get_cmip6_ECS-alt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from datetime import datetime\n",
    "from collections import Counter\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate ECS in CMIP6 models\n",
    "## Goal: Use Gregory regression to calculate ECS $\\Delta T$ [Gregory et al 2004](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2003GL018747) \n",
    "\n",
    "### For each model, average over all members of abrupt-4xCO2 simulations available \n",
    "\n",
    "### CMIP6 data repo: NCAR CGD local sync of the ETH repository (including utilities)\n",
    "\n",
    "## Potentially useful utilities to get the pathnames \n",
    "\n",
    "### imitate cell area from cosine latitude weighting for most models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For some model, experiment, and variables, get all available variants (ensemble members) and filenames. Define a class to put them in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getOneModelExperimentVar(model,experiment,var):\n",
    "    basedir = '/project/cmip6/'\n",
    "    # new: basedir/experiment/Amon/\"+variable+\" / model / variant / gridlabel /\n",
    "    # old: basedir/institution/model/experiment/variant/Amon/\"+variable / gridlabel / version {latest/vDATE} \n",
    "    thisModelExpVar = []\n",
    "    models = os.listdir(basedir+experiment+\"/Amon/\"+var)\n",
    "    hasmodel = any(elem == model for elem in models)\n",
    "    if hasmodel:\n",
    "        # get ensemble members with data and also their end paths\n",
    "        variants,gridlabels = getvariants(basedir+experiment+\"/Amon/\"+var+\"/\"+model+\"/\")\n",
    "        if len(variants)>0:\n",
    "            thisModelExpVar = ModelExpVar(model,experiment,var,basedir+experiment+\"/Amon/\"+var+\"/\"+model,variants,gridlabels)\n",
    "    else:\n",
    "        print(experiment+\" doesn't have \"+model)\n",
    "    return thisModelExpVar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getvariants(pathtoexp):\n",
    "    variants = []\n",
    "    gridlabels = [] \n",
    "    for variant in sorted(os.listdir(pathtoexp)):\n",
    "        thispath = pathtoexp+variant\n",
    "        #print(thispath)\n",
    "        thesegridlabels=os.listdir(thispath)\n",
    "        #print(gridlabels)\n",
    "        if len(thesegridlabels)==1:\n",
    "            gridlabel=thesegridlabels[0]\n",
    "            files = os.listdir(thispath+\"/\"+gridlabel)\n",
    "            #print(files)\n",
    "            ncfiles = [item for item in files if item.endswith('.nc')]\n",
    "            if len(ncfiles)>0:\n",
    "                variants.append(variant)\n",
    "                gridlabels.append(gridlabel)\n",
    "        else:\n",
    "            print(len(thesegridlabels)+\"grid labels - skipping\")\n",
    "    return variants,gridlabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelExpVar:\n",
    "  def __init__(self, model, experiment, variable,startpath,variants,gridlabels):\n",
    "    self.model = model\n",
    "    self.experiment = experiment\n",
    "    self.variable = variable\n",
    "    # The rest are derived\n",
    "    ## basedir/experiment/Amon/\"+variable+\" / model / variant / gridlabel /\n",
    "    paths = []\n",
    "    files = []\n",
    "    for i in range(len(variants)):\n",
    "        #print(variants[i])\n",
    "        thepath = startpath+\"/\"+variants[i]+\"/\"+gridlabels[i]\n",
    "        ensfiles = os.listdir(thepath)\n",
    "        thisensfiles = []\n",
    "        for file in ensfiles:\n",
    "            if file.endswith(\".nc\"):\n",
    "                # deals with duplicate files in GISS-E2-1-G\n",
    "                if (model+\"_\"+experiment in file):  \n",
    "                    thisensfiles.append(thepath+\"/\"+file)\n",
    "        startdates=[]\n",
    "        lengthinfo = {}\n",
    "        fileinfo = {}\n",
    "        for file in thisensfiles:\n",
    "            dates=file.split(\"_\")[-1].split(\".\")[0].split(\"-\")\n",
    "            dt = datetime.strptime(dates[1], '%Y%m').year - datetime.strptime(dates[0], '%Y%m').year\n",
    "            if dates[0] in startdates:\n",
    "                # we have a duplicate\n",
    "                ## this should only happen for E3SM piControl\n",
    "                duplicate=True\n",
    "                onefile=[]\n",
    "                if model==\"E3SM-1-0\":\n",
    "                    print('duplicate fix designed for E3SM-1-0 piControl')\n",
    "                    print('current model and experiment: '+model+', '+experiment)\n",
    "                    ## deal with duplicate files in E3SM piControl\n",
    "                    ## assume that if there are duplicates, they are of the form that all files are concatenated into one (eg e3sm)\n",
    "                    ## check for duplicate start dates. if there's more than one, take the longer one and delete all the others.\n",
    "                    if dt>lengthinfo[dates[0]]:\n",
    "                        onefile.append(file)\n",
    "                    else:\n",
    "                        onefile.append(fileinfo[dates[0]])\n",
    "                    thisensfiles=onefile\n",
    "                elif model==\"GFDL-CM4\":\n",
    "                    onefile\n",
    "                    print('duplicate fix designed for GFDL-CM4 abrupt4xCO2')\n",
    "                    print('current model and experiment: '+model+', '+experiment)\n",
    "                    ## deal with duplicate files in GFDL-CM4 abrupt4xCO2\n",
    "                    ## keep the longer of the two files, get rid of the other\n",
    "                    ## don't get rid of the other files\n",
    "                    if dt>lengthinfo[dates[0]]:\n",
    "                        thisensfiles.remove(fileinfo[dates[0]])\n",
    "                    else:\n",
    "                        thisensfiles.remove(file)\n",
    "                else:\n",
    "                    print('duplicate file start date, not fixed ')\n",
    "                    print('current model and experiment: '+model+', '+experiment)        \n",
    "                    sys.exit(1)\n",
    "                break\n",
    "            startdates.append(dates[0])\n",
    "            lengthinfo[dates[0]]=dt\n",
    "            fileinfo[dates[0]]=file\n",
    "        files.append(sorted(thisensfiles))\n",
    "    self.variants = dict(zip(variants, files))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For experiment and variable, get all models with data (and their variants [ensemble members] and filenames). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getModelsForExperimentVar(experiment,var):\n",
    "    basedir = '/project/cmip6/'\n",
    "    ## basedir/experiment/Amon/\"+variable+\" / model / variant / gridlabel /\n",
    "    evd_ofmodels = {}\n",
    "    #models = os.listdir(basedir+experiment+\"/Amon/\"+var)\n",
    "    #print(models)\n",
    "    for model in os.listdir(basedir+experiment+\"/Amon/\"+var):\n",
    "        #print(model)\n",
    "        variants,gridlabels = getvariants(basedir+experiment+\"/Amon/\"+var+\"/\"+model+\"/\")\n",
    "        if len(variants)>0:\n",
    "            #print(model)\n",
    "            evd_ofmodels[model] = ModelExpVar(model,experiment,var,basedir+experiment+\"/Amon/\"+var+\"/\"+model,variants,gridlabels)\n",
    "    return evd_ofmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r1i1p1f1\n"
     ]
    }
   ],
   "source": [
    "model = \"CESM2\"\n",
    "cesm4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",\"ts\")\n",
    "imem = list(cesm4x.variants)[0]\n",
    "print(imem)\n",
    "#thisModelArea = getModelAreacella(model)\n",
    "#dts = getChangeTimeseries(model,\"ts\",imem,thisModelArea.areacella)\n",
    "#drsdt = getChangeTimeseries(model,\"rsdt\",imem,thisModelArea.areacella)\n",
    "#drsut = getChangeTimeseries(model,\"rsut\",imem,thisModelArea.areacella)\n",
    "#drlut = getChangeTimeseries(model,\"rlut\",imem,thisModelArea.areacella)\n",
    "#dN = drsdt - drsut - drlut\n",
    "#slope, intercept, r_value, p_value, std_err = stats.linregress(dts,dN)\n",
