Download2NetCDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directly Download from GC and save as netcdf files\n",
    "- This is for those who cannot use zarr/python for processing the CMIP6 datasets\n",
    "- Please note that the netcdf files have CF-compliant time grids, but might not be what you are used to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import gcsfs #google cloud file system. \n",
    "import xarray as xr\n",
    "import warnings\n",
    "from glob import glob # use * !\n",
    "import scipy.io as sio\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utls import search_df, add_time_info, get_zdict #extra functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_area_and_seasonal_mean(danom, xlim, ylim, slim, mask=1):\n",
    "    \"\"\"\n",
    "    Weights each grid point by the cos(latitude), computes area mean, normalizing by areaa mean of the weights\n",
    "    returns:\n",
    "        DataArray:  global mean for each model\n",
    "    \"\"\"  \n",
    "    xlim = np.array(xlim)\n",
    "    xlim += (xlim<0 )*360\n",
    "    if xlim[0]>xlim[1]:\n",
    "        lon_sel = (danom.lon>=xlim[0])+(danom.lon<xlim[1])\n",
    "    else:\n",
    "        #this was previously wrong only when xlim was fully positive or negative -- missing parentheses\n",
    "        lon_sel = (danom.lon>=xlim[0])*(danom.lon<xlim[1])\n",
    "    #print(sum(lon_sel.values))\n",
    "    if type(mask)!=int:\n",
    "        mask = (xr.ones_like(danom)*mask).isel({'lat': (danom.lat>=ylim[0])*(danom.lat<ylim[1]), 'lon': lon_sel})\n",
    "    #print(sum(((danom.lat>=ylim[0])*(danom.lat<ylim[1])).values))\n",
    "    danom = danom.isel({'lat': (danom.lat>=ylim[0])*(danom.lat<ylim[1]), \n",
    "                        'lon': lon_sel, \n",
    "                        'time': (danom['time'].dt.month >= slim[0])*(danom['time'].dt.month <= slim[1])\n",
    "                       })\n",
    "    coslat = np.cos(np.deg2rad(danom.lat))\n",
    "    #print(coslat)\n",
    "    weights = xr.ones_like(danom)*coslat*mask\n",
    "    #print(weights.hus.values)\n",
    "    weight_mean = weights.mean(['lat','lon'])#, keep_attrs=True, 'plev', 'time'\n",
    "    #print(weight_mean.hus.values)\n",
    "    #print('danom*weights')\n",
    "    #print(danom*weights)\n",
    "    #print('values')\n",
    "    #print((danom*weights).hus.values)\n",
    "    #print('area_mean part 1')\n",
    "    area_mean = (danom * weights).mean(['lat','lon'], keep_attrs=True, skipna = True)#\n",
    "    #print(area_mean.hus.values)\n",
    "    area_mean = area_mean/weight_mean\n",
    "    #print('area_mean values')\n",
    "    #print(area_mean.hus.values)\n",
    "    if area_mean.time.dtype!='datetime64[ns]':\n",
    "        area_mean['time'] = area_mean.indexes['time'].year\n",
    "        return area_mean.groupby('time').mean(dim='time', keep_attrs=True)\n",
    "        #area_mean.indexes['time'].to_datetimeindex()\n",
    "        #there are lots of dftime.DatetimeNoLeap. this would matter a touch if I used a weighted average.\n",
    "    else:   \n",
    "        #month_length = danom.time.dt.days_in_month\n",
    "        #this actually isn't right! Let's just do an unweighted mean for now :(\n",
    "        #weights = month_length/sum(month_length[slim[0]:slim[1]+1])\n",
    "        #Sm = (weights*area_mean)\n",
    "        #return Sm.groupby(grp).sum(dim='time', keep_attrs=True)\n",
    "        return area_mean.groupby(area_mean.time.dt.year).mean(dim='time', keep_attrs=True)\n",
    "    \n",
    "    #technically this is inconsistent handling; one makes a dimension called \"time\" and the other makes \"years\". I have to deal with that in my matlab code\n",
    "    \n",
    "plev19 = [100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n",
    "plev19_bnds = [100000,95000,90000,75000,65000,55000,45000,35000,27000,23000,17000,12500,8500,6500,4000,2500,1500,700,300,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#danom = ds0\n",
    "\n",
    "#xlim = [-20,40]\n",
    "#ylim = [12,18]\n",
    "#slim = [7,9]\n",
    "def calc_stp(dl):\n",
    "    l = dl.values\n",
    "    stp = max(l[1:] - l[0:-1])\n",
    "    return stp\n",
    "\n",
    "def compute_conv(danom, xlim, ylim, slim, plim):\n",
    "    \"\"\"\n",
    "    Calculates the mean flow convergence of moisture on monthly scale into the Sahel region.\n",
    "    returns convergence at each pressure level. add pressure level ranges to integrate over.\n",
    "    \"\"\"  \n",
    "    xlim = np.array(xlim)\n",
    "    xlim += (xlim<0)*360\n",
    "    lt_stp = calc_stp(danom.lat)\n",
    "    ln_stp = calc_stp(danom.lon)\n",
    "    if xlim[0]>xlim[1]:\n",
    "        lon_sel_2 = (danom.lon>(xlim[0]-ln_stp))+(danom.lon<(xlim[1]+ln_stp))\n",
    "    else:\n",
    "        lon_sel_2 = (danom.lon>(xlim[0]-ln_stp))*(danom.lon<(xlim+ln_stp))\n",
    "\n",
    "    danom = danom.isel({'time': (danom['time'].dt.month >= slim[0])*(danom['time'].dt.month <= slim[1]),\n",
    "                         'lat': (danom.lat>(ylim[0]-lt_stp))*(danom.lat<(ylim[1]+lt_stp)),\n",
    "                         'lon': lon_sel_2,\n",
    "                         'plev': danom.plev==plim #when I can figure out the bounds change this to an integral\n",
    "                       })\n",
    "    danom['qfu'] = danom['hus']*danom['ua']\n",
    "    danom['qfv'] = danom['hus']*danom['va']\n",
    "    if xlim[0]>xlim[1]:\n",
    "        lon_sel = (danom.lon>xlim[0])+(danom.lon<xlim[1])\n",
    "    else:\n",
    "        lon_sel = (danom.lon>xlim[0])*danom.lon<xlim[1]\n",
    "    lat_sel = (danom.lat>ylim[0])*(danom.lat<ylim[1])\n",
    "    danom.load()\n",
    "    dS = danom['qfv'].isel({'lat': danom.lat<(ylim[0]+lt_stp), 'lon': lon_sel}).mean(['lat'], keep_attrs=True)\n",
    "    dN = -danom['qfv'].isel({'lat': danom.lat>(ylim[1]-lt_stp), 'lon': lon_sel}).mean(['lat'], keep_attrs=True)\n",
    "    dW = danom['qfu'].isel({'lat': lat_sel, 'lon': danom.lon<(xlim[0]+ln_stp)}).mean(['lon'], keep_attrs=True)\n",
    "    dE = -danom['qfu'].isel({'lat': lat_sel, 'lon': danom.lon>(xlim[1]-ln_stp)}).mean(['lon'], keep_attrs=True)\n",
    "    coslat = np.cos(np.deg2rad(ylim))\n",
    "    d_conv = (coslat[0]*dS + coslat[1]*dN).sum(['lon'], keep_attrs=True)+(dW+dE).sum(['lat'], keep_attrs=True)\n",
    "    if d_conv.time.dtype!='datetime64[ns]':\n",
    "        d_conv['time'] = d_conv.indexes['time'].year\n",
    "        return d_conv.groupby('time').mean(dim='time', keep_attrs=True)\n",
    "    else:   \n",
    "        return d_conv.groupby(d_conv.time.dt.year).mean(dim='time', keep_attrs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Where to write local netcdf files:\n",
    "username = os.environ['JUPYTERHUB_USER']\n",
    "mach = os.uname()[1]\n",
    "\n",
    "zarr_local = f'/home/{username}/netcdf/cmip6/preprocessed'\n",
    "if not os.path.exists(zarr_local):\n",
    "    print(f'Please create the directory {zarr_local}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "      <th>status</th>\n",
       "      <th>severity</th>\n",
       "      <th>issue_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>6hrPlev</td>\n",
       "      <td>psl</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>rsdt</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>prw</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>rlus</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>rlds</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id     source_id       experiment_id member_id  \\\n",
