code/ERA5_SSW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb923732-fe9e-400a-ac31-a92a3c28b24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import more_itertools as mit\n",
    "from itertools import groupby\n",
    "import glob\n",
    "import datetime as dt \n",
    "#import vor_fast\n",
    "#import vor_fast_setup\n",
    "import pandas as pd\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7853b73-2f5b-4542-bba9-88fe22f48969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: more-itertools in /home/b/b381258/.local/lib/python3.9/site-packages (10.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --user more-itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8eb1e565-e813-4fd6-9a2f-00b9e9693a43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{Timestamp('2023-11-01 00:00:00'): 92, Timestamp('2024-02-01 00:00:00'): 152}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6e16452-741f-494d-9245-e8619740a80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths1=[]\n",
    "root_path = '/work/bd1022/from_Mistral/bd1022/b381258/ERA5/'\n",
    "for filepath1 in glob.iglob(f'{root_path}*ua*'):\n",
    "    filepaths1.append(filepath1)\n",
    "filepaths1.sort()\n",
    "\n",
    "# filepaths to obtain 10hPa GPH for northern hemisphere\n",
    "filepaths2=[]\n",
    "for filepath2 in glob.iglob(f'{root_path}*moments*'):\n",
    "    filepaths2.append(filepath2)\n",
    "filepaths2.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c6f61c3-1dc8-47d9-bba3-3033a4839709",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "E5pl00_1D_1995-09_129.nc  E5pl00_1D_2014-04_129.nc\n",
      "E5pl00_1D_1995-09_131.nc  E5pl00_1D_2014-04_131.nc\n",
      "E5pl00_1D_1995-10_129.nc  E5pl00_1D_2014-05_129.nc\n",
      "E5pl00_1D_1995-10_131.nc  E5pl00_1D_2014-05_131.nc\n",
      "E5pl00_1D_1995-11_129.nc  E5pl00_1D_2014-06_129.nc\n",
      "E5pl00_1D_1995-11_131.nc  E5pl00_1D_2014-06_131.nc\n",
      "E5pl00_1D_1995-12_129.nc  E5pl00_1D_2014-07_129.nc\n",
      "E5pl00_1D_1995-12_131.nc  E5pl00_1D_2014-07_131.nc\n",
      "E5pl00_1D_1996-01_129.nc  E5pl00_1D_2014-08_129.nc\n",
      "E5pl00_1D_1996-01_131.nc  E5pl00_1D_2014-08_131.nc\n",
      "E5pl00_1D_1996-02_129.nc  E5pl00_1D_2014-09_129.nc\n",
      "E5pl00_1D_1996-02_131.nc  E5pl00_1D_2014-09_131.nc\n",
      "E5pl00_1D_1996-03_129.nc  E5pl00_1D_2014-10_129.nc\n",
      "E5pl00_1D_1996-03_131.nc  E5pl00_1D_2014-10_131.nc\n",
      "E5pl00_1D_1996-04_129.nc  E5pl00_1D_2014-11_129.nc\n",
      "E5pl00_1D_1996-04_131.nc  E5pl00_1D_2014-11_131.nc\n",
      "E5pl00_1D_1996-05_129.nc  E5pl00_1D_2014-12_129.nc\n",
      "E5pl00_1D_1996-05_131.nc  E5pl00_1D_2014-12_131.nc\n",
      "E5pl00_1D_1996-06_129.nc  E5pl00_1D_2015-01_129.nc\n",
      "E5pl00_1D_1996-06_131.nc  E5pl00_1D_2015-01_131.nc\n",
      "E5pl00_1D_1996-07_129.nc  E5pl00_1D_2015-02_129.nc\n",
      "E5pl00_1D_1996-07_131.nc  E5pl00_1D_2015-02_131.nc\n",
      "E5pl00_1D_1996-08_129.nc  E5pl00_1D_2015-03_129.nc\n",
      "E5pl00_1D_1996-08_131.nc  E5pl00_1D_2015-03_131.nc\n",
      "E5pl00_1D_1996-09_129.nc  E5pl00_1D_2015-04_129.nc\n",
      "E5pl00_1D_1996-09_131.nc  E5pl00_1D_2015-04_131.nc\n",
      "E5pl00_1D_1996-10_129.nc  E5pl00_1D_2015-05_129.nc\n",
      "E5pl00_1D_1996-10_131.nc  E5pl00_1D_2015-05_131.nc\n",
      "E5pl00_1D_1996-11_129.nc  E5pl00_1D_2015-06_129.nc\n",
      "E5pl00_1D_1996-11_131.nc  E5pl00_1D_2015-06_131.nc\n",
      "E5pl00_1D_1996-12_129.nc  E5pl00_1D_2015-07_129.nc\n",
      "E5pl00_1D_1996-12_131.nc  E5pl00_1D_2015-07_131.nc\n",
      "E5pl00_1D_1997-01_129.nc  E5pl00_1D_2015-08_129.nc\n",
      "E5pl00_1D_1997-01_131.nc  E5pl00_1D_2015-08_131.nc\n",
      "E5pl00_1D_1997-02_129.nc  E5pl00_1D_2015-09_129.nc\n",
      "E5pl00_1D_1997-02_131.nc  E5pl00_1D_2015-09_131.nc\n",
      "E5pl00_1D_1997-03_129.nc  E5pl00_1D_2015-10_129.nc\n",
