{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['hatch.color']='g'\n",
    "import xarray as xr\n",
    "from misc.jupyter import end_interactive\n",
    "from scipy.stats import ttest_1samp\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from urllib.request import urlopen\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "#from metpy.units import masked_array, units\n",
    "from netCDF4 import Dataset\n",
    "\n",
    "#from geoplots import mapplot, xticksyear, xticksmonth, yticks2lat\n",
    "#import geoxarray\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get dates of volcanic eruptions (from two datasets; 500-1850AD and 1850-2006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "forcing1 = xr.open_dataset('/tigress/wenchang/data/cesm/LME/FORCING/IVI2LoadingLatHeight501-2000_L18_c20100518.nc')\n",
    "forcing2 = xr.open_dataset('/tigress/wenchang/data/cesm/LME/FORCING/CCSM4_volcanic_1850-2008_prototype1.nc')\n",
    "colmass1=forcing1.colmass\n",
    "colmass2=forcing2.colmass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "volc_dates2 = []\n",
    "for i in range(len(forcing2.date)):\n",
    "    if (colmass2[i-1].sum()< 7e-6) and (colmass2[i].sum() > 7e-6):\n",
    "        #print(i)\n",
    "        volc_dates2.append(forcing2.date[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "volc_dates1 = [ 5290415.,  5410415.,  5670415.,  5770415.,  5890415.,  6190415.,\n",
    "        6640415.,  7380415.,  7440415.,  8540415.,  8700415.,  8770415.,\n",
    "        8930415.,  9290415.,  9390415.,  9450415.,  9610415.,  9870415.,\n",
    "       10010415., 10180415., 10300415., 10400415., 10500415., 10600415.,\n",
    "       10810415., 10940415., 11100415., 11180415., 11320415., 11420415.,\n",
    "       11500415., 11580415., 11760515., 11880415., 12130415., 12320415.,\n",
    "       12580415., 12680415., 12750415., 12840415., 13070415., 13160415.,\n",
    "       13280415., 13410415., 13580415., 13700415., 13810415., 14160415.,\n",
    "       14520415., 14590415., 14740415., 15030415., 15120415., 15260415.,\n",
    "       15340615., 15840415., 15930415., 16000215., 16190415., 16410115.,\n",
    "       16730515., 16930615., 17110415., 17190415., 17290815., 17380815.,\n",
    "       17551015., 17610915., 17940315., 18090415., 18150415., 18310415.]\n",
    "      \n",
    "volc_dates2 = [18531215.,18560915.,18830815.,18900215.,19020515.,19070315.,19120615.,\n",
    "              19211215.,19280815.,19320415.,19530715.,19630315.,19680615.,\n",
    "              19741015.,19820515.,19910615.] #1849.9166"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "volc_dates = [18531215.,18560915.,18830815.,18900215.,19020515.,19070315.,19120615.,\n",
    "              19211215.,19280815.,19320415.,19530715.,19630315.,19680615.,\n",
    "              19741015.,19820515.,19910615.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eruption weights and Symmetry classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 500-1849\n",
    "erup_weights1=[0 for x in range(len(volc_dates1))]\n",
    "erup_sym1 = [0 for x in range(len(volc_dates1))]\n",
    "for i in range(len(volc_dates1)):\n",
    "    ind = np.where(forcing1.date==volc_dates1[i])\n",
    "    nh = 0\n",
    "    sh = 0\n",
    "    #print(i)\n",
    "    for j in range(48):\n",
    "        nh += np.average(colmass1.isel(lat=slice(32,64))[ind[0]+j],weights=np.cos(colmass1.isel(lat=slice(32,64)).lat*np.pi/180.),axis=1)\n",
    "        sh += np.average(colmass1.isel(lat=slice(0,32))[ind[0]+j],weights=np.cos(colmass1.isel(lat=slice(0,32)).lat*np.pi/180.),axis=1)\n",
    "        erup_weights1[i] += np.average(colmass1[ind[0]+j], weights=np.cos(colmass1.lat*np.pi/180.),axis=1)\n",
    "    erup_sym1[i] = [nh-sh,nh+sh]\n",
    "erup_weights1 = np.log(np.squeeze(erup_weights1))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1850-2006\n",
    "erup_weights2=[0 for x in range(len(volc_dates2))]\n",
    "erup_sym2 = [0 for x in range(len(volc_dates2))]\n",
    "for i in range(len(volc_dates2)):\n",
    "    ind = np.where(forcing2.date==volc_dates2[i])\n",
    "    nh = 0\n",
    "    sh = 0\n",
    "    #print(i)\n",
    "    for j in range(48):\n",
    "        nh += np.average(colmass2.isel(lat=slice(32,64))[ind[0]+j],weights=np.cos(colmass2.isel(lat=slice(32,64)).lat*np.pi/180.),axis=1)\n",
    "        sh += np.average(colmass2.isel(lat=slice(0,32))[ind[0]+j],weights=np.cos(colmass2.isel(lat=slice(0,32)).lat*np.pi/180.),axis=1)\n",
