')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.set_ylabel(\"log(probability density)\")\n",
    "    ax.set_xlabel(\"precipitation rate (mm/day)\")\n",
    "    fig.savefig(\"/Users/brianpm/Desktop/cmip6_aqua_pr_histgram.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in log10\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 460.8x316.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bctr = ahist['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist['CESM2-amip-r7i1p1f1'][1])\n",
    "with plt.style.context('seaborn-paper'):    \n",
    "    fig, ax = plt.subplots()\n",
    "    for i in ahist:\n",
    "        if 'lwoff' in i:\n",
    "            linsty = 'dashed'\n",
    "        else:\n",
    "            linsty = 'solid'\n",
    "        cval = mc[i[0:4]]\n",
    "        ax.plot(bctr, np.log10(ahist[i][0]), color=cval, linestyle=linsty, label=i)\n",
    "        nearest_idx = np.abs(bctr - aquan[i][-1]).argmin()\n",
    "        ax.plot(aquan[i][-1], np.log10(ahist[i][0][nearest_idx]), marker='o', color=cval, markersize=9)\n",
    "#     ax.legend(loc='lower left')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.set_ylabel(\"log(probability density)\")\n",
    "    ax.set_xlabel(\"precipitation rate (mm/day)\")\n",
    "    fig.savefig(\"/Users/brianpm/Desktop/cmip6_amip_pr_histgram.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip\n",
      "CESM2-amip-lwoff\n",
      "CESM2-aqua-control\n",
      "CESM2-aqua-control-lwoff\n",
      "IPSL-CM6A-LR-amip\n",
      "IPSL-CM6A-LR-amip-lwoff\n",
      "IPSL-CM6A-LR-aqua-control\n",
      "IPSL-CM6A-LR-aqua-control-lwoff\n",
      "CNRM-CM6-1-amip\n",
      "CNRM-CM6-1-amip-lwoff\n",
      "CNRM-CM6-1-aqua-control\n",
      "CNRM-CM6-1-aqua-control-lwoff\n",
      "MRI-ESM2-0-amip\n",
      "MRI-ESM2-0-amip-lwoff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:42: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'precipitation rate (mm/day)')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# histograms that combine the amip ensemble members\n",
    "#\n",
    "m_combine = dict()\n",
    "m_n = dict()\n",
    "m_q = dict()\n",
    "for m in models:\n",
    "    for ex in experiments:\n",
    "        s = \"-\".join([m,ex])\n",
    "        m_n[s] = 0\n",
    "        for ah in ahist:\n",
    "            if (s in ah) and (ah[len(s)+1] == 'r'):\n",
    "                if s in m_combine:\n",
    "                    m_combine[s].append(ahist[ah][0])\n",
    "                    m_q[s].append(aquan[ah][-1])\n",
    "                else:\n",
    "                    m_combine[s] = [ahist[ah][0]]\n",
    "                    m_q[s] = [aquan[ah][-1]]\n",
    "                m_n[s] += 1 \n",
    "m_toplot = dict()\n",
    "for m in m_combine:\n",
    "    if len(m_combine[m]) > 1:\n",
    "        m_toplot[m] = sum(m_combine[m])/m_n[m]\n",
    "        m_q[m] = sum(m_q[m])/m_n[m]\n",
    "    else:\n",
    "        m_toplot[m] = m_combine[m][0]\n",
    "        m_q[m] = m_q[m][0]\n",
    "\n",
    "bctr = ahist['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist['CESM2-amip-r7i1p1f1'][1]) # bin centers are the same for all histograms\n",
    "fig, ax = plt.subplots(ncols=2)\n",
    "for i in m_toplot:\n",
    "    print(i)\n",
    "    if 'lwoff' in i:\n",
    "        linsty = 'dashed'\n",
    "    else:\n",
    "        linsty = 'solid'\n",
    "    if 'aqua' in i:\n",
    "        a = ax[1]\n",
    "    else:\n",
    "        a = ax[0]\n",
    "    cval = select_color(i)\n",
    "    a.plot(bctr, np.log10(m_toplot[i]), color=cval, linestyle=linsty, label=i)\n",
    "    nearest_idx = np.abs(bctr - m_q[i]).argmin()\n",
    "    a.plot(m_q[i], np.log10(m_toplot[i][nearest_idx]), marker='o', color=cval, markersize=7)\n",
    "#     ax.legend(loc='lower left')\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "ax[0].set_ylabel(\"log(probability density)\")\n",
    "ax[0].set_xlabel(\"precipitation rate (mm/day)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip\n",
      "CESM2-amip-lwoff\n",
      "CESM2-aqua-control\n",
      "CESM2-aqua-control-lwoff\n",
      "IPSL-CM6A-LR-amip\n",
      "IPSL-CM6A-LR-amip-lwoff\n",
      "IPSL-CM6A-LR-aqua-control\n",
      "IPSL-CM6A-LR-aqua-control-lwoff\n",
      "CNRM-CM6-1-amip\n",
      "CNRM-CM6-1-amip-lwoff\n",
      "CNRM-CM6-1-aqua-control\n",
      "CNRM-CM6-1-aqua-control-lwoff\n",
      "MRI-ESM2-0-amip\n",
      "MRI-ESM2-0-amip-lwoff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 538.583x325.984 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Now can I get the zonal mean and distribution on to one plot\n",
    "#\n",
    "\n",
    "fig, axx = plt.subplots(figsize=(mm2inches(190), mm2inches(115)), ncols=2, nrows=2, constrained_layout=True)\n",
    "ax = axx[0,:]\n",
    "for ex in ZM:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "#             print(f\"working on {ex} - {m} - {b}\")\n",
    "            xlat = ZM[ex][m][b]['lat']\n",
    "            xzm = ZM[ex][m][b]\n",
    "            c = select_color(m)\n",
    "            if 'lwoff' in ex:\n",
    "                ls = 'dashed'\n",
    "            else:\n",
    "                ls = 'solid'\n",
    "            if max(xzm) < 1:\n",
    "                print(f\"WARNING: {ex} - {m} - {b}\")\n",
    "            if 'aqua' in ex:\n",
    "                a = ax[1]\n",
    "            else:\n",
    "                a = ax[0]\n",
    "            a.plot(xlat, xzm, color=c, linestyle=ls)\n",
    "ax[0].set_ylabel(\"Precipitation (mm/day)\")\n",
    "[a.set_xlabel(\"Latitude\") for a in ax]\n",
    "ax[0].set_title(\"AMIP\", loc='left')\n",
    "ax[1].set_title(\"AQUA\", loc='left')\n",
    "[a.set_xlim([-90,90]) for a in ax]\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "\n",
    "bctr = ahist['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist['CESM2-amip-r7i1p1f1'][1]) # bin centers are the same for all histograms\n",
    "ax = axx[1,:]\n",
    "for i in m_toplot:\n",
    "    print(i)\n",
    "    if 'lwoff' in i:\n",
    "        linsty = 'dashed'\n",
    "    else:\n",
    "        linsty = 'solid'\n",
    "    if 'aqua' in i:\n",
    "        a = ax[1]\n",
    "    else:\n",
    "        a = ax[0]\n",
    "    cval = select_color(i)\n",
    "    a.plot(bctr, np.log10(m_toplot[i]), color=cval, linestyle=linsty, label=i)\n",
    "    nearest_idx = np.abs(bctr - m_q[i]).argmin()\n",
    "    a.plot(m_q[i], np.log10(m_toplot[i][nearest_idx]), marker='o', color=cval, markersize=7)\n",
    "#     ax.legend(loc='lower left')\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "ax[0].set_ylabel(\"log(probability density)\")\n",
    "[a.set_xlabel(\"precipitation rate (mm/day)\") for a in ax]\n",
    "\n",
    "fig.savefig(\"/Users/brianpm/Dropbox/Manuscripts/M_cre_pex/amip_aqua_zonal_and_pdf.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'CESM2 Equatorial Precipitation (mm/day)')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hovmoller near equator\n",
    "datmp = (H['aqua-control']['CESM2']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "datmp2 = (H['aqua-control-lwoff']['CESM2']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, constrained_layout=True)\n",
    "\n",
    "\n",
    "vmn = 10.0\n",
    "vmx = 60.0\n",
    "stp = 5\n",
    "n = mpl.colors.Normalize(vmin=vmn, vmax=vmx-stp)\n",
    "levels = np.arange(vmn, vmx, stp)\n",
    "\n",
    "mlon, mtime = np.meshgrid(datmp['lon'], np.arange(1, 1+len(datmp['time'])))\n",
    "img = ax[0].contourf(mlon, mtime, datmp, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "mlon2, mtime2 = np.meshgrid(datmp2['lon'], np.arange(1, 1+len(datmp2['time'])))\n",
    "img2 = ax[1].contourf(mlon2, mtime2, datmp2, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "\n",
    "cb1 = fig.colorbar(img, ax=ax[0])\n",
    "cb2 = fig.colorbar(img2, ax=ax[1])\n",
    "\n",
    "ax[0].set_ylim([2500, 2700])\n",
    "ax[1].set_ylim([2500, 2700])\n",
    "ax[0].set_title(\"aqua-control\")\n",
    "ax[1].set_title(\"aqua-control-lwoff\")\n",
    "fig.suptitle(\"CESM2 Equatorial Precipitation (mm/day)\")\n",
    "# fig.savefig(\"/Users/brianpm/Desktop/cmip6_cesm_aqua_pr_hov.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'CNRM-CM6 Equatorial Precipitation (mm/day)')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datmp = (H['aqua-control']['CNRM-CM6-1']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "datmp2 = (H['aqua-control-lwoff']['CNRM-CM6-1']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "fig, ax = plt.subplots(ncols=2, constrained_layout=True)\n",
    "\n",
    "\n",
    "vmn = 10.0\n",
    "vmx = 60.0\n",
    "stp = 5\n",
    "n = mpl.colors.Normalize(vmin=vmn, vmax=vmx-stp)\n",
    "levels = np.arange(vmn, vmx, stp)\n",
    "\n",
    "mlon, mtime = np.meshgrid(datmp['lon'], np.arange(1, 1+len(datmp['time'])))\n",
    "img = ax[0].contourf(mlon, mtime, datmp, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "mlon2, mtime2 = np.meshgrid(datmp2['lon'], np.arange(1, 1+len(datmp2['time'])))\n",
    "img2 = ax[1].contourf(mlon2, mtime2, datmp2, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "\n",
    "cb1 = fig.colorbar(img, ax=ax[0])\n",
    "cb2 = fig.colorbar(img2, ax=ax[1])\n",
    "\n",
    "ax[0].set_ylim([2500, 2700])\n",
    "ax[1].set_ylim([2500, 2700])\n",
    "ax[0].set_title(\"aqua-control\")\n",
    "ax[1].set_title(\"aqua-control-lwoff\")\n",
    "fig.suptitle(\"CNRM-CM6 Equatorial Precipitation (mm/day)\")\n",
    "# fig.savefig(\"/Users/brianpm/Desktop/cmip6_CNRM-CM6_aqua_pr_hov.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'IPSL-CM6A Equatorial Precipitation (mm/day)')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datmp = (H['aqua-control']['IPSL-CM6A-LR']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "datmp2 = (H['aqua-control-lwoff']['IPSL-CM6A-LR']['pr'].sel(lat=slice(-2.5,2.5))*86400.).mean(dim='lat').compute()\n",
    "fig, ax = plt.subplots(ncols=2, constrained_layout=True)\n",
    "\n",
    "\n",
    "vmn = 10.0\n",
    "vmx = 60.0\n",
    "stp = 5\n",
    "n = mpl.colors.Normalize(vmin=vmn, vmax=vmx-stp)\n",
    "levels = np.arange(vmn, vmx, stp)\n",
    "\n",
    "mlon, mtime = np.meshgrid(datmp['lon'], np.arange(1, 1+len(datmp['time'])))\n",
    "img = ax[0].contourf(mlon, mtime, datmp, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "mlon2, mtime2 = np.meshgrid(datmp2['lon'], np.arange(1, 1+len(datmp2['time'])))\n",
    "img2 = ax[1].contourf(mlon2, mtime2, datmp2, levels=levels, norm=n, cmap=cc.cm.CET_L19)\n",
    "\n",
    "\n",
    "# cb1 = fig.colorbar(img, ax=ax[0])\n",
    "# cb2 = fig.colorbar(img2, ax=ax[1])\n",
    "cb = fig.colorbar(img2, ax=ax)\n",
    "ax[0].set_ylim([2500, 2700])\n",
    "ax[1].set_ylim([2500, 2700])\n",
    "ax[0].set_title(\"aqua-control\")\n",
    "ax[1].set_title(\"aqua-control-lwoff\")\n",
    "fig.suptitle(\"IPSL-CM6A Equatorial Precipitation (mm/day)\")\n",
    "# fig.savefig(\"/Users/brianpm/Desktop/cmip6_IPSL-CM6A_aqua_pr_hov.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-90.0, 90.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAEoCAYAAAAqrOTwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3xUVfr48c8zk0YaEBJKaCF06b1XG9ixd7Ghu2tb17a7rqv+vrq2RXTtuHZXUOwKKiIdBBKKdAgthJZCSSNt5vn9cSYxCRNIIMlkwnm/XvMacu+5956ZhPvc00VVsSzLsqz6xuHrDFiWZVlWTbABzrIsy6qXbICzLMuy6iUb4CzLsqx6yQY4y7Isq16yAc6yLMuql2yAO02IyE4ROcvX+agtItJGRLJFxFmdaatw/VkiclN1nc+yrKrzywAnIteKSILnprTPczMZ7tn3uIioiFxRKn2AZ1uc5+f3RKTAc/xBEZktIl1KpZ/oST+53HUv8Wx/7wT5aycibhF5rRo/tl/wfD8dfJ0PVU1W1XBVdVU1rYjME5HbKnstz9/cR+XOOV5V3696zi3Lqi5+F+BE5H5gCvA00AxoA7wGXFwq2UHgyRM8kT+nquFAS2AP8N9y+7cBV4lIQKltNwJbKpHNG4FDwNUiElyJ9JYXYvjd36hlWXWDX908RKQh8CTwJ1X9QlVzVLVQVb9V1QdLJf0BKACuP9E5VfUo8CnQu9yu/cBa4FzPtaOAocA3lcjqjcCjQCFwYbnP8KCn1LlXRG4pXeIpX3LwlCQXlfr5JRHZLSKZIpIoIiNK7XtcRGaIyHQRyRKRlSLSy1vmRGSgiCwVkcOevLwiIkGl9quI3CkiW0XkkIi8KiJSav8tIrLRs+9HEWnr2b7Ak2SNp3R8lZdrTxSRxSLyHxE5IiKbROTMUvvnichTIrIYyAXiRaSLp5R9UEQ2i8iVpdI3EJF/i8guz/kWebbFeT5HQKnz/ktElnvSfe35nVI6rYg8BYwAXvF8hleO992LyDjgb5iHoWwRWVP+dykiDhF51JPHVBH5wPO3XPraN4lIsoiki8jfvf3eLMuqGr8KcMAQIAT48gTpFPgH8E8RCTxeQhEJA64Bkrzs/gATrACuBr4G8k9wvhFAK2AaJnDeWGrfOOAB4GygI1DVNrEVmEAcBfwP+ExEQkrtvxj4rNT+ryr4/C7gz0A05js9E/hjuTQXAAOAXsCV/B7oL8Hc0C8FYoCFwCcAqjrSc2wvT5Xf9Ao+xyBgu+f6/wS+KA42HjcAk4AIIA2Y7fk8TTG/q9dEpJsn7QtAP8zDRxTwEOCu4Lo3ArcAsUAR8HL5BKr6d89nusvzGe7y7PL63avqD5jahOme9N4eKiZ6XmOAeCAceKVcmuFAZ8zv4jER6VrBZ7Asq5L8LcA1AdJVtehECVX1G8zNsaK2lAdE5DCQhbm53OAlzZfAaM/T9o2YgHciNwGzVPUQ5kY4XkSaevZdCbyrqutUNQd4vBLnK6GqH6lqhqoWqeq/gWDMTbFYoqrOUNVCYDLmYWCwl/MkquqvnvPsBN4ERpVL9oyqHlbVZGAuv5dw7wD+paobPb+Hp4HexaW4SkoFpnhK39OBzcD5pfa/p6rrPecfB+xU1Xc9+V0JfA5c7qm+vAW4V1X3qKpLVZeoakUPIR+W+u7/AVx5gmrsEpX47o/nOmCyqm5X1Wzgr5jq69LV30+o6lFVXQOswTxYWJZ1CvwtwGUA0eVuDMfzKPB3zI2+vBdUtREQBxzFy83KU335vec80aq6+HgXE5EGwBXAx57jlwLJwLWeJLHA7lKH7Krk5yg+/188VYNHPMG5IaYUVKzk3KrqBlI81yx/nk4i8p2I7BeRTEyQii6XbH+pf+diSh0AbYGXPNWbhzHtnYJpy6ysPVp2lu9d5fJZ+jtqCwwqvp7nmtcBzT15DsG0l1ZG+e8+kGM/t1eV+O6PJ5ayv+tdQACmDblYRd+3ZVknyd8C3FIgD7ikMolVdTam6rF89VvpNMnAvZibdgMvST4A/gJ8WIlLTgAiMVVo+0VkP+bGX1xNuQ9oXSp9m3LH5wChpX5uXvwPT9Xnw5hSYGNPcD6CCS7FWpdK78BUle71ks/XgU1AR1WNxFQ5ipd03uwG7lDVRqVeDVR1SSWPB2hZuk0P8z2Uzmfp4LcbmF/ueuGq+gcgHfP30L6S1y3/3Rd6zlFemSU2KvHdn2hJjr2YQF362kXAgUrm27Ksk+BXAU5VjwCPAa+K6bIfKiKBIjJeRJ6r4LC/Y9pljnfe2Zib0CQvu+dj2sz+U4ks3gS8A/TAVOn1BoZhqvB6YNrkJorIGSISiml/Km01cKnnc3UAbi21LwJzU0wDAkTkMUwwLa2fiFzqKeHeh2kv/NVLPiOATCBbzPCIP1TisxV7A/hrcRuYiDSUUkMyMDft+BOcoylwj+d3dwXQFZhZQdrvgE4icoMnfaCIDBCRrp5S6jvAZBGJFRGniAyRinuuXl/qu38SmFHBMILyn+FE3/0BIE4q7vH5CfBnMcNHwvm9ze6EVe2WZZ08vwpwAKo6GbgfU22YhnnCvwv4qoL0i4HllTj188BD5W+OasxR1YPHO1hEWmI6CExR1f2lXomYXp03qeoszBCHXzAly1/KneZFTO/PA8D7eKo6PX4EZmGGKezClFx2lzv+a+AqzBCFG4BLPe1x5T2AqTbNAqYCFXUGOYaqfgk8C0zzVG+uA8aXSvI48L6nOvFKL6cAWIbpZJMOPAVcrqoZFVwvCzgH08lnL6Yq71lMG1jxZ1mL6QRy0LOvor/rD4H3POcIAe6pIN1LmDa+QyLyMif+7j/zvGeIyEov53vHc+0FwA7P8XdXcG3LsqqJ2AVPfUtEFFNV6K0XZ1XO8zjQQVVPODTCl0RkInCbqg6v5evOAz5S1bdr87qWZfmO35XgLMuyLKsybICzLMuy6iVbRWlZlmXVSzVeghORdzzTE60rtS1KzNRLWz3vjWs6H5ZlWdbppTaqKN/DzEZR2iPAHFXtCMzx/GxZlmVZ1aZWqijFLFPznap29/y8GRitqvtEpAUwT1VPOO1RdHS0xsXF1WRWLcuyLD+SmJiYrqox3vZVdsqr6tZMVfcBeIJc0xMdABAXF0dCQkLN5syyLMvyGyJS4ZSHdb4XpYhMErO4aUJaWpqvs2NZlmX5CV8FuAOeqkk876kVJVTVt1S1v6r2j4nxWgq1rHolr9DFpv2Zvs6GZfk9X1VRfoOZt/EZz/vXPsqHZdUJqsoXK/fww/r9LNyaRl6hmzeu78e47s1PfHApOflF/OXTNfRp04ibhsYRElip1YAsq16q8QAnIp8AozHL3KRgJhh+BvhURG7FLCdzRcVnsKz677vf9vGXz9YQ2zCEK/u3ZsGWNKb8vIVzzmiGw1HZhR7gtXlJ/LB+Pz+s38/7S3Zy/zmdubRPyyqdw/KdwsJCUlJSyMvL83VW6pyQkBBatWpFYOBx17Auo8YDnKpeU8GuM2v62pblL37ZlEpUWBALHx6L0yF8tWoP901fzU8b9jOue4tKnWP3wVymLtzBJb1juXJAa56ZtYkHPlvD0YIibhgSV7MfwKoWKSkpREREEBcXR9kVpU5vqkpGRgYpKSm0a9eu0sfV+U4mllXfud3Kgi1pjOgYjdNT0rqwVyzx0WFM+XkrbnflhvI8M2sTDoGHxnVhaPtovvrjMDo0DeenDXbZOX+Rl5dHkyZNbHArR0Ro0qRJlUu2NsBZlo9t2JdJRk4Bozr93onK6RDuObMjm/Zn8dOG/cc52li+4yDfr93HnaPaE9vIrNvrcAjDO0SzYudB8gq9LXtn1UU2uHl3Mt+LDXCW5WPzt5jhLyM6lu0lXNlSnNutPPndemIbhnDHyLKLmw/vEE1eoZuVyYeqP+NWvbV//36uvvpq2rdvzxlnnMF5553Hli1bqnyeKVOmkJubW+XjwsPDq3yMNzbAWZaPzd+SRrfYSGIiyi5EXroUN3PdvgqP/3rNHtbtyeTh8V1oEFS21+Sg+CicDmFxUnqN5N2qf1SVCRMmMHr0aLZt28aGDRt4+umnOXCg6lXdxwtwLlfN1yrYAGdZPpSVV8jKXYfKVE+WdmGvWDo3i+CFHzdT6HIfsz+/yMW/f9pCt9hILuwZe8z+iJBA+rRuxKIkrwumW9Yx5s6dS2BgIHfeeWfJtt69ezN8+HAefPBBunfvTo8ePZg+fToA8+bNY/To0Vx++eV06dKF6667DlXl5ZdfZu/evYwZM4YxY8YApmT22GOPMWjQIJYuXcrkyZPp3r073bt3Z8qUKdX+WXw1Ds6yLGDJtgyK3MrICgKc0yE8NK4zt76fwPQVu7l+cNsy+/+3LJmUQ0d5ekKPCocCDOsQzX9+2cqR3EIahla+i7XlW098u54Ne6t3wP8ZsZH888Jux02zbt06+vXrd8z2L774gtWrV7NmzRrS09MZMGAAI0eOBGDVqlWsX7+e2NhYhg0bxuLFi7nnnnuYPHkyc+fOJTo6GoCcnBy6d+/Ok08+SWJiIu+++y7Lli1DVRk0aBCjRo2iT58+1fZ5bQnOsnxo/pY0woMD6Num4hWjxnZpyoC4xrw0Zyu5BUUl27Pzi3jllySGtm/CiI7RFR4/vGM0boWl2201pXXyFi1axDXXXIPT6aRZs2aMGjWKFStWADBw4EBatWqFw+Ggd+/e7Ny50+s5nE4nl112Wcn5JkyYQFhYGOHh4Vx66aUsXLiwWvNsS3CW5SOqZnjAkPZNCAqo+FlTRHhkfBcue30p7yzawV1jOwIwdcF2MnIKeHhcl+P2MOvduhFhQU4WJaVXekyd5XsnKmnVlG7dujFjxoxjth9v5Zng4N/bj51OJ0VFRV7ThYSE4HQ6T3i+6mJLcJblI9vTc0g5dLTC9rfS+rWN4uwzmvHG/O3c/+lqrn97GW/M38b5PVrQq3Wj4x4b6HQwOL4Ji207nFUJY8eOJT8/n6lTp5ZsW7FiBY0bN2b69Om4XC7S0tJYsGABAwcOPO65IiIiyMrK8rpv5MiRfPXVV+Tm5pKTk8OXX37JiBEjqvWz2ABnWT6yYsdBwLSRVcbD4zoTEuhg2faD5BQUcW635jx6QddKHTusQzQ70nNIOVT1LtvW6UVE+PLLL5k9ezbt27enW7duPP7441x77bX07NmTXr16MXbsWJ577jmaNz/+XKmTJk1i/PjxJZ1MSuvbty8TJ05k4MCBDBo0iNtuu61a29+glhY8rS79+/dXux6cVV888e16pi3fzfonzq3xuSK3Hsji7BcX8OxlPbhqQJsavZZ18jZu3EjXrpV7aDkdeft+RCRRVft7S29LcJblI1sPZNOxWXitTITcoWk4kSEBrN1zpMavZVl1hQ1wluUjmw9k0alZRK1cS0To2CyCrQeya+V6llUX+DTAicifRWS9iKwTkU9EJMSX+bGs2nIop4C0rHw6NaueKYkqo2PTcJJSbYCzTh8+C3Ai0hK4B+ivqt0BJ3C1r/JjWbVpywHTs6y2SnBgqikzcgrIyM6vtWtali/5uooyAGggIgFAKLDXx/mxrFqxxVOSqs0AV3ytrbYUZ50mfBbgVHUP8AJmRe99wBFV/al8OhGZJCIJIpKQlpZW29m0rBqxZX8WEcEBtGhYe7XyHT3VoTbAWacLX1ZRNgYuBtoBsUCYiFxfPp2qvqWq/VW1f0zMiQfEWpY/2HIgi47Nwmt17a/mkSGEBweQdMD7wFvLgrJL1agq0dHRHDpkllvat28fIsKiRYtK0sTExJCRUfEkAg8++CDdunXjwQcfJC0tjUGDBtGnT59qn5bLG19WUZ4F7FDVNFUtBL4AhvowP5ZVK1SVLQey6Ny89qonwfSk7NA03JbgrEoTkZKZ/wGWLFlCnz59WLJkCQCbN28mOjqaJk2aVHiON998k5UrV/L8888zZ84cunTpwqpVq6p91hJvfBngkoHBIhIq5jH2TGCjD/NjWbUiPbuAQ7mFdGxauwEOTE/KLXaogFUFw4YNKwloS5Ys4f777y8T8IYOHYqqel1K56KLLiInJ4dBgwbx7LPP8tBDDzFz5kx69+7N0aNHazzvPptsWVWXicgMYCVQBKwC3vJVfiyrtviiB2WxTs0i+CwxhUM5BTQOC6r161tVMOsR2L+2es/ZvAeMf6ZKhwwdOpQnn3wSgOXLl/PEE0+UrN22ZMkShg0bVuFSOt988w3h4eGsXr0agGbNmpGQkMArr7xSvZ+rAj7tRamq/1TVLqraXVVvUFXbf9mq90oCXPPaGwNXrIOno0lSmi3FWZUzcOBAVq1aRU5ODoWFhYSHhxMfH09SUlJJCe54S+n4kl0ux7Jq2ZYD2TQKDSQmPPjEiatZx6aenpQHshkQF1Xr17eqoIolrZoSGhpKhw4deOedd+jbty8AgwcPZubMmaSmptK5c+daWfrmZPh6HJxlnXa2eKboqs0elMViGzYgNMhZUoq0rMoYNmwYU6ZMYciQIQAMGTKEl156icGDByMijBw5sspL6dQGG+AsqxYV96CszSm6SnM4xE7ZZR1Xbm4urVq1KnlNnjyZYcOGsX379pIA17dvX1JSUhg61HR8nzBhQpWX0qkNtorSsmrR/sw8svKK6OyDDibFOjSNYFGSnTTB8s7tdnvdXroaMjg4mPz837tMiAjPP/88zz///DHHZWf//jA1ceJEJk6cWH2ZPYEqleBE5AUR8c066pZVDxR30e/owwDXsVk4BzLzOXK00Gd5sKzaUNUqyk3AWyKyTETuFJGGNZEpy6qv0ret4k7nN3Rhp8/y0LFpOKHkkXQg02d5sKzaUKUqSlV9G3hbRDoDNwO/ichiYKqqzq2JDFpWvZCfBfOe4ZJlr+MMdMEH08yYpL43Qb+J4Ays+Twc2AAbv2HEhplsCFlD2td9YOLH0LBlzV/bsnygyp1MRMQJdPG80oE1wP0iMq2a82ZZ9cP+tfDKAFj6CnMbnMPdTabC+OdBnDDzAXh3PBzaVXPXd7tg3rPw+lCY9wyBQcG86z6fyCOb4Y3hsHV2zV3bqrK62uXe107me6lqG9xkYDNwHvC0qvZT1WdV9UKgT5WvblmngzlPgqsAbv2ZRwpvJbRFFxg0Ce6YD5e/C2mb4Y0RsP7L6r92dhp8dCnMexp6XgkPbEVum82nTf7A481fgchY+PhyWPlB9V/bqrKQkBAyMjJskCtHVcnIyCAkpGqrb1S1F+U64FFVzfWyz/eDHiyrrjmwHrb+BGMe5XCTXqRnz6Z907Df93e/FFr2hRm3wmcTTZAb94wJPKdCFdZ9Dj/+DfKOwIUvQ98bwTP2Lj4mjCV7ouC+n+HDCTDn/0GPKyCwwald1zolrVq1IiUlBbs02LFCQkJo1apVlY6pahvcOyLSWES6AyGlti9Q1SNVurJlnQ4WvwyBYTDgVral5QDQPqbcGLjGcXDLD7D4JVjwPCTNgTF/g97XQYNGVb9m6kaY+SDsXAgtesP1n5v2vlLaR4cxa+0+8iWI4DF/h/cvgJUfmpKl5TOBgYG0a9fO19moN6oU4ETkNuBeoBWwGhgMLAXGVn/WLMvPHd4N62bAwEkQGsW2tN2AlwAHppPJyAdMie77B0zJa/Zj0GYIdDwbIluaYBfcEMQB6gZ1QeFRKMyFo4cgZQXsXAQZSRDSCM6fbDqwOJzHXC4+Jhy3wq6MXDrFDYfWg02A7TcRAuwkzFb9UNUqynuBAcCvqjpGRLoAT5zsxUWkEfA20B1Q4BZVXXqy57OsOuXX101V4eA/ArA9LYdAp9Cq8XGqAaPiTYkrZQVsnglbfjSBrjKCI01A7HsT9L4WwqIrTFocZLenZZtVDUY+CB9fBr9NM1WZllUPVDXA5alqnoggIsGquskzZOBkvQT8oKqXi0gQEHoK57KsuuPoIUh8D3pcDo1aA7AtLZu4JmEEOE/Qt0sEWg80r7Meh5x088o7DHmesWviAAECQ80rOAIatQVn5f5Lt4sJ8+TJVJvS4UxTnbnoReh1baXPY1l1WVX/ilM8pa6vgNkicgjYezIXFpFIYCQwEUBVC4CCkzmXZdU5q/8HhTkw9J6STdvSsul0MouchkUftzR2MsKDA2gWGcz24gAnYqpIp19vOrr0vKJar2dZvlClYQKqOkFVD6vq48A/gP8Cl5zkteOBNOBdEVklIm+LSNiJDrIsv7B5FjTtBs27A1DocpOckVu2B6WPxUeHsz291KTLnc+HqPaw+iPfZcqyqlGlApyIRJV/AWuBRcDJToseAPQFXlfVPkAO8IiXa08SkQQRSbBdZy2/kHcEkpeaziEeuzJyKXKr9w4mPhIfE8a21Ozfx1w5HND1Ati52HwGy/JzlS3BJQIJnvc0YAuw1fPvxJO8dgqQoqrLPD/PwAS8MlT1LVXtr6r9Y2JiTvJSllWLts0FdxF0Ovf3TZ4VtOtSgGsfE05mXhEZOaVaBjqfB+5CM1TBsvxcpQKcqrZT1XjgR+BCVY1W1SbABcAXJ3NhVd0P7C7VSeVMYMPJnMuy6pStsyGkIbT6fe6D4gAXH1OHqig9eSlphwNoNQBCm5gqVsvyc1Wdi3KAqs4s/kFVZwGjTuH6dwMfi8hvQG/g6VM4l2X5ntttZi5pf2aZnojbUnNoFhlMREgtTKpcSaWHCpRwOKHTONj6I7jscjqWf6tqgEsXkUdFJE5E2orI34GMk724qq72VD/2VNVLVPXQyZ7LsuqEfashJ7VM9SSYElxdqp4EiG3UgKAAR0npskTn8Z52xF99kzHLqiZVDXDXADHAl55XjGebZVngmZlfoMNZJZtUtU4GOKdDaNckrGwVJUD8GHAGwZYffJMxy6omle1F+VcR6aOqB1X1XlXto6p9VfU+VT1Y05m0LL+x9Udo2a/MuLW07Hyy8orqVPtbsfiYMLanlwtwweHQbhRs+t7MxGJZfqqyJbgdwL2e8WrvichVItK4JjNmWX4nOw32rDy2ejK1gkmW64D2MeEkH8yloMhddkfn8XBoB6Rv8U3GLKsaVLYX5TRVnegZr/YSZpD2FyKyQEQeExG7VI5lJf0MKHQ8p+zm4iECTetegIuPCcPlVpIPllsBq9M4877p+9rPlGVVkyqv6K2qq1T1X6o6BjNMYD1wW7XnzLL8zbZfICwGmvcss3njvkwiQgKIbVi1xRprQ7ynVHlMR5OGLc3n2PaLD3JlWdWjqsvlOIHzgbjSx6qqXUTKOr2pwo75pu3KUfa5ccPeTM5oEYl4FhutS7yOhSvZOQqWvQkFuRBk50G3/E9VS3DfYiZHbgJEeF51r97Fsmpb2ibIPgDxo8tsdrmVTfsz6Rbb0CfZOpHIkECaRQaz9UDWsTvbjQZXgZl2zLL8UFVXE2ilqj1PnMyyTjPb55n3+NFlNu9IzyGv0M0ZsZG1naNKO6NFJOv3Zh67o+0QcASakmmHM2s/Y5Z1iqpagpslIuecOJllnWa2zzOLlXrWfiu2fq+ZtPiMFnU3wHWLbUhSWjZ5ha6yO4LCzNRd2+f7JmOWdYqqGuB+Bb4UkaMikikiWSLi5dHPsk4jrkIzA3/86GN2bdiXSaBT6FAHe1AW6xYbicutbN7vpZoyfhTsWwO5drir5X+qGuD+DQwBQlU1UlUjVLXuPppaVm3YsxIKsrwHuL2ZdGwaQVBAlTss15ri9kGv1ZTtRgEKOxfVbqYsqxpU9X/dVmCdqp3ewLJKbJ8HCMSNKLNZVdmwN5Nudbj9DaB1VAMiQgJKqlPLaNkPAsNMO5xl+ZmqdjLZB8wTkVlAfvFGVZ1crbmyLH+yYz606AWhUWU2p2Xlk5FTUKc7mACISMUdTQKCoO1Q2w5n+aWqluB2AHOAQMzwgOLXSRMRp2cKsO9O5TyW5RP52bB7udfqyeKAUZc7mBTrFtuQTfszcbm9VM7Ej4KMrZC5t/YzZlmnoKoBbiZm3bYJwBWe1+WnmId7gY2neA7L8o3kpWYF7PjRx+zasM8EuK51vAQHpqNJXqG77Npwxdp5lnw8DUpxGdn57D+SR3p2Ppl5dj08f1fVKsqPgAeAdYD7BGlPSERaYWZGeQq4/1TPZ/mO261s3J/Juj2mHScowEFoUACD2kXRKDTIx7mrQdt+AWcwtBl8zK4NezNpExVKZB1a5LQi3VqaILx+byYdm0WU3dmsu1nle/tc6F2/VsdSVZbvOMjPGw8wb3MaW1PLBvj2MWGcdUYzzu7ajL5tGuNw1L3ZaKyKVTXApanqt9V4/SnAQ5gZUbwSkUnAJIA2bdpU46WtU6WqLEpK55PlySzdlsGh3GOfeAMcwtAO0VzQowUX94klOMDpg5zWEFXYPAvajYTABsfs3rAv0y+qJ8GsKhAU4GD93iNc0qdl2Z0Oh1mhPOlncLvMqt/1wN7DR/nHV+uYsymVQKcwsF0Ul/ZtRcMGgRS53WTnF7F0WwbvLNrBm/O3071lJH8d35VhHaJPfHKrTqhqgPuniLyNaYcr3cnki6peWEQuAFJVNVFERleUTlXfAt4C6N+/v+29WQe43cqsdft5fX4S6/ZkEh0ezJldmzEkvgn94xoT4HRQUOTmYE4+P204wKy1+3no8994e9F2nr+8F71aN/L1R6ge6VvMkjJD7z5mV3Z+ETszcphQPljUUYFOB12aR3jvaALQeRys/RRSVngtrfoTVeWDpbt47odNuBX+fl5Xrh3UhrDgY2+Hfxzdgcy8Qn5Yt5+Xft7KdW8vY2SnGJ68qBtx0XVvfT+rrKoGuJuBLphOJsVVlApUOcABw4CLROQ8IASIFJGPVPX6kziXVUsy8wq5f/oaft54gHbRYTx7WQ8u6dPSa8msXXQY/dpG8ci4LszdnMrfv1zHhNcWc/vIeO4/u5P/l+Y2zzLvxUvLlLJpXyaq/tHBpFi32Ehmrt2Pqh47MXSHs8ARYD6zHwe4IpebR75Yy4zEFEZ2iuGpS7rTOur4E0lHhgRyZf/WXNQrlg+X7uI/v2zl0teX8N+b+tOnjV0Wsy6raieTXqraX1VvUtWbPa9bTubCqvpXVW2lqnHA1cAvNrjVbUmp2Vzy6mLmbk7lHxecwc/3j+KqAW1OGKhEhLFdmvHjn0dy1YDWvDl/O5M+SDx2aivB9+kAACAASURBVCh/s3mWWVKm4bGltOK2yLo+RKC0M2IbcuRoIXsOHz12Z0hDM1xgyw+1n7Fqklfo4s6PEpmRmMK9Z3bk/ZsHnDC4lRYS6OT2kfF8fddwwoMDuGbqr8zZeKAGc2ydqipP1SUiZ9RITqw6be6mVC55dTFHcgv5+LZB3Dq8Hc4qNrhHhgTyr0t78uxlPViwNY3b3k/gaIGfBrmcDEhZbla+9mLp9gxaNmpAizq4BlxFigekV1hN2Wm8WTXh4I5azFX1yMor5Mb/LmfOplSevLgbfz6700kvX9QuOozP/zCUjk0juP2DBD5N2F3NubWqS1UD3HBgtYhsFpHfRGStiPx2qplQ1XmqesGpnseqGdNXJHPbBwm0bRLKt3cPZ3B8k1M631UD2vDC5b1Ysi2dm99bTk5+UTXltBZt/QnU7TXAudzKkm0ZDO8QXSfXgKtI1+aROATW7/EyowmYdjjwu1JcQZGbOz9KJDH5EC9f3Ycbh8Sd8jljIoKZNmkwwzpE89CM3/jo112nnlGr2lU1wI0DOgLnABdiVvS+sLozZdUNqsqLs7fw8OdrGdYhmul3DCG20bG9BU/GZf1a8eJVvVm+4yB3f7KKItcpjzqpXZtnQkQLaNH7mF2/pRwmK6+I4R39q7ddgyAnXVtE8uuOCiZWjoqHmC6/tz36AbdbeXDGGhYnZfDsZT25sFdstZ07LDiAqTf2Z2yXpjz61TreWeR/Jdv6rkoBTlV3eXvVVOYs33G7lX98vY6X5mzlin6t+O9N/Qn30svsVFzcuyVPXtydXzal8uR3G/CbKU6L8s34t07ngpcS2qKt6QAMbX9qJV1fGNUphsRdhyoe5NxpHOxaDHkVlPLqmH/N2sjXq/fy4Lmdubxfq2o/f0igkzeu78e53Zrx5HcbeGP+tmq/hnXyqveOZdULRS43D3++ls9XpnDHqHgeGdel4qq2zL2wJ9Esp5KfCUV50KSDGRwcFX/CMVPXD25L8sFc3lqwnTZRodw2Ir4GPlE127kICrKh83ledy9KSqdbbCRNwoNrOWOnbkyXprw2bxuLtqZzXo8WxyboPB4WT4GkOdD90trPYBV8umI3Uxfu4KYhbfnj6PaVO8jtgkM7IW0zZCSBM9B0sAlpaP6mG7U55qEmKMDBK9f25f5P1/DMrE0UFLm558yO1f+BrCqzAc4qo9Dl5r7pq/n+t33cf3Yn7h7b4djglroRlr8FOxaYm0BFAkPNE3/PK81A4QDvM5o8Mq4Luw/m8tTMjcQ1MTNH1Gkbvjafrd3IY3bl5BexMvkQtwxv54OMnbo+rRsRGRLAvM2p3gNcqwFmVpNN39fpAJeUmsVj36xjaPsmPHZht+O3hapCSgKs+gDWfWmWPqpIZCvTm7TfRIgbVrI50OlgylW9CXQIk2dvoaDIzV/OOfmOLFb1sAHOKlHkcnPftNV8v3YffzuvC5NGlnvqPbIH5j0Nq/8HAQ2g3QjodzO0HQJhTSEkEhyBZmLeA+vNoOD1X8H6L8xNcfj9MHDSMYHO4RBevKo3KW8s5c+frub7u0fQpknlu2/XqrwjsHaGubl7mb1k+c6DFLqU4X4620WA08GITjHM25zmfTycwwlnXAKrPjI9ScPqXjVsXqGLu/63irCgAF68qnfFvX1VYcuPMOcJSN1glgXqdokJYDFdTE2Eus3vPDfDrPuXvAS2zTGD3rteBGc/CVHmYcbpEJ6/opcp0c1Nwq3KQ+O61OInt8qzAc4CTJvbw5+v5fu1+3j0/K5lqwpdRbDkJZj/nPkPP/iPMOIvxywPU6JFL/PqfS2Mf85UZy1/E376OyS+B+OegY5nlTkkJNDJa9f15YL/LOIPHyfy+R+GEhJYBweCr5kOhTnQ/1avuxdtTScowMGAuAq+Gz8wulMM3/+2jw37MksWQy1j4O2Q8F9T4hn+59rP4Ak89f1GNu3P4t2bB9AssoJhGulJ8MMjkDQbojvBRf+BbhMg2MusgaFRJoi16g+DJkFBLix9FRa9aHqUDrkLRj4IQaE4HcLTE3ogIrw2bxuRDQK5c1Qlq0etald3lxm2ao2q8vi36/l8ZQp/PqtT2eC2fx28PRbmPAkdz4G7EuDcpyoObuU5A0338uu/gGs/NQHy48vgizsgv2xVUOuoUF68qhfr92by+Dfrq/ETVhNVc2OP7Qst+3pNsmhrOgPjoupmcK6kUZ1jAJi3Oc17gqZdzeKuK94xbVZ1yA/r9vHhr7u4fUQ7xnRuemwCtxuW/AdeGwzJv8I5T8EflkDfG70HN2+CQmHUg3B3InS/DBZNhtcGmdIgpkbi/y7pzgU9W/DMrE18sjy5Gj+hVRU2wJ3m3G7liW838MHSXdwxMp57zuxQvAMWToa3RpmOJFe8D1d9CI3bntyFREyvwz/+CqMeNlU8b46CvavKJBvbpRl/GtOeaSt281ldG0C7a7EZ6DzAe+ktNSuPzQey/G54QHlNI0Lo3jKSeZtTK0408HY4klxyU68LUg7l8tCM3+jZqiEPnuulajBrP3x0Kfz0qPlbvDsRht5lHsJORmQLmPAGTPzeVNn/70r47GbIPYjTIUy+sjejOsXwty/X8u0au5aeL9gAdxordLm5/9PVvLdkJ7cOb8cj4z29JbMOwEcTTNtEl/Phj8tM20R1CAiCMX+Dm741PS7fPhtW/LdMkj+f1Ykh8U149Kt1bKhoVg1fWPE2hDSCbt47VyxOMsMD/LX9rbTRnZqyMvkwR7ysEAFA5/MhsqXpbFQHFLnc3DttNW6F/1zTh6CAcre2pDnw+lBTartgClz1EURUU2emuOFw5yIY8yhs/NaUDrf8SFCAgzeu78eAtlHcO20V01fYklxtswHuNHW0wMWkDxL4yjNG6NHzu5rgtmMhvDHM3AgufMmU3GqiI0HxTaH9GPj+fvjufnCZm2mA08HL1/ShYYNA/vhxYt1YeDLrgLl59b7OVFF58XniHlo0DPGrCZYrMqZLDC63sjCpgmpKZwD0v9msEZe+tXYz58WUn7eSuOsQT03oTtsmpWb5d7thwfPw0WUQ3gzuWGDyXd29GwOCTLXl7b9AaLQpzc16mAYB8N4tAxjRMYaHP1/LWwvsOLnaZAPcaehIbiE3/HcZ87ak8fSEHvxpjGcowJpp8OEEaNAYJs0zXaFrsptzaBRcMw2G3Wfatj642PTMw0yF9Op1fdl96CgPfLrG94PAE94BdxH09z63eFJqFouS0rl+cNt6sShm79aNaRQayA/r9lecqO9EcAbBsjdrLV/eLNyaxqvzkriyfysu7l1q4uu8TJh2Lfzyf9DjCrjtZ4jpVLOZadETJs2FQX+AZW/AtOsIJZ+pN/bn/J4teHrmJp77YZPv/55PEzbAnWZSM/O46q2l/JZyhFev7cu1g9qYzhPznoUv7zBd/m+dbToS1AaHE85+Ai592wwYf+dcOGza3gbERfHX8V34acMBXpvnwyffg9vN4OauF0J0B69J3l+yi6AAB1cPaF3LmasZTodwRb9WzFq3n90Hc70nCo8xPWUT3oGUxNrNoMfug7nc/ckqOjYN5/GLuv2+I2s/vHee6SU5/nm49C0IqqX12wKCYfwzcN4LsPVHePc8go6m8vLVfbhmYBtem7eNR79ah8ttg1xN81mAE5HWIjJXRDaKyHoRuddXeTld7EjP4bI3lpB8MJd3Jg4wA3kLcuDz28z4tl7XwnWfQwMfLEja8wq44UvITjVBLm0zALcOb8eFvWJ5/sfNzFy7r/bzpQrf/dmUVMY/5zVJZl4hn69M4cKesX45e0lFbh0ej0Ng6sLtFSc66wmIaA5f3QmFXpbZqUHFy9+43MqbN/QnNMgz6ik9Cf57NmRsh2unm679vhhwPfB2uPoTU4X71micexJ4ekJ3/jC6PR8vS+a+6aspKPKzOVj9jC9LcEXAX1S1KzAY+JNdiqfmzN5wgIteWUR2XhH/u32w6emXngRvn2UGYp/5GFzyWoWzjdSKtkPh5u9NVeA74yB5GSLC85f3pG+bRvx5+mpW7z5cu3laMw22z4Oz/gmR3ifqnZGQQm6Bi4lD42o1azWtecMQLu3TiukrdpOene89UYNGcPGrZnXzOU/WWt5Ulb99uZb1ezOZclVv2hWvrp28DN45x4xVm/itWajVlzqPg1t/MqW6d8cjie/y8LmdeWR8F75ds5eb3llOamaeb/NYj/kswKnqPlVd6fl3FrAROHblSOuUFLncPPfDJm7/IIG4JmF8c9dwerduBBu/g6ljIPuAGaM24i++ecotr3kPuOVHc+N8/wJY+QEhgU7eurE/TSODue39BFIOVVBlVt1y0uHHv0HrQdDPe9ub2618+Osu+rRpRI9WXgZF+7lJo+IpcLl5b/HOihO1HwMDbodfXzOdlGrBG/O388XKPdx7ZkfO7OrpDbnyQ3jvfDNv5K0/Qct+tZKXE2reHW6fC/GjTG3AN3dz59CW/PuKXqzafYjzXl7I/C0VdOaxTkmdaIMTkTigD7DMtzmpX5JSs7nqrV95bd42rhnYhs/uHELrhkHw8+Mw/TozFdGk+eYGVZdEtYPb5kDbYfDN3fD9A0Q3cPDuxAHkF7m4ZuqvFbcLVZfCPNMmmZ9lepM6vP9Xmb81jR3pOfWu9FasfUw447o154OlO8k+3rp9Zz9hJtf+7KYab4/7YOlOnv1hExf2iuXeMzuamXZmPQLf3GV6597+CzSpY7OHhEaZiQ5GPgirPoR3x3FZe+Wbu4YTFRbETe8s56nvN/jvAsB1lM8DnIiEA58D96nqMYOeRGSSiCSISEJamn3KqYxCl5tX5yZx3ksL2ZaWzZSrevOvS3sQkn/QDHRd9KKZQ/KWH6BRHe0UERoF180w0yCtmArvjqdDQBof3TqIzKNFXPnmUnak59TMtQuPmt53ST/D+f+usMNNTn4RT367gZaNGjC+u5eJieuJO0e1JzOviA+XHmdlrKAw8/sKjjClqM01syjqZwm7eezr9ZzVtRmTr+yFI2OraW9b9rqZQu66GaYXcF3kcMLYR+Gqj03zwFuj6JSdwNd/Gs51g9owdeEOzpkynwW2NFdtxJfdVUUkEPgO+FFVJ58off/+/TUhIaHmM+bHFmxJ4/++38CWA9mc36MFj1/UjZjwIFj3Ocx6yHQqOX8y9LnO11mtvHVfwLf3gbrgvBfYEHMe17+zHKdD+Pi2QXRqVskpliqjIBemXQPb55v5CfveUGHSBz9bw4yVKXxy++BTXuW8rrv1vRUs3JrOJ5MG0a/tcaZpy06Fj6+A/b/BuU+bybVPsGRSZc1ITOGhGWsY1iGaqTf0JSRxqpmMIDAULphs5pL0F+lbYdp1kL7ZPMSN/Qe/7s7hb1+sZXt6Dhf3juWv47vSvGEFc2laJUQkUVX7e93nqwAnZpry94GDqnpfZY6xAa5im/Zn8twPm/llUyptokJ59PyunNOtuVkBYOaDsPl70yZx8au1NwSgOh1ONvNXJi+Bjueys99fueLzDPILXbxxQz+Gtq+G2UN2LoKZD5mZ5S95HXpfU2HSb9fs5e5PVnHXmA48cG7nU792HXc4t4BLXl1Mdn4RX981nJbHW9k9Pxtm3GK6yDfvaSbXLrW0TFWpKq/OTeKFn7YwrEMT3hmeRfDcJ+DAWrMc04UvV9+sJLWpIAdmP2ZmyInpChPeIC+mB6/N28Yb87fhFOFPY9pz24h4v57btKbV1QA3HFgIrAWK+8r+TVVnVnSMDXBludzKL5tSeXfxDpZsyyAiOIC7z+zATUPjCD6aZqoiE941nUfGPmqqcKrpadon3C4zi/uC56Egh6zuN3DLjjGsPhTEM5f25LKTXbH54HbTA3D9l9CwDZz/gpmrsAIph3IZ/9JCOjQN59M7hhDo9HlNf61ISs1iwqtLaB0Vyow/DPm9W743qqbWYPY/ITPFLA476A5oN6pKnZmKXG4e+2Y9/1uWzL1dMrlXpuHYMQ8atTU9f7tfVjc6R52KrbPh6z+Z0m+va2DsoyQXNeapmRv4cf0BmkUGc8Pgtlw7qC1RYT7s5VxH1ckAdzJsgDPta79uz+DH9fv5af0BUrPyadEwhBuGtOWaAW1orIdh0RQzM4ir0FRFjnjg5CdJroty0mHevyDhXdQRwLzgMTx9aCwjh43gL+d0Ov6Nt/Q5Nn4Dv30KyUshIMSsVzfsHq/rvBXbuC+T295P4MjRQmbeU4fXrashczencut7KxjUrglTru5d8XI0xQpyYekr8OvrcPSg6djU53rocDY063bc4LQjPYcHPluDJi/jhaY/EH/kV2gQBaMeMjPKBNSfMYccPWQmN1/2pvlOBt0BQ+9hyT54ff42FnqWYTq/RwvGdW/OyI4xNAjy44fVamQDnB87WuBia2oWK3YeYnFSOsu2Z5BT4KJBoJPRnWO4oGcs53RrRuDRdLMMyPKp4Mo3T4IjHzA92+qrjG2w9FV09cdIUR4b3a3ZFtiRTr1H0KlzN9PxITDUTOqcnWpeB9aaeTbTt5hzRHeGXleZ76uCcW7FZm84wL3TVhEREsDUG/vTs5UPBsTXAZ8npvD3r9bSINDJvy7tybjuzU98UGEebPjKVMelrDDbwpv9vrhodEezWra6cBcVsHDlOlLX/sxgxwZac8DM7zjsHrMOX3B4zX5AXzqcbKYW++1T87c78DYYchdbcxrw7pKdfLdmL5l5RYQEOhjWPprB8U0Y2C6KbrGRBJwmNQnl2QBXx6kqqVn5bE/LYWdGDjvSzWvrgSx2Hcyl+FcUHx3G0A5NGNkxhpGdYghxYia7TXwfNs80a631uAJGPlThlFL1Uk4GrHyfIxvnovtW0+jYzri/C2kIbYZAm8EQP8YszHqCKq49h48ydcF23l+6kx4tGzL1xv4nLrnUc9vSsrlv2mrW7jnCBT1bcPfYjnRuXsnOPkf2mL/bbXNNsDucDBx7H8p2RBDQbjghXc4yDyC1NdVWXZC22VTFr/scxGkGjPe+nsL4sSzflcmP6/ezcGt6SU/ioAAH8dFhxMeEER8dTvum5r1dTBiRISe5HJCfqFcB7osfF7DrYA4hgU5CApwEBgiqpsrf7fksxR8pONBBSICTkEAHwQFOggMdBAc4zMTC1cDtVgpcbgpd7pL/nqqm+/iRo4Ulr8xS75l5Rcds33P4KLmlxr8EBTiIaxJK+5hwOjePoEvzCHq2akRsZDAc3GbWJds+D3YsgNwMCG1ibgD9Jpon4dNYQaGL6XOW8tPytRTlZdEjxsmILq3o3qUjjWNam++qgjFtpeUXuUjceYjpCbv57jczRdgV/Vrxzwu72aohj4IiN6/MTeLthdvJLXBx9hnNuHlYHAPjoqpUmjh0+AjLE1ewaNU6th3Mp2nDcC4e0o3Rw4YjzkpUN9dn6Vsh8T0zo05uuhkC0W6keTiLH8UBZwuW7zzEbymH2Z6Ww/b0HJIP5paZ5zIiJICWjRrQvGEIDRsE0rBBIJEhnvcGASU/R3reGwQ5zSvQidNHE4erKkVupdDlptClFHneRSDAIQQ4HOb+HuisXwFu4nOf8MJPW076HCIQHGC+mCCno+ThvfTXUPobKfv1KAVF5osudLkpquJkqSIQERxAw9Df/8AaNgikecMQ4qPDiIsOI76h0FwO4czeB5n7IGuveU/bCHvXQP4Rc7KIFuaPvNM5pgG/PrVHVIO8QhefJexm6sIdJHsGhfds1ZCBcVF0ah5Bp2YRNIsMximCiHAot4CtB7JJSs0mMfkQy3dkkFfoJizIyTUD23Dz8HbH7zl4GjuUU8B7S3by3pKdHDlaSOPQQMZ0acrgdk1o3jCEFg1DiGwQiMutuNzKkaOFbE/PYUdaDit2HmTp9gxcbiU+Ooy7xnbgol6xp211W4WKCmDrT6amZttcc18AU3XbagDE9jHNEVHxFES0IvloMNsy8tiZnsO+I3mkHDpKalZemQftykz2HOR0EOIJJA7PzVIEisNe+cKCCGXuqcUFD/MygctdvM2tpfab9+KfK3NvvaxvK/59Za/6FeDmTLmDwMS3KXIE43IEUSSBgIA4PN+s+beK4NLfX0UquBBcbvPvIsXsw4lbnKg4cIsTt+dntwTgFicuz7tbAnBJAE6HA6dDcDocOBwOAhyCw1Hq2kBwYAANggIICQog1Oki1OEixFFIsBbicOVDUb5pF3Llm7aJ3HQz+3nmvt8DWGlBEWZmhpZ9IbYvtB4I0Z38v/dYLXC7lQ37Mpm3OZW5m9NYt+cI+ceZ4FbEVAWP6BjD8A7RDG7fhPDg07wUUUk5+UXM35LG7A0H+GVTKkeOHn8dv+Lv+txuzTmvRwu6xUZWW+1KvaZq2pB3LYbdKyBlOWQklU0jDtMhJywGwqLNq0FjcASCw4mKk0IV8twO8osgzwV5RUq+CwrcUOh5L3BBoefdjaAIbhw41IXgwqkuRF041IUDz3upn80d1bw71FXyb0Fx4Mahpf5dvF3dOMRsc+LGUbJfATfidqGq5LQ9k7jL/69+BbiE9x+FtZ+ZAFHkCRbqBtS8a6n34m1ul2dbuZfbZQYPu11mgl91m/fil9bATN+OANNjLyAYnMFmcuPQJqZEFhlb9j2iBUS2MLNDWNXC5VaSD+ayeX8Wh3ILSp4eI4ID6NA0nPYx4bYKshoUudzsPZzH/kzzysorJMDzYBgeHEB8TBhtokLt+K7qUpALh3eZIS9HUkwv4Zw0zyvdPEQfPey5txXf92roPucINPc5R4BpDij5d4BpT3Q4zLs4zMvh/L2AUrzdUWq/OD37Sm93mrk9h/ypngW42uxk4naDu9D8Ebg87yWBs6TVrdy2cu/OIBPMioOaP49Dsyyr/imuS/RWCCjzKpXG4fS8SgeugEq1b1e34wU4W/dyPA4HOIIB275lWVY9VdJwVv/aPevfJ7Isy7IsbICzLMuy6im/aoMTkTTgOGt2nLRoIL0GzlsTbF5rjj/l1+a15vhTfv0pr1Az+W2rqjHedvhVgKspIpJQUSNlXWPzWnP8Kb82rzXHn/LrT3mF2s+vraK0LMuy6iUb4CzLsqx6yQY44y1fZ6AKbF5rjj/l1+a15vhTfv0pr1DL+bVtcJZlWVa9ZEtwlmVZVr1kA5xlWZZVL9kAZ1mWZdVLNsBZlmVZ9ZINcJZlWVa9ZAOcZVmWVS/ZAGdZlmXVS/UuwInIGyLyj1I//0FEDohItog0EZFhIrLV8/MlvsyrZVmWVXP8bqC3iOwEmgFFgAvYAHwAvKVadu11EQkEMoHBqrrGs20O8I2qvlSb+bYsy7Jql7+W4C5U1QigLfAM8DDwXy/pmgEhwPpS29qW+7nSRMSugG5ZluUn/DXAAaCqR1T1G+Aq4CYR6S4i74nI/4lIJ2CzJ+lhEflFRLYB8cC3nirKYBFpKCL/FZF9IrLHc6wTQEQmishiEXlRRA4Cj3uOeUFEkj1Vn2+ISANP+tEikiIifxGRVM85by7Or4g0EJF/i8guETkiIotKHTtYRJaIyGERWSMio2vvm7Qsy6p//DrAFVPV5UAKMKLUti1AN8+PjVR1rKq2B5IxJcBwVc0H3sdUd3YA+gDnALeVOv0gYDvQFHgKeBboBPT2HNMSeKxU+uZAQ8/2W4FXRaSxZ98LQD9gKBAFPAS4RaQl8D3wf57tDwCfi4jXRfwsy7KsE6sXAc5jLyY4VJqINAPGA/epao6qpgIvAleXPq+q/kdVi4A84Hbgz6p6UFWzgKfLpS8EnlTVQlWdCWQDnUXEAdwC3Kuqe1TVpapLPEH2emCmqs5UVbeqzgYSgPNO4nuwLMuygPrUptQSOFjFY9oCgcA+ESne5gB2l0pT+t8xQCiQWCq9AM5SaTI8wbBYLhCOWao9BNhWQT6uEJELS20LBOZW5cNYlmVZv6sXAU5EBmAC3CJMlWJl7QbygehyQam00t1M04GjQDdV3VPFbKZjSoDtgTVe8vGhqt5exXNalmVZFfDrKkoRiRSRC4BpwEequrYqx6vqPuAn4N+eczlEpL2IjKogvRuYCrwoIk09eWgpIudW4lpu4B1gsojEiohTRIaISDDwEXChiJzr2R7i6bDSqiqfx7Isy/qdvwa4b0UkC1Py+TswGbj5+IdU6EYgCDOe7hAwA2hxnPQPA0nAryKSCfwMdK7ktR4A1gIrMNWpzwIOVd0NXAz8DUjDfK4H8d/fj2VZls/53UBvy7Isy6oMW0KwLMuy6iUb4CzLsqx6yQY4y7Isq16yAc6yLMuql/xqHFx0dLTGxcX5OhuWZVlWHZGYmJiuql6nNfSrABcXF0dCQoKvs2FZlmXVESKyq6J9torSsizLqpdsgLMsy7LqJRvgLMsqw+1W1qYc4fV521iZfMjX2bGsk+ZXbXCWZdWsN+Zv4+2FO0jPzgegc7MIfrhvBKVWz7Asv2FLcJZVTfYfyWPa8mSO5Bb6Oisn5WiBixd+3EybqAZMvrIXfx3fhc0HskjcZUtxln+yAc6yqsk/vl7HI1+sZfC/5vCPr9axIz3H11mqknV7j1DkVv44ugOX9m3FDUPaEhEcwMfLkn2dNcs6KTbAWVY12LQ/k9kbDnD1gNZc0LMF01fs5qL/LCIrz39Kc6s87W292zQCIDQogEv7tuT73/ZxMKfAl1mzrJNiA5xlVYPX5m4jLMjJI+O78PwVvXjzxn5k5RexZvcRX2et0lbvPkzrqAZEhweXbLtucFsKXG5mJO4+zpGWVTfZAGdZp2hneg7f/baX6we3pVFoEAD92jZGBFbv9p/2q1XJh+nTunGZbZ2aRTAwLor/LUvG7bZLa1n+xQY4yzpFr8/bRoDTwa0j2pVsiwwJpH1MOKuSD/swZ5W3/0ge+47k0bt1o2P2XTe4DTszclmyLcMHObOsk+ezACcirUVkrohsFJH1InKvr/JiWSdr7+GjfLEqhav6t6ZpREiZfX1aN2LV7sP4w6LCxSXNhASCNAAAIABJREFUPm2ODXDjujcnKiyIT1bYziaWf/FlCa4I+IuqdgUGA38SkTN8mB/LqrL3l+7ErXDHqPhj9vVp05iDOQUkH8yt/YxV0arkwwQ5HZwRG3nMvuAAJ+f1aM4vG1M5WuDyQe4s6+T4LMCp6j5VXen5dxawEWjpq/xYVlW53MrXq/YyulMMrRqHHrO/uDTkD9WUq5IPc0ZsJMEBTq/7x3dvwdFCF/O3pNVyzizr5NWJNjgRiQP6AMu87JskIgkikpCWZv9zWXXHsu0Z7M/M45I+3p/LOjWLIDTIWdL9vq4qcrn5bc9hr9WTxQa1i6JxaCA/rNtXizmzrFPj8wAnIuHA58B9qppZfr+qvqWq/VW1f0yM1yV/LMsnvlq9h7AgJ2d1beZ1v9Mh9Gpl2uHqsk37s8grdNOnTeMK0/z/9u48vK3ySvz490iyLO/7Fju7kxAnZIcEQkLZ9xLKUjrQMjCUttPyK+30N4VCCwMdutBCaWcKwwBlGCi07CklQNjXEJIQSEx2Z7Xj2Fm8W7IlvfPHvXJM4sRKbFnX8vk8jx5d3UX32JZ17vved/G4XZxRUcTra+oIBLWaUg0OcU1wIpKEldweN8Y8G89YlDoS/s4Qi1bVctbkYlK8PVfrgVVN+XlNE/5O5yaFSAKe3kMLyu7OmVxCcyDIBxu1NaUaHOLZilKAh4A1xpi74xWHUkfjzbV1NAeCLJh2+NvG00fkEAwbVlc7t8P3ym0N5Kd7KctJOex+J5bnkZHs4aVVWk2pBod4luDmAl8HThWRlfbj3DjGo1TUnvukmoKMZE4cm3fY/SL9ylY6uJryk+37mDY8p9cZA5I9bk6bWMjiNbvoDIUHKDqljl48W1G+Z4wRY8wUY8w0+/FSvOJRKlqNbZ28ta6eC6YMw+M+/L9QQUYyZTkpjm1JGQyF2bK7lYklGVHtf86xJTS0dfJR1d4YR6ZU38W9kYlSg81Lq3fSEQqzYPqwqPafPiLHsS0pa5v8hA2UZh++ejLi5PEFpHrdvKStKdUgoAlOqSNgjOGxJVsZV5jOsaVZUR0ztSyLmkZ/1ySiTlLT4AdgWJQJzpfk5syKIv62soa2jmAsQ1OqzzTBKXUElm/dR2VNE1edOCrqWa5H5qUBUL2vPZahHZXqBmuUldJeGph0d8WckTQHgixcWROrsJTqF5rglDoCf3p/C5k+a560aA3LtsaorGlwXoLrKsFlRZ/gZo3MYUJRBo99tHVQjLOphi5NcEpFqaahnZcra7n8+BGkej1RH1eWbQ3jVe3ABFfd0E5umvewffkOJCJcOWcEq6ub+HSHc7s/KKUJTqkoPbbEKrF8fc7IIzouM8VDmtftzAS3rz3qBibdLZheSqrXzWNLtsYgKqX6hyY4paLg7wzxxNJtnD6xiOG5Bw+sfDgiwrDsFIdWUbZ3VaEeiQxfEguml/K3T2toaOuIQWRK9Z0mOKWi8MLKava1dfKPc0cd1fFWgvP3b1B9ZIyhuqGd0uwjS9gRV84eSSAY5unlO/o5MqX6hyY4pXrREghyz+INTC7N5IQxhx+55FCcWIJrbO+krSN0VCU4gIphmRw3Kof7365ijwO7QCilCU6pXtyzeD27mv3cfuHkqLsGHKgsJ4U9rR2OGnR5h91t4WjuwUXcfuFkmto7ufm51dqiUjmOJjilDuPzmiYe+WALlx83ghmHmU6mN5FSkpMamkRKlEfSB+5AE0sy+eGZ43m5spZnV1T3V2hK9QtNcEodQjhsuOX5VWSnJPHjsyf06b0i/cycVE0ZiSXaUUwO5ZvzxnD8qFxuW1jpqASulCY4pQ7h8aXbWLGtgZvOnUh2qrdP7xVJIk5KcNUN7SR7XOSl9e1nc7uE3142lbAxfOex5dQ36/045QxRJTgR+Y2ITIp1MEo5xcura7ltYSXzxuVz8RGMWnIoxVk+XALVDmpJWdPgpzQ75ajvK3Y3PDeVey+fzvpdzSz4z/dZV9vcDxEq1TfRluDWAg+IyEci8m0RiW6UWaUGoTfX1nH9EyuYWpbFfVfO7JcEkOR2UZTpc9R4lDsa2vtcPdnd6RVF/PVbJ9AZCnPxfR/w5tq6fntvpY5GVAnOGPOgMWYu8A1gFPCZiPxZRE6JZXBKDbQ31u7iW48tZ0JxBn+6+njSk6Mfkqs3TusqUNNwdKOYHM6Usmxe+N5cRuSmcvUjH/OzF1brrAMqbqK+BycibuAY+7Eb+BT4oYg8GaPYlBow9c0BbnjyE655ZBlj8tP432tmk5WS1K/nGJadQk2jMxKcvzNEfXOgX0twESVZKTz7zydyzdzRPPrhVs69912WbdEJUtXAi/Ye3N3AOuBc4E5jzExjzK+MMRcA02MZoFKxFAyFefTDLZz227d4aVUt/++0cTz/3bnk9LHhRU+GZfvY2eAnHI5/f7HaRuteYF+6CByOL8nNzy6o4IlvziEYNlz6Xx/yk+dW0djWGZPzKdWTaOtfVgO3GGPaeth2fD/Go9SAMMbw5ro67nxpLRvrWjhxbB53LJjM2IL0mJ2zLDuFjlCY3S0BCjOPbvSQ/rK/i0Bs4zhhbB6v3DCfexav508fbOGV1bXccv5EFkwr7Zd7m0odTlQJzhjzsIjkiMhkwNdt/TvGGJ0vQw0qa3Y28e9/X8N7G3czOj+N//7GLE6fWBjzL9xIdWB1Q3vcE9wOO8GVHeU4lEciLdnDLedXcNGMUm5+bjU/+Mun/PXjHdyxYDLlhbG7oFAqqgQnItcC3wfKgJXAHOBD4NTYhaZU/6pr8vPbV9fz1+XbyfQlcesFFVwxeyRez8B0B93fF87P9BEDcspDqmloRwSKspIH7JyThmXx7HdO5ImPt/GrRWs55953+M6Xyrn+1HKS3NolV/W/aKsovw8cBywxxpwiIscA/xa7sJTqP+0dIR58t4r73t5EZyjMNXNHc/2p5X3uvH2knNTZu3pfOwXpySR7op/otD+4XMIVs0dyZkUxd760ht+/voG31tXxu69OY0wMq4fV0BTtZZPfGOMHEJFkY8xaoG9jF1nvdbaIrBORjSJyY1/fT6nujDE8/0k1p/72LX67eD3zxxWw+Acn89PzKwY8uQFk+jykJ3scMZxVTWN7zBqYRKMgI5l7vjqN+66YwdY9bZz3+/d4Yuk2HbBZ9atoS3A7RCQbeB5YLCL7gJq+nNjudvCfwBnADuBjEVlojPm8L++rFMCGXc3c8vxqPtq8l2NLs/jdV6cx+yinuukvIkJpdoojElz1vnYmlcZ/vIZzji1h+ogc/uWpldz07CreXFvHLy+eQm4MWrGqoSfaRiYX2Yu3icibQBbwch/PfTyw0RhTBWD3p7sQ0ASnjlprIMjv39jAQ+9uJi3Zw50XHcvlxw3H5XJGi71h2b64V1EaY6hp9HPmpOK4xhFRnOXjf6+ZzUPvbeauV9Zx1u/e4TeXTuXk8QXxDk0NcodNcCKS28PqVfZzOtCX3pulwPZur3cAs/vwfmoIM8bwSmUtt//tc2oa/Vw6s4wbzzmGvPSBa0QRjWHZKazc3hDXGPa2dtARDFOSFd+WnN25XMI354/hxPI8vv/kSq56eCnnTynh5vMmUpIVv6pUNbj1VoJbDhhAgBHAPns5G9gGjO7DuXu6pD6oAl5ErgOuAxgxou9Nz4KhMB2hMB1B6xEIWq+DIYPX4yLZ48KX5O56djvkyl8d2tY9rdy6sJK31tVzTHEGv//adGaN6unaLP6GZaewr62Tto4gqd7+GwbsSNQ2WZ28i+PcVaEnk4Zl8eL1J3HfW5u4/+1NvL6mju+dWs6180YPeIMYdfTCYUPYGELGYAyEwvZyGELGEAobjL3dWrb2CRv7uDD2s5USIrdmDaZrOTfNy/Dcw3dzOex/mDFmNICI3A8sNMa8ZL8+Bzi9L78ArBLb8G6vy+jhvp4x5gHgAYBZs2aZB9+t4g9vbMTtElxi3ddwCbhErIdr/3JXAguGupLakQ4ikeQWkj1ufEkukj1u0pLdpCVbjQXSkz1dyx6X4HIJIpCa5CHDF3kkfWE5Pdla9iXpP2tf+TtD3P/2Jv741ia8bhc/Pb+Cq04YicfBTc5Lu7WkLC/MiEsMkVFMih1UguvOl+TmB2eM55KZZdzx4ufc9co6nlq2nZ9dUMGpxxTFO7xBLRQ2tPiDNPk7aWzvpMnfSVN70H62H/6g/bx/W7M/SDBsfX92Ja+wsV53LVuvQwM0Us8lM8v4zaVTD7tPtJeQxxljvh15YYxZJCJ39CU44GNgnIiMBqqBy4F/6O2g8UUZLJg2rOsXGzZW9VQk60euCsIGvG5XV6nM63F1vT5wOdnjwuNy0RkK4+8MEQj2/OzvDNPWEaQlYD1qG/20BoI0B4L2H926OukIhnv94b1uFxk+D+l2y7o0r4fUZDfZKUnkpSeTl+4lP816zktPJi/NS356MileTYwAb62r49aFlWzd08YFU4dxy3kTKXJgieRAkaRS2xiIX4KzS3BOr/obnpvKA9+Yxdvr6/m3hZVc88gyTp9YyJ1fOZbCDOf/rftbSyDI3pYOWjuCtAaCtHaEaLW/i1rtR3MgSIs/2LW+2R/cn7DaO2kOHH7gaxHISPaQmZJEpi+JzBQPI3JTSfd5SHK5ugoQVgFD7GW7UOES3JECh0sO2I+DlyP7iFUwcLus/cVe53ZFCjBWLZp0i1EEijN7//xGm+B2i8gtwGNY1YhXAnuiPLZHxpigiHwPeAVwAw8bYyp7O27++ALmO/zmc2cobH3Y7CulFr+13BzotK+erNctAevKyPpwhtjb2kFVfSt7WgK0doR6fO9Ur5vctP1JLy/NS266115OJtdOjMOyfY67/9QfahrauePFz1m0upYxBWk8fu1s5pbnxzusqEXue+2M46DLtY1+XAL56YOjpeLJ4wt4+Yb5/On9zdy9eD3n3vsud182zfHfA9EwxuDvDNPk72R3S4DaRj+1TX7r2V7e2ehnV6O/1+QE4PW4yEi2LpzTvNZzWU4KmSWZZKZ47KSVRKbPSmJZ3RJZZkoS6V6PYxpk9YdoE9zXgFuB57AS3Dv2uj6xqzxf6uv7OE2S20V2qrdPfa3aO0LsaQ2wp6WD3S32c2uA3c0d7G0NsKe1g9pGP5/XNFmNBkIHlxqLMpOZNCyLScMymTQsk4qSLIbn9s8ElwPNGMOfl27jzr+vIWQM//+sCYPyvkyklBmpJoyHnY1+CjN8jq7KPZDX4+JbJ4/lSxMKuf6JFXzj4aV8++Sx/OjM8YPi5zDGsG1vGx9V7eXznU1sqm+hqr6VumY/naGDq/RcAoUZPoqyfJQXpHNSeT7FWT7y0rxdt0Yit0vSvPtfD7b/h1jrrRXlTcDLxphPsEYzUQMkxeumzJtKWU7vYwUaY2i2qy/2tHawpyXAtr1tVNY0UVnTyNvr67vqxTN8HipKMvcnvtJMxhakO3qopOqGdm585jPe3bCbueV5/PIrU3q9uexUviSrBL6zKX4JbleTnyKH3n/rzYTiDF747knc/mIl97+9iXW1TfzhH2b067x9/aU1EOSd9fUsXrOLDzbu6aoaTvW6GVuQznGjcijOSrFKUSkeclO9FGf5KMlKIT/dOygSt9P19qnYDHxfRKZizf+2CHjVGLMv5pGpqImIVc3gS2JUftpB2/2dIdbVNnclvMqaJv68dCv+TqvU5/W4qCjJZG55HnPL85k5MscRV4LGGP7y8XZ+/vc1hI3h5wsmc8XsEYOyBNpdcaYv7iW48kE8LFaK180vvjKFScOyuHVhJZfc9wEP/eNx/T5569EwxvDext08+uFW3l5XT0coTFZKEieV5zNnbB4njMllbEH6oP8MDxa9taJ8EngSQESmA2cDz9qjkLyGVbpbGvMoVZ/4ktxMHZ7N1OHZXeuCoTCbd7d2Jb0V2xq4/+0q/vPNTaR63ZxyTCHnTC7mlAmFpMXh6nhnYzs/fmYV76yv54Qxefz6ksFbajtQSZaPmjgmuNpGPycNovuWh3LlnJGMyE3lu4+v4ML/eJ8/XjGD40fHp3tIIBji6eU7+NP7W9hY10J+upcr54zkjIoijhuVo6WxOIn6m8uupvwE+IWIZGINsXUtoAluEPK4XYwrymBcUQYLppcC0OzvZEnVXt5YW8erlbX8/bOdJHtcnDy+gHOPLeHUiYVk+vp3lusDdYbCPPrhVn63eD3BsOH2Cydx5eyRCXXjuzjLx4pt8akEafZ30hIIOraLwJGaP76AZ/75RK57dBlf++8l3Hj2MVw7b/SAlZA6Q2GeXbGD37++keqGdqaUZXH3ZVM5b0qJI2pBhrpop8txA+cBo7ofY4y5LjZhqXjI8CVxRkURZ1QU8fMFk/l4y14WrdrJy5W1vPr5LrxuFyeNy+fsycWcWVHU7wMWf7hpD7ctrGTdrmbmjy/gjgsnMTLv4CrXwS7S2dvfGRrw/pC7uroIJEaCA6vr0MLrT+Jfn/qMf39pDcu27uXXF08lKzV2F2PtHSGeXr6d/353M9v2tjF1eDa/+MqxzBuXr9WPDhJtCe5vgB9rmK5Icz0d9juBuV3CnDF5zBmTx60XTOKT7Q0sWrWTRatreWNtHT9xCSeMzeOcySWcOamI/D50Sfh4y17ufW0D723cTVlOCg98fSZnVBQl7BdFcbeWlD3dM42l2sbAF2JIFJm+JO67cgYPvbeZXy5ay9n3WuNZ9ncXkt0tAR79cCv/++EW9rV1Mm14Nj89v2JAJsxVRy7aBFdmjJkS00iUY7lcwsyROcwcmcPN501kVXUji1bXsmjVTn7y3CpueX4Vx4/O5ZzJJZw9uTiqDtftHSFe/byWJ5du58OqPeSne7n53IlcOWdkwndm398XbuATXKT/XaJUUXYnIlw7bwzHj87lhr+s5IoHP+KfThrNDaePI6OPVeub6lt46L3NPLN8B4FgmNMnFvGtk8cwa2SOJjYHizbBLRKRM40xr8Y0GuV4IsKUsmymlGXzr2dNYG1tc1eyu3VhJbcurGRUXiozRuQwdXg22alJpHo9JLmF+uYAOxv9bKpv4fU1dbQEggzL8nHzuRO5Ys6IuI3NONC6RjNpGvjO3pHWm4Nh1JejNaUsm79fP49fLFrDQ+9t5qll27l67miumTv6iKot65sD/P2zGl74tIZPtjXg9bi4eEYZ184bzdhB3Ap1KIn2G2UJ8JyIuIBOrFFTjDEmM2aRKccTESaWZDKxJJMfnjGejXUtvL5mF8u37uOdDfU8+0l1j8cVZCRz7rHFXDS9jNmjcxOqAUk0iruV4AZabZOfnNSkhB8LNcXr5vYLJ3PZrOH84Y0N3Pv6Bh58t4r54ws4ZUIh88bnU5zp6yp9hcOGnU1+NuxqZknVXt7bWE9lTRPGwDHFGfz47GO4ZGYZBRmJNzpQIos2wf0WOAFYZXTKXXUI5YXplBdaV7bGGOqaAzT7g/Z4niHy05MpyvQl/Jdrb1K9HrJSkuLSF6620U+xw8eg7E+TS7P4r6/PYm1tE//zwRbeXFvPotW1gHWfOTsliXSfh9pGPwF7DNkktzB9RA4/OH08Z00qZkJxfMYMVX0XbYLbAKzW5KaiJSIUZfoo0jJ+j0qyfNQ0xKcEl0gtKKN1THEmv/jKFIwxrK1tZknVHna3BNjXZo0He2ZFMqPz0xmdn8aUsqy49P1U/S/av+JO4C0RWQQEIiuNMXfHJCqlElxxli9u9+CmlGX3vmOC6l6trhJftAlus/1Ish9KqT4oyfKxurpxQM8ZCIbY09oxJEtwamiKNsG9BPyEL3b0NsDtMYhJqYRXnJnC7pYOAsHQgI14Uddk94HTBKeGiGgT3GPAj4DV7O/orZQ6SpFSVF1TYMDG2Iy02ky0Tt5KHUq0Ca7eGPO3mEai1BDSvavAwCU4656fVlGqoSLaBHeriDwIvM4XG5k8G5OolEpw8ZjZOzIO5WCdC06pIxVtgrsaOAargUn3sSg1wSl1FLpGMxnAvnA7G/2ked1kaBN4NURE+0mfaow5NqaRKDWEZPiSSE/2DOhoJrua/BRn+XTsRDVkRDsL3xIRqYhpJEoNMcVZAzuz985Gv7agVENKtAnuJGCliKwTkc9EZJWIfBbLwJRKdCVZPnY2DVyCq230U5w5dIbpUiraKsqzYxqFUkNQcaaP9bvqB+RcwVCYuuYAxVk6WLAaOqJKcMaYrf15UhG5C7gA6AA2AVcbYxr68xxKOV1Jdgp1zQE6Q2GS3NFWphydHfvaCYVNQs6QrtShxPa/6tAWA5PtSVTXAzfFKQ6l4qYky4cxUNcc6H3nPqra3QLA2AJNcGroiEuCM8a8aowJ2i+XAGXxiEOpeBqeY3Xw3rq7Nebnqqq3zjEmXyfqVENHvEpw3V0DLIp3EEoNtMjceRvrW2J+rk31reSkJpGT5o35uZRyipj1+BSR14DiHjbdbIx5wd7nZiAIPH6Y97kOuA5gxIgRMYhUqfgoykwmPdnDxrrYJ7iq+hbGFGjpTQ0tMUtwxpjTD7ddRK4CzgdOO9xEqsaYB4AHAGbNmqUTrqqEISKMLUhj0wCU4Kp2t/Kl8QUxP49SThKXKkoRORv4MfBlY0xbPGJQygnGFqbHvATX7O+kvjmgJTg15MTrHtx/ABnAYhFZKSL3xykOpeKqvDCdXU0BmvydMTtHVwMTbUGphpi4jLpqjCmPx3mVcppyu1RVVd/KtOHZve7f0NZBqteD1xP9tal2EVBDlQ4rrlQcjY20pKxr6TXBVdY0csEf3gOgNCeF8YUZ3LFgMsOyDz/8VlV9Ky6BEbma4NTQ4oRuAkoNWSNzU0lyS1T34Z5Yuo0kt4vvnlLO9OE5vLOhngfeqer1uKr6Vobnph5RqU+pRKCfeKXiyON2MSovrdcE5+8M8cLKGs6ZXMy/nDmB339tOmdWFPPCymo6guHDHrupvoUx+Vp6U0OPJjil4qy8MJ2qXroKvFJZS7M/yGWzhnetu2RmGfvaOnlj7a5DHhcOG7bsadUWlGpI0gSnVJyNLUhn6962w5bEnlq2g7KcFOaMyetaN29cPoUZyTy9fMchj6tpbMffGdYWlGpI0gSnVJyVF6YTsktaPdmxr433N+3mkplluFz7Z+P2uF1cNKOUN9fVU9fc87xyOgalGso0wSkVZ+XdWlL25Jnl1RgDF884eEzyS2eWEQobXvikpsdjI1Wf2kVADUWa4JSKs0j14aYeElw4bHhq+XbmlucxPDf1oO3lhRlMG57N08t30NOId1W7W0lP9lCQoROdqqFHE5xScZbq