    "#cesmECS = -intercept / slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "{'BCC-CSM2-MR': <__main__.ModelExpVar object at 0x7f05fba27e10>, 'BCC-ESM1': <__main__.ModelExpVar object at 0x7f05fba27630>, 'CAMS-CSM1-0': <__main__.ModelExpVar object at 0x7f05fba27cf8>, 'CESM2-WACCM': <__main__.ModelExpVar object at 0x7f05fba274a8>, 'CESM2': <__main__.ModelExpVar object at 0x7f06cd480748>, 'CNRM-CM6-1': <__main__.ModelExpVar object at 0x7f06cd480ba8>, 'CNRM-ESM2-1': <__main__.ModelExpVar object at 0x7f06cd480908>, 'CanESM5': <__main__.ModelExpVar object at 0x7f06cd480898>, 'E3SM-1-0': <__main__.ModelExpVar object at 0x7f06cd480a58>, 'EC-Earth3-Veg': <__main__.ModelExpVar object at 0x7f06cd480be0>, 'GISS-E2-1-G': <__main__.ModelExpVar object at 0x7f06cd480b70>, 'GISS-E2-1-H': <__main__.ModelExpVar object at 0x7f06cd4807b8>, 'HadGEM3-GC31-LL': <__main__.ModelExpVar object at 0x7f06cd4808d0>, 'IPSL-CM6A-LR': <__main__.ModelExpVar object at 0x7f06cd4806a0>, 'MIROC6': <__main__.ModelExpVar object at 0x7f06cd480630>, 'MRI-ESM2-0': <__main__.ModelExpVar object at 0x7f06cd480860>, 'SAM0-UNICON': <__main__.ModelExpVar object at 0x7f05fba35080>, 'UKESM1-0-LL': <__main__.ModelExpVar object at 0x7f05fba352b0>, 'GFDL-CM4': <__main__.ModelExpVar object at 0x7f05fba35438>, 'GFDL-ESM4': <__main__.ModelExpVar object at 0x7f05fba35320>, 'NESM3': <__main__.ModelExpVar object at 0x7f05fba35358>, 'MIROC-ES2L': <__main__.ModelExpVar object at 0x7f05fba354a8>, 'NorESM2-LM': <__main__.ModelExpVar object at 0x7f05fba35400>, 'MCM-UA-1-0': <__main__.ModelExpVar object at 0x7f05fba353c8>}\n"
     ]
    }
   ],
   "source": [
    "var=\"ts\"\n",
    "ts4x_models = getModelsForExperimentVar(\"abrupt-4xCO2\",var)\n",
    "print(ts4x_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deal with grid cell area.  Get areacella files if they exist (when I made this only CESM2 and CESM2-WACCM did). Otherwise look in folder areacella with manually-downloaded files for each model. Otherwise, use dlat * cos(lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelArea:\n",
    "  def __init__(self,model,file,areacella):\n",
    "    self.model = model\n",
    "    self.variable = \"areacella\"\n",
    "    self.file = file\n",
    "    self.areacella = areacella\n",
    "    \n",
    "def getModelAreacella(model):\n",
    "    #basedir = \"/glade/collections/cmip/CMIP6/CMIP/\"\n",
    "    experiment=\"piControl\"\n",
    "    #var=\"areacella\"\n",
    "    thisModelExpVar = []\n",
    "    #institutions = []\n",
    "    thisModelArea = []\n",
    "    # this first method will work for CESM2. Maybe nothing else. \n",
    "#    for institution in os.listdir(basedir):\n",
    "        #print(institution)\n",
    "#        models = os.listdir(basedir+institution)\n",
    "#        hasmodel = any(elem == model for elem in models)\n",
    "#        if hasmodel:\n",
    "#            institutions.append(institution)        \n",
    "#    if len(institutions)==1:\n",
    "#        institution = institutions[0]\n",
    "#        exps = os.listdir(basedir+institution+\"/\"+model)\n",
    "#        hasexp = any(elem == experiment for elem in exps)\n",
    "#        if (hasexp) : \n",
    "            # get ensemble members with data and also their end paths\n",
    "#            variants,endpaths,extraVarFolder = getFXvariants(basedir+institution+\"/\"+model+\"/\"+experiment+\"/\",var)\n",
    "#            if len(variants)>0:\n",
    "#                thepath = basedir+institution+\"/\"+model+\"/\"+experiment+\"/\"+variants[0]+\"/fx/\"+var+endpaths[0]\n",
    "#                if extraVarFolder:\n",
    "#                    thepath = thepath+var+\"/\"\n",
    "#                files = os.listdir(thepath)\n",
    "#                thisModelArea = ModelArea(model,thepath+files[0],[])\n",
    "    # If that didn't work, then we try something else. Next step is the folder with areacella files I downloaded. \n",
    "    #if thisModelArea==[]:\n",
    "    #    areacelladir=\"/glade/work/apgrass/pythonhackathon/areacella/\"\n",
    "    #    files = os.listdir(areacelladir)\n",
    "    #    thefile = [item for item in files if model in item]\n",
    "    #    #print(thefile)\n",
    "    #    if len(thefile)>0:\n",
    "    #        thisModelArea = ModelArea(model,areacelladir + thefile[0],[])\n",
    "    #        print(model+\": Using manually downloaded areacella\")\n",
    "#    if thisModelArea!=[]:\n",
    "#        ds = xr.open_mfdataset(thisModelArea.file)\n",
    "#        areacella = ds.get(thisModelArea.variable)\n",
    "#        thisModelArea.areacella = areacella\n",
    "#        print(model+\": Successfully retrieved areacella from CMIP analysis platform\")\n",
    "#    else:\n",
    "# Move the next block right one indent to get it to work in the loop\n",
    "    # Finally, if that still didn't work, we just do lat/lon weighting. Leave it empty\n",
    "    thismodeltas = getOneModelExperimentVar(model,experiment,\"tas\")\n",
    "    ds = xr.open_dataset(thismodeltas.variants[list(thismodeltas.variants)[0]][0])\n",
    "    tas1time = ds.tas.sel(time=(ds.time[-1]))\n",
    "    l1=np.array(tas1time.lat)\n",
    "    l2=np.copy(l1)\n",
    "    l2[1:]=l1[0:-1]\n",
    "    l2[0]=-90\n",
    "    l3=np.copy(l1)\n",
    "    l3[0:-1]=l1[1:]\n",
    "    l3[-1]=90\n",
    "    lw = np.cos(l1*np.pi/180)*(l3-l2)\n",
    "    areacella = xr.DataArray(np.transpose(np.tile(lw,[len(tas1time.lon),1])),coords = tas1time.coords, dims = tas1time.dims)\n",
    "    thisModelArea = ModelArea(model,[],areacella)\n",
    "    print(model+\": Area from cos lat\")\n",
    "    return thisModelArea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/project/cmip6/piControl/Amon/tas/CESM2/r1i1p1f1/gn/tas_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "CESM2: Area from cos lat\n"
     ]
    }
   ],
   "source": [
    "model=\"CESM2\"\n",
    "#basedir = \"/glade/collections/cmip/CMIP6/CMIP/\"\n",
    "experiment=\"piControl\"\n",
    "#var=\"areacella\"\n",
    "thisModelExpVar = []\n",
    "#institutions = []\n",
    "thisModelArea = []\n",
    "thismodeltas = getOneModelExperimentVar(model,experiment,\"tas\")\n",
    "print(thismodeltas.variants[list(thismodeltas.variants)[0]][0])\n",
    "ds = xr.open_dataset(thismodeltas.variants[list(thismodeltas.variants)[0]][0])\n",
    "tas1time = ds.tas.sel(time=(ds.time[-1]))\n",
    "l1=np.array(tas1time.lat)\n",
    "l2=np.copy(l1)\n",
    "l2[1:]=l1[0:-1]\n",
    "l2[0]=-90\n",
    "l3=np.copy(l1)\n",
    "l3[0:-1]=l1[1:]\n",
    "l3[-1]=90\n",
    "lw = np.cos(l1*np.pi/180)*(l3-l2)\n",
    "areacella = xr.DataArray(np.transpose(np.tile(lw,[len(tas1time.lon),1])),coords = tas1time.coords, dims = tas1time.dims)\n",
    "thisModelArea = ModelArea(model,[],areacella)\n",
    "print(model+\": Area from cos lat\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## this is not yet needed for the ETH version\n",
    "\n",
    "def getFXvariants(pathtoexp,var):\n",
    "    variants = []\n",
    "    endpaths = [] \n",
    "    extrabit = []\n",
    "    for variant in os.listdir(pathtoexp):\n",
    "        try: \n",
    "            # check that there is actually a path for this particular variable in this experiment\n",
    "            os.listdir(pathtoexp+variant+\"/fx/\"+var)\n",
    "            #print(variant)\n",
    "            # we will now call a function that gets the end path. this is not trivial; it includes the grid label and finding the latest version. \n",
    "            thispath = pathtoexp+variant+\"/fx/\"+var\n",
    "            #print(thispath)\n",
    "            thisendpath,extrabit = getendpath(thispath,var)\n",
    "            # if this all worked, then add the variant / ensemble member, and its path info, to the list of existing ones\n",
    "            if len(thisendpath)>0:\n",
    "                endpaths.append(thisendpath)\n",
    "                variants.append(variant)\n",
    "        except:\n",
    "            # in case there is not actually any data for this model + variant / ensemble member\n",
    "            #print(pathtoexp+\" \"+variant+\" is missing\")\n",
    "            pass\n",
    "    #print(variants)\n",
    "    return variants,endpaths,extrabit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define functions that use the tools to calculate things \n",