    "        erup_weights2[i] += np.average(colmass2[ind[0]+j], weights=np.cos(colmass2.lat*np.pi/280.),axis=1)\n",
    "    erup_sym2[i] = [nh-sh,nh+sh]\n",
    "erup_weights2 = np.log(np.squeeze(erup_weights2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the two periods\n",
    "erup_weights =  erup_weights2.tolist()\n",
    "erup_sym = erup_sym2\n",
    "#erup_weights = erup_weights1.tolist() + erup_weights2.tolist()\n",
    "#erup_sym = erup_sym1 + erup_sym2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn 2D symmetry array into symmetry angle; use threshold of +/- pi/6 \n",
    "erup_sym_angle = [0 for x in range(len(erup_sym))]\n",
    "erup_sym_bool = [0 for x in range(len(erup_sym))]\n",
    "for i in range(len(erup_sym)):\n",
    "    erup_sym_angle[i] = np.arctan2(erup_sym[i][0][0],erup_sym[i][1][0])\n",
    "    if erup_sym_angle[i] > np.pi/6.:\n",
    "        erup_sym_bool[i] = 1 \n",
    "    if erup_sym_angle[i] < -np.pi/6.:\n",
    "         erup_sym_bool[i] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# then add constant so that Pinatubo has a value of ~-3.5 so that it reflects radiative forcing\n",
    "const=3.5-erup_weights[-1]\n",
    "erup_weights=[x + const for x in erup_weights]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 0, 1, 1, -1, 0, -1, 1, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erup_sym_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "erup_weights = [4.198237317545125, 4.11317437628273, 2.5833139325809586, 3.732058245107523, 0.9924984136303561, 2.79283115288076, 3.4265538889861054, 3.9984521796451284, 2.8321465786886524, 3.2482627838000946, 3.242621426160893, 1.4495030197677465, 1.0393538974801952, 2.4835817008137973, 0.6236851082082655, 1.442270278426033, 2.9588215298379668, 2.1065409758468814, 3.2157890567837715, 1.9514104983161449, 2.1480671344023072, 1.0188180677312104, 1.3873616495790415, 0.9394558936035153, 3.019479757401429, 1.966749958244412, 1.5307012012069947, 1.4086387222775638, 1.069355587054586, 0.16409038126405662, 1.1127360560506396, 3.4716583296523247, 3.8186015443521972, 1.6882203841000134, 4.266144752999342, 2.9932089911743276, 5.7239010261335785, 3.1021600383979537, 4.483634329276291, 4.224648497538364, 0.5268338705386917, 0.9656359096635967, 2.9761685099574056, 3.7422597001664046, 0.4474966033595944, 1.6331256003960135, 2.0233506825058853, 2.442551521896249, 5.180288997849326, 3.0820366141048634, 2.9221753604930907, 0.5378743211690828, 1.440113250588773, 1.2596762437388502, 1.7063160175549097, 3.181902636669732, 2.616813442693976, 4.11168889913466, 2.0157316569862562, 4.0806252375111605, 3.011310693331951, 3.6551824862841533, 1.7119872778761316, 3.445537421341607, 2.4934618458805, 1.5596875694082168, 2.0827590152349362, 4.532320699397075, 2.181273220676223, 4.177328531336492, 4.882304759994825, 3.097861029411047, 1.7222165894144066, 0.8741522795533356, 3.707315193119152, 3.0385066922712225, 3.495901112130311, 0.9218316417593364, 2.1720797374364267, 0.829325220613498, 1.9447801042363242, 1.0348675509224456, 0.2277647716490847, 3.1483197288649905, 1.6290306207867928, 1.6281306076900162, 2.995415406861043, 3.500000000000001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "erup_sym_bool = [1, -1, 1, -1, 1, 1, 0, 0, 0, 0, 0, 1, 1, -1, -1, -1, 0, 1, 0, 1, -1, 1, -1, 1, 0, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 0, -1, 0, 0, -1, -1, 1, 0, -1, 1, -1, 0, 0, 1, 0, 1, 1, 1, -1, 1, -1, 0, -1, 0, 0, -1, -1, 1, 1, -1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, -1, 0, -1, 1, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LME precip for the 13 full-forcing ensemble members are only available split into convective (PRECC) & large-scale precip (PRECL), have to add them together into PRECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<xarray.DataArray 'date' ()>\n",
      "array(18531215.)\n",
      "Coordinates:\n",
      "    time     float32 1853.9166\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(18560915.)\n",
      "Coordinates:\n",
      "    time     float32 1856.6666\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(18830815.)\n",
      "Coordinates:\n",
      "    time     float32 1883.5833\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(18900215.)\n",
      "Coordinates:\n",
      "    time     float32 1890.0833\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19020515.)\n",
      "Coordinates:\n",
      "    time     float32 1902.3333\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19070315.)\n",
      "Coordinates:\n",
      "    time     float32 1907.1666\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19120615.)\n",