9VCandLjrAIfVu1hx752Lp05vIcjLZfOKmPdrmY+29F40Laq+lbGFKQhIj0cqVRi0wSnlAMcakzKP3+0jezUJM6e3NPEHJbzpwwj1evmkQ+2HLStSrsIqCFME5xSDlBekM6m+hbC4f3VjHXNfl6prOXiGWX4ktyHPDYrJYnLjxvB3z6toaahvWv9jn1t1DT6GatdBNQQpQlOKQcoL0zH3xn+QkvKp5btIBg2fO343udBvOakURjg4fc2d637+Ytr8CW5+MrMgxunKDUUaIJTygFOKs/Hl+Tipy+sJhQ2hMOGJz/expwxuV2tLA+nLCeV86eU8MTSbTS2d/L2+nperqzl+lPHUdrLWJVKJSpNcEo5wIi8VP7ty5N4f+Me/vjmRt7duJvte9v5h9kjo36P6+aPobUjxCPvb+G2hZWMzk/j2nmjYxi1Us6mswko5RCXzRrOB5v2cM9r6xlflEFumpezJhVFffykYVnMG5fP715fjzHwyNXHkew59L07pRKdluCUcggR4d8vOpYRuamsrW3m0lllR5ygrps/BmPgrElFfGlCYYwiVWpw0BKcUg6Snuzhj1fM5NevrOWqE0Yd8fEnledzz1enMn9cQf8Hp9QgIz2NfuBUs2bNMsuWLYt3GEoppRxCRJYbY2b1tE2rKJVSSiUkTXBKKaUSkiY4pZRSCWlQ3YMTkXpgawzeOh/YHYP3jQWNNXYGU7waa+wMpngHU6wQm3hHGmN6bFU1qBJcrIjIskPdpHQajTV2BlO8GmvsDKZ4B1OsMPDxahWlUkqphKQJTimlVELSBGd5IN4BHAGNNXYGU7waa+wMpngHU6wwwPHqPTillFIJSUtwSimlEtKQTXAiMk1ElojIShFZJiLHd9t2k4hsFJF1InJWPOPsTkSut2OqFJFfd1vv1Hh/JCJGRPK7rXNUrCJyl4isFZHPROQ5Ecnuts1RsUaIyNl2TBtF5MZ4x9OdiAwXkTdFZI39Of2+vT5XRBaLyAb7OSfesUaIiFtEPhGRF+3XTo41W0Setj+za0TkBKfGKyI/sD8Dq0XkCRHxDXisxpgh+QBeBc6xl88F3rKXK4BPgWRgNLAJcDsg3lOA14Bk+3Whw+MdDryC1W8x36mxAmcCHnv5V8CvnBqrHZfbjmUM4LVjrIh3XN3iKwFm2MsZwHr7d/lr4EZ7/Y2R37MTHsAPgT8DL9qvnRzr/wDX2steINuJ8QKlwGYgxX79V+AfBzrWIVuCAwyQaS9nATX28oXAk8aYgDFmM7AROL6H4wfad4BfGmMCAMaYOnu9U+O9B/hXrN9zhONiNca8aowJ2i+XAGX2suNitR0PbDTGVBljOoAnsWJ1BGPMTmPMCnu5GViD9WV3IdaXM/bzgvhE+EUiUgacBzzYbbVTY80E5gMPARhjOowxDTg0XqzZalJExAOkYn3HDmisQznB3QDcJSLbgd8AN9nrS4Ht3fbbYa+Lt/HAPBH5SETeFpHj7PWOi1dEvgxUG2M+PWCT42I9wDXAInvZqbE6Na6DiMgoYDrwEVBkjNkJVhIEnDJZ3e+wLsTC3dY5NdYxQD3wJ7tK9UERScOB8RpjqrG+V7cBO4FGY8yrDHCsCT0fnIi8BhT3sOlm4DTgB8aYZ0TkMqyrotMB6WH/AWlq2ku8HiAHmAMcB/xVRMYQp3h7ifUnWFUvjfpaAAAEXUlEQVR/Bx3Ww7q4xmqMecHe52YgCDweOayH/Z3Q5NipcX2BiKQDzwA3GGOaRHoKO75E5HygzhizXES+FO94ouABZgDXG2M+EpF7sar5HMe+t3YhVvV+A/CUiFw50HEkdIIzxpx+qG0i8ijwffvlU+yvotiBdf8oooz91Zcx1Uu83wGeNVbl9VIRCWON6xaXeA8Vq4gci/Wh/tT+UisDVtiNeBwVa4SIXAWcD5xm/34hjp+DXjg1ri4ikoSV3B43xjxrr94lIiXGmJ0iUgLUHfodBsxc4Msici7gAzJF5DGcGStYf/sdxpiP7NdPYyU4J8Z7OrDZGFMPICLPAicywLEO5SrKGuBke/lUYIO9vBC4XESSRWQ0MA5YGof4DvQ8VpyIyHisG8y7cVi8xphVxphCY8woY8worH/KGcaYWqfFClaLRODHwJeNMW3dNjkuVtvHwDgRGS0iXuByrFgdQayrmoeANcaYu7ttWghcZS9fBbww0LEdyBhzkzGmzP6cXg68YYy5EgfGCmD/D20XkQn2qtOAz3FmvNuAOSKSan8mTsO6HzugsSZ0Ca4X3wTutW+A+oHrAIwxlSLyV6wPThD4rjEmFL8wuzwMPCwiq4EO4Cq7tOHUeA/i0N/tf2C1lFxslziXGGO+7dBYMcYEReR7WC1U3cDDxpjKOIfV3Vzg68AqEVlpr/sJ8EusavV/wvryuzRO8UXDybFeDzxuX9xUAVdjFVQcFa9dhfo0sALr/+cTrFFM0hnAWHUkE6WUUglpKFdRKqWUSmCa4JRSSiUkTXBKKaUSkiY4pZRSCUkTnFJKqYSkCU6pASQiLb1szxaRf+72epjd3DoyA8a5R3HO20TkR0cerVKDmyY4pZwlG+hKcMaYGmPMJfbLaVgzXyiloqAJTqk4EJF0EXldRFaIyCoRicwI8EtgrFjzFN4lIqPs+bS8wO3AV+1tXz2wZGbvN8pevlmsOeNeAyZ022esiLwsIstF5F0ROWbAfmilBthQHslEqXjyAxfZAxHnA0tEZCHW2IKTjTHToGtEfowxHSLyM2CWMeZ79rbbenpjEZmJNfTUdKz/8RXAcnvzA8C3jTEbRGQ28EfsIeCUSjSa4JSKDwHuFJH5WFO1lAJF/fTe84DnImNr2okzMsL/iVgju0f2Te6ncyrlOJrglIqPK4ACYKYxplNEtmCNaH8kgnzxNkP343sag88FNERKh0olOr0Hp1R8ZGHNRdYpIqcAI+31zUDGIY45cNsWrPnBEJEZWNMUAbwDXCQiKSKSAVwAYIxpAjaLyKX2MSIiU/vvR1LKWTTBKRUfjwOzRGQZVmluLYAxZg/wvt1g5K4DjnkTqIg0MsGacy3XHrX/O8B6+z1WAH8BVtr7vNvtPa4A/klEPgUqsSalVCoh6WwCSimlEpKW4JRSSiUkTXBKKaUSkiY4pZRSCUkTnFJKqYSkCU4ppVRC0gSnlFIqIWmCU0oplZA0wSmllEpI/we3FYR9Rj723AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, constrained_layout=True, sharex=True)\n",
    "\n",
    "ax[0].plot(ds1['lat'], 86400*ds1['pr'].mean(dim=('time','lon')), label='Control')\n",
    "ax[0].plot(ds2['lat'], 86400*ds2['pr'].mean(dim=('time','lon')), label='LWoff')\n",
    "ax[0].set_title(\"CNRM Aquaplanet precipitation\", loc='left')\n",
    "ax[0].legend()\n",
    "ax[0].set_ylabel(\"mm/day\")\n",
    "ax[1].plot(z.lat, 86400*z)\n",
    "ax[1].set_title(\"Difference\", loc='left')\n",
    "ax[1].set_ylabel(\"mm/day\")\n",
    "ax[1].set_xlabel(\"latitude\")\n",
    "ax[1].set_xlim([-90,90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now look at wap (OMEGA)\n",
    "ds1wap = xr.open_dataset(\"/Volumes/Glyph6TB/CMIP6/wap_Amon_CNRM-CM6-1_aqua-control_r1i1p1f2_gr_197901-198812.nc\")\n",
    "ds2wap = xr.open_dataset(\"/Volumes/Glyph6TB/CMIP6/wap_Amon_CNRM-CM6-1_aqua-control-lwoff_r1i1p1f2_gr_197901-198812.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide data repr</title>\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide attributes</title>\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c01d6303-bef6-4b01-a967-a860669abbba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c01d6303-bef6-4b01-a967-a860669abbba' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 128</li><li><span class='xr-has-index'>lon</span>: 256</li><li><span class='xr-has-index'>plev</span>: 19</li><li><span class='xr-has-index'>time</span>: 120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57a24f56-ec26-48c2-bbd0-458c5719720e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57a24f56-ec26-48c2-bbd0-458c5719720e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-88.93 -87.54 ... 87.54 88.93</div><input id='attrs-a0454593-ae2f-4509-9336-4fd7f33913d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0454593-ae2f-4509-9336-4fd7f33913d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-748c94a1-f7ab-49ac-a091-4d88077b075a' class='xr-var-data-in' type='checkbox'><label for='data-748c94a1-f7ab-49ac-a091-4d88077b075a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-88.927735, -87.538705, -86.141472, -84.742386, -83.342596, -81.942466,\n",
       "       -80.542146, -79.14171 , -77.741196, -76.340629, -74.940023, -73.539389,\n",
       "       -72.138732, -70.738059, -69.337372, -67.936673, -66.535966, -65.135251,\n",
       "       -63.73453 , -62.333803, -60.933072, -59.532337, -58.131598, -56.730857,\n",
       "       -55.330112, -53.929366, -52.528617, -51.127867, -49.727115, -48.326361,\n",
       "       -46.925606, -45.52485 , -44.124093, -42.723335, -41.322576, -39.921816,\n",
       "       -38.521056, -37.120294, -35.719532, -34.31877 , -32.918007, -31.517244,\n",
       "       -30.11648 , -28.715716, -27.314951, -25.914186, -24.513421, -23.112655,\n",
       "       -21.71189 , -20.311124, -18.910357, -17.509591, -16.108824, -14.708057,\n",
       "       -13.30729 , -11.906523, -10.505756,  -9.104989,  -7.704221,  -6.303454,\n",
       "        -4.902687,  -3.501919,  -2.101151,  -0.700384,   0.700384,   2.101151,\n",
       "         3.501919,   4.902687,   6.303454,   7.704221,   9.104989,  10.505756,\n",
       "        11.906523,  13.30729 ,  14.708057,  16.108824,  17.509591,  18.910357,\n",
       "        20.311124,  21.71189 ,  23.112655,  24.513421,  25.914186,  27.314951,\n",
       "        28.715716,  30.11648 ,  31.517244,  32.918007,  34.31877 ,  35.719532,\n",
       "        37.120294,  38.521056,  39.921816,  41.322576,  42.723335,  44.124093,\n",
       "        45.52485 ,  46.925606,  48.326361,  49.727115,  51.127867,  52.528617,\n",
       "        53.929366,  55.330112,  56.730857,  58.131598,  59.532337,  60.933072,\n",
       "        62.333803,  63.73453 ,  65.135251,  66.535966,  67.936673,  69.337372,\n",
       "        70.738059,  72.138732,  73.539389,  74.940023,  76.340629,  77.741196,\n",
       "        79.14171 ,  80.542146,  81.942466,  83.342596,  84.742386,  86.141472,\n",
       "        87.538705,  88.927735])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.406 2.812 ... 357.2 358.6</div><input id='attrs-80c87859-7174-4209-9c32-b663c5625698' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80c87859-7174-4209-9c32-b663c5625698' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a8178b0-36b4-4f72-9653-52d0882f422a' class='xr-var-data-in' type='checkbox'><label for='data-1a8178b0-36b4-4f72-9653-52d0882f422a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100000.0 92500.0 ... 500.0 100.0</div><input id='attrs-f1afa305-5c21-4461-9e3d-5e4be556064e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1afa305-5c21-4461-9e3d-5e4be556064e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd5f3363-ba11-4773-a309-8025c64085ee' class='xr-var-data-in' type='checkbox'><label for='data-fd5f3363-ba11-4773-a309-8025c64085ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>plev</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><pre class='xr-var-data'>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n",
       "        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n",
       "         1000.,    500.,    100.], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 1988-12-16T12:00:00</div><input id='attrs-41bcef89-0934-4d4d-9fb7-140df42ceb9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41bcef89-0934-4d4d-9fb7-140df42ceb9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dddc641-f203-45ae-b0bd-d8fbbb9ea103' class='xr-var-data-in' type='checkbox'><label for='data-7dddc641-f203-45ae-b0bd-d8fbbb9ea103' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, &#x27;1979-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-05-16T12:00:00.000000000&#x27;, &#x27;1979-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-07-16T12:00:00.000000000&#x27;, &#x27;1979-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1979-09-16T00:00:00.000000000&#x27;, &#x27;1979-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1979-11-16T00:00:00.000000000&#x27;, &#x27;1979-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-16T12:00:00.000000000&#x27;, &#x27;1980-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-16T12:00:00.000000000&#x27;, &#x27;1980-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-05-16T12:00:00.000000000&#x27;, &#x27;1980-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-07-16T12:00:00.000000000&#x27;, &#x27;1980-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-09-16T00:00:00.000000000&#x27;, &#x27;1980-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-11-16T00:00:00.000000000&#x27;, &#x27;1980-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-16T12:00:00.000000000&#x27;, &#x27;1981-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-03-16T12:00:00.000000000&#x27;, &#x27;1981-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-05-16T12:00:00.000000000&#x27;, &#x27;1981-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-07-16T12:00:00.000000000&#x27;, &#x27;1981-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-16T00:00:00.000000000&#x27;, &#x27;1981-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-16T00:00:00.000000000&#x27;, &#x27;1981-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-16T12:00:00.000000000&#x27;, &#x27;1982-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-03-16T12:00:00.000000000&#x27;, &#x27;1982-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-05-16T12:00:00.000000000&#x27;, &#x27;1982-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-07-16T12:00:00.000000000&#x27;, &#x27;1982-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-09-16T00:00:00.000000000&#x27;, &#x27;1982-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-11-16T00:00:00.000000000&#x27;, &#x27;1982-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1983-01-16T12:00:00.000000000&#x27;, &#x27;1983-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-03-16T12:00:00.000000000&#x27;, &#x27;1983-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-05-16T12:00:00.000000000&#x27;, &#x27;1983-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-07-16T12:00:00.000000000&#x27;, &#x27;1983-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1983-09-16T00:00:00.000000000&#x27;, &#x27;1983-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1983-11-16T00:00:00.000000000&#x27;, &#x27;1983-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-01-16T12:00:00.000000000&#x27;, &#x27;1984-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-03-16T12:00:00.000000000&#x27;, &#x27;1984-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-05-16T12:00:00.000000000&#x27;, &#x27;1984-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-07-16T12:00:00.000000000&#x27;, &#x27;1984-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-09-16T00:00:00.000000000&#x27;, &#x27;1984-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-11-16T00:00:00.000000000&#x27;, &#x27;1984-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-16T12:00:00.000000000&#x27;, &#x27;1985-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-03-16T12:00:00.000000000&#x27;, &#x27;1985-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-05-16T12:00:00.000000000&#x27;, &#x27;1985-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-07-16T12:00:00.000000000&#x27;, &#x27;1985-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1985-09-16T00:00:00.000000000&#x27;, &#x27;1985-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1985-11-16T00:00:00.000000000&#x27;, &#x27;1985-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1986-01-16T12:00:00.000000000&#x27;, &#x27;1986-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-03-16T12:00:00.000000000&#x27;, &#x27;1986-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-05-16T12:00:00.000000000&#x27;, &#x27;1986-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-07-16T12:00:00.000000000&#x27;, &#x27;1986-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1986-09-16T00:00:00.000000000&#x27;, &#x27;1986-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1986-11-16T00:00:00.000000000&#x27;, &#x27;1986-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1987-01-16T12:00:00.000000000&#x27;, &#x27;1987-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-03-16T12:00:00.000000000&#x27;, &#x27;1987-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-05-16T12:00:00.000000000&#x27;, &#x27;1987-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-07-16T12:00:00.000000000&#x27;, &#x27;1987-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1987-09-16T00:00:00.000000000&#x27;, &#x27;1987-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1987-11-16T00:00:00.000000000&#x27;, &#x27;1987-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-01-16T12:00:00.000000000&#x27;, &#x27;1988-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-03-16T12:00:00.000000000&#x27;, &#x27;1988-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-05-16T12:00:00.000000000&#x27;, &#x27;1988-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-07-16T12:00:00.000000000&#x27;, &#x27;1988-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-09-16T00:00:00.000000000&#x27;, &#x27;1988-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-11-16T00:00:00.000000000&#x27;, &#x27;1988-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-4f350943-8184-4833-a676-e72e06413174' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f350943-8184-4833-a676-e72e06413174' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ebe329b9-c562-4d26-a779-891bc2a5f1f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebe329b9-c562-4d26-a779-891bc2a5f1f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-625e677c-c50b-4a34-ac6f-e0668188e38c' class='xr-var-data-in' type='checkbox'><label