    "\n",
    "### Global annual mean timeseries, climatological global mean (one value), a timeseries of the difference of those two, and ECS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def globalannualmean(aModelExpVar,areacella,mem):\n",
    "    weight= areacella / areacella.sum(dim=('lon','lat'))\n",
    "    weight.sum(dim=('lon','lat'))\n",
    "    try:\n",
    "        ds = xr.open_mfdataset(aModelExpVar.variants[mem][:])\n",
    "        it = ds.get(aModelExpVar.variable)\n",
    "        itGm=(np.tile(weight,[len(ds.time),1,1])*it).sum(dim=('lat','lon'))\n",
    "        if len(ds.time)>=1800:\n",
    "            # this is to get the first 150 years from abrupt-4xCO2\n",
    "            starttimes = ds.time[0:150*12]\n",
    "            its=itGm.sel(time=slice(starttimes[0],starttimes[-1]))\n",
    "            itGm_annual = its.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "        else:\n",
    "            itGm_annual = itGm.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "    except:\n",
    "    # IPSL-CM6A-LR abrupt-4xCO2 has a member that goes for 900 yrs and breaks this. if it fails, just get the first file\n",
    "        print(\"globalannualmean: \"+aModelExpVar.model+\" \"+mem+\" trying just first file\")\n",
    "        ds = xr.open_dataset(aModelExpVar.variants[mem][0])\n",
    "        it = ds.get(aModelExpVar.variable)\n",
    "        itGm=(np.tile(weight,[len(ds.time),1,1])*it).sum(dim=('lat','lon'))\n",
    "        starttimes = ds.time[0:150*12]\n",
    "        its=itGm.sel(time=slice(starttimes[0],starttimes[-1]))\n",
    "        itGm_annual = its.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "    return itGm_annual\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def globalannualmean(aModelExpVar,areacella,mem):\n",
    "    weight= areacella / areacella.sum(dim=('lon','lat'))\n",
    "    weight.sum(dim=('lon','lat'))\n",
    "    try:\n",
    "        ds = xr.open_mfdataset(aModelExpVar.variants[mem][:])\n",
    "        it = ds.get(aModelExpVar.variable)\n",
    "        itGm=(np.tile(weight,[len(ds.time),1,1])*it).sum(dim=('lat','lon'))\n",
    "        if len(ds.time)>=1800:\n",
    "            # this is to get the first 150 years from abrupt-4xCO2\n",
    "            starttimes = ds.time[0:150*12]\n",
    "            its=itGm.sel(time=slice(starttimes[0],starttimes[-1]))\n",
    "            itGm_annual = its.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "        else:\n",
    "            itGm_annual = itGm.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "    except:\n",
    "        if aModelExpVar.model==\"IPSL-CM6A-LR\":\n",
    "        # IPSL-CM6A-LR abrupt-4xCO2 has a member that goes for 900 yrs and breaks this. if it fails, just get the first file\n",
    "            print(\"globalannualmean: \"+aModelExpVar.model+\" \"+mem+\" trying just first file\")\n",
    "            ds = xr.open_dataset(aModelExpVar.variants[mem][0])\n",
    "            it = ds.get(aModelExpVar.variable)\n",
    "            itGm=(np.tile(weight,[len(ds.time),1,1])*it).sum(dim=('lat','lon'))\n",
    "            starttimes = ds.time[0:150*12]\n",
    "            its=itGm.sel(time=slice(starttimes[0],starttimes[-1]))\n",
    "            itGm_annual = its.resample(time='A',label='left',loffset='6M').mean(dim='time')\n",
    "    return itGm_annual\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def climglobalmean(aModelExpVar,areacella):\n",
    "    weight= areacella / areacella.sum(dim=('lon','lat'))\n",
    "    weight.sum(dim=('lon','lat'))\n",
    "    # average across all piControl members\n",
    "    c = []\n",
    "    for mem in list(aModelExpVar.variants):\n",
    "        print(aModelExpVar.variants[mem][0])\n",
    "        try:\n",
    "            ds = xr.open_mfdataset(aModelExpVar.variants[mem][:])\n",
    "        except:\n",
    "            ds = xr.open_dataset(aModelExpVar.variants[mem][0])\n",
    "        endtimes = ds.time[-(12*30):]\n",
    "        print(aModelExpVar.variable)\n",
    "        it = ds.get(aModelExpVar.variable)\n",
    "        it_c=it.sel(time=slice(endtimes[0],endtimes[-1])).mean(dim=('time'))\n",
    "        itGm=(it_c*weight).sum(dim=('lat','lon'))\n",
    "        c.append(itGm.values)\n",
    "        c.append(itGm.values)\n",
    "    itGm_c = np.average(c)\n",
    "    return itGm_c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getChangeTimeseries(model,var,mem,areacella):\n",
    "    v4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",var)\n",
    "    vpi = getOneModelExperimentVar(model,\"piControl\",var)\n",
    "    vGAm = globalannualmean(v4x,areacella,mem)\n",
    "    vpi_c = climglobalmean(vpi,areacella)\n",
    "    change = vGAm-vpi_c\n",
    "    return change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcECSts(model,mem):\n",
    "    thisModelArea = getModelAreacella(model)\n",
    "    dts = getChangeTimeseries(model,\"ts\",mem,thisModelArea.areacella)\n",
    "    drsdt = getChangeTimeseries(model,\"rsdt\",mem,thisModelArea.areacella)\n",
    "    drsut = getChangeTimeseries(model,\"rsut\",mem,thisModelArea.areacella)\n",
    "    drlut = getChangeTimeseries(model,\"rlut\",mem,thisModelArea.areacella)\n",
    "    dN = drsdt - drsut - drlut\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(dts,dN)\n",
    "    ECS = -intercept / slope\n",
    "    return ECS,intercept\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the tools with CESM2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.ModelExpVar at 0x7f05fadcb0f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = \"CESM2\"\n",
    "cesm4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",\"ts\")\n",
    "cesm4x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2: Area from cos lat\n"
     ]
    }
   ],
   "source": [
    "imem = list(cesm4x.variants)[0]\n",
    "thisModelArea = getModelAreacella(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CESM2/r1i1p1f1/gn/ts_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CESM2/r1i1p1f1/gn/rsdt_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CESM2/r1i1p1f1/gn/rsut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CESM2/r1i1p1f1/gn/rlut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rlut\n"
     ]
    }
   ],
   "source": [
    "model = \"CESM2\"\n",
    "cesm4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",\"ts\")\n",
    "imem = list(cesm4x.variants)[0]\n",
    "thisModelArea = getModelAreacella(model)\n",
    "dts = getChangeTimeseries(model,\"ts\",imem,thisModelArea.areacella)\n",
    "drsdt = getChangeTimeseries(model,\"rsdt\",imem,thisModelArea.areacella)\n",
    "drsut = getChangeTimeseries(model,\"rsut\",imem,thisModelArea.areacella)\n",
    "drlut = getChangeTimeseries(model,\"rlut\",imem,thisModelArea.areacella)\n",
    "dN = drsdt - drsut - drlut\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(dts,dN)\n",
    "cesmECS = -intercept / slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.237067067918854\n",
      "5.118533533959427\n"
     ]
    }
   ],
   "source": [
    "print(cesmECS)\n",
    "print(cesmECS/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r1i1p1f1'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cesm4x.variants)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CESM2/r1i1p1f1/gn/ts_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CESM2/r1i1p1f1/gn/rsdt_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CESM2/r1i1p1f1/gn/rsut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CESM2/r1i1p1f1/gn/rlut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rlut\n",
      "5.118533533959427\n"
     ]
    }
   ],
   "source": [
    "# test: does it work for CESM2? \n",
    "cesmECS,F = calcECSts(\"CESM2\",imem)\n",
    "print(cesmECS/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next: Make it work for CMIP6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a function that reconciles two dicts of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keepModelsWithAllData(modeldict1,modeldict2):\n",
    "    missingmodel = set(list(modeldict1)).symmetric_difference(list(modeldict2))\n",
    "    for model in missingmodel:\n",
    "        #print(model)\n",
    "        try: \n",