      "Coordinates:\n",
      "    time     float32 1912.4166\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19211215.)\n",
      "Coordinates:\n",
      "    time     float32 1921.9166\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19280815.)\n",
      "Coordinates:\n",
      "    time     float32 1928.5833\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19320415.)\n",
      "Coordinates:\n",
      "    time     float32 1932.25\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19530715.)\n",
      "Coordinates:\n",
      "    time     float32 1953.5\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19630315.)\n",
      "Coordinates:\n",
      "    time     float32 1963.1666\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19680615.)\n",
      "Coordinates:\n",
      "    time     float32 1968.4166\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19741015.)\n",
      "Coordinates:\n",
      "    time     float32 1974.75\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19820515.)\n",
      "Coordinates:\n",
      "    time     float32 1982.3334\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd, <xarray.DataArray 'date' ()>\n",
      "array(19910615.)\n",
      "Coordinates:\n",
      "    time     float32 1991.4166\n",
      "Attributes:\n",
      "    long_name:  Date\n",
      "    units:      yyyymmdd]\n"
     ]
    }
   ],
   "source": [
    "# add eruption dates lists\n",
    "volc_dates=volc_dates2\n",
    "#volc_dates = volc_dates1 + volc_dates2\n",
    "print(volc_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONVECTIVE PRECIP - get 13 LME ensembles\n",
    "preccs = [0 for x in range(13)]\n",
    "precc_1mil = '/tigress/gvecchi/DATA/CESM/LME/PRECC/b.e11.BLMTRC5CN.f19_g16.0{:02d}.cam.h0.PRECC.085001-184912.nc'\n",
    "precc_2mil = '/tigress/gvecchi/DATA/CESM/LME/PRECC/b.e11.BLMTRC5CN.f19_g16.0{:02d}.cam.h0.PRECC.185001-200512.nc'\n",
    "\n",
    "for i in range(1,13):\n",
    "    preccs[i-1] = xr.open_mfdataset([precc_1mil.format(i),precc_2mil.format(i)])\n",
    "\n",
    "preccs[12] = xr.open_mfdataset(['/tigress/gvecchi/DATA/CESM/LME/PRECC/b.e11.BLMTRC5CN.f19_g16.013.cam.h0.PRECC.085001-184912.nc','/tigress/gvecchi/DATA/CESM/LME/PRECC/b.e11.BLMTRC5CN.f19_g16.013.cam.h0.PRECC.185001-200512.nc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LARGE-SCALE PRECIP - get 13 LME ensembles\n",
    "precls = [0 for x in range(13)]\n",
    "precl_1mil = '/tigress/wenchang/data/cesm/LME/PRECL/b.e11.BLMTRC5CN.f19_g16.0{:02d}.cam.h0.PRECL.085001-184912.nc'\n",
    "precl_2mil = '/tigress/wenchang/data/cesm/LME/PRECL/b.e11.BLMTRC5CN.f19_g16.0{:02d}.cam.h0.PRECL.185001-200512.nc'\n",
    "                                                   \n",
    "\n",
    "for i in range(1,13):\n",
    "    precls[i-1] = xr.open_mfdataset([precl_1mil.format(i),precl_2mil.format(i)])\n",
    "\n",
    "precls[12] = xr.open_mfdataset(['/tigress/wenchang/data/cesm/LME/PRECL/b.e11.BLMTRC5CN.f19_g16.013.cam.h0.PRECL.085001-184912.nc','/tigress/wenchang/data/cesm/LME/PRECL/b.e11.BLMTRC5CN.f19_g16.013.cam.h0.PRECL.185001-200512.nc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create PRECT list of datasets\n",
    "PRECT = [0 for x in range(13)]\n",
    "for i in range(13):\n",
    "    PRECT[i] = precls[i].PRECL + preccs[i].PRECC\n",
    "    PRECT[i] = PRECT[i] * 8.64e+7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only keep eruption dates after 850AD\n",
    "volc_dates = [int(d - 14) for d in volc_dates if d>8550101]\n",
    "erup_sym_bool=erup_sym_bool[10:]\n",
    "erup_weights=erup_weights[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (time: 130, lat: 96, lon: 144)>\n",
       "dask.array<shape=(130, 96, 144), dtype=float32, chunksize=(130, 96, 144)>\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time     (time) object 0865-03-01 00:00:00 ... 0875-12-01 00:00:00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is practice selecting time slices from xarray datasets\n",
    "year = 870\n",
    "month=3\n",
    "PRECT[0].sel(time=slice(f'{year-5:04d}-{month:02d}', f'{year+5:04d}'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=[]\n",
    "month=[]\n",
    "for date in volc_dates:\n",
    "    s = str(int(date))\n",
    "    year.append(int(s[0:-4]))\n",
    "    month.append(int(s[-4:-2]))\n",
    "    #print(date,year,month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[18531215.0,\n",
       " 18560915.0,\n",
       " 18830815.0,\n",
       " 18900215.0,\n",