       "0  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "1  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "2  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "3  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "4  HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present  r1i1p1f1   \n",
       "\n",
       "  table_id variable_id grid_label  \\\n",
       "0  6hrPlev         psl         gn   \n",
       "1     Amon        rsdt         gn   \n",
       "2     Amon         prw         gn   \n",
       "3     Amon        rlus         gn   \n",
       "4     Amon        rlds         gn   \n",
       "\n",
       "                                              zstore dcpp_init_year   version  \\\n",
       "0  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...            NaN  20170706   \n",
       "1  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...            NaN  20170706   \n",
       "2  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...            NaN  20170706   \n",
       "3  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...            NaN  20170706   \n",
       "4  gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...            NaN  20170706   \n",
       "\n",
       "  status severity issue_url  \n",
       "0   good     none      none  \n",
       "1   good     none      none  \n",
       "2   good     none      none  \n",
       "3   good     none      none  \n",
       "4   good     none      none  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the master CMIP6 Google Cloud catalog\n",
    "df_cloud = pd.read_csv('https://cmip6.storage.googleapis.com/cmip6-zarr-consolidated-stores-noQC.csv', dtype='unicode')\n",
    "df_cloud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose basic configuration parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we search the CMIP6 data for the datasets you need - using the same keywords as at the ESGF sites\n",
    "#       https://esgf-node.llnl.gov/search/cmip6/\n",
    "\n",
    "debug = False\n",
    "\n",
    "# must choose ONE table_id  (only works for *mon or *day)\n",
    "table_id = 'Amon'\n",
    "\n",
    "#must choose LIST of experiments, variables\n",
    "experiments = ['historical']#,'hist-aer', , 'hist-nat', 'hist-GHG', 'piControl' 'amip-hist',\n",
    "variables = ['ts']#, 'va', 'ua']\n",
    "\n",
    "location = 'Ocean'#'Sahel'#\n",
    "\n",
    "# can specify 'All' or give a list or string\n",
    "#sources = ['GFDL-ESM4']#GFDL-CM4']#'ACCESS-ESM1-5']#'CNRM-ESM2-1']#'NorESM2-LM']# 'GFDL-ESM4'['CanESM5-CanOE']#CMCC-CM2-SR5']#'CIESM']#MCM-UA-1-0']  #AWI-CM-1-1-MR']#SAM0-UNICON']# omit the [] to get all models with CESM2 in their name\n",
    "sources = 'All'\n",
    "#members = ['r3i1p1f1']#r1i1p1f1']#'r9i1p1f2']#\n",
    "members = 'All'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of matching datasets 555\n"
     ]
    }
   ],
   "source": [
    "search = {'table_id':table_id}\n",
    "search['experiment_id'] = experiments\n",
    "search['variable_id'] = variables\n",
    "if sources != 'All':\n",
    "    search['source_id'] = sources\n",
    "if members != 'All':\n",
    "    search['member_id'] = members\n",
    "    \n",
    "df_available = search_df(df_cloud, **search)\n",
    "\n",
    "print('number of matching datasets',len(df_available))\n",
    "\n",
    "#523 historical simulations < 536 on the cite directly. Do I want to figure out which simulations are missing? \n",
    "# Or do I trust that they are missing for a reason?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "      <th>status</th>\n",
       "      <th>severity</th>\n",
       "      <th>issue_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13151</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r3i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ts</td>\n",
       "      <td>gr1</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-ESM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r2i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ts</td>\n",
       "      <td>gr1</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21159</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NOAA-GFDL</td>\n",
       "      <td>GFDL-CM4</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ts</td>\n",
       "      <td>gr1</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180701</td>\n",
       "      <td>wontfix</td>\n",
       "      <td>medium</td>\n",
       "      <td>https://errata.es-doc.org/static/view.html?uid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22165</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>IPSL</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r12i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ts</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180803</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22908</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>IPSL</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r15i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ts</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20180803</td>\n",
       "      <td>good</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      activity_id institution_id     source_id experiment_id  member_id  \\\n",
       "13151        CMIP      NOAA-GFDL     GFDL-ESM4    historical   r3i1p1f1   \n",
       "13401        CMIP      NOAA-GFDL     GFDL-ESM4    historical   r2i1p1f1   \n",
       "21159        CMIP      NOAA-GFDL      GFDL-CM4    historical   r1i1p1f1   \n",
       "22165        CMIP           IPSL  IPSL-CM6A-LR    historical  r12i1p1f1   \n",
       "22908        CMIP           IPSL  IPSL-CM6A-LR    historical  r15i1p1f1   \n",
       "\n",
       "      table_id variable_id grid_label  \\\n",
       "13151     Amon          ts        gr1   \n",
       "13401     Amon          ts        gr1   \n",
       "21159     Amon          ts        gr1   \n",
       "22165     Amon          ts         gr   \n",
       "22908     Amon          ts         gr   \n",
       "\n",
       "                                                  zstore dcpp_init_year  \\\n",
       "13151  gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...            NaN   \n",
       "13401  gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...            NaN   \n",
       "21159  gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...            NaN   \n",
       "22165  gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...            NaN   \n",
       "22908  gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...            NaN   \n",
       "\n",
       "        version   status severity  \\\n",
       "13151  20180701     good     none   \n",
       "13401  20180701     good     none   \n",
       "21159  20180701  wontfix   medium   \n",
       "22165  20180803     good     none   \n",
       "22908  20180803     good     none   \n",
       "\n",
       "                                               issue_url  \n",
       "13151                                               none  \n",
       "13401                                               none  \n",
       "21159  https://errata.es-doc.org/static/view.html?uid...  \n",
       "22165                                               none  \n",
       "22908                                               none  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_available.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For proper debugging, it is helpful to add time grid information to dataframe:\n",