    "            modeldict1.pop(model)\n",
    "        except:\n",
    "            pass\n",
    "        try: \n",
    "            modeldict2.pop(model)\n",
    "        except:\n",
    "            pass\n",
    "    return modeldict1,modeldict2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get models with surface temperature for abrupt-4xCO2 and piControl experiments, then models that have the other fields we need too (net TOA radiative fluxes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n"
     ]
    }
   ],
   "source": [
    "e3smpi = getOneModelExperimentVar(\"E3SM-1-0\",\"piControl\",\"ts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "E3SM-1-0: Area from cos lat\n",
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "/project/cmip6/piControl/Amon/ts/E3SM-1-0/r1i1p1f1/gr/ts_Amon_E3SM-1-0_piControl_r1i1p1f1_gr_000101-050012.nc\n",
      "ts\n"
     ]
    }
   ],
   "source": [
    "model=\"E3SM-1-0\"\n",
    "imem = list(e3smpi.variants)[0]\n",
    "thisModelArea = getModelAreacella(model)\n",
    "dts = getChangeTimeseries(model,\"ts\",imem,thisModelArea.areacella)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n"
     ]
    }
   ],
   "source": [
    "gfdl = getOneModelExperimentVar(\"GFDL-CM4\",\"abrupt-4xCO2\",\"ts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['BCC-CSM2-MR',\n",
       " 'BCC-ESM1',\n",
       " 'CAMS-CSM1-0',\n",
       " 'CESM2-WACCM',\n",
       " 'CESM2',\n",
       " 'CNRM-CM6-1',\n",
       " 'CNRM-ESM2-1',\n",
       " 'CanESM5',\n",
       " 'E3SM-1-0',\n",
       " 'EC-Earth3-Veg',\n",
       " 'GISS-E2-1-G',\n",
       " 'GISS-E2-1-H',\n",
       " 'HadGEM3-GC31-LL',\n",
       " 'IPSL-CM6A-LR',\n",
       " 'MIROC6',\n",
       " 'MRI-ESM2-0',\n",
       " 'SAM0-UNICON',\n",
       " 'UKESM1-0-LL',\n",
       " 'GFDL-CM4',\n",
       " 'NESM3',\n",
       " 'MIROC-ES2L',\n",
       " 'NorESM2-LM']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var=\"ts\"\n",
    "ts4x_models = getModelsForExperimentVar(\"abrupt-4xCO2\",var)\n",
    "tsPi_models = getModelsForExperimentVar(\"piControl\",var)\n",
    "ts4x_models,tsPi_models = keepModelsWithAllData(ts4x_models,tsPi_models)\n",
    "variables = [\"rsdt\",\"rsut\",\"rlut\"]\n",
    "for var in variables:\n",
    "    #print(var)\n",
    "    v4x_models = getModelsForExperimentVar(\"abrupt-4xCO2\",var)\n",
    "    vPi_models = getModelsForExperimentVar(\"piControl\",var)\n",
    "    v4x_models,vPi_models = keepModelsWithAllData(v4x_models,vPi_models)\n",
    "    ts4x_models,v4x_models = keepModelsWithAllData(ts4x_models,v4x_models)\n",
    "list(ts4x_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(ts4x_models))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPSL-CM6A-LR: Area from cos lat\n"
     ]
    }
   ],
   "source": [
    "model=\"IPSL-CM6A-LR\"\n",
    "thisModelArea = getModelAreacella(model)\n",
    "#vGAm = globalannualmean(ts4x_models[model],thisModelArea.areacella,mem) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/project/cmip6/abrupt-4xCO2/Amon/ts/IPSL-CM6A-LR/r1i1p1f1/gr/ts_Amon_IPSL-CM6A-LR_abrupt-4xCO2_r1i1p1f1_gr_185001-214912.nc',\n",
       " '/project/cmip6/abrupt-4xCO2/Amon/ts/IPSL-CM6A-LR/r1i1p1f1/gr/ts_Amon_IPSL-CM6A-LR_abrupt-4xCO2_r1i1p1f1_gr_215001-244912.nc',\n",
       " '/project/cmip6/abrupt-4xCO2/Amon/ts/IPSL-CM6A-LR/r1i1p1f1/gr/ts_Amon_IPSL-CM6A-LR_abrupt-4xCO2_r1i1p1f1_gr_245001-274912.nc']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=\"IPSL-CM6A-LR\"\n",
    "ts4x_models[model].variants[\"r1i1p1f1\"]\n",
    "#vGAm = globalannualmean(ts4x_models[model],thisModelArea.areacella,ts4x_models[model].variants[\"r1i1p1f1\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCC-CSM2-MR: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "BCC-ESM1: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "CAMS-CSM1-0: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "r2i1p1f1\n",
      "150\n",
      "CESM2-WACCM: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "CESM2: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "CNRM-CM6-1: Area from cos lat\n",
      "r1i1p1f2\n",
      "150\n",
      "r2i1p1f2\n",
      "10\n",
      "r3i1p1f2\n",
      "10\n",
      "r4i1p1f2\n",
      "10\n",
      "r5i1p1f2\n",
      "10\n",
      "r6i1p1f2\n",
      "10\n",
      "CNRM-ESM2-1: Area from cos lat\n",
      "r1i1p1f2\n",
      "150\n",
      "r2i1p1f2\n",
      "150\n",
      "r3i1p1f2\n",
      "150\n",
      "CanESM5: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "r1i1p2f1\n",
      "150\n",
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "E3SM-1-0: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "EC-Earth3-Veg: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "GISS-E2-1-G: Area from cos lat\n",
      "r102i1p1f1\n",
      "150\n",
      "r1i1p1f1\n",
      "150\n",
      "r1i1p1f3\n",
      "50\n",
      "GISS-E2-1-H: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "HadGEM3-GC31-LL: Area from cos lat\n",
      "r1i1p1f3\n",
      "150\n",
      "IPSL-CM6A-LR: Area from cos lat\n",
      "r10i1p1f1\n",
      "6\n",
      "r11i1p1f1\n",
      "6\n",
      "r12i1p1f1\n",
      "6\n",
      "r1i1p1f1\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "globalannualmean: IPSL-CM6A-LR r1i1p1f1 trying just first file\n",
      "150\n",
      "r2i1p1f1\n",
      "6\n",
      "r3i1p1f1\n",
      "6\n",
      "r4i1p1f1\n",
      "6\n",
      "r5i1p1f1\n",
      "6\n",
      "r6i1p1f1\n",
      "6\n",
      "r7i1p1f1\n",
      "6\n",
      "r8i1p1f1\n",
      "6\n",
      "r9i1p1f1\n",
      "6\n",
      "MIROC6: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "r10i1p1f1\n",
      "150\n",
      "r11i1p1f1\n",
      "6\n",
      "r12i1p1f1\n",
      "6\n",
      "r13i1p1f1\n",
      "150\n",
      "r1i1p1f1\n",
      "150\n",
      "r2i1p1f1\n",
      "6\n",
      "r3i1p1f1\n",
      "6\n",
      "r4i1p1f1\n",
      "150\n",
      "r5i1p1f1\n",
      "6\n",
      "r6i1p1f1\n",
      "6\n",
      "r7i1p1f1\n",
      "150\n",
      "r8i1p1f1\n",
      "6\n",
      "r9i1p1f1\n",
      "6\n",
      "SAM0-UNICON: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "UKESM1-0-LL: Area from cos lat\n",
      "r1i1p1f2\n",
      "150\n",
      "GFDL-CM4: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "NESM3: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n",
      "MIROC-ES2L: Area from cos lat\n",
      "r1i1p1f2\n",
      "150\n",
      "NorESM2-LM: Area from cos lat\n",
      "r1i1p1f1\n",
      "150\n"
     ]
    }
   ],
   "source": [
    "# get rid of variants with less than 150 years of data for abrupt 4xCO2\n",
    "for model in list(ts4x_models):\n",
    "    thisModelArea = getModelAreacella(model)\n",
    "    for mem in list(ts4x_models[model].variants):\n",
    "        print(mem)\n",
    "        vGAm = globalannualmean(ts4x_models[model],thisModelArea.areacella,mem) \n",
    "        print(len(vGAm))\n",
    "        if len(vGAm)<150:\n",
    "            ts4x_models[model].variants.pop(mem)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "1\n",
      "1\n",
      "1\n",
      "3\n",
      "2\n",
      "1\n",
      "1\n",
      "2\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "5\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# Make sure our models still have variants\n",
    "for model in list(ts4x_models):\n",
    "    print(len(ts4x_models[model].variants))\n",
    "    if len(ts4x_models[model].variants)==0:\n",
    "        ts4x_models.pop(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ts4x_models)\n",
    "#ts4x_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check and make sure areacella works for models other than NCAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2: Area from cos lat\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f04a45bd240>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = \"CESM2\"\n",
    "cesm4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",\"ts\")\n",
    "imem = list(cesm4x.variants)[0]\n",
    "thisModelArea = getModelAreacella(model)\n",
    "#dts = getChangeTimeseries(model,\"ts\",imem,thisModelArea.areacella)\n",