      "E5pl00_1D_1997-03_131.nc  E5pl00_1D_2015-10_131.nc\n",
      "E5pl00_1D_1997-04_129.nc  E5pl00_1D_2015-11_129.nc\n",
      "E5pl00_1D_1997-04_131.nc  E5pl00_1D_2015-11_131.nc\n",
      "E5pl00_1D_1997-05_129.nc  E5pl00_1D_2015-12_129.nc\n",
      "E5pl00_1D_1997-05_131.nc  E5pl00_1D_2015-12_131.nc\n",
      "E5pl00_1D_1997-06_129.nc  ERA5_moments@10and100hPa_NHonly.nc\n",
      "E5pl00_1D_1997-06_131.nc  ERA5_moments@10and50and100hPa_NHonly.nc\n",
      "E5pl00_1D_1997-07_129.nc  ml00_1H\n",
      "E5pl00_1D_1997-07_131.nc\n"
     ]
    }
   ],
   "source": [
    "!ls /work/bd1022/from_Mistral/bd1022/b381258/ERA5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da48c614-9839-48d6-a388-549408543a44",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2023.4.2'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "feb05e03-1ab2-4e06-af32-dd8ac2a450ba",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979-01\n",
      "1979-02\n",
      "1979-03\n",
      "1979-04\n",
      "1979-05\n",
      "1979-06\n",
      "1979-07\n",
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      "1979-12\n",
      "1980-01\n",
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      "1980-04\n",
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      "1980-06\n",
      "1980-07\n",
      "1980-08\n",
      "1980-09\n",
      "1980-10\n",
      "1980-11\n",
      "1980-12\n",
      "1981-01\n",
      "1981-02\n",
      "1981-03\n",
      "1981-04\n",
      "1981-05\n",
      "1981-06\n",
      "1981-07\n",
      "1981-08\n",
      "1981-09\n",
      "1981-10\n",
      "1981-11\n",
      "1981-12\n",
      "1982-01\n",
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      "1982-03\n",
      "1982-04\n",
      "1982-05\n",
      "1982-06\n",
      "1982-07\n",
      "1982-08\n",
      "1982-09\n",
      "1982-10\n",
      "1982-11\n",
      "1982-12\n",
      "1983-01\n",
      "1983-02\n",
      "1983-03\n",
      "1983-04\n",
      "1983-05\n",
      "1983-06\n",
      "1983-07\n",
      "1983-08\n",
      "1983-09\n",
      "1983-10\n",
      "1983-11\n",
      "1983-12\n",
      "1984-01\n",
      "1984-02\n",
      "1984-03\n",
      "1984-04\n",
      "1984-05\n",
      "1984-06\n",
      "1984-07\n",
      "1984-08\n",
      "1984-09\n",
      "1984-10\n",
      "1984-11\n",
      "1984-12\n",
      "1985-01\n",
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      "1985-03\n",
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      "1986-01\n",
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      "1989-01\n",
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      "1989-03\n",
      "1989-04\n",
      "1989-05\n",
      "1989-06\n",
      "1989-07\n",
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      "1989-11\n",
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      "1990-02\n",
      "1990-03\n",
      "1990-04\n",
      "1990-05\n",
      "1990-06\n",
      "1990-07\n",
      "1990-08\n",
      "1990-09\n",
      "1990-10\n",
      "1990-11\n",
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      "1991-01\n",
      "1991-02\n",
      "1991-03\n",
      "1991-04\n",
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      "1991-06\n",
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      "2009-01\n",
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      "2009-03\n",
      "2009-04\n",
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      "2009-07\n",
      "2009-08\n",
      "2009-09\n",
      "2009-10\n",
      "2009-11\n",
      "2009-12\n",
      "2010-01\n",
      "2010-02\n",
      "2010-03\n",
      "2010-04\n",
      "2010-05\n",
      "2010-06\n",
      "2010-07\n",
      "2010-08\n",
      "2010-09\n",
      "2010-10\n",
      "2010-11\n",
      "2010-12\n",