    "if debug:\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        dfa = add_time_info(df_available)\n",
    "else:\n",
    "    dfa = df_available.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of mask datasets 464\n"
     ]
    }
   ],
   "source": [
    "#only use the MASK code for TS!\n",
    "if location=='Ocean':\n",
    "    search_mask = {'table_id':'fx'}\n",
    "    search_mask['experiment_id'] = ['historical', 'piControl', '1pctCO2','hist-resIPO','hist-1950HC']\n",
    "    search_mask['variable_id'] = ['sftlf']\n",
    "    if sources != 'All':\n",
    "        search_mask['source_id'] = sources\n",
    "    if members != 'All':\n",
    "        search_mask['member_id'] = members\n",
    "    historical_mask = search_df(df_cloud, **search_mask)\n",
    "\n",
    "    print('number of mask datasets',len(historical_mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/historical/r3i1p1f1/Amon/ts/gr1/v20180701/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/historical/r2i1p1f1/Amon/ts/gr1/v20180701/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/ts/gr1/v20180701/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r12i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r15i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r10i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r11i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r1i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r20i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r17i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r21i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r9i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r6i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r7i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r13i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r8i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r14i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r3i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r28i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r25i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r27i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r23i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r31i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r22i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r4i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r5i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r19i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r18i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r24i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r16i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r2i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r26i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r29i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r30i1p1f1/Amon/ts/gr/v20180803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r2i1p1f1/Amon/ts/gn/v20180827/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r1i1p1f1/Amon/ts/gn/v20180827/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r3i1p1f1/Amon/ts/gn/v20180828/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r4i1p1f1/Amon/ts/gn/v20180828/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r5i1p1f1/Amon/ts/gn/v20180828/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r6i1p1f1/Amon/ts/gn/v20180829/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r7i1p1f1/Amon/ts/gn/v20180829/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r8i1p1f1/Amon/ts/gn/v20180830/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r10i1p1f1/Amon/ts/gn/v20180830/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/historical/r9i1p1f1/Amon/ts/gn/v20180830/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r1i1p1f2/Amon/ts/gr/v20180917/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-CSM2-MR/historical/r2i1p1f1/Amon/ts/gn/v20181115/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-CSM2-MR/historical/r3i1p1f1/Amon/ts/gn/v20181119/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-CSM2-MR/historical/r1i1p1f1/Amon/ts/gn/v20181126/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r2i1p1f2/Amon/ts/gr/v20181126/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-ESM2-1/historical/r1i1p1f2/Amon/ts/gr/v20181206/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r3i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r6i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r5i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r10i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r4i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r7i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r2i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r8i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r9i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r1i1p1f1/Amon/ts/gn/v20181212/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-ESM1/historical/r2i1p1f1/Amon/ts/gn/v20181214/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-ESM1/historical/r1i1p1f1/Amon/ts/gn/v20181214/'\n",
      " 'gs://cmip6/CMIP6/CMIP/BCC/BCC-ESM1/historical/r3i1p1f1/Amon/ts/gn/v20181214/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r7i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r10i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-ESM2-1/historical/r4i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-ESM2-1/historical/r5i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r8i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-ESM2-1/historical/r3i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-ESM2-1/historical/r2i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r9i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r3i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r4i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r6i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r5i1p1f2/Amon/ts/gr/v20190125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r3i1p1f1/Amon/ts/gn/v20190222/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r5i1p1f1/Amon/ts/gn/v20190222/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r4i1p1f1/Amon/ts/gn/v20190222/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r2i1p1f1/Amon/ts/gn/v20190222/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r1i1p1f1/Amon/ts/gn/v20190222/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r3i1p1f1/Amon/ts/gn/v20190227/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r1i1p1f1/Amon/ts/gn/v20190227/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/historical/r2i1p1f1/Amon/ts/gn/v20190227/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6i1p1f1/Amon/ts/gn/v20190308/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9i1p1f1/Amon/ts/gn/v20190311/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7i1p1f1/Amon/ts/gn/v20190311/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8i1p1f1/Amon/ts/gn/v20190311/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/ts/gn/v20190313/'\n",