       " 19020515.0,\n",
       " 19070315.0,\n",
       " 19120615.0,\n",
       " 19211215.0,\n",
       " 19280815.0,\n",
       " 19320415.0,\n",
       " 19530715.0,\n",
       " 19630315.0,\n",
       " 19680615.0,\n",
       " 19741015.0,\n",
       " 19820515.0,\n",
       " 19910615.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volc_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"erup_precip = [0 for x in range(len(volc_dates))]\\nda = [0 for x in range(13)]\\nmytime = pd.date_range('1990-01', end='2000-12', freq='MS')\\nfor j in range(13):\\n    for i in range(len(volc_dates)):\\n        #ind = np.where(volc_dates[i]==preccs[j].date)[0][0] - 60 #find index 5 years before eruption\\n        erup_precip[i] = PRECT[j].sel(time=slice(f'{year[i]-5:04d}', f'{year[i]+5:04d}')).assign_coords(time=mytime)\\n    da[j] = xr.concat(erup_precip, dim=pd.Index(range(len(volc_dates)), name='iv'))\\n    print(j)\\ndaa = xr.concat(da,dim=pd.Index(range(13),name='en'))\\ndaa.to_dataset(name='lme_precip').to_netcdf('/tigress/tessj/lme_precip.nc')\\n\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run this only when u need to recreate \"daa\" array of selected precip data\n",
    "'''erup_precip = [0 for x in range(len(volc_dates))]\n",
    "da = [0 for x in range(13)]\n",
    "mytime = pd.date_range('1990-01', end='2000-12', freq='MS')\n",
    "for j in range(13):\n",
    "    for i in range(len(volc_dates)):\n",
    "        #ind = np.where(volc_dates[i]==preccs[j].date)[0][0] - 60 #find index 5 years before eruption\n",
    "        erup_precip[i] = PRECT[j].sel(time=slice(f'{year[i]-5:04d}', f'{year[i]+5:04d}')).assign_coords(time=mytime)\n",
    "    da[j] = xr.concat(erup_precip, dim=pd.Index(range(len(volc_dates)), name='iv'))\n",
    "    print(j)\n",
    "daa = xr.concat(da,dim=pd.Index(range(13),name='en'))\n",
    "daa.to_dataset(name='lme_precip').to_netcdf('/tigress/tessj/lme_precip.nc')\n",
    "'''\n",
    "#daa.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warning - its 7.4gb\n",
    "da = xr.open_dataset('/tigress/tessj/lme_precip.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = da.sel(iv=slice(62,78))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "#make list of control climatology in the sahel (first 5 years of selected data for each eruption)\n",
    "ctl_sahel = []\n",
    "for i in range(len(da.iv)):\n",
    "    print(i)\n",
    "    ctl_sahel.append(da.isel(time=slice(0,60),iv=i,lon=(da.lon<=10)|(da.lon>=340)).sel(lat=slice(10,20)).mean(['lon','lat','en']).groupby('time.month').mean('time').load())\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "# take a closer look at that SNOUT (control precipitation climatology for each of the 78 eruptions)\n",
    "for pr in ctl_sahel: pr.lme_precip.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#daa = anomaly dataset (subtract out ctl climatologies)\n",
    "\n",
    "daa = da.isel(lon=(da.lon<=10)|(da.lon>=340)).sel(lat=slice(10,20)).groupby('time.month') - da.isel(time=slice(0,60),lon=(da.lon<=10)|(da.lon>=340)).sel(lat=slice(10,20)).mean(['lon','lat','en']).groupby('time.month').mean('time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "DateParseError",
     "evalue": "month must be in 1..12",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string_with_reso\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: month must be in 1..12",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mDateParseError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-bebe72c5e4e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merup_sym_bool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mnh_precip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{1995}-{month[i]:02d}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{2000}-{month[i]:02d}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'lon'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'en'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0merup_weights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merup_sym_bool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0msym_precip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{1995}-{month[i]:02d}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{2000}-{month[i]:02d}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'lon'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'en'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0merup_weights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36msel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   