      "2011-01\n",
      "2011-02\n",
      "2011-03\n",
      "2011-04\n",
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      "2011-06\n",
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      "2011-08\n",
      "2011-09\n",
      "2011-10\n",
      "2011-11\n",
      "2011-12\n",
      "2012-01\n",
      "2012-02\n",
      "2012-03\n",
      "2012-04\n",
      "2012-05\n",
      "2012-06\n",
      "2012-07\n",
      "2012-08\n",
      "2012-09\n",
      "2012-10\n",
      "2012-11\n",
      "2012-12\n",
      "2013-01\n",
      "2013-02\n",
      "2013-03\n",
      "2013-04\n",
      "2013-05\n",
      "2013-06\n",
      "2013-07\n",
      "2013-08\n",
      "2013-09\n",
      "2013-10\n",
      "2013-11\n",
      "2013-12\n",
      "2014-01\n",
      "2014-02\n",
      "2014-03\n",
      "2014-04\n",
      "2014-05\n",
      "2014-06\n",
      "2014-07\n",
      "2014-08\n",
      "2014-09\n",
      "2014-10\n",
      "2014-11\n",
      "2014-12\n",
      "2015-01\n",
      "2015-02\n",
      "2015-03\n",
      "2015-04\n",
      "2015-05\n",
      "2015-06\n",
      "2015-07\n",
      "2015-08\n",
      "2015-09\n",
      "2015-10\n",
      "2015-11\n",
      "2015-12\n"
     ]
    }
   ],
   "source": [
    "for date in pd.date_range('1979-01-01', '2015-12-31', freq = '1M'):\n",
    "    date_s = date.strftime('%Y-%m')\n",
    "    print(date_s)\n",
    "    infile = f'{root_path}{sub_dir}E5pl00_1D_{date_s}_{variable}.nc'\n",
    "    ds = xr.open_dataset(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1365d1cf-e9e2-427e-9735-6b850436f1ea",
   "metadata": {
    "scrolled": true,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    444     444   31524\n"
     ]
    }
   ],
   "source": [
    "!ls /work/bd1022/from_Mistral/bd1022/b381258/ERA5/E5pl00_1D_*131.nc | wc"
   ]
  },
  {
   "cell_type": "code",
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   "id": "eeb7e3e0-0301-4db2-ac8f-771100a8bc3d",
   "metadata": {
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   },
   "outputs": [
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 31, lon: 1, lat: 1, plev: 1)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.015e+07 2.015e+07 ... 2.015e+07 2.015e+07\n",
       "  * lon      (lon) float64 0.0\n",
       "  * lat      (lat) float64 60.0\n",
       "  * plev     (plev) float64 1e+03\n",
       "Data variables:\n",
       "    U        (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 23:45:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8c8544d-5f70-45b5-b53a-dc0840763c8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8c8544d-5f70-45b5-b53a-dc0840763c8c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>plev</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c39b3a54-bcea-4e12-bc4c-54eb41d838ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c39b3a54-bcea-4e12-bc4c-54eb41d838ce' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.015e+07 2.015e+07 ... 2.015e+07</div><input id='attrs-51e318ca-fb8d-47ca-a9bc-d0c1eec2992e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e318ca-fb8d-47ca-a9bc-d0c1eec2992e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91898e11-244e-45e7-94be-2447189c2644' class='xr-var-data-in' type='checkbox'><label for='data-91898e11-244e-45e7-94be-2447189c2644' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([20151201.479167, 20151202.479167, 20151203.479167, 20151204.479167,\n",
       "       20151205.479167, 20151206.479167, 20151207.479167, 20151208.479167,\n",
       "       20151209.479167, 20151210.479167, 20151211.479167, 20151212.479167,\n",
       "       20151213.479167, 20151214.479167, 20151215.479167, 20151216.479167,\n",
       "       20151217.479167, 20151218.479167, 20151219.479167, 20151220.479167,\n",
       "       20151221.479167, 20151222.479167, 20151223.479167, 20151224.479167,\n",