      " 'gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/historical/r1i1p1f1/Amon/ts/gn/v20190323/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r8i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r7i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r6i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r9i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r4i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r5i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r3i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r2i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r1i1p1f1/Amon/ts/gn/v20190403/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/historical/r10i1p1f1/Amon/ts/gn/v20190403/'\n",
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      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r27i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r50i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r20i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r26i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r24i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r47i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r41i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r11i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r40i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r38i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r39i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r44i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r45i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r12i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r18i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r30i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r31i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r42i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r15i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r32i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r16i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r43i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r13i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r14i1p1f1/Amon/ts/gn/v20200519/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/r17i1p1f1/Amon/ts/gn/v20200519/'\n",
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      " 'gs://cmip6/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r29i1p1f2/Amon/ts/gr/v20200529/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r4i1p1f1/Amon/ts/gn/v20200529/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MOHC/HadGEM3-GC31-MM/historical/r3i1p1f3/Amon/ts/gn/v20200601/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MOHC/HadGEM3-GC31-MM/historical/r4i1p1f3/Amon/ts/gn/v20200601/'\n",
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      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/ts/gn/v20200605/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/historical/r1i1p1f1/Amon/ts/gn/v20200616/'\n",
      " 'gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/Amon/ts/gn/v20200623/'\n",
      " 'gs://cmip6/CMIP6/CMIP/E3SM-Project/E3SM-1-1-ECA/historical/r1i1p1f1/Amon/ts/gr/v20200623/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3-AerChem/historical/r1i1p1f1/Amon/ts/gr/v20200624/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorESM2-MM/historical/r3i1p1f1/Amon/ts/gn/v20200702/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r9i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r8i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r5i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r6i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r7i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r4i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r3i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r30i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r2i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r29i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r27i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r26i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r25i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r28i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r21i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r24i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r23i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r22i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r19i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r18i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r15i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r16i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r17i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r20i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r1i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r12i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r14i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r11i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/historical/r13i1p1f1/Amon/ts/gn/v20200724/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r3i1p1f1/Amon/ts/gr/v20200730/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r19i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r14i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r15i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r18i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r20i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r12i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r11i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r13i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r16i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r17i1p1f1/Amon/ts/gn/v20200803/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r14i1p1f2/Amon/ts/gn/v20200805/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/historical/r1i1p1f1/Amon/ts/gn/v20200904/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r26i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r27i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r29i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r28i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r21i1p1f1/Amon/ts/gn/v20200922/'\n",
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      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r22i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r24i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r23i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r25i1p1f1/Amon/ts/gn/v20200922/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3-Veg/historical/r12i1p1f1/Amon/ts/gr/v20200925/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3-Veg/historical/r14i1p1f1/Amon/ts/gr/v20200925/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historical/r1i1000p1f1/Amon/ts/gn/v20201029/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3-AerChem/historical/r4i1p1f1/Amon/ts/gr/v20201214/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r2i1p1f1/Amon/ts/gr/v20201215/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r7i1p1f1/Amon/ts/gr/v20201217/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r14i1p1f1/Amon/ts/gr/v20201218/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r10i1p1f1/Amon/ts/gr/v20201221/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r16i1p1f1/Amon/ts/gr/v20201230/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r1i1000p1f2/Amon/ts/gn/v20210107/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/historical/r1i1p1f1/Amon/ts/gn/v20210114/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r17i1p1f1/Amon/ts/gr/v20210120/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r18i1p1f1/Amon/ts/gr/v20210121/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r21i1p1f1/Amon/ts/gr/v20210223/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3/historical/r19i1p1f1/Amon/ts/gr/v20