    "thisModelArea.areacella.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BCC-CSM2-MR'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ts4x_models)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCC-CSM2-MR: Area from cos lat\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f05facbe9b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=list(ts4x_models)[0]\n",
    "cnrm4x = getOneModelExperimentVar(model,\"abrupt-4xCO2\",\"ts\")\n",
    "imem = list(cnrm4x.variants)[0]\n",
    "thisModelArea = getModelAreacella(model)\n",
    "#dts = getChangeTimeseries(model,\"ts\",imem,thisModelArea.areacella)\n",
    "thisModelArea.areacella.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use our tools to get the data and calculate ECS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ECS_models = ts4x_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BCC-CSM2-MR': <__main__.ModelExpVar at 0x7f04be937438>,\n",
       " 'BCC-ESM1': <__main__.ModelExpVar at 0x7f04be937550>,\n",
       " 'CAMS-CSM1-0': <__main__.ModelExpVar at 0x7f04be937208>,\n",
       " 'CESM2-WACCM': <__main__.ModelExpVar at 0x7f04be93e908>,\n",
       " 'CESM2': <__main__.ModelExpVar at 0x7f04be937128>,\n",
       " 'CNRM-CM6-1': <__main__.ModelExpVar at 0x7f04be937278>,\n",
       " 'CNRM-ESM2-1': <__main__.ModelExpVar at 0x7f04be93ec88>,\n",
       " 'CanESM5': <__main__.ModelExpVar at 0x7f04be9370b8>,\n",
       " 'E3SM-1-0': <__main__.ModelExpVar at 0x7f04be9375c0>,\n",
       " 'EC-Earth3-Veg': <__main__.ModelExpVar at 0x7f04be937198>,\n",
       " 'GISS-E2-1-G': <__main__.ModelExpVar at 0x7f05fab140b8>,\n",
       " 'GISS-E2-1-H': <__main__.ModelExpVar at 0x7f05fab142e8>,\n",
       " 'HadGEM3-GC31-LL': <__main__.ModelExpVar at 0x7f05fab143c8>,\n",
       " 'IPSL-CM6A-LR': <__main__.ModelExpVar at 0x7f05fab14198>,\n",
       " 'MIROC6': <__main__.ModelExpVar at 0x7f05fab14128>,\n",
       " 'MRI-ESM2-0': <__main__.ModelExpVar at 0x7f05fab14908>,\n",
       " 'SAM0-UNICON': <__main__.ModelExpVar at 0x7f05fab14438>,\n",
       " 'UKESM1-0-LL': <__main__.ModelExpVar at 0x7f05fab14b70>,\n",
       " 'GFDL-CM4': <__main__.ModelExpVar at 0x7f05fab14208>,\n",
       " 'NESM3': <__main__.ModelExpVar at 0x7f05fab14ba8>,\n",
       " 'MIROC-ES2L': <__main__.ModelExpVar at 0x7f05fab14668>,\n",
       " 'NorESM2-LM': <__main__.ModelExpVar at 0x7f05fab14390>}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts4x_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCC-CSM2-MR\n",
      "r1i1p1f1\n",
      "BCC-ESM1\n",
      "r1i1p1f1\n",
      "CAMS-CSM1-0\n",
      "r1i1p1f1\n",
      "r2i1p1f1\n",
      "CESM2-WACCM\n",
      "r1i1p1f1\n",
      "CESM2\n",
      "r1i1p1f1\n",
      "CNRM-CM6-1\n",
      "r1i1p1f2\n",
      "CNRM-ESM2-1\n",
      "r1i1p1f2\n",
      "r2i1p1f2\n",
      "r3i1p1f2\n",
      "CanESM5\n",
      "r1i1p1f1\n",
      "r1i1p2f1\n",
      "E3SM-1-0\n",
      "r1i1p1f1\n",
      "EC-Earth3-Veg\n",
      "r1i1p1f1\n",
      "GISS-E2-1-G\n",
      "r102i1p1f1\n",
      "r1i1p1f1\n",
      "GISS-E2-1-H\n",
      "r1i1p1f1\n",
      "HadGEM3-GC31-LL\n",
      "r1i1p1f3\n",
      "IPSL-CM6A-LR\n",
      "r1i1p1f1\n",
      "MIROC6\n",
      "r1i1p1f1\n",
      "MRI-ESM2-0\n",
      "r10i1p1f1\n",
      "r13i1p1f1\n",
      "r1i1p1f1\n",
      "r4i1p1f1\n",
      "r7i1p1f1\n",
      "SAM0-UNICON\n",
      "r1i1p1f1\n",
      "UKESM1-0-LL\n",
      "r1i1p1f2\n",
      "GFDL-CM4\n",
      "r1i1p1f1\n",
      "NESM3\n",
      "r1i1p1f1\n",
      "MIROC-ES2L\n",
      "r1i1p1f2\n",
      "NorESM2-LM\n",
      "r1i1p1f1\n"
     ]
    }
   ],
   "source": [
    "ECS={}\n",
    "for model in list(ECS_models):\n",
    "    print(model)\n",
    "    allECS = []\n",
    "    for mem in list(ECS_models[model].variants):\n",
    "        print(mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCC-CSM2-MR\n",
      "r1i1p1f1\n",
      "BCC-CSM2-MR: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/BCC-CSM2-MR/r1i1p1f1/gn/ts_Amon_BCC-CSM2-MR_piControl_r1i1p1f1_gn_185001-244912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/BCC-CSM2-MR/r1i1p1f1/gn/rsdt_Amon_BCC-CSM2-MR_piControl_r1i1p1f1_gn_185001-244912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/BCC-CSM2-MR/r1i1p1f1/gn/rsut_Amon_BCC-CSM2-MR_piControl_r1i1p1f1_gn_185001-244912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/BCC-CSM2-MR/r1i1p1f1/gn/rlut_Amon_BCC-CSM2-MR_piControl_r1i1p1f1_gn_185001-244912.nc\n",
      "rlut\n",
      "2.9312830532206493\n",
      "BCC-ESM1\n",
      "r1i1p1f1\n",
      "BCC-ESM1: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/BCC-ESM1/r1i1p1f1/gn/ts_Amon_BCC-ESM1_piControl_r1i1p1f1_gn_185001-230012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/BCC-ESM1/r1i1p1f1/gn/rsdt_Amon_BCC-ESM1_piControl_r1i1p1f1_gn_185001-230012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/BCC-ESM1/r1i1p1f1/gn/rsut_Amon_BCC-ESM1_piControl_r1i1p1f1_gn_185001-230012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/BCC-ESM1/r1i1p1f1/gn/rlut_Amon_BCC-ESM1_piControl_r1i1p1f1_gn_185001-230012.nc\n",
      "rlut\n",
      "3.3252584356072408\n",
      "CAMS-CSM1-0\n",
      "r1i1p1f1\n",
      "CAMS-CSM1-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CAMS-CSM1-0/r1i1p1f1/gn/ts_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CAMS-CSM1-0/r1i1p1f1/gn/rsdt_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CAMS-CSM1-0/r1i1p1f1/gn/rsut_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CAMS-CSM1-0/r1i1p1f1/gn/rlut_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rlut\n",
      "2.2213378801730026\n",
      "r2i1p1f1\n",
      "CAMS-CSM1-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CAMS-CSM1-0/r1i1p1f1/gn/ts_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CAMS-CSM1-0/r1i1p1f1/gn/rsdt_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CAMS-CSM1-0/r1i1p1f1/gn/rsut_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CAMS-CSM1-0/r1i1p1f1/gn/rlut_Amon_CAMS-CSM1-0_piControl_r1i1p1f1_gn_290001-314912.nc\n",
      "rlut\n",
      "2.222036591914794\n",
      "CESM2-WACCM\n",
      "r1i1p1f1\n",
      "CESM2-WACCM: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CESM2-WACCM/r1i1p1f1/gn/ts_Amon_CESM2-WACCM_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CESM2-WACCM/r1i1p1f1/gn/rsdt_Amon_CESM2-WACCM_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CESM2-WACCM/r1i1p1f1/gn/rsut_Amon_CESM2-WACCM_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CESM2-WACCM/r1i1p1f1/gn/rlut_Amon_CESM2-WACCM_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rlut\n",
      "4.603034591347171\n",
      "CESM2\n",
      "r1i1p1f1\n",
      "CESM2: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CESM2/r1i1p1f1/gn/ts_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CESM2/r1i1p1f1/gn/rsdt_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CESM2/r1i1p1f1/gn/rsut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CESM2/r1i1p1f1/gn/rlut_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc\n",
      "rlut\n",
      "5.118533533959427\n",
      "CNRM-CM6-1\n",
      "r1i1p1f2\n",
      "CNRM-CM6-1: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CNRM-CM6-1/r1i1p1f2/gr/ts_Amon_CNRM-CM6-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CNRM-CM6-1/r1i1p1f2/gr/rsdt_Amon_CNRM-CM6-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CNRM-CM6-1/r1i1p1f2/gr/rsut_Amon_CNRM-CM6-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CNRM-CM6-1/r1i1p1f2/gr/rlut_Amon_CNRM-CM6-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rlut\n",
      "4.7949075411770545\n",
      "CNRM-ESM2-1\n",
      "r1i1p1f2\n",
      "CNRM-ESM2-1: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CNRM-ESM2-1/r1i1p1f2/gr/ts_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CNRM-ESM2-1/r1i1p1f2/gr/rsdt_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CNRM-ESM2-1/r1i1p1f2/gr/rsut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CNRM-ESM2-1/r1i1p1f2/gr/rlut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rlut\n",
      "4.684017334024744\n",
      "r2i1p1f2\n",
      "CNRM-ESM2-1: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CNRM-ESM2-1/r1i1p1f2/gr/ts_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CNRM-ESM2-1/r1i1p1f2/gr/rsdt_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CNRM-ESM2-1/r1i1p1f2/gr/rsut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CNRM-ESM2-1/r1i1p1f2/gr/rlut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rlut\n",