for='data-625e677c-c50b-4a34-ac6f-e0668188e38c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-02-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1979-02-01T00:00:00.000000000&#x27;, &#x27;1979-03-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1979-03-01T00:00:00.000000000&#x27;, &#x27;1979-04-01T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;1988-10-01T00:00:00.000000000&#x27;, &#x27;1988-11-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1988-11-01T00:00:00.000000000&#x27;, &#x27;1988-12-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1988-12-01T00:00:00.000000000&#x27;, &#x27;1989-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>wap</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.0019248935 ... -5.1684023e-05</div><input id='attrs-90d4a3a0-b697-4bf0-96c0-af158a17f55e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90d4a3a0-b697-4bf0-96c0-af158a17f55e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0faac709-0b08-4cf4-83e2-89211ea02d18' class='xr-var-data-in' type='checkbox'><label for='data-0faac709-0b08-4cf4-83e2-89211ea02d18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>omega (=dp/dt)</dd><dt><span>units :</span></dt><dd>Pa s-1</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>interval_operation :</span></dt><dd>900 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>description :</span></dt><dd>commonly referred to as &quot;omega&quot;, this represents the vertical component of velocity in pressure coordinates (positive down)</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div><pre class='xr-var-data'>array([[[[-1.924893e-03, ..., -1.924893e-03],\n",
       "         ...,\n",
       "         [ 7.167562e-05, ...,  7.167562e-05]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-1.744600e-04, ..., -1.744600e-04],\n",
       "         ...,\n",
       "         [-1.812471e-04, ..., -1.812471e-04]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[-3.422337e-03, ..., -3.422337e-03],\n",
       "         ...,\n",
       "         [ 9.322703e-03, ...,  9.322703e-03]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-2.327669e-04, ..., -2.327669e-04],\n",
       "         ...,\n",
       "         [-5.168402e-05, ..., -5.168402e-05]]]], dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-4e9df106-f6c1-4f76-8de5-efeb19c14a32' class='xr-section-summary-in' type='checkbox'  ><label for='section-4e9df106-f6c1-4f76-8de5-efeb19c14a32' class='xr-section-summary' >Attributes: <span>(51)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>creation_date :</span></dt><dd>2018-09-20T17:17:22Z</dd><dt><span>description :</span></dt><dd>Extended version of CMIP5/CFMIP-2 aquaControl experiment. Aquaplanet (no land) experiment with no seasonal cycle forced with specified zonally symmetric SSTs</dd><dt><span>title :</span></dt><dd>CNRM-CM6-1 model output prepared for CMIP6 / CFMIP aqua-control</dd><dt><span>activity_id :</span></dt><dd>CFMIP</dd><dt><span>contact :</span></dt><dd>contact.cmip@meteo.fr</dd><dt><span>data_specs_version :</span></dt><dd>01.00.21</dd><dt><span>dr2xml_version :</span></dt><dd>1.13</dd><dt><span>experiment_id :</span></dt><dd>aqua-control</dd><dt><span>experiment :</span></dt><dd>aquaplanet control</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>2</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS.CNRM-CM6-1.aqua-control.none.r1i1p1f2</dd><dt><span>grid :</span></dt><dd>data regridded to a T127 gaussian grid (128x256 latlon) from a native atmosphere T127l reduced gaussian grid</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>comment :</span></dt><dd>a 20-year spin-up starting from a uniform state at rest was performed before runnning the experiment</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution_id :</span></dt><dd>CNRM-CERFACS</dd><dt><span>institution :</span></dt><dd>CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France)</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CNRM-CERFACS is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at http://www.umr-cnrm.fr/cmip6/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>http://www.umr-cnrm.fr/cmip6/references</dd><dt><span>source :</span></dt><dd>CNRM-CM6-1 (2017):  aerosol: prescribed monthly fields computed by TACTIC_v2 scheme atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km) atmosChem: OZL_v2 land: Surfex 8.0c ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m) seaIce: Gelato 6.1</dd><dt><span>source_id :</span></dt><dd>CNRM-CM6-1</dd><dt><span>source_type :</span></dt><dd>AGCM</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>variable_id :</span></dt><dd>wap</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f2</dd><dt><span>EXPID :</span></dt><dd>CNRM-CM6-1_aqua-control_r1i1p1f2</dd><dt><span>CMIP6_CV_version :</span></dt><dd>cv=6.2.3.0-7-g2019642</dd><dt><span>dr2xml_md5sum :</span></dt><dd>92ddb3d0d8ce79f498d792fc8e559dcf</dd><dt><span>xios_commit :</span></dt><dd>1442-shuffle</dd><dt><span>nemo_gelato_commit :</span></dt><dd>49095b3accd5d4c_6524fe19b00467a</dd><dt><span>arpege_minor_version :</span></dt><dd>6.3.2</dd><dt><span>branch_method :</span></dt><dd>no parent</dd><dt><span>parent_activity_id :</span></dt><dd>no parent</dd><dt><span>parent_experiment_id :</span></dt><dd>no parent</dd><dt><span>parent_mip_era :</span></dt><dd>no parent</dd><dt><span>parent_source_id :</span></dt><dd>no parent</dd><dt><span>parent_time_units :</span></dt><dd>no parent</dd><dt><span>parent_variant_label :</span></dt><dd>no parent</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/38c16b97-7447-4595-bafe-e10b55114cde</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (axis_nbounds: 2, lat: 128, lon: 256, plev: 19, time: 120)\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "  * lon          (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "  * plev         (plev) float32 100000.0 92500.0 85000.0 ... 1000.0 500.0 100.0\n",
       "  * time         (time) datetime64[ns] 1979-01-16T12:00:00 ... 1988-12-16T12:00:00\n",
       "Dimensions without coordinates: axis_nbounds\n",
       "Data variables:\n",
       "    time_bounds  (time, axis_nbounds) datetime64[ns] ...\n",
       "    wap          (time, plev, lat, lon) float32 -0.0019248935 ... -5.1684023e-05\n",
       "Attributes:\n",
       "    Conventions:           CF-1.7 CMIP-6.2\n",
       "    creation_date:         2018-09-20T17:17:22Z\n",
       "    description:           Extended version of CMIP5/CFMIP-2 aquaControl expe...\n",
       "    title:                 CNRM-CM6-1 model output prepared for CMIP6 / CFMIP...\n",
       "    activity_id:           CFMIP\n",
       "    contact:               contact.cmip@meteo.fr\n",
       "    data_specs_version:    01.00.21\n",
       "    dr2xml_version:        1.13\n",
       "    experiment_id:         aqua-control\n",
       "    experiment:            aquaplanet control\n",
       "    external_variables:    areacella\n",
       "    forcing_index:         2\n",
       "    frequency:             mon\n",
       "    further_info_url:      https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS....\n",
       "    grid:                  data regridded to a T127 gaussian grid (128x256 la...\n",
       "    grid_label:            gr\n",
       "    nominal_resolution:    250 km\n",
       "    comment:               a 20-year spin-up starting from a uniform state at...\n",
       "    initialization_index:  1\n",
       "    institution_id:        CNRM-CERFACS\n",
       "    institution:           CNRM (Centre National de Recherches Meteorologique...\n",
       "    license:               CMIP6 model data produced by CNRM-CERFACS is licen...\n",
       "    mip_era:               CMIP6\n",
       "    physics_index:         1\n",
       "    product:               model-output\n",
       "    realization_index:     1\n",
       "    realm:                 atmos\n",
       "    references:            http://www.umr-cnrm.fr/cmip6/references\n",
       "    source:                CNRM-CM6-1 (2017):  aerosol: prescribed monthly fi...\n",
       "    source_id:             CNRM-CM6-1\n",
       "    source_type:           AGCM\n",
       "    sub_experiment_id:     none\n",
       "    sub_experiment:        none\n",
       "    table_id:              Amon\n",
       "    variable_id:           wap\n",
       "    variant_label:         r1i1p1f2\n",
       "    EXPID:                 CNRM-CM6-1_aqua-control_r1i1p1f2\n",
       "    CMIP6_CV_version:      cv=6.2.3.0-7-g2019642\n",
       "    dr2xml_md5sum:         92ddb3d0d8ce79f498d792fc8e559dcf\n",
       "    xios_commit:           1442-shuffle\n",
       "    nemo_gelato_commit:    49095b3accd5d4c_6524fe19b00467a\n",
       "    arpege_minor_version:  6.3.2\n",
       "    branch_method:         no parent\n",
       "    parent_activity_id:    no parent\n",
       "    parent_experiment_id:  no parent\n",
       "    parent_mip_era:        no parent\n",
       "    parent_source_id:      no parent\n",
       "    parent_time_units:     no parent\n",
       "    parent_variant_label:  no parent\n",
       "    history:               none\n",
       "    tracking_id:           hdl:21.14100/38c16b97-7447-4595-bafe-e10b55114cde"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1wap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wap1z = 864. * ds1wap['wap'].mean(dim=('time','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "wap2z = 864. * ds2wap['wap'].mean(dim=('time','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "lat, pres = np.meshgrid(wap1z['lat'], wap1z['plev']/100)\n",
    "fig2, ax2 = plt.subplots(nrows=2, ncols=2, sharey=True)\n",
    "ax2[0,0].contourf(lat, pres, wap1z)\n",
    "ax2[0,1].contourf(lat, pres, wap2z)\n",
    "ax2[1,0].contourf(lat, pres, wap2z-wap1z, cmap='coolwarm_r')\n",
    "ax2[0,0].invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## repeat for IPSL\n",
    "\n",
    "ds_ipsl_wap_c = xr.open_mfdataset(\"/Volumes/Glyph6TB/CMIP6/wap_Amon_IPSL-CM6A-LR_aqua-control_r1i1p1f1_gr_*\", combine='by_coords')\n",
    "ds_ipsl_wap_l = xr.open_dataset(\"/Volumes/Glyph6TB/CMIP6/wap_Amon_IPSL-CM6A-LR_aqua-control-lwoff_r1i1p1f1_gr_197901-198912.nc\")\n",
    "\n",
    "ds_ipsl_pr_c = xr.open_mfdataset(\"/Volumes/Glyph6TB/CMIP6/pr_Amon_IPSL-CM6A-LR_aqua-control_r1i1p1f1_gr*\", combine='by_coords')\n",
    "ds_ipsl_pr_l = xr.open_dataset(\"/Volumes/Glyph6TB/CMIP6/pr_Amon_IPSL-CM6A-LR_aqua-control-lwoff_r1i1p1f1_gr_197901-198912.nc\")\n",
    "\n",
    "ipsl_wap_c = 864*ds_ipsl_wap_c['wap'].mean(dim=('time','lon'))\n",
    "ipsl_wap_l = 864*ds_ipsl_wap_l['wap'].mean(dim=('time','lon'))\n",
    "\n",
    "ipsl_pr_c = 86400*ds_ipsl_pr_c['pr'].mean(dim=('time','lon'))\n",
    "ipsl_pr_l = 86400*ds_ipsl_pr_l['pr'].mean(dim=('time','lon'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'OMEGA (hPa/day)')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, constrained_layout=True, sharex=True)\n",
    "\n",
    "ax[0].plot(ipsl_pr_c['lat'], ipsl_pr_c, label='Control')\n",
    "ax[0].plot(ipsl_pr_l['lat'], ipsl_pr_l, label='LWoff')\n",
    "ax[0].set_title(\"IPSL Aquaplanet precipitation\", loc='left')\n",
    "ax[0].legend()\n",
    "ax[0].set_ylabel(\"mm/day\")\n",
    "ax[1].plot(ipsl_pr_c.lat, ipsl_pr_l - ipsl_pr_c)\n",
    "ax[1].set_title(\"Difference\", loc='left')\n",
    "ax[1].set_ylabel(\"mm/day\")\n",
    "ax[1].set_xlabel(\"latitude\")\n",
    "ax[1].set_xlim([-90,90])\n",
    "\n",
    "lat, pres = np.meshgrid(ipsl_wap_c['lat'], ipsl_wap_c['plev']/100)\n",
    "fig2, ax2 = plt.subplots(nrows=2, ncols=2, sharey=True)\n",
    "ax2[0,0].contourf(lat, pres, ipsl_wap_c)\n",
    "ax2[0,1].contourf(lat, pres, ipsl_wap_l)\n",
    "ax2[1,0].contourf(lat, pres, ipsl_wap_l-ipsl_wap_c, cmap='coolwarm_r')\n",
    "ax2[0,0].invert_yaxis()\n",
    "fig2.suptitle(\"OMEGA (hPa/day)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Zonal mean response is pretty similar --- look at precipitation distribution\n",
    "#\n",
    "\n",
    "cnrm_pr_day_ds = xr.open_dataset(\"/Volumes/Glyph6TB/CMIP6/pr_day_CNRM-CM6-1_aqua-control_r1i1p1f2_gr_19790101-19881231.nc\")\n",
    "cnrm_pr_day = cnrm_pr_day_ds['pr'].sel(lat=slice(-30,30))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.59286092993716e-08\n",
      "0.0\n",
      "6.408838970628494e-08\n",
      "0.0\n",
      "5.277151515770129e-08\n",
      "0.0\n",
      "5.912330891533202e-08\n",
      "0.0\n",
      "6.06271512992862e-08\n",
      "0.0\n",
      "6.165975463545692e-08\n",
      "0.0\n",
      "5.855545381905358e-08\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "checkfils = ['pr_day_CESM2_amip_r1i1p1f1_gn_19500101-19591231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_19600101-19691231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_19700101-19791231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_19800101-19891231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_19900101-19991231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_20000101-20091231.nc',\n",
    "'pr_day_CESM2_amip_r1i1p1f1_gn_20100101-20150101.nc']\n",
    "for cf in checkfils:\n",