1608\u001b[0m         \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m         pos_indexers, new_indexes = remap_label_indexers(\n\u001b[0;32m-> 1610\u001b[0;31m             self, indexers=indexers, method=method, tolerance=tolerance)\n\u001b[0m\u001b[1;32m   1611\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_replace_indexes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_indexes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/xarray/core/coordinates.py\u001b[0m in \u001b[0;36mremap_label_indexers\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     pos_indexers, new_indexes = indexing.remap_label_indexers(\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m     )\n\u001b[1;32m    357\u001b[0m     \u001b[0;31m# attach indexer's coordinate to pos_indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36mremap_label_indexers\u001b[0;34m(data_obj, indexers, method, tolerance)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             idxr, new_idx = convert_label_indexer(index, label,\n\u001b[0;32m--> 258\u001b[0;31m                                                   dim, method, tolerance)\n\u001b[0m\u001b[1;32m    259\u001b[0m             \u001b[0mpos_indexers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midxr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36mconvert_label_indexer\u001b[0;34m(index, label, index_name, method, tolerance)\u001b[0m\n\u001b[1;32m    141\u001b[0m         indexer = index.slice_indexer(_sanitize_slice_element(label.start),\n\u001b[1;32m    142\u001b[0m                                       \u001b[0m_sanitize_slice_element\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m                                       _sanitize_slice_element(label.step))\n\u001b[0m\u001b[1;32m    144\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;31m# unlike pandas, in xarray we never want to silently convert a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   1104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1107\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m             \u001b[0;31m# For historical reasons DatetimeIndex by default supports\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4670\u001b[0m         \"\"\"\n\u001b[1;32m   4671\u001b[0m         start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[0;32m-> 4672\u001b[0;31m                                                  kind=kind)\n\u001b[0m\u001b[1;32m   4673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4674\u001b[0m         \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4869\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4870\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4871\u001b[0;31m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4872\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4873\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4795\u001b[0m         \u001b[0;31m# For datetime indices label may be a string that has to be converted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4796\u001b[0m         \u001b[0;31m# to datetime boundary according to its resolution.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4797\u001b[0;31m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4799\u001b[0m         \u001b[0;31m# we need to look up the label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3p7/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36m_maybe_cast_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   1056\u001b[0m             freq = getattr(self, 'freqstr',\n\u001b[1;32m   1057\u001b[0m                            getattr(self, 'inferred_freq', None))\n\u001b[0;32m-> 1058\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreso\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_time_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1059\u001b[0m             \u001b[0mlower\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parsed_string_to_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreso\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;31m# lower, upper form the half-open interval:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_time_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string_with_reso\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mDateParseError\u001b[0m: month must be in 1..12"
     ]
    }
   ],
   "source": [
    "nh_precip=[]\n",
    "sh_precip=[]\n",
    "sym_precip=[]\n",
    "\n",
    "for i in range(len(daa.iv)):\n",
    "    if erup_sym_bool[i] > 0:\n",