      "4.555762403524064\n",
      "r3i1p1f2\n",
      "CNRM-ESM2-1: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CNRM-ESM2-1/r1i1p1f2/gr/ts_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CNRM-ESM2-1/r1i1p1f2/gr/rsdt_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CNRM-ESM2-1/r1i1p1f2/gr/rsut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CNRM-ESM2-1/r1i1p1f2/gr/rlut_Amon_CNRM-ESM2-1_piControl_r1i1p1f2_gr_185001-234912.nc\n",
      "rlut\n",
      "4.722622159200362\n",
      "CanESM5\n",
      "r1i1p1f1\n",
      "CanESM5: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CanESM5/r1i1p1f1/gn/ts_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/CanESM5/r1i1p2f1/gn/ts_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CanESM5/r1i1p1f1/gn/rsdt_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/CanESM5/r1i1p2f1/gn/rsdt_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CanESM5/r1i1p1f1/gn/rsut_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/CanESM5/r1i1p2f1/gn/rsut_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CanESM5/r1i1p1f1/gn/rlut_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/CanESM5/r1i1p2f1/gn/rlut_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rlut\n",
      "5.599088772026379\n",
      "r1i1p2f1\n",
      "CanESM5: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/CanESM5/r1i1p1f1/gn/ts_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/CanESM5/r1i1p2f1/gn/ts_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/CanESM5/r1i1p1f1/gn/rsdt_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/CanESM5/r1i1p2f1/gn/rsdt_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/CanESM5/r1i1p1f1/gn/rsut_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/CanESM5/r1i1p2f1/gn/rsut_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/CanESM5/r1i1p1f1/gn/rlut_Amon_CanESM5_piControl_r1i1p1f1_gn_520101-540012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/CanESM5/r1i1p2f1/gn/rlut_Amon_CanESM5_piControl_r1i1p2f1_gn_555001-560012.nc\n",
      "rlut\n",
      "5.583513443397019\n",
      "E3SM-1-0\n",
      "r1i1p1f1\n",
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "E3SM-1-0: Area from cos lat\n",
      "duplicate fix designed for E3SM-1-0 piControl\n",
      "current model and experiment: E3SM-1-0, piControl\n",
      "/project/cmip6/piControl/Amon/ts/E3SM-1-0/r1i1p1f1/gr/ts_Amon_E3SM-1-0_piControl_r1i1p1f1_gr_000101-050012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/E3SM-1-0/r1i1p1f1/gr/rsdt_Amon_E3SM-1-0_piControl_r1i1p1f1_gr_000101-002512.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/E3SM-1-0/r1i1p1f1/gr/rsut_Amon_E3SM-1-0_piControl_r1i1p1f1_gr_000101-002512.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/E3SM-1-0/r1i1p1f1/gr/rlut_Amon_E3SM-1-0_piControl_r1i1p1f1_gr_000101-002512.nc\n",
      "rlut\n",
      "5.289402301061932\n",
      "EC-Earth3-Veg\n",
      "r1i1p1f1\n",
      "EC-Earth3-Veg: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/EC-Earth3-Veg/r1i1p1f1/gr/ts_Amon_EC-Earth3-Veg_piControl_r1i1p1f1_gr_185001-185012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/EC-Earth3-Veg/r1i1p1f1/gr/rsdt_Amon_EC-Earth3-Veg_piControl_r1i1p1f1_gr_185001-185012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/EC-Earth3-Veg/r1i1p1f1/gr/rsut_Amon_EC-Earth3-Veg_piControl_r1i1p1f1_gr_185001-185012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/EC-Earth3-Veg/r1i1p1f1/gr/rlut_Amon_EC-Earth3-Veg_piControl_r1i1p1f1_gr_185001-185012.nc\n",
      "rlut\n",
      "4.03204661998398\n",
      "GISS-E2-1-G\n",
      "r102i1p1f1\n",
      "GISS-E2-1-G: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r101i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r102i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r1i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r1i1p1f3/gn/ts_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r2i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r101i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r102i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f2/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f3/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r2i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r101i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r102i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f2/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f3/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r2i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r101i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r102i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f2/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f3/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r2i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rlut\n",
      "2.5481714201384333\n",
      "r1i1p1f1\n",
      "GISS-E2-1-G: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r101i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r102i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r1i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r1i1p1f3/gn/ts_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-G/r2i1p1f1/gn/ts_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r101i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r102i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f2/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r1i1p1f3/gn/rsdt_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-G/r2i1p1f1/gn/rsdt_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r101i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r102i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f2/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r1i1p1f3/gn/rsut_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-G/r2i1p1f1/gn/rsut_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r101i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r101i1p1f1_gn_185001-190012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r102i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r102i1p1f1_gn_185001-190012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f1_gn_415001-420012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f2/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f2_gn_755001-760012.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r1i1p1f3/gn/rlut_Amon_GISS-E2-1-G_piControl_r1i1p1f3_gn_290001-294912.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-G/r2i1p1f1/gn/rlut_Amon_GISS-E2-1-G_piControl_r2i1p1f1_gn_290001-294912.nc\n",
      "rlut\n",
      "2.5843226199084524\n",
      "GISS-E2-1-H\n",
      "r1i1p1f1\n",
      "GISS-E2-1-H: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/GISS-E2-1-H/r1i1p1f1/gn/ts_Amon_GISS-E2-1-H_piControl_r1i1p1f1_gn_318001-323012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/GISS-E2-1-H/r1i1p1f1/gn/rsdt_Amon_GISS-E2-1-H_piControl_r1i1p1f1_gn_318001-323012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/GISS-E2-1-H/r1i1p1f1/gn/rsut_Amon_GISS-E2-1-H_piControl_r1i1p1f1_gn_318001-323012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/GISS-E2-1-H/r1i1p1f1/gn/rlut_Amon_GISS-E2-1-H_piControl_r1i1p1f1_gn_318001-323012.nc\n",
      "rlut\n",
      "2.9637867927069497\n",
      "HadGEM3-GC31-LL\n",
      "r1i1p1f3\n",
      "HadGEM3-GC31-LL: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/HadGEM3-GC31-LL/r1i1p1f1/gn/ts_Amon_HadGEM3-GC31-LL_piControl_r1i1p1f1_gn_185001-194912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/HadGEM3-GC31-LL/r1i1p1f1/gn/rsdt_Amon_HadGEM3-GC31-LL_piControl_r1i1p1f1_gn_185001-194912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/HadGEM3-GC31-LL/r3i1p1f1/gn/rsdt_Amon_HadGEM3-GC31-LL_piControl_r3i1p1f1_gn_185001-186912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/HadGEM3-GC31-LL/r1i1p1f1/gn/rsut_Amon_HadGEM3-GC31-LL_piControl_r1i1p1f1_gn_185001-194912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/HadGEM3-GC31-LL/r3i1p1f1/gn/rsut_Amon_HadGEM3-GC31-LL_piControl_r3i1p1f1_gn_185001-186912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/HadGEM3-GC31-LL/r1i1p1f1/gn/rlut_Amon_HadGEM3-GC31-LL_piControl_r1i1p1f1_gn_185001-194912.nc\n",