       "       20151225.479167, 20151226.479167, 20151227.479167, 20151228.479167,\n",
       "       20151229.479167, 20151230.479167, 20151231.479167])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-62102820-ce60-4188-ba85-622de2b4dce4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62102820-ce60-4188-ba85-622de2b4dce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f58ccb-4ff3-4678-8766-934b2b0ffa03' class='xr-var-data-in' type='checkbox'><label for='data-32f58ccb-4ff3-4678-8766-934b2b0ffa03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-83b65834-4c84-43b3-9447-6b259e3f3f7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83b65834-4c84-43b3-9447-6b259e3f3f7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a64d4d3-4780-4cdc-a145-38e035dcdade' class='xr-var-data-in' type='checkbox'><label for='data-2a64d4d3-4780-4cdc-a145-38e035dcdade' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([59.99997])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-fb476bf7-3fe1-40d0-a22e-e46696fc745e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb476bf7-3fe1-40d0-a22e-e46696fc745e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14874dea-1242-49fc-b829-5fc30ac2fce9' class='xr-var-data-in' type='checkbox'><label for='data-14874dea-1242-49fc-b829-5fc30ac2fce9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([1000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3157fba8-f135-462c-afe9-4f9141e43530' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3157fba8-f135-462c-afe9-4f9141e43530' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-564f23a9-3a32-475f-9298-0afc19cd9eb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-564f23a9-3a32-475f-9298-0afc19cd9eb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22eee73d-28a9-4eb7-bfd8-0ee005fc99d2' class='xr-var-data-in' type='checkbox'><label for='data-22eee73d-28a9-4eb7-bfd8-0ee005fc99d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1678258d-8082-4e53-9609-854242a9b781' class='xr-section-summary-in' type='checkbox'  ><label for='section-1678258d-8082-4e53-9609-854242a9b781' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea29ce69-e755-4e60-8e02-404f37c1e872' class='xr-index-data-in' type='checkbox'/><label for='index-ea29ce69-e755-4e60-8e02-404f37c1e872' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([20151201.479166668, 20151202.479166668, 20151203.479166668,\n",
       "       20151204.479166668, 20151205.479166668, 20151206.479166668,\n",
       "       20151207.479166668, 20151208.479166668, 20151209.479166668,\n",
       "       20151210.479166668, 20151211.479166668, 20151212.479166668,\n",
       "       20151213.479166668, 20151214.479166668, 20151215.479166668,\n",
       "       20151216.479166668, 20151217.479166668, 20151218.479166668,\n",
       "       20151219.479166668, 20151220.479166668, 20151221.479166668,\n",
       "       20151222.479166668, 20151223.479166668, 20151224.479166668,\n",
       "       20151225.479166668, 20151226.479166668, 20151227.479166668,\n",
       "       20151228.479166668, 20151229.479166668, 20151230.479166668,\n",
       "       20151231.479166668],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1912e097-68f4-40cf-886a-2ac3eec537b7' class='xr-index-data-in' type='checkbox'/><label for='index-1912e097-68f4-40cf-886a-2ac3eec537b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-30a15b3f-2dbb-4731-a196-b567e84516f3' class='xr-index-data-in' type='checkbox'/><label for='index-30a15b3f-2dbb-4731-a196-b567e84516f3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([59.999969762058264], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcf519c0-4583-4d40-8f0a-470ea7e2b340' class='xr-index-data-in' type='checkbox'/><label for='index-fcf519c0-4583-4d40-8f0a-470ea7e2b340' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c90abf59-7f7d-46a5-b142-a13a95a5ab70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c90abf59-7f7d-46a5-b142-a13a95a5ab70' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.6 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Sat Apr 29 23:45:10 2023: cdo -zonmean -sellevel,1000 -sellonlatbox,0.,359.7,59.75,60.25 /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc /work/bd1022/from_Mistral/bd1022/b381258/ERA5/E5pl00_1D_2015-12_131.nc\n",