    "        nh_precip.append(daa.isel(iv=i).sel(time=slice(f'{1995}-{month[i]:02d}', f'{2000}-{month[i]:02d}')).mean(['lat','lon','en']) * (1.0/erup_weights[i]))\n",
    "    if erup_sym_bool[i] == 0:\n",
    "        sym_precip.append(daa.isel(iv=i).sel(time=slice(f'{1995}-{month[i]:02d}', f'{2000}-{month[i]:02d}')).mean(['lat','lon','en']) * (1.0/erup_weights[i]))\n",
    "    if erup_sym_bool[i] < 0:\n",
    "        sh_precip.append(daa.isel(iv=i).sel(time=slice(f'{1995}-{month[i]:02d}', f'{2000}-{month[i]:02d}')).mean(['lat','lon','en']) * (1.0/erup_weights[i]))\n",
    "#daa.sel(time=slice(f'{1995}-{month[i]:02d}', f'{2000}-{month[i]:02d}')).mean(['lat','lon','en']).groupby('time.month').mean(['time']).lme_precip.plot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "del nh_precip[-1]\n",
    "del nh_precip[12]\n",
    "#nh_precip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mean=0\n",
    "avg_clim=nh_precip[0].groupby('time.month').mean('time').lme_precip - nh_precip[0].groupby('time.month').mean('time').lme_precip\n",
    "for pr in nh_precip: \n",
    "    pr.groupby('time.month').mean('time').lme_precip.plot()\n",
    "    mean+= pr.lme_precip.mean()\n",
    "    avg_clim+=pr.groupby('time.month').mean('time').lme_precip\n",
    "mean = mean/len(nh_precip)\n",
    "avg_clim = avg_clim/len(nh_precip)\n",
    "plt.axhline(y=mean, color='k', linestyle=\"--\",label='mean anomaly (-0.029)')\n",
    "avg_clim.plot(color='k',linewidth=2,label='mean climatology')\n",
    "plt.title('NH LME eruptions, Sahel precip anomaly (5-yr response)')\n",
    "plt.ylabel('precip anomaly / eruption forcing [mm/day / W/m$^2$] ')\n",
    "mean\n",
    "plt.legend()\n",
    "#plt.savefig('nh_precip.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lme_precip' ()>\n",
       "array(-0.028959)\n",
       "Coordinates:\n",
       "    iv       int64 1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lme_precip' (month: 12)>\n",
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)\n",
       "Coordinates:\n",
       "    iv       int64 1\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nh_precip[0].groupby('time.month').mean('time').lme_precip - nh_precip[0].groupby('time.month').mean('time').lme_precip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sh_precip[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mean=0\n",
    "avg_clim=sh_precip[0].groupby('time.month').mean('time').lme_precip - sh_precip[0].groupby('time.month').mean('time').lme_precip\n",
    "for pr in sh_precip: \n",
    "    pr.groupby('time.month').mean('time').lme_precip.plot()\n",
    "    mean+= pr.lme_precip.mean()\n",
    "    avg_clim+=pr.groupby('time.month').mean('time').lme_precip\n",
    "mean = mean/len(sh_precip)\n",
    "avg_clim = avg_clim/len(sh_precip)\n",
    "plt.axhline(y=mean, color='k', linestyle=\"--\",label='mean anomaly (0.0199)')\n",
    "avg_clim.plot(color='k',linewidth=2,label='mean climatology')\n",
    "plt.title('SH LME eruptions, Sahel precip anomaly (5-yr response)')\n",
    "plt.ylabel('precip anomaly / eruption forcing [mm/day / W/m$^2$] ')\n",
    "mean\n",
    "plt.legend()\n",
    "#plt.savefig('sh_precip.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lme_precip' ()>\n",
       "array(0.019913)\n",
       "Coordinates:\n",
       "    iv       int64 3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mean=0\n",
    "avg_clim=sym_precip[0].groupby('time.month').mean('time').lme_precip - sym_precip[0].groupby('time.month').mean('time').lme_precip\n",
    "for pr in sym_precip: \n",
    "    pr.groupby('time.month').mean('time').lme_precip.plot()\n",
    "    mean+= pr.lme_precip.mean()\n",
    "    avg_clim+=pr.groupby('time.month').mean('time').lme_precip\n",
    "mean = mean/len(sym_precip)\n",
    "avg_clim = avg_clim/len(sym_precip)\n",
    "plt.axhline(y=mean, color='k', linestyle=\"--\",label='mean anomaly (-0.004)')\n",
    "avg_clim.plot(color='k',linewidth=2,label='mean climatology')\n",
    "plt.title('Symmetric LME eruptions, Sahel precip anomaly (5-yr response)')\n",
    "plt.ylabel('precip anomaly / eruption forcing [mm/day / W/m$^2$] ')\n",
    "mean\n",
    "plt.legend()\n",
    "#plt.savefig('sym_precip.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lme_precip' (month: 12)>\n",
       "array([-0.002656, -0.001728, -0.005218, -0.021343,  0.167177, -0.103978,\n",