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/HadGEM3-GC31-LL/r3i1p1f1/gn/rlut_Amon_HadGEM3-GC31-LL_piControl_r3i1p1f1_gn_185001-186912.nc\n",
      "rlut\n",
      "5.407428220075852\n",
      "IPSL-CM6A-LR\n",
      "r1i1p1f1\n",
      "IPSL-CM6A-LR: Area from cos lat\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "globalannualmean: IPSL-CM6A-LR r1i1p1f1 trying just first file\n",
      "/project/cmip6/piControl/Amon/ts/IPSL-CM6A-LR/r1i1p1f1/gr/ts_Amon_IPSL-CM6A-LR_piControl_r1i1p1f1_gr_185001-234912.nc\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts\n",
      "/project/cmip6/piControl/Amon/ts/IPSL-CM6A-LR/r1i2p1f1/gr/ts_Amon_IPSL-CM6A-LR_piControl_r1i2p1f1_gr_185001-209812.nc\n",
      "ts\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "globalannualmean: IPSL-CM6A-LR r1i1p1f1 trying just first file\n",
      "/project/cmip6/piControl/Amon/rsdt/IPSL-CM6A-LR/r1i1p1f1/gr/rsdt_Amon_IPSL-CM6A-LR_piControl_r1i1p1f1_gr_185001-234912.nc\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsdt/IPSL-CM6A-LR/r1i2p1f1/gr/rsdt_Amon_IPSL-CM6A-LR_piControl_r1i2p1f1_gr_185001-209812.nc\n",
      "rsdt\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "globalannualmean: IPSL-CM6A-LR r1i1p1f1 trying just first file\n",
      "/project/cmip6/piControl/Amon/rsut/IPSL-CM6A-LR/r1i1p1f1/gr/rsut_Amon_IPSL-CM6A-LR_piControl_r1i1p1f1_gr_185001-234912.nc\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rsut\n",
      "/project/cmip6/piControl/Amon/rsut/IPSL-CM6A-LR/r1i2p1f1/gr/rsut_Amon_IPSL-CM6A-LR_piControl_r1i2p1f1_gr_185001-209812.nc\n",
      "rsut\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "globalannualmean: IPSL-CM6A-LR r1i1p1f1 trying just first file\n",
      "/project/cmip6/piControl/Amon/rlut/IPSL-CM6A-LR/r1i1p1f1/gr/rlut_Amon_IPSL-CM6A-LR_piControl_r1i1p1f1_gr_185001-234912.nc\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.parse_timedelta_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'datetime.timedelta' has no len()",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\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--> 415\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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    416\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedeltaIndex\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    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mCFTimeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pytimedelta\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/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         tdarr = TimedeltaArray._from_sequence(\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         )\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtdarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, data, dtype, copy, freq, unit)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_infer_freq\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[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\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    273\u001b[0m         \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_inferred_freq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minferred_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq_infer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    972\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    974\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   1099\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.algos.are_diff'\n",
      "Traceback (most recent call last):\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/xarray/coding/cftimeindex.py\", line 415, in __sub__\n",
      "    return pd.TimedeltaIndex(np.array(self) - np.array(other))\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/indexes/timedeltas.py\", line 258, in __new__\n",
      "    data, freq=freq, unit=unit, dtype=dtype, copy=copy\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 272, in _from_sequence\n",
      "    data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 973, in sequence_to_td64ns\n",
      "    data = objects_to_td64ns(data, unit=unit, errors=errors)\n",
      "  File \"/project/ccr/apgrass/miniconda3/envs/cmip6/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py\", line 1098, in objects_to_td64ns\n",
      "    result = array_to_timedelta64(values, unit=unit, errors=errors)\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 236, in pandas._libs.tslibs.timedeltas.array_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 199, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64\n",
      "  File \"pandas/_libs/tslibs/timedeltas.pyx\", line 144, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds\n",
      "OverflowError: Python int too large to convert to C long\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rlut\n",
      "/project/cmip6/piControl/Amon/rlut/IPSL-CM6A-LR/r1i2p1f1/gr/rlut_Amon_IPSL-CM6A-LR_piControl_r1i2p1f1_gr_185001-209812.nc\n",
      "rlut\n",
      "4.483081612984019\n",
      "MIROC6\n",
      "r1i1p1f1\n",
      "MIROC6: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MIROC6/r1i1p1f1/gn/ts_Amon_MIROC6_piControl_r1i1p1f1_gn_320001-329912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MIROC6/r1i1p1f1/gn/rsdt_Amon_MIROC6_piControl_r1i1p1f1_gn_320001-329912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MIROC6/r1i1p1f1/gn/rsut_Amon_MIROC6_piControl_r1i1p1f1_gn_320001-329912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MIROC6/r1i1p1f1/gn/rlut_Amon_MIROC6_piControl_r1i1p1f1_gn_320001-329912.nc\n",
      "rlut\n",
      "2.5378744943208553\n",
      "MRI-ESM2-0\n",
      "r10i1p1f1\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MRI-ESM2-0/r1i1p1f1/gn/ts_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MRI-ESM2-0/r1i1p1f1/gn/rsdt_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MRI-ESM2-0/r1i1p1f1/gn/rsut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MRI-ESM2-0/r1i1p1f1/gn/rlut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rlut\n",
      "3.4489822010805886\n",
      "r13i1p1f1\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MRI-ESM2-0/r1i1p1f1/gn/ts_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MRI-ESM2-0/r1i1p1f1/gn/rsdt_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MRI-ESM2-0/r1i1p1f1/gn/rsut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MRI-ESM2-0/r1i1p1f1/gn/rlut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rlut\n",
      "3.542870237748016\n",
      "r1i1p1f1\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MRI-ESM2-0/r1i1p1f1/gn/ts_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MRI-ESM2-0/r1i1p1f1/gn/rsdt_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MRI-ESM2-0/r1i1p1f1/gn/rsut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MRI-ESM2-0/r1i1p1f1/gn/rlut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rlut\n",
      "3.084033537286304\n",
      "r4i1p1f1\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MRI-ESM2-0/r1i1p1f1/gn/ts_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MRI-ESM2-0/r1i1p1f1/gn/rsdt_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MRI-ESM2-0/r1i1p1f1/gn/rsut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MRI-ESM2-0/r1i1p1f1/gn/rlut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rlut\n",
      "3.1327302729756195\n",
      "r7i1p1f1\n",
      "MRI-ESM2-0: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MRI-ESM2-0/r1i1p1f1/gn/ts_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MRI-ESM2-0/r1i1p1f1/gn/rsdt_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MRI-ESM2-0/r1i1p1f1/gn/rsut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MRI-ESM2-0/r1i1p1f1/gn/rlut_Amon_MRI-ESM2-0_piControl_r1i1p1f1_gn_185001-255012.nc\n",
      "rlut\n",
      "3.3013004502251806\n",
      "SAM0-UNICON\n",
      "r1i1p1f1\n",
      "SAM0-UNICON: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/SAM0-UNICON/r1i1p1f1/gn/ts_Amon_SAM0-UNICON_piControl_r1i1p1f1_gn_000101-001012.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/SAM0-UNICON/r1i1p1f1/gn/rsdt_Amon_SAM0-UNICON_piControl_r1i1p1f1_gn_000101-001012.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/SAM0-UNICON/r1i1p1f1/gn/rsut_Amon_SAM0-UNICON_piControl_r1i1p1f1_gn_000101-001012.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/SAM0-UNICON/r1i1p1f1/gn/rlut_Amon_SAM0-UNICON_piControl_r1i1p1f1_gn_000101-001012.nc\n",