    "    d0 = xr.open_dataset(f\"/Volumes/Glyph6TB/CMIP6/{cf}\")\n",
    "    print(d0['pr'].max().item())\n",
    "    print(d0['pr'].min().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide data repr</title>\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide attributes</title>\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd7dc0b0-d65c-4bae-b047-f3e5cea7947d' class='xr-array-in' type='checkbox' ><label for='section-bd7dc0b0-d65c-4bae-b047-f3e5cea7947d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.18891923 0.19271329 0.20474608 ... 0.6073528 0.5986861 0.5482787</span></div><pre class='xr-array-data'>array([0.18891923, 0.19271329, 0.20474608, 0.23002607, 0.25951794,\n",
       "       0.26536247, 0.24715553, 0.23719686, 0.24032034, 0.2501488 ,\n",
       "       0.27648154, 0.30864388, 0.35853013, 0.44124156, 0.58506197,\n",
       "       0.7249854 , 0.78559667, 0.8406295 , 0.8830549 , 0.90821487,\n",
       "       0.9940262 , 1.1296446 , 1.2967832 , 1.4599524 , 1.6384772 ,\n",
       "       1.8239747 , 1.973309  , 2.058771  , 2.116375  , 2.187573  ,\n",
       "       2.287707  , 2.3952167 , 2.500273  , 2.6026947 , 2.6990933 ,\n",
       "       2.78239   , 2.8438547 , 2.885619  , 2.9033992 , 2.9034474 ,\n",
       "       2.8995938 , 2.8952715 , 2.89003   , 2.8837337 , 2.8845105 ,\n",
       "       2.8658662 , 2.8428214 , 2.8295763 , 2.819911  , 2.8083048 ,\n",
       "       2.7854476 , 2.7656593 , 2.738131  , 2.694825  , 2.6500082 ,\n",
       "       2.5960593 , 2.5306652 , 2.4630797 , 2.4027703 , 2.3463805 ,\n",
       "       2.2982025 , 2.2609644 , 2.2291214 , 2.1951241 , 2.1627727 ,\n",
       "       2.1268806 , 2.0903819 , 2.0560017 , 2.0298119 , 2.0093007 ,\n",
       "       2.0041113 , 2.0242028 , 2.0563319 , 2.0929396 , 2.1533208 ,\n",
       "       2.2242296 , 2.3118188 , 2.4164886 , 2.53525   , 2.662864  ,\n",
       "       2.7751179 , 2.8988445 , 2.9849474 , 3.0434906 , 3.1414757 ,\n",
       "       3.2643893 , 3.432414  , 3.6525803 , 3.8828723 , 4.0634804 ,\n",
       "       4.1865964 , 4.198184  , 4.09302   , 3.8907843 , 3.6809251 ,\n",
       "       3.5742705 , 3.5898488 , 3.794216  , 4.147405  , 4.612255  ,\n",
       "       5.1018224 , 5.5257564 , 5.787471  , 5.822771  , 5.5995736 ,\n",
       "       5.238774  , 4.852706  , 4.4705725 , 4.0789003 , 3.7046928 ,\n",
       "       3.3848014 , 3.1089602 , 2.8775165 , 2.6901443 , 2.5404212 ,\n",
       "       2.406142  , 2.3082297 , 2.204197  , 2.1107671 , 2.0463545 ,\n",
       "       2.013963  , 1.9934846 , 1.97902   , 1.9939603 , 2.1279817 ,\n",
       "       2.217591  , 2.1690588 , 2.075863  , 2.0556223 , 2.091141  ,\n",
       "       2.1501496 , 2.2132137 , 2.279117  , 2.3382292 , 2.3743477 ,\n",
       "       2.3887722 , 2.3911977 , 2.3771634 , 2.3588097 , 2.3417091 ,\n",
       "       2.298028  , 2.2655663 , 2.2337158 , 2.2022424 , 2.1928086 ,\n",
       "       2.1951249 , 2.1999843 , 2.2025325 , 2.2150931 , 2.2221642 ,\n",
       "       2.215972  , 2.2100897 , 2.1891718 , 2.1586287 , 2.1417298 ,\n",
       "       2.136851  , 2.1219885 , 2.1016552 , 2.1157265 , 2.1487632 ,\n",
       "       2.0522087 , 1.8992461 , 1.7975292 , 1.7491956 , 1.6918284 ,\n",
       "       1.6105186 , 1.5337862 , 1.4528075 , 1.3564537 , 1.2753806 ,\n",
       "       1.1992465 , 1.1273851 , 1.0591217 , 0.9972394 , 0.9535025 ,\n",
       "       0.92509407, 0.89592206, 0.86027855, 0.81170934, 0.7691284 ,\n",
       "       0.731584  , 0.709964  , 0.6950741 , 0.6869871 , 0.6839129 ,\n",
       "       0.6752545 , 0.64803755, 0.6333795 , 0.6207541 , 0.6073528 ,\n",
       "       0.5986861 , 0.5482787 ], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-08f10682-bdab-4d88-9c28-432d09eaf846' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08f10682-bdab-4d88-9c28-432d09eaf846' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-8a230244-972e-4781-a04b-49565ce6b311' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a230244-972e-4781-a04b-49565ce6b311' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05525618-667f-402d-a154-1d16937abe12' class='xr-var-data-in' type='checkbox'><label for='data-05525618-667f-402d-a154-1d16937abe12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><pre class='xr-var-data'>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-fb79d608-15d3-4804-937e-ca8fc08bb995' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb79d608-15d3-4804-937e-ca8fc08bb995' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (lat: 192)>\n",
       "array([0.18891923, 0.19271329, 0.20474608, 0.23002607, 0.25951794,\n",
       "       0.26536247, 0.24715553, 0.23719686, 0.24032034, 0.2501488 ,\n",
       "       0.27648154, 0.30864388, 0.35853013, 0.44124156, 0.58506197,\n",
       "       0.7249854 , 0.78559667, 0.8406295 , 0.8830549 , 0.90821487,\n",
       "       0.9940262 , 1.1296446 , 1.2967832 , 1.4599524 , 1.6384772 ,\n",
       "       1.8239747 , 1.973309  , 2.058771  , 2.116375  , 2.187573  ,\n",
       "       2.287707  , 2.3952167 , 2.500273  , 2.6026947 , 2.6990933 ,\n",
       "       2.78239   , 2.8438547 , 2.885619  , 2.9033992 , 2.9034474 ,\n",
       "       2.8995938 , 2.8952715 , 2.89003   , 2.8837337 , 2.8845105 ,\n",
       "       2.8658662 , 2.8428214 , 2.8295763 , 2.819911  , 2.8083048 ,\n",
       "       2.7854476 , 2.7656593 , 2.738131  , 2.694825  , 2.6500082 ,\n",
       "       2.5960593 , 2.5306652 , 2.4630797 , 2.4027703 , 2.3463805 ,\n",
       "       2.2982025 , 2.2609644 , 2.2291214 , 2.1951241 , 2.1627727 ,\n",
       "       2.1268806 , 2.0903819 , 2.0560017 , 2.0298119 , 2.0093007 ,\n",
       "       2.0041113 , 2.0242028 , 2.0563319 , 2.0929396 , 2.1533208 ,\n",
       "       2.2242296 , 2.3118188 , 2.4164886 , 2.53525   , 2.662864  ,\n",
       "       2.7751179 , 2.8988445 , 2.9849474 , 3.0434906 , 3.1414757 ,\n",
       "       3.2643893 , 3.432414  , 3.6525803 , 3.8828723 , 4.0634804 ,\n",
       "       4.1865964 , 4.198184  , 4.09302   , 3.8907843 , 3.6809251 ,\n",
       "       3.5742705 , 3.5898488 , 3.794216  , 4.147405  , 4.612255  ,\n",
       "       5.1018224 , 5.5257564 , 5.787471  , 5.822771  , 5.5995736 ,\n",
       "       5.238774  , 4.852706  , 4.4705725 , 4.0789003 , 3.7046928 ,\n",
       "       3.3848014 , 3.1089602 , 2.8775165 , 2.6901443 , 2.5404212 ,\n",
       "       2.406142  , 2.3082297 , 2.204197  , 2.1107671 , 2.0463545 ,\n",
       "       2.013963  , 1.9934846 , 1.97902   , 1.9939603 , 2.1279817 ,\n",
       "       2.217591  , 2.1690588 , 2.075863  , 2.0556223 , 2.091141  ,\n",
       "       2.1501496 , 2.2132137 , 2.279117  , 2.3382292 , 2.3743477 ,\n",
       "       2.3887722 , 2.3911977 , 2.3771634 , 2.3588097 , 2.3417091 ,\n",
       "       2.298028  , 2.2655663 , 2.2337158 , 2.2022424 , 2.1928086 ,\n",
       "       2.1951249 , 2.1999843 , 2.2025325 , 2.2150931 , 2.2221642 ,\n",
       "       2.215972  , 2.2100897 , 2.1891718 , 2.1586287 , 2.1417298 ,\n",
       "       2.136851  , 2.1219885 , 2.1016552 , 2.1157265 , 2.1487632 ,\n",
       "       2.0522087 , 1.8992461 , 1.7975292 , 1.7491956 , 1.6918284 ,\n",
       "       1.6105186 , 1.5337862 , 1.4528075 , 1.3564537 , 1.2753806 ,\n",
       "       1.1992465 , 1.1273851 , 1.0591217 , 0.9972394 , 0.9535025 ,\n",
       "       0.92509407, 0.89592206, 0.86027855, 0.81170934, 0.7691284 ,\n",
       "       0.731584  , 0.709964  , 0.6950741 , 0.6869871 , 0.6839129 ,\n",
       "       0.6752545 , 0.64803755, 0.6333795 , 0.6207541 , 0.6073528 ,\n",
       "       0.5986861 , 0.5482787 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K['amip']['CESM2']['r1i1p1f1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r1i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r3i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r5i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r6i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r10i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r8i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r4i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r9i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r2i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - CESM2 - r7i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - IPSL-CM6A-LR - r1i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip - IPSL-CM6A-LR - r3i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r5i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r6i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r10i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r8i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r4i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r9i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r11i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r2i1p1f1\n",
      "working on amip - IPSL-CM6A-LR - r7i1p1f1\n",
      "working on amip - CNRM-CM6-1 - r1i1p1f2\n",
      "working on amip - MRI-ESM2-0 - r1i1p1f1\n",
      "working on amip - MRI-ESM2-0 - r2i1p1f1\n",
      "working on amip - MRI-ESM2-0 - r3i1p1f1\n",
      "working on amip-lwoff - CESM2 - r1i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on amip-lwoff - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on amip-lwoff - CNRM-CM6-1 - r1i1p1f2\n",
      "working on amip-lwoff - MRI-ESM2-0 - r1i1p1f1\n",
      "working on aqua-control - CESM2 - r1i1p1f1\n",
      "working on aqua-control - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on aqua-control - CNRM-CM6-1 - r1i1p1f2\n",
      "working on aqua-control-lwoff - CESM2 - r1i1p1f1\n",
      "working on aqua-control-lwoff - IPSL-CM6A-LR - r1i1p1f1\n",
      "working on aqua-control-lwoff - CNRM-CM6-1 - r1i1p1f2\n"
     ]
    }
   ],
   "source": [
    "# Zonal Mean without LAND\n",
    "\n",
    "# zonal average for each simulation\n",
    "ZMnl = dict()\n",
    "for ex in K:\n",
    "    ZMnl[ex] = dict()\n",
    "    for m in K[ex]:\n",
    "        ZMnl[ex][m] = dict()\n",
    "        for b in K[ex][m]:\n",
    "            print(f\"working on {ex} - {m} - {b}\")\n",
    "            tmp = (K[ex][m][b]['pr'].compute()*86400.)\n",
    "            if (ex in LAND) and (m in LAND[ex]) and (b in LAND[ex][m]):\n",
    "                landfrac = LAND[ex][m][b]['sftlf']\n",
    "                tmp = tmp.where(landfrac <= 0)\n",
    "            ZMnl[ex][m][b] = tmp.mean(dim=('time','lon'))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 538.583x325.984 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(mm2inches(190), mm2inches(115)), ncols=2, constrained_layout=True, sharey=True)\n",
    "for ex in ZMnl:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "#             print(f\"working on {ex} - {m} - {b}\")\n",
    "            xlat = ZMnl[ex][m][b]['lat']\n",
    "            xzm = ZMnl[ex][m][b]\n",
    "            c = select_color(m)\n",
    "            if 'lwoff' in ex:\n",
    "                ls = 'dashed'\n",
    "            else:\n",
    "                ls = 'solid'\n",
    "            if max(xzm) < 1:\n",
    "                print(f\"WARNING: {ex} - {m} - {b}\")\n",
    "            if 'aqua' in ex:\n",
    "                a = ax[1]\n",
    "            else:\n",
    "                a = ax[0]\n",
    "            a.plot(xlat, xzm, color=c, linestyle=ls)\n",
    "ax[0].set_ylabel(\"Precipitation (mm/day)\")\n",
    "ax[0].set_xlabel(\"Latitude\")\n",
    "ax[0].set_title(\"AMIP (ocean only)\", loc='left')\n",
    "ax[1].set_title(\"AQUA\", loc='left')\n",
    "[a.set_xlim([-90,90]) for a in ax]\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip-r1i1p1f1\n",
      "CESM2-amip-r3i1p1f1\n",
      "CESM2-amip-r5i1p1f1\n",
      "CESM2-amip-r6i1p1f1\n",
      "CESM2-amip-r10i1p1f1\n",
      "CESM2-amip-r8i1p1f1\n",
      "CESM2-amip-r4i1p1f1\n",
      "CESM2-amip-r9i1p1f1\n",
      "CESM2-amip-r2i1p1f1\n",
      "CESM2-amip-r7i1p1f1\n",
      "IPSL-CM6A-LR-amip-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-r3i1p1f1\n",
      "IPSL-CM6A-LR-amip-r5i1p1f1\n",
      "IPSL-CM6A-LR-amip-r6i1p1f1\n",
      "IPSL-CM6A-LR-amip-r10i1p1f1\n",
      "IPSL-CM6A-LR-amip-r8i1p1f1\n",
      "IPSL-CM6A-LR-amip-r4i1p1f1\n",
      "IPSL-CM6A-LR-amip-r9i1p1f1\n",
      "IPSL-CM6A-LR-amip-r11i1p1f1\n",
      "IPSL-CM6A-LR-amip-r2i1p1f1\n",
      "IPSL-CM6A-LR-amip-r7i1p1f1\n",
      "CNRM-CM6-1-amip-r1i1p1f2\n",
      "MRI-ESM2-0-amip-r1i1p1f1\n",
      "MRI-ESM2-0-amip-r2i1p1f1\n",
      "MRI-ESM2-0-amip-r3i1p1f1\n",
      "CESM2-amip-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-amip-lwoff-r1i1p1f2\n",
      "MRI-ESM2-0-amip-lwoff-r1i1p1f1\n",
      "CESM2-aqua-control-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-r1i1p1f2\n",
      "CESM2-aqua-control-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2\n",
      "CPU times: user 9min 37s, sys: 1min 58s, total: 11min 35s\n",
      "Wall time: 17min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# well, kde is super slow, what about just old fashioned histogram\n",
    "B = np.arange(0, 101, 1)\n",
    "ahist_nl = dict()\n",
    "aquan_nl = dict()\n",
    "quantiles = [0.5, 0.75, 0.9, 0.95, 0.99, 0.999]\n",
    "for ex in K:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "            k = \"-\".join([m,ex,b])\n",
    "            print(k)\n",
    "            if (ex in LAND) and (m in LAND[ex]) and (b in LAND[ex][m]):\n",
    "                datmp = (K[ex][m][b]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "                landfrac = LAND[ex][m][b]['sftlf'].sel(lat=slice(-20,20))\n",
    "                datmp = datmp.where(landfrac <= 0)            \n",
    "                arr = datmp.values.flatten()\n",
    "                weights = np.cos(np.radians(datmp['lat'])).broadcast_like(datmp) \n",
    "                ahist_nl[k] = np.histogram(arr, bins=B, weights=weights.values.flatten(), density=True)\n",
    "                aquan_nl[k] = np.nanquantile(arr, quantiles)\n",
    "            else:\n",
    "                ahist_nl[k] = ahist[k]\n",
    "                aquan_nl[k] = aquan[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 225 µs, sys: 7 µs, total: 232 µs\n",