       "        0.173444, -0.098417, -0.201309,  0.537697, -0.421088, -0.00348 ],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    iv       int64 1\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nh_precip[0].groupby('time.month').mean('time').lme_precip * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2277647716490847"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erup_weights[72]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 1\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.010065633 0.04407686 ... -0.006971043,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 2\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.066071965 -0.000211733 ... -0.04230302,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 7\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.0022448285 -0.047584053 ... -0.017369257,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 9\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.005614325 0.018904146 ... 0.014285644,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 11\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.026120385 -0.034798138 ... -0.0048091435,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 13\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0416069 0.05390653 ... -0.02072858 0.012077333,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 22\n",
       "     month       (time) int64 5 6 7 8 9 10 11 12 1 2 3 ... 8 9 10 11 12 1 2 3 4 5\n",
       "   * time        (time) datetime64[ns] 1995-05-01 1995-06-01 ... 2000-05-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.007395305 -0.03507241 ... -0.04494348,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 23\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.018515764 -0.02664631 ... 0.012827553,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 24\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.006574594 -0.07457361 ... -0.004235828,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 32\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0052003334 -0.027487513 ... 0.006993321,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 35\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.047087748 0.032112088 ... -0.021048155,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 39\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.014967074 -0.00084998197 ... 7.761381e-05,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 41\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.0040484485 0.18291639 ... -0.11275105,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 42\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0031413992 -0.08896516 ... 0.009860599,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 43\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.010334037 0.19929263 ... 0.0008120892,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 45\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.009394412 -0.025694264 ... -0.008428859,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 53\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.016454333 -0.037470575 ... -0.002820232,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 54\n",
       "     month       (time) int64 8 9 10 11 12 1 2 3 4 5 6 ... 11 12 1 2 3 4 5 6 7 8\n",
       "   * time        (time) datetime64[ns] 1995-08-01 1995-09-01 ... 2000-08-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.14086421 0.097142406 ... 0.096177295,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 56\n",
       "     month       (time) int64 10 11 12 1 2 3 4 5 6 7 8 ... 1 2 3 4 5 6 7 8 9 10\n",
       "   * time        (time) datetime64[ns] 1995-10-01 1995-11-01 ... 2000-10-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.094742775 0.006224413 ... -0.082112595,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 57\n",
       "     month       (time) int64 9 10 11 12 1 2 3 4 5 6 7 ... 12 1 2 3 4 5 6 7 8 9\n",
       "   * time        (time) datetime64[ns] 1995-09-01 1995-10-01 ... 2000-09-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.056261033 0.010161982 ... 0.06654223 0.06414299,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 61\n",
       "     month       (time) int64 4 5 6 7 8 9 10 11 12 1 2 ... 7 8 9 10 11 12 1 2 3 4\n",
       "   * time        (time) datetime64[ns] 1995-04-01 1995-05-01 ... 2000-04-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.010204688 0.021656448 ... -0.013842619,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 62\n",
       "     month       (time) int64 12 1 2 3 4 5 6 7 8 9 10 ... 3 4 5 6 7 8 9 10 11 12\n",
       "   * time        (time) datetime64[ns] 1995-12-01 1996-01-01 ... 2000-12-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0015641234 -0.0075000683 ... -0.03683397,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 63\n",