      "rlut\n",
      "3.718017280560372\n",
      "UKESM1-0-LL\n",
      "r1i1p1f2\n",
      "UKESM1-0-LL: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/UKESM1-0-LL/r1i1p1f2/gn/ts_Amon_UKESM1-0-LL_piControl_r1i1p1f2_gn_196001-204912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/UKESM1-0-LL/r1i1p1f2/gn/rsdt_Amon_UKESM1-0-LL_piControl_r1i1p1f2_gn_196001-204912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/UKESM1-0-LL/r1i1p1f2/gn/rsut_Amon_UKESM1-0-LL_piControl_r1i1p1f2_gn_196001-204912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/UKESM1-0-LL/r1i1p1f2/gn/rlut_Amon_UKESM1-0-LL_piControl_r1i1p1f2_gn_196001-204912.nc\n",
      "rlut\n",
      "5.266521017402457\n",
      "GFDL-CM4\n",
      "r1i1p1f1\n",
      "GFDL-CM4: Area from cos lat\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "/project/cmip6/piControl/Amon/ts/GFDL-CM4/r1i1p1f1/gr1/ts_Amon_GFDL-CM4_piControl_r1i1p1f1_gr1_015101-025012.nc\n",
      "ts\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "/project/cmip6/piControl/Amon/rsdt/GFDL-CM4/r1i1p1f1/gr1/rsdt_Amon_GFDL-CM4_piControl_r1i1p1f1_gr1_015101-025012.nc\n",
      "rsdt\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "/project/cmip6/piControl/Amon/rsut/GFDL-CM4/r1i1p1f1/gr1/rsut_Amon_GFDL-CM4_piControl_r1i1p1f1_gr1_015101-025012.nc\n",
      "rsut\n",
      "duplicate fix designed for GFDL-CM4 abrupt4xCO2\n",
      "current model and experiment: GFDL-CM4, abrupt-4xCO2\n",
      "/project/cmip6/piControl/Amon/rlut/GFDL-CM4/r1i1p1f1/gr1/rlut_Amon_GFDL-CM4_piControl_r1i1p1f1_gr1_015101-025012.nc\n",
      "rlut\n",
      "3.8351141938059388\n",
      "NESM3\n",
      "r1i1p1f1\n",
      "NESM3: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/NESM3/r1i1p1f1/gn/ts_Amon_NESM3_piControl_r1i1p1f1_gn_050001-059912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/NESM3/r1i1p1f1/gn/rsdt_Amon_NESM3_piControl_r1i1p1f1_gn_050001-059912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/NESM3/r1i1p1f1/gn/rsut_Amon_NESM3_piControl_r1i1p1f1_gn_050001-059912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/NESM3/r1i1p1f1/gn/rlut_Amon_NESM3_piControl_r1i1p1f1_gn_050001-059912.nc\n",
      "rlut\n",
      "4.461691731810879\n",
      "MIROC-ES2L\n",
      "r1i1p1f2\n",
      "MIROC-ES2L: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/MIROC-ES2L/r1i1p1f2/gn/ts_Amon_MIROC-ES2L_piControl_r1i1p1f2_gn_185001-204912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/MIROC-ES2L/r1i1p1f2/gn/rsdt_Amon_MIROC-ES2L_piControl_r1i1p1f2_gn_185001-204912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/MIROC-ES2L/r1i1p1f2/gn/rsut_Amon_MIROC-ES2L_piControl_r1i1p1f2_gn_185001-204912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/MIROC-ES2L/r1i1p1f2/gn/rlut_Amon_MIROC-ES2L_piControl_r1i1p1f2_gn_185001-204912.nc\n",
      "rlut\n",
      "2.675450675014949\n",
      "NorESM2-LM\n",
      "r1i1p1f1\n",
      "NorESM2-LM: Area from cos lat\n",
      "/project/cmip6/piControl/Amon/ts/NorESM2-LM/r1i1p1f1/gn/ts_Amon_NorESM2-LM_piControl_r1i1p1f1_gn_160001-160912.nc\n",
      "ts\n",
      "/project/cmip6/piControl/Amon/rsdt/NorESM2-LM/r1i1p1f1/gn/rsdt_Amon_NorESM2-LM_piControl_r1i1p1f1_gn_160001-160912.nc\n",
      "rsdt\n",
      "/project/cmip6/piControl/Amon/rsut/NorESM2-LM/r1i1p1f1/gn/rsut_Amon_NorESM2-LM_piControl_r1i1p1f1_gn_160001-160912.nc\n",
      "rsut\n",
      "/project/cmip6/piControl/Amon/rlut/NorESM2-LM/r1i1p1f1/gn/rlut_Amon_NorESM2-LM_piControl_r1i1p1f1_gn_160001-160912.nc\n",
      "rlut\n",
      "2.5701754915985298\n"
     ]
    }
   ],
   "source": [
    "ECS={}\n",
    "for model in list(ECS_models):\n",
    "    print(model)\n",
    "    allECS = []\n",
    "    for mem in list(ECS_models[model].variants):\n",
    "        print(mem)\n",
    "        thisECS,F = calcECSts(model,mem)\n",
    "        (model,mem)\n",
    "        allECS.append(thisECS)\n",
    "        print(thisECS/2)\n",
    "    ECS[model] = np.average(allECS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make an ordered list of the ECS values, plot ECS/2 in a bar plot (to get ECS for CO2-doubling, which is more familiar), and save the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CAMS-CSM1-0': 4.443374472087797, 'MIROC6': 5.075748988641711, 'GISS-E2-1-G': 5.132494040046886, 'NorESM2-LM': 5.1403509831970595, 'MIROC-ES2L': 5.350901350029898, 'BCC-CSM2-MR': 5.862566106441299, 'GISS-E2-1-H': 5.927573585413899, 'MRI-ESM2-0': 6.603966679726284, 'BCC-ESM1': 6.6505168712144815, 'SAM0-UNICON': 7.436034561120744, 'GFDL-CM4': 7.6702283876118775, 'EC-Earth3-Veg': 8.06409323996796, 'NESM3': 8.923383463621757, 'IPSL-CM6A-LR': 8.966163225968039, 'CESM2-WACCM': 9.206069182694343, 'CNRM-ESM2-1': 9.308267931166114, 'CNRM-CM6-1': 9.589815082354109, 'CESM2': 10.237067067918854, 'UKESM1-0-LL': 10.533042034804915, 'E3SM-1-0': 10.578804602123864, 'HadGEM3-GC31-LL': 10.814856440151704, 'CanESM5': 11.182602215423397}\n",
      "{'CAMS-CSM1-0': 4.443374472087797, 'MIROC6': 5.075748988641711, 'GISS-E2-1-G': 5.132494040046886, 'NorESM2-LM': 5.1403509831970595, 'MIROC-ES2L': 5.350901350029898, 'BCC-CSM2-MR': 5.862566106441299, 'GISS-E2-1-H': 5.927573585413899, 'MRI-ESM2-0': 6.603966679726284, 'BCC-ESM1': 6.6505168712144815, 'SAM0-UNICON': 7.436034561120744, 'GFDL-CM4': 7.6702283876118775, 'EC-Earth3-Veg': 8.06409323996796, 'NESM3': 8.923383463621757, 'IPSL-CM6A-LR': 8.966163225968039, 'CESM2-WACCM': 9.206069182694343, 'CNRM-ESM2-1': 9.308267931166114, 'CNRM-CM6-1': 9.589815082354109, 'CESM2': 10.237067067918854, 'UKESM1-0-LL': 10.533042034804915, 'E3SM-1-0': 10.578804602123864, 'HadGEM3-GC31-LL': 10.814856440151704, 'CanESM5': 11.182602215423397}\n"
     ]
    }
   ],
   "source": [
    "orderedECS = sorted(ECS.items(), key=lambda x: x[1])\n",
    "oECS = {}\n",
    "for pair in orderedECS:\n",
    "    #print(pair[0])\n",
    "    #print(pair[1])\n",
    "    oECS[pair[0]]=pair[1]\n",
    "    \n",
    "print(oECS)\n",
    "oECS = { pair[0]:pair[1] for pair in orderedECS }\n",
    "print(oECS)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ECS for CMIP6 models (16 Octoboer 2019)')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecs2x = []\n",
    "for val in oECS.values():\n",
    "    ecs2x.append(val/2)\n",
    "\n",
    "\n",
    "objects = oECS.keys()\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = ecs2x\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('ECS_2x [K]')\n",
    "plt.title('ECS for CMIP6 models (16 Octoboer 2019)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('cmip6-ecs-16102019-cgd.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = csv.writer(open(\"cmip6-ecs-16102019-cgd.csv\", \"w\"))\n",
    "for key, val in oECS.items():\n",
    "    w.writerow([key, val])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CAMS-CSM1-0': 4.443374472087797,\n",
       " 'MIROC6': 5.075748988641711,\n",
       " 'GISS-E2-1-G': 5.132494040046886,\n",
       " 'NorESM2-LM': 5.1403509831970595,\n",
       " 'MIROC-ES2L': 5.350901350029898,\n",
       " 'BCC-CSM2-MR': 5.862566106441299,\n",
       " 'GISS-E2-1-H': 5.927573585413899,\n",
       " 'MRI-ESM2-0': 6.603966679726284,\n",
       " 'BCC-ESM1': 6.6505168712144815,\n",
       " 'SAM0-UNICON': 7.436034561120744,\n",
       " 'GFDL-CM4': 7.6702283876118775,\n",
       " 'EC-Earth3-Veg': 8.06409323996796,\n",
       " 'NESM3': 8.923383463621757,\n",
       " 'IPSL-CM6A-LR': 8.966163225968039,\n",
       " 'CESM2-WACCM': 9.206069182694343,\n",
       " 'CNRM-ESM2-1': 9.308267931166114,\n",
       " 'CNRM-CM6-1': 9.589815082354109,\n",
       " 'CESM2': 10.237067067918854,\n",
       " 'UKESM1-0-LL': 10.533042034804915,\n",
       " 'E3SM-1-0': 10.578804602123864,\n",
       " 'HadGEM3-GC31-LL': 10.814856440151704,\n",
       " 'CanESM5': 11.182602215423397}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oECS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = csv.writer(open(\"cmip6-ecs-16102019-cgd.csv\", \"w\"))\n",
    "for key, val in oECS.items():\n",
    "    w.writerow([key, val])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}