210305/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MOHC/HadGEM3-GC31-LL/historical/r5i1p1f3/Amon/ts/gn/v20210315/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r30i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r22i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r12i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r29i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r21i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r28i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r27i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r13i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r14i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r26i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r25i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r17i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r16i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r19i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r20i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r15i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r24i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r11i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r18i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2L/historical/r23i1p1f2/Amon/ts/gn/v20210317/'\n",
      " 'gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r2i1p1f1/Amon/ts/gn/v20210416/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NIMS-KMA/UKESM1-0-LL/historical/r15i1p1f2/Amon/ts/gn/v20210426/']\n"
     ]
    }
   ],
   "source": [
    "#only use MASK code for TS!\n",
    "if location=='Ocean':\n",
    "    def get_ids(dfa, id_name):\n",
    "        zdicts = list(map(get_zdict, list(dfa.zstore.values)))\n",
    "        return set(map(lambda x: (x[id_name], x['grid_label']), zdicts))\n",
    "\n",
    "    id_name = 'source_id'\n",
    "    historical_mask_models = get_ids(historical_mask, id_name)\n",
    "    sst_models = get_ids(dfa, id_name)\n",
    "\n",
    "    maskable_models = sst_models.intersection(historical_mask_models)\n",
    "\n",
    "    gsurls = np.array([gsurl for gsurl in dfa.zstore.values if (get_zdict(gsurl)['source_id'], get_zdict(gsurl)['grid_label']) in maskable_models])\n",
    "    missing = np.array([gsurl for gsurl in dfa.zstore.values if not (get_zdict(gsurl)['source_id'], get_zdict(gsurl)['grid_label']) in maskable_models])\n",
    "\n",
    "    masks = historical_mask.groupby(['source_id', 'grid_label']).first()\n",
    "\n",
    "    print(gsurls)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gs://cmip6/CMIP6/CMIP/NUIST/NESM3/historical/r2i1p1f1/Amon/ts/gn/v20190628/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NUIST/NESM3/historical/r5i1p1f1/Amon/ts/gn/v20190628/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NUIST/NESM3/historical/r1i1p1f1/Amon/ts/gn/v20190630/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NUIST/NESM3/historical/r3i1p1f1/Amon/ts/gn/v20190630/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NUIST/NESM3/historical/r4i1p1f1/Amon/ts/gn/v20190630/'\n",
      " 'gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/historical/r1i1p1f2/Amon/ts/gn/v20190731/'\n",
      " 'gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/historical/r1i1p1f1/Amon/ts/gn/v20190731/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NIMS-KMA/KACE-1-0-G/historical/r1i1p1f1/Amon/ts/gr/v20190910/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NIMS-KMA/KACE-1-0-G/historical/r3i1p1f1/Amon/ts/gr/v20190919/'\n",
      " 'gs://cmip6/CMIP6/CMIP/NIMS-KMA/KACE-1-0-G/historical/r2i1p1f1/Amon/ts/gr/v20190920/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CCCR-IITM/IITM-ESM/historical/r1i1p1f1/Amon/ts/gn/v20191226/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CAS/CAS-ESM2-0/historical/r1i1p1f1/Amon/ts/gn/v20200302/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CAS/CAS-ESM2-0/historical/r4i1p1f1/Amon/ts/gn/v20200303/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CAS/CAS-ESM2-0/historical/r3i1p1f1/Amon/ts/gn/v20200303/'\n",
      " 'gs://cmip6/CMIP6/CMIP/CAS/CAS-ESM2-0/historical/r2i1p1f1/Amon/ts/gn/v20200303/'\n",
      " 'gs://cmip6/CMIP6/CMIP/THU/CIESM/historical/r1i1p1f1/Amon/ts/gr/v20200417/'\n",
      " 'gs://cmip6/CMIP6/CMIP/THU/CIESM/historical/r3i1p1f1/Amon/ts/gr/v20200417/'\n",
      " 'gs://cmip6/CMIP6/CMIP/THU/CIESM/historical/r2i1p1f1/Amon/ts/gr/v20200417/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM5A2-INCA/historical/r1i1p1f1/Amon/ts/gr/v20200729/'\n",
      " 'gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/historical/r3i1p1f1/Amon/ts/gn/v20200911/'\n",
      " 'gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/historical/r1i1p1f1/Amon/ts/gn/v20200921/'\n",
      " 'gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/historical/r2i1p1f1/Amon/ts/gn/v20200921/'\n",
      " 'gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-Earth3-CC/historical/r1i1p1f1/Amon/ts/gr/v20210113/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2H/historical/r1i1p2f2/Amon/ts/gn/v20210125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2H/historical/r1i1p1f2/Amon/ts/gn/v20210125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/MIROC/MIROC-ES2H/historical/r1i1p3f2/Amon/ts/gn/v20210125/'\n",
      " 'gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR-INCA/historical/r1i1p1f1/Amon/ts/gr/v20210216/'\n",
      " 'gs://cmip6/CMIP6/CMIP/KIOST/KIOST-ESM/historical/r1i1p1f1/Amon/ts/gr1/v20210601/']\n"
     ]
    }
   ],
   "source": [
    "if location=='Ocean':\n",
    "    print(missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for pr:\n",
    "if location=='Sahel':\n",
    "    gsurls = np.array(dfa.zstore.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "if debug:\n",
    "    pd.set_option('display.max_rows', 500)\n",
    "    pd.set_option('display.max_columns', 500)\n",
    "    pd.set_option('display.width', 1000)\n",
    "\n",
    "    dm = dfa[['experiment_id','source_id','member_id','variable_id','start','stop']].groupby([\n",
    "             'experiment_id','start','stop','source_id']).nunique()[['member_id']]\n",
    "\n",
    "    table = pd.DataFrame.pivot_table(dm,\n",
    "                                     values='member_id',\n",
    "                                     index=['source_id','start','stop'],\n",
    "                                     columns=['experiment_id'],\n",
    "                                     aggfunc=np.sum,\n",
    "                                     fill_value=0)\n",
    "    print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['none',\n",
       "       'https://errata.es-doc.org/static/view.html?uid=2f6b5963-f87e-b2df-a5b0-2f12b6b68d32',\n",
       "       'https://errata.es-doc.org/static/view.html?uid=5ebabff0-388f-07bc-b2cf-d44dcbb2940f',\n",
       "       'https://errata.es-doc.org/static/view.html?uid=ad9e4213-f254-e930-a143-030a4b0f8a32',\n",
       "       'https://errata.es-doc.org/static/view.html?uid=e6b642cf-d449-dfa6-a9da-1d1d1e1c0ecd'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa.issue_url.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(token='anon', access='read_only') #the actual files, not the list of files woohoo FILE SYSTEM\n",
    "#fs.get_mapper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-260-1112a380e4f6>, line 64)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-260-1112a380e4f6>\"\u001b[0;36m, line \u001b[0;32m64\u001b[0m\n\u001b[0;31m    mask_ds = xr.open_zarr(fs.get_mapper(masks['zstore'][(model, grd]),consolidated=True)\u001b[0m\n\u001b[0m                                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#gsurls = dfa.zstore.values #zstore is the url where the data is stored\n",