      "Wall time: 237 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#\n",
    "# histograms that combine the amip ensemble members\n",
    "#\n",
    "m_combine_nl = dict()\n",
    "m_n_nl = dict()\n",
    "m_q_nl = dict()\n",
    "for m in models:\n",
    "    for ex in experiments:\n",
    "        s = \"-\".join([m,ex])\n",
    "        m_n_nl[s] = 0\n",
    "        for ah in ahist_nl:\n",
    "            if (s in ah) and (ah[len(s)+1] == 'r'):\n",
    "                if s in m_combine_nl:\n",
    "                    m_combine_nl[s].append(ahist_nl[ah][0])\n",
    "                    m_q_nl[s].append(aquan_nl[ah][-1])\n",
    "                else:\n",
    "                    m_combine_nl[s] = [ahist_nl[ah][0]]\n",
    "                    m_q_nl[s] = [aquan_nl[ah][-1]]\n",
    "                m_n_nl[s] += 1 \n",
    "m_toplot_nl = dict()\n",
    "for m in m_combine_nl:\n",
    "    if len(m_combine_nl[m]) > 1:\n",
    "        m_toplot_nl[m] = sum(m_combine_nl[m])/m_n_nl[m]\n",
    "        m_q_nl[m] = sum(m_q_nl[m])/m_n_nl[m]\n",
    "    else:\n",
    "        m_toplot_nl[m] = m_combine_nl[m][0]\n",
    "        m_q_nl[m] = m_q_nl[m][0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index: 63, X-Value: 63.76187115364068, Y-Value: 2.5422138609296548e-05\n",
      "Index: 60, X-Value: 60.34962719726536, Y-Value: 6.839302121610936e-05\n",
      "Index: 70, X-Value: 70.50351337432863, Y-Value: 5.877780473461169e-05\n",
      "Index: 41, X-Value: 41.828793449402326, Y-Value: 0.0001311118680597205\n",
      "Index: 99, X-Value: 108.09620172535283, Y-Value: 1.4122340326897527e-05\n",
      "Index: 48, X-Value: 48.478301071166925, Y-Value: 6.477816396332388e-05\n",
      "Index: 87, X-Value: 87.81021453857443, Y-Value: 4.37577947179839e-05\n",
      "Index: 31, X-Value: 31.32345362472536, Y-Value: 9.35823292560978e-05\n",
      "Index: 94, X-Value: 94.21379080963106, Y-Value: 4.683370912838017e-05\n",
      "Index: 84, X-Value: 84.1965376205435, Y-Value: 5.699354556399809e-05\n",
      "Index: 79, X-Value: 79.4539430694573, Y-Value: 7.054444613702733e-05\n",
      "Index: 79, X-Value: 79.15798419952313, Y-Value: 7.020272334166798e-05\n",
      "Index: 64, X-Value: 64.9563751525875, Y-Value: 2.190599484082386e-05\n",
      "Index: 47, X-Value: 47.5054039688107, Y-Value: 0.00011232297847041706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 538.583x325.984 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Now can I get the zonal mean and distribution on to one plot\n",
    "#\n",
    "\n",
    "fig, axx = plt.subplots(figsize=(mm2inches(190), mm2inches(115)), ncols=2, nrows=2, constrained_layout=True)\n",
    "ax = axx[0,:]\n",
    "for ex in ZMnl:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "#             print(f\"working on {ex} - {m} - {b}\")\n",
    "            xlat = ZMnl[ex][m][b]['lat']\n",
    "            xzm = ZMnl[ex][m][b]\n",
    "            c = select_color(m)\n",
    "            if 'lwoff' in ex:\n",
    "                ls = 'dashed'\n",
    "            else:\n",
    "                ls = 'solid'\n",
    "            if max(xzm) < 1:\n",
    "                print(f\"WARNING: {ex} - {m} - {b}\")\n",
    "            if 'aqua' in ex:\n",
    "                a = ax[1]\n",
    "            else:\n",
    "                a = ax[0]\n",
    "            a.plot(xlat, xzm, color=c, linestyle=ls)\n",
    "ax[0].set_ylabel(\"Precipitation (mm/day)\")\n",
    "[a.set_xlabel(\"Latitude\") for a in ax]\n",
    "ax[0].set_title(\"AMIP (Ocean Only)\", loc='left')\n",
    "ax[1].set_title(\"AQUA\", loc='left')\n",
    "[a.set_xlim([-90,90]) for a in ax]\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "\n",
    "bctr = ahist_nl['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist_nl['CESM2-amip-r7i1p1f1'][1]) # bin centers are the same for all histograms\n",
    "ax = axx[1,:]\n",
    "for i in m_toplot_nl:\n",
    "#     print(i)\n",
    "    if 'lwoff' in i:\n",
    "        linsty = 'dashed'\n",
    "    else:\n",
    "        linsty = 'solid'\n",
    "    if 'aqua' in i:\n",
    "        a = ax[1]\n",
    "    else:\n",
    "        a = ax[0]\n",
    "    cval = select_color(i)\n",
    "    a.plot(bctr, np.log10(m_toplot_nl[i]), color=cval, linestyle=linsty, label=i)\n",
    "    nearest_idx = np.abs(bctr - m_q_nl[i]).argmin()\n",
    "    print(f\"Index: {nearest_idx}, X-Value: {m_q_nl[i]}, Y-Value: {m_toplot_nl[i][nearest_idx]}\")\n",
    "    a.plot(m_q_nl[i], np.log10(m_toplot_nl[i][nearest_idx]), marker='o', alpha=0.5, color=cval, markersize=7)\n",
    "#     ax.legend(loc='lower left')\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "ax[0].set_ylabel(\"log(probability density)\")\n",
    "[a.set_xlabel(\"precipitation rate (mm/day)\") for a in ax]\n",
    "\n",
    "fig.savefig(\"/Users/brianpm/Dropbox/Manuscripts/M_cre_pex/amip_aqua_zonal_and_pdf_noland.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip-r1i1p1f1\n",
      "CESM2-amip-r3i1p1f1\n",
      "CESM2-amip-r5i1p1f1\n",
      "CESM2-amip-r6i1p1f1\n",
      "CESM2-amip-r10i1p1f1\n",
      "CESM2-amip-r8i1p1f1\n",
      "CESM2-amip-r4i1p1f1\n",
      "CESM2-amip-r9i1p1f1\n",
      "CESM2-amip-r2i1p1f1\n",
      "CESM2-amip-r7i1p1f1\n",
      "IPSL-CM6A-LR-amip-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-r3i1p1f1\n",
      "IPSL-CM6A-LR-amip-r5i1p1f1\n",
      "IPSL-CM6A-LR-amip-r6i1p1f1\n",
      "IPSL-CM6A-LR-amip-r10i1p1f1\n",
      "IPSL-CM6A-LR-amip-r8i1p1f1\n",
      "IPSL-CM6A-LR-amip-r4i1p1f1\n",
      "IPSL-CM6A-LR-amip-r9i1p1f1\n",
      "IPSL-CM6A-LR-amip-r11i1p1f1\n",
      "IPSL-CM6A-LR-amip-r2i1p1f1\n",
      "IPSL-CM6A-LR-amip-r7i1p1f1\n",
      "CNRM-CM6-1-amip-r1i1p1f2\n",
      "MRI-ESM2-0-amip-r1i1p1f1\n",
      "MRI-ESM2-0-amip-r2i1p1f1\n",
      "MRI-ESM2-0-amip-r3i1p1f1\n",
      "CESM2-amip-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-amip-lwoff-r1i1p1f2\n",
      "MRI-ESM2-0-amip-lwoff-r1i1p1f1\n",
      "CESM2-aqua-control-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-r1i1p1f2\n",
      "CESM2-aqua-control-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2\n",
      "CPU times: user 8min 41s, sys: 2min 17s, total: 10min 58s\n",
      "Wall time: 17min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#\n",
    "# Now we repeat again, but we don't allow \"dry days\" -- my motivation here is only to try to limit the impact of the very dry subtropics, which might be different sizes in different data\n",
    "#\n",
    "# well, kde is super slow, what about just old fashioned histogram\n",
    "B = np.arange(0, 101, 1)\n",
    "ahist_nldry = dict()\n",
    "aquan_nldry = dict()\n",
    "quantiles = [0.5, 0.75, 0.9, 0.95, 0.99, 0.999]\n",
    "for ex in K:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "            k = \"-\".join([m,ex,b])\n",
    "            print(k)\n",
    "            datmp = (K[ex][m][b]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "            if (ex in LAND) and (m in LAND[ex]) and (b in LAND[ex][m]):\n",
    "                landfrac = LAND[ex][m][b]['sftlf'].sel(lat=slice(-20,20))\n",
    "                datmp = datmp.where(landfrac <= 0)\n",
    "            datmp = datmp.where(datmp>=1) # datmp in mm/day, set threshold for \"dry day\" to 1mm\n",
    "            arr = datmp.values.flatten()\n",
    "            weights = np.cos(np.radians(datmp['lat'])).broadcast_like(datmp) \n",
    "            ahist_nldry[k] = np.histogram(arr, bins=B, weights=weights.values.flatten(), density=True)\n",
    "            aquan_nldry[k] = np.nanquantile(arr, quantiles)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip\n",
      "CESM2-amip-lwoff\n",
      "CESM2-aqua-control\n",
      "CESM2-aqua-control-lwoff\n",
      "IPSL-CM6A-LR-amip\n",
      "IPSL-CM6A-LR-amip-lwoff\n",
      "IPSL-CM6A-LR-aqua-control\n",
      "IPSL-CM6A-LR-aqua-control-lwoff\n",
      "CNRM-CM6-1-amip\n",
      "CNRM-CM6-1-amip-lwoff\n",
      "CNRM-CM6-1-aqua-control\n",
      "CNRM-CM6-1-aqua-control-lwoff\n",
      "MRI-ESM2-0-amip\n",
      "MRI-ESM2-0-amip-lwoff\n",
      "CPU times: user 131 ms, sys: 16.7 ms, total: 147 ms\n",
      "Wall time: 237 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'precipitation rate (mm/day)')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "#\n",
    "# histograms that combine the amip ensemble members\n",
    "#\n",
    "m_combine_nldry = dict()\n",
    "m_n_nldry = dict()\n",
    "m_q_nldry = dict()\n",
    "for m in models:\n",
    "    for ex in experiments:\n",
    "        s = \"-\".join([m,ex])\n",
    "        m_n_nldry[s] = 0\n",
    "        for ah in ahist_nldry:\n",
    "            if (s in ah) and (ah[len(s)+1] == 'r'):\n",
    "                if s in m_combine_nldry:\n",
    "                    m_combine_nldry[s].append(ahist_nldry[ah][0])\n",
    "                    m_q_nldry[s].append(aquan_nldry[ah][-1])\n",
    "                else:\n",
    "                    m_combine_nldry[s] = [ahist_nldry[ah][0]]\n",
    "                    m_q_nldry[s] = [aquan_nldry[ah][-1]]\n",
    "                m_n_nldry[s] += 1 \n",
    "m_toplot_nldry = dict()\n",
    "for m in m_combine_nldry:\n",
    "    if len(m_combine_nldry[m]) > 1:\n",
    "        m_toplot_nldry[m] = sum(m_combine_nldry[m])/m_n_nldry[m]\n",
    "        m_q_nldry[m] = sum(m_q_nldry[m])/m_n_nldry[m]\n",
    "    else:\n",
    "        m_toplot_nldry[m] = m_combine_nldry[m][0]\n",
    "        m_q_nldry[m] = m_q_nldry[m][0]\n",
    "\n",
    "\n",
    "\n",
    "bctr = ahist_nldry['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist_nldry['CESM2-amip-r7i1p1f1'][1]) # bin centers are the same for all histograms\n",
    "fig, ax = plt.subplots(ncols=2)\n",
    "for i in m_toplot:\n",
    "    print(i)\n",
    "    if 'lwoff' in i:\n",
    "        linsty = 'dashed'\n",
    "    else:\n",
    "        linsty = 'solid'\n",
    "    if 'aqua' in i:\n",
    "        a = ax[1]\n",
    "    else:\n",
    "        a = ax[0]\n",
    "    cval = select_color(i)\n",
    "    a.plot(bctr, np.log10(m_toplot_nldry[i]), color=cval, linestyle=linsty, label=i)\n",
    "    nearest_idx = np.abs(bctr - m_q_nldry[i]).argmin()\n",
    "    a.plot(m_q_nldry[i], np.log10(m_toplot_nldry[i][nearest_idx]), marker='o', color=cval, markersize=7)\n",
    "#     ax.legend(loc='lower left')\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "ax[0].set_ylabel(\"log(probability density)\")\n",
    "ax[0].set_xlabel(\"precipitation rate (mm/day)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip-r1i1p1f1\n",
      "CESM2-amip-r3i1p1f1\n",
      "CESM2-amip-r5i1p1f1\n",
      "CESM2-amip-r6i1p1f1\n",
      "CESM2-amip-r10i1p1f1\n",
      "CESM2-amip-r8i1p1f1\n",
      "CESM2-amip-r4i1p1f1\n",
      "CESM2-amip-r9i1p1f1\n",
      "CESM2-amip-r2i1p1f1\n",
      "CESM2-amip-r7i1p1f1\n",
      "IPSL-CM6A-LR-amip-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-r3i1p1f1\n",
      "IPSL-CM6A-LR-amip-r5i1p1f1\n",
      "IPSL-CM6A-LR-amip-r6i1p1f1\n",
      "IPSL-CM6A-LR-amip-r10i1p1f1\n",
      "IPSL-CM6A-LR-amip-r8i1p1f1\n",
      "IPSL-CM6A-LR-amip-r4i1p1f1\n",
      "IPSL-CM6A-LR-amip-r9i1p1f1\n",
      "IPSL-CM6A-LR-amip-r11i1p1f1\n",
      "IPSL-CM6A-LR-amip-r2i1p1f1\n",
      "IPSL-CM6A-LR-amip-r7i1p1f1\n",
      "CNRM-CM6-1-amip-r1i1p1f2\n",
      "MRI-ESM2-0-amip-r1i1p1f1\n",
      "MRI-ESM2-0-amip-r2i1p1f1\n",
      "MRI-ESM2-0-amip-r3i1p1f1\n",
      "CESM2-amip-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-amip-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-amip-lwoff-r1i1p1f2\n",
      "MRI-ESM2-0-amip-lwoff-r1i1p1f1\n",
      "CESM2-aqua-control-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-r1i1p1f2\n",
      "CESM2-aqua-control-lwoff-r1i1p1f1\n",
      "IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1\n",
      "CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2\n",
      "CPU times: user 8min 17s, sys: 2min 18s, total: 10min 35s\n",
      "Wall time: 16min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#\n",
    "# A different way to try to remove subtropics is to mask based on mean precipitation, which has the advantage of keeping deep tropical dry days\n",
    "# Just apply common rule-of-thumb of 4mm/day\n",
    "#\n",
    "B = np.arange(0, 101, 1)\n",
    "ahist_nl4mm = dict()\n",
    "aquan_nl4mm = dict()\n",
    "quantiles = [0.5, 0.75, 0.9, 0.95, 0.99, 0.999]\n",
    "for ex in K:\n",
    "    for m in K[ex]:\n",
    "        for b in K[ex][m]:\n",
    "            k = \"-\".join([m,ex,b])\n",
    "            print(k)\n",
    "            datmp = (K[ex][m][b]['pr'].sel(lat=slice(-20,20))*86400.).compute()\n",
    "            datmpclimo = datmp.mean(dim='time')\n",
    "            if (ex in LAND) and (m in LAND[ex]) and (b in LAND[ex][m]):\n",
    "                landfrac = LAND[ex][m][b]['sftlf'].sel(lat=slice(-20,20))\n",
    "                datmp = datmp.where(landfrac <= 0)\n",
    "            datmp = datmp.where(datmpclimo>=4) # datmp in mm/day, set threshold of 4mm/day for \"Tropics\"\n",
    "            arr = datmp.values.flatten()\n",
    "            weights = np.cos(np.radians(datmp['lat'])).broadcast_like(datmp) \n",
    "            ahist_nl4mm[k] = np.histogram(arr, bins=B, weights=weights.values.flatten(), density=True)\n",
    "            aquan_nl4mm[k] = np.nanquantile(arr, quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2-amip\n",
      "CESM2-amip-lwoff\n",
      "CESM2-aqua-control\n",
      "CESM2-aqua-control-lwoff\n",
      "IPSL-CM6A-LR-amip\n",
      "IPSL-CM6A-LR-amip-lwoff\n",
      "IPSL-CM6A-LR-aqua-control\n",
      "IPSL-CM6A-LR-aqua-control-lwoff\n",
      "CNRM-CM6-1-amip\n",
      "CNRM-CM6-1-amip-lwoff\n",
      "CNRM-CM6-1-aqua-control\n",
      "CNRM-CM6-1-aqua-control-lwoff\n",
      "MRI-ESM2-0-amip\n",
      "MRI-ESM2-0-amip-lwoff\n",
      "CPU times: user 76.9 ms, sys: 71.1 ms, total: 148 ms\n",
      "Wall time: 271 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Users/brianpm/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'precipitation rate (mm/day)')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "#\n",