       "     month       (time) int64 9 10 11 12 1 2 3 4 5 6 7 ... 12 1 2 3 4 5 6 7 8 9\n",
       "   * time        (time) datetime64[ns] 1995-09-01 1995-10-01 ... 2000-09-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.1518753 0.51284754 ... -0.23471546 -0.22999783,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 67\n",
       "     month       (time) int64 3 4 5 6 7 8 9 10 11 12 1 ... 6 7 8 9 10 11 12 1 2 3\n",
       "   * time        (time) datetime64[ns] 1995-03-01 1995-04-01 ... 2000-03-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0064891335 0.14605974 ... -0.0028487549,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 68\n",
       "     month       (time) int64 6 7 8 9 10 11 12 1 2 3 4 ... 9 10 11 12 1 2 3 4 5 6\n",
       "   * time        (time) datetime64[ns] 1995-06-01 1995-07-01 ... 2000-06-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 -0.0026047854 0.076499835 ... 0.025607897,\n",
       " <xarray.Dataset>\n",
       " Dimensions:     (time: 61)\n",
       " Coordinates:\n",
       "     iv          int64 72\n",
       "     month       (time) int64 7 8 9 10 11 12 1 2 3 4 5 ... 10 11 12 1 2 3 4 5 6 7\n",
       "   * time        (time) datetime64[ns] 1995-07-01 1995-08-01 ... 2000-07-01\n",
       " Data variables:\n",
       "     lme_precip  (time) float32 0.8087693 -1.4705442 ... -0.52133954 -0.64559984]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nh_precip"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lme_precip' (time: 132)>\n",
       "array([-1.934315e-03, -6.297513e-03,  2.521764e-02, -1.502099e-01,\n",
       "       -1.629207e-01, -9.812341e-01,  2.076313e-01,  6.990368e-01,\n",
       "        9.504895e-01,  8.252771e-01,  8.280255e-02, -8.050422e-02,\n",
       "       -4.602713e-03,  1.530519e-02,  7.100793e-03,  1.626563e-01,\n",
       "        5.651847e-01,  4.823292e-01,  1.050115e-01,  2.601465e-01,\n",
       "        9.142239e-02,  8.315755e-01, -1.000481e+00, -1.548136e-01,\n",
       "        2.600692e-02,  2.702253e-02, -4.450531e-02, -1.549389e-01,\n",
       "       -2.361922e-01,  4.519886e-01,  5.666916e-01,  1.090259e-01,\n",
       "       -7.944878e-01,  4.562421e-01,  8.391504e-01, -2.031167e-02,\n",
       "       -2.422390e-03, -2.581671e-02, -2.220554e-02, -1.001198e-01,\n",
       "       -1.312840e-01,  8.609200e-02, -6.193694e-01,  4.830213e-01,\n",
       "        6.246460e-01, -1.107926e+00, -6.735368e-01, -2.199029e-02,\n",
       "       -1.704751e-02, -1.021351e-02,  3.439238e-02,  2.426124e-01,\n",
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       "       -8.720616e-01, -1.005162e+00,  7.520668e-01,  2.776194e-01,\n",
       "       -4.937691e-03,  4.542825e-02,  3.832299e-02,  1.086427e-01,\n",
       "        4.490631e-01, -1.005039e-01,  8.087693e-01, -1.470544e+00,\n",
       "       -1.789926e+00, -1.101331e+00, -1.207610e+00,  1.560718e-01,\n",
       "       -1.344249e-02,  8.190214e-03, -1.857122e-02,  1.904639e-02,\n",
       "       -5.314864e-01, -6.570253e-01,  1.536419e+00,  2.321296e-01,\n",
       "       -1.865506e+00, -1.022655e-01, -4.619581e-01,  1.455231e-01,\n",
       "        3.295895e-03, -8.912429e-03, -2.945570e-02,  2.175428e-02,\n",
       "        2.175772e-01,  4.163424e-01,  1.357903e+00,  8.770623e-01,\n",
       "        9.995077e-01,  8.809558e-01, -3.778790e-01, -1.153403e-01,\n",
       "       -1.939692e-02, -2.135161e-02, -2.904545e-02,  5.323618e-02,\n",
       "        2.087473e-01,  7.057874e-01,  1.459217e-01, -5.390154e-01,\n",
       "       -1.255872e+00,  1.450732e+00,  3.151183e-01, -1.503763e-02,\n",
       "        1.088056e-03, -8.558974e-04,  1.677365e-02, -2.202728e-01,\n",
       "        1.823220e-01,  1.509433e+00,  2.570579e-01, -8.245732e-01,\n",
       "       -3.052358e+00, -2.009608e+00, -3.485277e-01, -1.286805e-02,\n",
       "       -2.455275e-02,  3.014593e-02, -5.173186e-02, -1.497720e-01,\n",
       "       -4.366902e-01, -5.213395e-01, -6.455998e-01, -4.388865e-01,\n",
       "       -8.804573e-01, -1.310791e+00, -9.984307e-01, -2.354704e-01],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    iv       int64 72\n",
       "  * time     (time) datetime64[ns] 1990-01-01 1990-02-01 ... 2000-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daa.sel(iv=72).mean(['lat','lon','en']).lme_precip * (1.0/0.2277647716490847)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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