    "\n",
    "conv = False\n",
    "bndries = False\n",
    "replace = False\n",
    "start_month = 7\n",
    "end_month = 9\n",
    "\n",
    "ds_list = []\n",
    "ds_failed_list = []\n",
    "\n",
    "def update_vars(ds):\n",
    "    for var in [var for var in ds.coords]:\n",
    "        if 'bounds' in var:\n",
    "            nvar = var.replace('bounds','bnds')\n",
    "            #print(var,nvar)\n",
    "            ds = ds.rename({var:nvar})\n",
    "        if 'latitude' in var:\n",
    "            nvar = var.replace('latitude','lat')\n",
    "            #print(var,nvar)\n",
    "            ds = ds.rename({var:nvar})\n",
    "        if 'longitude' in var:\n",
    "            nvar = var.replace('longitude','lon')\n",
    "            #print(var,nvar)\n",
    "            ds = ds.rename({var:nvar})\n",
    "    return ds\n",
    "\n",
    "if conv:\n",
    "    search_uv = search.copy()\n",
    "    search_uv['source_id'] = [model]\n",
    "    search_uv['member_id'] = [run]\n",
    "    search_u = search_uv.copy()\n",
    "    search_v = search_uv.copy()\n",
    "    search_u['variable_id'] = ['ua']\n",
    "    search_v['variable_id'] = ['va']\n",
    "    \n",
    "for gsurl in gsurls:\n",
    "    zdict = get_zdict(gsurl) #naomi func for metadata\n",
    "    institution = zdict['institution_id']\n",
    "    model = zdict['source_id']\n",
    "    grd = zdict['grid_label']\n",
    "    run = zdict['member_id']\n",
    "    variable = zdict['variable_id']\n",
    "    expt = zdict['experiment_id']\n",
    "    if conv:\n",
    "        filename = f'{variable}_conv_925_{institution}_{model}_{run}'\n",
    "    elif bndries:\n",
    "        filename = f'{variable}_bndries_{institution}_{model}_{run}'\n",
    "    else:\n",
    "        filename = f'{variable}_{institution}_{model}_{run}'\n",
    "    ncdir = f'{zarr_local}/{expt}/{start_month}-{end_month}'\n",
    "    ncfile = f'{ncdir}/{filename}.nc'\n",
    "    \n",
    "    if(not replace):\n",
    "        ncfiles = glob(ncfile) #check not to double-download files\n",
    "        if len(ncfiles) > 0:\n",
    "            print(ncfiles, 'already exists')\n",
    "            continue\n",
    "            \n",
    "    with warnings.catch_warnings():\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        ds0 = xr.open_zarr(fs.get_mapper(gsurl),consolidated=True) #gets info about the file. get_mapper! always use consolidated=True\n",
    "        if variable=='ts':\n",
    "            mask_ds = xr.open_zarr(fs.get_mapper(masks['zstore'][(model, grd)]),consolidated=True) \n",
    "        #if variable=='ta' or variable=='zg':\n",
    "         #   ds0 = ds0.isel({'plev': ds0.plev==100})\n",
    "        if conv:\n",
    "            U = np.array(search_df(df_cloud, **search_u).zstore.values)\n",
    "            V = np.array(search_df(df_cloud, **search_v).zstore.values)\n",
    "            if len(U) == 1 and len(V) == 1:\n",
    "                dsU = xr.open_zarr(fs.get_mapper(U[0]),consolidated=True)\n",
    "                dsV = xr.open_zarr(fs.get_mapper(V[0]),consolidated=True)\n",
    "                ds0['va'] = dsV['va']\n",
    "                ds0['ua'] = dsU['ua']\n",
    "            else:\n",
    "                continue\n",
    "        \n",
    "    #month_length = ds.time.dt.days_in_month #for some reason the first one doesn't have this...\n",
    "        \n",
    "    ds0 = update_vars(ds0)\n",
    "    \n",
    "    if location=='Ocean':\n",
    "        mask_ds = update_vars(mask_ds)\n",
    "        ls_mask = np.floor(1-mask_ds.sftlf.values/100)\n",
    "        new_lon = (mask_ds.lon+180)%360-180\n",
    "        new_mask = (mask_ds.lat>4)*(6*mask_ds.lat - 5*new_lon <164) + (mask_ds.lat<4)*(6*mask_ds.lat + 5*new_lon > -116)\n",
    "        new_mask = xr.ones_like(ds0)*new_mask.values\n",
    "        if sum(ds0.lat == new_mask.lat) != ds0.lat.size:\n",
    "            print('dimension mismatch:', sum(ds0.lat == new_mask.lat).values, '!=', ds0.lat.size)\n",
    "            continue\n",
    "    \n",
    "    try:\n",
    "        #if variable=='hus':\n",
    "        #    ds = ds0.isel({'plev':ds0.plev==8.5e4})\n",
    "        #else:\n",
    "        ds = ds0\n",
    "        #THINGS THAT WERE WRONG: NA, TA, W and E\n",
    "        if location=='Ocean':\n",
    "            #TODO don't assume it's ts. make NARI and p1 only for ts.\n",
    "            GG = compute_area_and_seasonal_mean(ds, [-28,20], [-35,15], [start_month, end_month], mask=ls_mask*new_mask)\n",
    "            NA = compute_area_and_seasonal_mean(ds, [-75,-15], [10,40], [start_month, end_month])\n",
    "            GT = compute_area_and_seasonal_mean(ds, [0,360],[-20,20],[start_month, end_month], mask=ls_mask)\n",
    "            SA = compute_area_and_seasonal_mean(ds, [-30, 20], [-20,10], [start_month, end_month], mask=ls_mask)\n",
    "            md = compute_area_and_seasonal_mean(ds, [-6,36], [30,40], [start_month, end_month], mask=ls_mask)\n",
    "            TA = compute_area_and_seasonal_mean(ds, [-75,-15], [5,15], [start_month, end_month])\n",
    "            Sm = NA\n",
    "            Sm = Sm.rename_vars({'ts':'NA'})\n",
    "            Sm['GT'] = GT.ts\n",
    "            if(variable=='ts'):\n",
    "                Sm['NARI'] = NA.ts - GT.ts\n",
    "                Sm['p1'] = NA.ts + GT.ts\n",
    "            Sm['SA'] = SA.ts\n",
    "            Sm['md'] = md.ts\n",
    "            Sm['TA'] = TA.ts\n",
    "            #Sm = GG\n",
    "            #Sm = Sm.rename_vars({'ts':'GG'})\n",
    "        elif conv:\n",
    "            Sm = compute_conv(ds, [-20,40], [12,18], [start_month, end_month], 92500)\n",
    "        elif bndries:\n",
    "            Sm = compute_area_and_seasonal_mean(ds, [-20,40], [12-calc_stp(ds.lat), 12], [start_month, end_month])\n",
    "            Sm = Sm.rename_vars({variable:(variable+'_South')})\n",
    "            N = compute_area_and_seasonal_mean(ds, [-20,40], [18, 18+calc_stp(ds.lat)], [start_month, end_month])\n",
    "            Sm[variable+'_North'] = N[variable]\n",
    "            W = compute_area_and_seasonal_mean(ds, [-20-calc_stp(ds.lon), -20], [12,18], [start_month, end_month])\n",
    "            Sm[variable+'_West'] = W[variable]\n",
    "            E = compute_area_and_seasonal_mean(ds, [40, 40+calc_stp(ds.lon)], [12,18], [start_month, end_month])\n",
    "            Sm[variable+'_East'] = E[variable]\n",
    "        else:\n",
    "            Sm = compute_area_and_seasonal_mean(ds, [-20,40], [12,18], [start_month, end_month])\n",
    "  \n",
    "    except ValueError:\n",
    "        print(f'value error for {ncfile}')\n",
    "        ds_failed_list += [ds]\n",
    "        continue\n",
    "    #have to customize this to the variable I'm using!\n",
    "    if variable=='ts' or variable=='tas' or variable=='ta':\n",
    "        if not ds[variable].attrs['units']=='K':\n",
    "            print(\"cannot comprehend units ({}), skipping model {}\".format(ds[variable].attrs['units'], model))\n",
    "            continue\n",
    "    elif variable=='pr':\n",
    "        if ds.pr.attrs['units'] == 'kg m-2 s-1':\n",
    "            Sm *= 86400\n",
    "        else:\n",