    "# histograms that combine the amip ensemble members\n",
    "#\n",
    "m_combine_nl4mm = dict()\n",
    "m_n_nl4mm = dict()\n",
    "m_q_nl4mm = dict()\n",
    "for m in models:\n",
    "    for ex in experiments:\n",
    "        s = \"-\".join([m,ex])\n",
    "        m_n_nl4mm[s] = 0\n",
    "        for ah in ahist_nl4mm:\n",
    "            if (s in ah) and (ah[len(s)+1] == 'r'):\n",
    "                if s in m_combine_nl4mm:\n",
    "                    m_combine_nl4mm[s].append(ahist_nl4mm[ah][0])\n",
    "                    m_q_nl4mm[s].append(aquan_nl4mm[ah][-1])\n",
    "                else:\n",
    "                    m_combine_nl4mm[s] = [ahist_nl4mm[ah][0]]\n",
    "                    m_q_nl4mm[s] = [aquan_nl4mm[ah][-1]]\n",
    "                m_n_nl4mm[s] += 1 \n",
    "m_toplot_nl4mm = dict()\n",
    "for m in m_combine_nl4mm:\n",
    "    if len(m_combine_nl4mm[m]) > 1:\n",
    "        m_toplot_nl4mm[m] = sum(m_combine_nl4mm[m])/m_n_nl4mm[m]\n",
    "        m_q_nl4mm[m] = sum(m_q_nl4mm[m])/m_n_nl4mm[m]\n",
    "    else:\n",
    "        m_toplot_nl4mm[m] = m_combine_nl4mm[m][0]\n",
    "        m_q_nl4mm[m] = m_q_nl4mm[m][0]\n",
    "\n",
    "\n",
    "\n",
    "bctr = ahist_nl4mm['CESM2-amip-r7i1p1f1'][1][0:-1] + 0.5*np.diff(ahist_nl4mm['CESM2-amip-r7i1p1f1'][1]) # bin centers are the same for all histograms\n",
    "fig, ax = plt.subplots(ncols=2)\n",
    "for i in m_toplot:\n",
    "    print(i)\n",
    "    if 'lwoff' in i:\n",
    "        linsty = 'dashed'\n",
    "    else:\n",
    "        linsty = 'solid'\n",
    "    if 'aqua' in i:\n",
    "        a = ax[1]\n",
    "    else:\n",
    "        a = ax[0]\n",
    "    cval = select_color(i)\n",
    "    a.plot(bctr, np.log10(m_toplot_nl4mm[i]), color=cval, linestyle=linsty, label=i)\n",
    "    nearest_idx = np.abs(bctr - m_q_nl4mm[i]).argmin()\n",
    "    a.plot(m_q_nl4mm[i], np.log10(m_toplot_nl4mm[i][nearest_idx]), marker='o', color=cval, markersize=7)\n",
    "#     ax.legend(loc='lower left')\n",
    "[a.spines['top'].set_visible(False) for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "ax[0].set_ylabel(\"log(probability density)\")\n",
    "ax[0].set_xlabel(\"precipitation rate (mm/day)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CESM2-amip-r1i1p1f1': array([ 3.17018795,  7.21953297, 14.26799936, 21.8773632 , 42.2167318 ,\n",
       "        72.01569913]),\n",
       " 'CESM2-amip-r3i1p1f1': array([ 3.14798844,  7.20434427, 14.28914356, 21.96644602, 42.47471722,\n",
       "        72.79954743]),\n",
       " 'CESM2-amip-r5i1p1f1': array([ 3.16638923,  7.22335386, 14.31626997, 21.98626137, 42.46724972,\n",
       "        72.6063737 ]),\n",
       " 'CESM2-amip-r6i1p1f1': array([ 3.17291427,  7.22932005, 14.3011055 , 21.94252672, 42.39194771,\n",
       "        72.24495023]),\n",
       " 'CESM2-amip-r10i1p1f1': array([ 3.17488718,  7.25021958, 14.37227821, 22.06069441, 42.61854172,\n",
       "        72.41149641]),\n",
       " 'CESM2-amip-r8i1p1f1': array([ 3.16059864,  7.21264696, 14.26431894, 21.87402296, 42.2167057 ,\n",
       "        72.05730839]),\n",
       " 'CESM2-amip-r4i1p1f1': array([ 3.16619873,  7.22465503, 14.32365608, 21.99637213, 42.50555092,\n",
       "        72.61707967]),\n",
       " 'CESM2-amip-r9i1p1f1': array([ 3.16254079,  7.20245016, 14.24806604, 21.88762741, 42.36905548,\n",
       "        72.02377172]),\n",
       " 'CESM2-amip-r2i1p1f1': array([ 3.16123354,  7.20552397, 14.26235867, 21.89592552, 42.24774017,\n",
       "        71.880457  ]),\n",
       " 'CESM2-amip-r7i1p1f1': array([ 3.16985643,  7.22044361, 14.29315472, 21.9162178 , 42.31916428,\n",
       "        72.33560954]),\n",
       " 'IPSL-CM6A-LR-amip-r1i1p1f1': array([  4.37048817,   6.90192723,  10.0818882 ,  14.08732343,\n",
       "         57.70169025, 129.78739381]),\n",
       " 'IPSL-CM6A-LR-amip-r3i1p1f1': array([  4.34414148,   6.83988523,   9.96606998,  13.89135323,\n",
       "         56.16853516, 125.95692661]),\n",
       " 'IPSL-CM6A-LR-amip-r5i1p1f1': array([  4.32818365,   6.82017934,   9.95887003,  13.95886235,\n",
       "         56.65596874, 126.72660052]),\n",
       " 'IPSL-CM6A-LR-amip-r6i1p1f1': array([  4.3489933 ,   6.83655596,   9.97640419,  13.95062656,\n",
       "         56.50995575, 126.30923816]),\n",
       " 'IPSL-CM6A-LR-amip-r10i1p1f1': array([  4.34426379,   6.84111404,   9.9876976 ,  13.97153668,\n",
       "         56.59227093, 126.44539572]),\n",
       " 'IPSL-CM6A-LR-amip-r8i1p1f1': array([  4.35191989,   6.83684063,   9.96826925,  13.9420188 ,\n",
       "         56.50826164, 126.59589841]),\n",
       " 'IPSL-CM6A-LR-amip-r4i1p1f1': array([  4.34539628,   6.84075928,   9.99765501,  14.01313167,\n",
       "         56.94141647, 127.11226691]),\n",
       " 'IPSL-CM6A-LR-amip-r9i1p1f1': array([  4.34771299,   6.83883691,   9.98698235,  13.99267387,\n",
       "         56.76595421, 126.69210815]),\n",
       " 'IPSL-CM6A-LR-amip-r11i1p1f1': array([  4.33495426,   6.82338333,   9.9679287 ,  13.96929703,\n",
       "         56.61215012, 126.596638  ]),\n",
       " 'IPSL-CM6A-LR-amip-r2i1p1f1': array([  4.33457494,   6.81721759,   9.94095383,  13.87356935,\n",
       "         56.29447357, 126.86332275]),\n",
       " 'IPSL-CM6A-LR-amip-r7i1p1f1': array([  4.36098337,   6.84298444,   9.98565006,  14.00349398,\n",
       "         56.79629761, 127.1846958 ]),\n",
       " 'CNRM-CM6-1-amip-r1i1p1f2': array([  1.80843556,   9.30649853,  21.0914135 ,  31.15194511,\n",
       "         58.58631592, 104.18358856]),\n",
       " 'MRI-ESM2-0-amip-r1i1p1f1': array([ 3.31738067,  9.59362507, 18.18020458, 25.17098475, 43.01739052,\n",
       "        73.49440891]),\n",
       " 'MRI-ESM2-0-amip-r2i1p1f1': array([ 3.30502033,  9.56969929, 18.1195816 , 25.10065842, 42.93358368,\n",
       "        73.69514944]),\n",
       " 'MRI-ESM2-0-amip-r3i1p1f1': array([ 3.25994503,  9.51420379, 18.07483673, 25.06556463, 42.95321083,\n",
       "        73.65825729]),\n",
       " 'CESM2-amip-lwoff-r1i1p1f1': array([ 4.06913948,  7.80546904, 13.49141026, 19.02705727, 35.97987747,\n",
       "        66.2299201 ]),\n",
       " 'IPSL-CM6A-LR-amip-lwoff-r1i1p1f1': array([ 5.1295495 ,  7.03029144,  9.29598751, 11.30456862, 21.78903177,\n",
       "        53.0295401 ]),\n",
       " 'CNRM-CM6-1-amip-lwoff-r1i1p1f2': array([ 2.66646624,  9.87427235, 21.10375023, 30.54269218, 54.53577805,\n",
       "        92.74640198]),\n",
       " 'MRI-ESM2-0-amip-lwoff-r1i1p1f1': array([ 4.51922894,  9.85630703, 16.58893661, 21.43070335, 32.59931519,\n",
       "        50.77145326]),\n",
       " 'CESM2-aqua-control-r1i1p1f1': array([ 6.9026413 , 12.65209866, 22.16087646, 30.82613459, 50.0178133 ,\n",
       "        87.8968057 ]),\n",
       " 'IPSL-CM6A-LR-aqua-control-r1i1p1f1': array([ 5.4731338 ,  7.30063021,  9.69540224, 14.38818369, 48.71165543,\n",
       "        90.85573137]),\n",
       " 'CNRM-CM6-1-aqua-control-r1i1p1f2': array([ 4.05176258, 11.09680557, 21.68872185, 30.63928223, 53.1641214 ,\n",
       "        85.25985016]),\n",
       " 'CESM2-aqua-control-lwoff-r1i1p1f1': array([ 4.09157801,  7.10344481, 11.08569241, 14.39257894, 23.99742786,\n",
       "        41.71471122]),\n",
       " 'IPSL-CM6A-LR-aqua-control-lwoff-r1i1p1f1': array([ 5.20706606,  6.9242878 ,  8.67890177,  9.84700785, 13.06959223,\n",
       "        26.39725547]),\n",
       " 'CNRM-CM6-1-aqua-control-lwoff-r1i1p1f2': array([ 2.55714226,  7.3209331 , 16.31016731, 25.17699585, 50.17077072,\n",
       "        83.35874054])}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aquan_nl4mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide data repr</title>\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide attributes</title>\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sftlf'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-feead83a-25b0-4f07-8824-249ff84e1415' class='xr-array-in' type='checkbox' ><label for='section-feead83a-25b0-4f07-8824-249ff84e1415' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0</span></div><pre class='xr-array-data'>array(0., dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-a0fb5d98-6d96-4f53-a1fe-369c8862657e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0fb5d98-6d96-4f53-a1fe-369c8862657e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-fcca4a20-7b08-436e-b9de-1bc4bb9b2532' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fcca4a20-7b08-436e-b9de-1bc4bb9b2532' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sftlf' ()>\n",
       "array(0., dtype=float32)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(barland[100,:,:]-barland[41,:,:]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'r1i1p1f2': <xarray.Dataset>\n",
       " Dimensions:      (axis_nbounds: 2, lat: 128, lon: 256, time: 3653)\n",
       " Coordinates:\n",
       "   * lat          (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "   * lon          (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "   * time         (time) datetime64[ns] 1979-01-01T12:00:00 ... 1988-12-31T12:00:00\n",
       " Dimensions without coordinates: axis_nbounds\n",
       " Data variables:\n",
       "     time_bounds  (time, axis_nbounds) datetime64[ns] ...\n",
       "     pr           (time, lat, lon) float32 9.652909e-06 ... 4.525816e-06\n",
       " Attributes:\n",
       "     Conventions:           CF-1.7 CMIP-6.2\n",
       "     creation_date:         2018-09-20T17:03:41Z\n",
       "     description:           Extended version of CMIP5/CFMIP-2 aquaControl expe...\n",
       "     title:                 CNRM-CM6-1 model output prepared for CMIP6 / CFMIP...\n",
       "     activity_id:           CFMIP\n",
       "     contact:               contact.cmip@meteo.fr\n",
       "     data_specs_version:    01.00.21\n",
       "     dr2xml_version:        1.13\n",
       "     experiment_id:         aqua-control\n",
       "     experiment:            aquaplanet control\n",
       "     external_variables:    areacella\n",
       "     forcing_index:         2\n",
       "     frequency:             day\n",
       "     further_info_url:      https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS....\n",
       "     grid:                  data regridded to a T127 gaussian grid (128x256 la...\n",
       "     grid_label:            gr\n",
       "     nominal_resolution:    250 km\n",
       "     comment:               a 20-year spin-up starting from a uniform state at...\n",
       "     initialization_index:  1\n",
       "     institution_id:        CNRM-CERFACS\n",
       "     institution:           CNRM (Centre National de Recherches Meteorologique...\n",
       "     license:               CMIP6 model data produced by CNRM-CERFACS is licen...\n",
       "     mip_era:               CMIP6\n",
       "     physics_index:         1\n",
       "     product:               model-output\n",
       "     realization_index:     1\n",
       "     realm:                 atmos\n",
       "     references:            http://www.umr-cnrm.fr/cmip6/references\n",
       "     source:                CNRM-CM6-1 (2017):  aerosol: prescribed monthly fi...\n",
       "     source_id:             CNRM-CM6-1\n",
       "     source_type:           AGCM\n",
       "     sub_experiment_id:     none\n",
       "     sub_experiment:        none\n",
       "     table_id:              day\n",
       "     variable_id:           pr\n",
       "     variant_label:         r1i1p1f2\n",
       "     EXPID:                 CNRM-CM6-1_aqua-control_r1i1p1f2\n",
       "     CMIP6_CV_version:      cv=6.2.3.0-7-g2019642\n",
       "     dr2xml_md5sum:         92ddb3d0d8ce79f498d792fc8e559dcf\n",
       "     xios_commit:           1442-shuffle\n",
       "     nemo_gelato_commit:    49095b3accd5d4c_6524fe19b00467a\n",
       "     arpege_minor_version:  6.3.2\n",
       "     branch_method:         no parent\n",
       "     parent_activity_id:    no parent\n",
       "     parent_experiment_id:  no parent\n",
       "     parent_mip_era:        no parent\n",
       "     parent_source_id:      no parent\n",
       "     parent_time_units:     no parent\n",
       "     parent_variant_label:  no parent\n",
       "     history:               none\n",
       "     tracking_id:           hdl:21.14100/22cb7cb0-b35f-4582-8e67-85fcf300c436}"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K['aqua-control'][m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import label, generate_binary_structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i'm curious about whether we can do very simplistic \"BLOB\" detection \n",
    "m = \"CNRM-CM6-1\"\n",
    "r = \"r1i1p1f2\"\n",
    "foo = K['aqua-control'][m][r]['pr'] * 86400.\n",
    "foo_mask = foo.where(foo >= aquan_nl4mm[f'{m}-aqua-control-{r}'][-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.52 s, sys: 332 ms, total: 6.85 s\n",
      "Wall time: 6.76 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "labeled_arrays = []\n",
    "n_features = []\n",
    "for t in foo_mask['time']:\n",
    "    la, nf = label(foo_mask.sel(time=t).values, structure=generate_binary_structure(2,2))\n",
    "    la = xr.DataArray(la, dims=(\"lat\",\"lon\"), coords={\"lat\":foo.lat, \"lon\":foo.lon})\n",
    "    labeled_arrays.append(la)\n",
    "    n_features.append(nf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# provides a quick & dirty count of precipitating features:\n",
    "nf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fad6e43c590>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foo_mask.isel(time=1000).plot.pcolormesh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fad6613be90>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_arrays[1000].plot.pcolormesh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function scipy.ndimage.measurements.label(input, structure=None, output=None)>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}