    "            print(\"cannot comprehend units ({}), skipping model {}\".format(ds.pr.attrs['units'], model))\n",
    "            continue\n",
    "    elif variable=='zg':\n",
    "        if not ds[variable].attrs['units']=='m':\n",
    "            print(\"cannot comprehend units ({}), skipping model {}\".format(ds[variable].attrs['units'], model))\n",
    "            continue\n",
    "    elif (variable=='huss'):\n",
    "        Sm *= 1000/float(ds.huss.attrs['units']) #convert to g/kg WHY DID I DO THIS?!\n",
    "    elif (variable=='hus'):\n",
    "        print(float(ds.hus.attrs['units']))\n",
    "        Sm *= 1000/float(ds.hus.attrs['units']) #convert to g/kg\n",
    "    elif (variable=='va' or variable=='ua'):\n",
    "        if not ds[variable].attrs['units']=='m/s':\n",
    "            print(\"cannot comprehend units ({}), skipping model {}\".format(ds[variable].attrs['units'], model))\n",
    "            continue\n",
    "    else:\n",
    "        print(\"need to make new units case for variable {} with units {}\".format(variable, ds[variable].attrs['units']))\n",
    "\n",
    "    os.system(f'mkdir -p {ncdir}')\n",
    "    try:\n",
    "        Sm.to_netcdf(ncfile,mode='w',unlimited_dims=['time','year'])  #saves the file. Don't have to do this before I'm ready! But ds is replaced each time...\n",
    "        ds_list += [Sm]\n",
    "        print(f'saving file {ncfile}')\n",
    "    except ValueError:\n",
    "        print(f'value error for {ncfile}')\n",
    "        ds_failed_list += [Sm]\n",
    "        continue    \n",
    "        \n",
    "    #ok I got an error for a model which uses i/j coordinates instead of lat lon! OY VEY...\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/fx/sftlf/gr2/v20180701/'"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks['zstore'][model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/historical/r1i1p1f1/Amon/ts/gr1/v20180701/'"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsurl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Dataset does not contain the dimensions: ['plev']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-223-450bff961547>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mGG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_area_and_seasonal_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstart_month\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_month\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mls_mask\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mGG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-150-43ee9975a140>\u001b[0m in \u001b[0;36mcompute_area_and_seasonal_mean\u001b[0;34m(danom, xlim, ylim, slim, mask)\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdanom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcoslat\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;31m#print(weights.hus.values)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mweight_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\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'plev'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#, keep_attrs=True\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0;31m#print(weight_mean.hus.values)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;31m#print('danom*weights')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36mwrapped_func\u001b[0;34m(self, dim, skipna, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                     \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                     \u001b[0mallow_lazy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                     \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m                 )\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/naomi/.conda/envs/my3.6/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mreduce\u001b[0;34m(self, func, dim, keep_attrs, keepdims, numeric_only, allow_lazy, **kwargs)\u001b[0m\n\u001b[1;32m   3886\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing_dimensions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3887\u001b[0m             raise ValueError(\n\u001b[0;32m-> 3888\u001b[0;31m                 \u001b[0;34m\"Dataset does not contain the dimensions: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmissing_dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3889\u001b[0m             )\n\u001b[1;32m   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Dataset does not contain the dimensions: ['plev']"
     ]
    }
   ],
   "source": [
    "GG = compute_area_and_seasonal_mean(ds, [-28,20], [-35,15], [start_month, end_month], mask=ls_mask*new_mask)\n",
    "GG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'lon' ()>\n",
      "array(43)\n",
      "<xarray.DataArray 'lat' ()>\n",
      "array(1)\n",
      "<xarray.DataArray 'lon' ()>\n",
      "array(43)\n",
      "<xarray.DataArray 'lat' ()>\n",
      "array(1)\n",
      "<xarray.DataArray 'lon' ()>\n",
      "array(242)\n",
      "<xarray.DataArray 'lat' ()>\n",
      "array(4)\n",
      "<xarray.DataArray 'lon' ()>\n",
      "array(30)\n",
      "<xarray.DataArray 'lat' ()>\n",
      "array(4)\n"
     ]
    }
   ],
   "source": [
    "            Sm = compute_area_and_seasonal_mean(ds, [-20,40], [12-calc_stp(ds.lat), 12], [start_month, end_month])\n",
    "            Sm = Sm.rename_vars({variable:(variable+'_South')})\n",
    "            N = compute_area_and_seasonal_mean(ds, [-20,40], [18, 18+calc_stp(ds.lat)], [start_month, end_month])\n",
    "            Sm[variable+'_North'] = N[variable]\n",
    "            W = compute_area_and_seasonal_mean(ds, [-20-calc_stp(ds.lon), -20], [12,18], [start_month, end_month])\n",
    "            Sm[variable+'_West'] = W[variable]\n",
    "            E = compute_area_and_seasonal_mean(ds, [40, 40+calc_stp(ds.lon)], [12,18], [start_month, end_month])\n",
    "            Sm[variable+'_East'] = E[variable]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "slim = [start_month, end_month]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lat' ()>\n",
       "array(56)\n",
       "Coordinates:\n",
       "    lat      float64 -88.57"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = 0\n",
    "F = 0\n",
    "c = 0\n",
    "for i in range(96):\n",
    "    T += danom.lat[i] == mask.lat[i]\n",
    "    F += danom.lat[i] != mask.lat[i]\n",
    "    c += 1\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lat' (lat: 96)>\n",
       "array([-88.572169, -86.722531, -84.86197 , -82.998942, -81.134977, -79.270559,\n",
       "       -77.405888, -75.541061, -73.676132, -71.811132, -69.946081, -68.080991,\n",
       "       -66.215872, -64.35073 , -62.485571, -60.620396, -58.755209, -56.890013,\n",
       "       -55.024808, -53.159595, -51.294377, -49.429154, -47.563926, -45.698694,\n",
       "       -43.833459, -41.96822 , -40.102979, -38.237736, -36.372491, -34.507243,\n",
       "       -32.641994, -30.776744, -28.911492, -27.046239, -25.180986, -23.315731,\n",
       "       -21.450475, -19.585219, -17.719962, -15.854704, -13.989446, -12.124187,\n",
       "       -10.258928,  -8.393669,  -6.528409,  -4.66315 ,  -2.79789 ,  -0.93263 ,\n",
       "         0.93263 ,   2.79789 ,   4.66315 ,   6.528409,   8.393669,  10.258928,\n",
       "        12.124187,  13.989446,  15.854704,  17.719962,  19.585219,  21.450475,\n",
       "        23.315731,  25.180986,  27.046239,  28.911492,  30.776744,  32.641994,\n",
       "        34.507243,  36.372491,  38.237736,  40.102979,  41.96822 ,  43.833459,\n",
       "        45.698694,  47.563926,  49.429154,  51.294377,  53.159595,  55.024808,\n",
       "        56.890013,  58.755209,  60.620396,  62.485571,  64.35073 ,  66.215872,\n",
       "        68.080991,  69.946081,  71.811132,  73.676132,  75.541061,  77.405888,\n",
       "        79.270559,  81.134977,  82.998942,  84.86197 ,  86.722531,  88.572169])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -88.57 -86.72 -84.86 -83.0 ... 83.0 84.86 86.72 88.57\n",
       "Attributes:\n",
       "    axis:           Y\n",
       "    bounds:         lat_bnds\n",
       "    long_name:      Latitude\n",
       "    standard_name:  latitude\n",
       "    units:          degrees_north"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ds0\n",
    "#new_mask\n",
    "#sum(ds0.lat == new_mask.lat)\n",
    "danom.lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 96, lon: 192, time: 1980)\n",
       "Coordinates:\n",
       "    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
       "  * lat        (lat) float64 -88.57 -86.72 -84.86 -83.0 ... 84.86 86.72 88.57\n",
       "    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
       "  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    ts         (time, lat, lon) float32 dask.array<chunksize=(990, 96, 192), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    cmor_version:           3.5.0\n",
       "    contact:                david.neubauer@env.ethz.ch\n",
       "    creation_date:          2019-11-24T15:53:32Z\n",
       "    data_specs_version:     01.00.30\n",
       "    experiment:             all-forcing simulation of the recent past\n",
       "    experiment_id:          historical\n",
       "    external_variables:     areacella\n",
       "    forcing_index:          1\n",
       "    frequency:              mon\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.MPI-M.MPI-ES...\n",
       "    grid:                   gn\n",
       "    grid_label:             gn\n",
       "    history:                2019-11-24T15:53:32Z ; CMOR rewrote data to be co...\n",
       "    initialization_index:   1\n",
       "    institution:            ETH Zurich, Switzerland; Max Planck Institut fur ...\n",
       "    institution_id:         HAMMOZ-Consortium\n",
       "    license:                CMIP6 model data produced by the HAMMOZ-Consortiu...\n",
       "    mip_era:                CMIP6\n",
       "    nominal_resolution:     250 km\n",
       "    parent_activity_id:     CMIP\n",
       "    parent_experiment_id:   piControl\n",
       "    parent_mip_era:         CMIP6\n",
       "    parent_source_id:       MPI-ESM-1-2-HAM\n",
       "    parent_time_units:      days since 1850-1-1 00:00:00\n",
       "    parent_variant_label:   r1i1p1f1\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    project_id:             CMIP6\n",
       "    realization_index:      1\n",
       "    realm:                  atmos\n",
       "    references:             MPI-ESM1.2-HAM: Mauritsen, T. et al. (2019), Deve...\n",
       "    source:                 MPI-ESM1.2-HAM (2017): \\naerosol: HAM2.3\\natmos: ...\n",
       "    source_id:              MPI-ESM-1-2-HAM\n",
       "    source_type:            AOGCM\n",
       "    status:                 2020-02-06;created; by gcs.cmip6.ldeo@gmail.com\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               Amon\n",
       "    table_info:             Creation Date:(09 May 2019) MD5:e6ef8ececc8f33864...\n",
       "    title:                  MPI-ESM-1-2-HAM output prepared for CMIP6\n",
       "    tracking_id:            hdl:21.14100/06b11e76-c6e8-4c3a-acbc-ac381f607ae0...\n",
       "    variable_id:            ts\n",
       "    variant_label:          r1i1p1f1\n",
       "    netcdf_tracking_ids:    hdl:21.14100/06b11e76-c6e8-4c3a-acbc-ac381f607ae0...\n",
       "    version_id:             v20190627"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.ones_like(danom)*mask.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.42108547e-14, -1.42108547e-14,  7.10542736e-15, -1.42108547e-14,\n",
       "       -1.42108547e-14,  7.10542736e-15,  1.42108547e-14,  1.06581410e-14,\n",
       "        7.10542736e-15, -7.10542736e-15, -7.10542736e-15,  1.77635684e-15,\n",
       "       -7.10542736e-15,  1.06581410e-14,  1.06581410e-14,  1.77635684e-15,\n",
       "       -8.88178420e-15,  5.32907052e-15, -1.15463195e-14,  1.34336986e-14,\n",
       "       -1.34336986e-14,  1.15463195e-14, -5.32907052e-15,  8.88178420e-15,\n",
       "       -1.77635684e-15, -1.06581410e-14, -1.06581410e-14,  7.10542736e-15,\n",
       "       -1.77635684e-15,  7.10542736e-15,  7.10542736e-15, -7.10542736e-15,\n",
       "       -1.06581410e-14, -1.42108547e-14, -7.10542736e-15,  1.42108547e-14,\n",
       "        1.42108547e-14, -7.10542736e-15,  1.42108547e-14, -1.42108547e-14])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffs = danom.lat.values - mask.lat.values\n",
    "diffs[danom.lat.values != mask.lat.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-73.67613231, -69.94608065, -56.8900126 , -55.02480754,\n",
       "       -41.96822027, -38.23773599, -34.50724334, -28.91149237,\n",
       "       -21.45047504, -19.58521861, -17.71996153, -15.85470387,\n",
       "       -13.98944571, -12.12418712, -10.25892817,  -8.39366891,\n",
       "        -6.5284094 ,  -4.66314971,  -2.79788988,  -0.93262997,\n",
       "         0.93262997,   2.79788988,   4.66314971,   6.5284094 ,\n",
       "         8.39366891,  10.25892817,  12.12418712,  13.98944571,\n",
       "        15.85470387,  17.71996153,  19.58521861,  21.45047504,\n",
       "        28.91149237,  34.50724334,  38.23773599,  41.96822027,\n",
       "        55.02480754,  56.8900126 ,  69.94608065,  73.67613231])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.lat.values[danom.lat.values != mask.lat.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4b3c7689e8>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8,5)\n",
    "masked = ds0*xr.ones_like(ds0)*ls_mask\n",
    "masked = masked.isel({'lat': (masked.lat>-35)*(masked.lat<15),'lon': (masked.lon>-28)*(masked.lon<20)})\n",
    "masked['ts'] = masked['ts'] - 273\n",
    "new_mask = (masked.lat>4)*(6*masked.lat - 5*masked.lon <164) + (masked.lat<4)*(6*masked.lat + 5*masked.lon > -116)\n",
    "(masked*xr.ones_like(masked)*new_mask).ts[0].sortby(masked.lon).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gs://cmip6/CMIP6/CMIP/HAMMOZ-Consortium/MPI-ESM-1-2-HAM/historical/r1i1p1f1/Amon/ts/gn/v20190627/'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsurl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! tree -L 9 ~/CMIP6-downloads #unix tree of created files; I didn't create any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! du -sh ~/CMIP6-downloads/*/*/*/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('/home/naomi/CMIP6-downloads/CMIP/NCAR/CESM2/historical/r1i1p1f1/Amon/sfcWind/gn/sfcWind.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.sfcWind.plot(vmin=0,vmax=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert longitude coordinates from 0-359 to -180-179:\n",
    "\n",
    "ds2 = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)).sortby('lon')\n",
    "\n",
    "#or\n",
    "\n",
    "ds.coords['lon'] = (ds.coords['lon'] + 180) % 360 - 180\n",
    "ds = ds.sortby(ds.lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2.sfcWind[0].plot()"
   ]
  }
 ],
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