       "Sat Apr 29 23:44:52 2023: cdo -s -f nc4 -t ecmwf setgridtype,regular /pool/data/ERA5/E5/pl/an/1D/131/E5pl00_1D_2015-12_131.grb /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.6 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 31, lon: 1, lat: 1, plev: 1)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.015e+07 2.015e+07 ... 2.015e+07 2.015e+07\n",
       "  * lon      (lon) float64 0.0\n",
       "  * lat      (lat) float64 60.0\n",
       "  * plev     (plev) float64 1e+03\n",
       "Data variables:\n",
       "    U        (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 23:45:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ds"
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  {
   "cell_type": "code",
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   "id": "d8fad656-2d08-4fea-9993-5638ac016201",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/work/bd1022/from_Mistral/bd1022/b381258/ERA5/E5pl00_1D_*131.nc\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 13514, lon: 1, lat: 1, plev: 1)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2015-12-31\n",
       "  * lon      (lon) float64 0.0\n",
       "  * lat      (lat) float64 60.0\n",
       "  * plev     (plev) float64 1e+03\n",
       "Data variables:\n",
       "    U        (time, plev, lat, lon) float32 dask.array&lt;chunksize=(31, 1, 1, 1), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0fb89f57-b9e4-482f-9b21-17280b8b0fb0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0fb89f57-b9e4-482f-9b21-17280b8b0fb0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 13514</li><li><span class='xr-has-index'>lon</span>: 1</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>plev</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e7e0ce6b-a97b-4924-b892-5c2cb43f6fb3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7e0ce6b-a97b-4924-b892-5c2cb43f6fb3' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2015-12-31</div><input id='attrs-d319cabf-4e6d-425d-b777-f330d1a7d0da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d319cabf-4e6d-425d-b777-f330d1a7d0da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb06caf8-0e11-4bb1-83f4-21dc7948e29b' class='xr-var-data-in' type='checkbox'><label for='data-eb06caf8-0e11-4bb1-83f4-21dc7948e29b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2015-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-12-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-3ec73366-eff3-4d1f-85c4-48f696d7292f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ec73366-eff3-4d1f-85c4-48f696d7292f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eccb54fe-9b79-44d9-b4b8-eb9bd9e9d6d7' class='xr-var-data-in' type='checkbox'><label for='data-eccb54fe-9b79-44d9-b4b8-eb9bd9e9d6d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-b267ad3d-6b50-4f07-8139-06781cba426a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b267ad3d-6b50-4f07-8139-06781cba426a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a9091b4-4de7-421c-992c-635b45ac5328' class='xr-var-data-in' type='checkbox'><label for='data-4a9091b4-4de7-421c-992c-635b45ac5328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([59.99997])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-b75e5da0-e547-4d07-b841-d5a096180db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b75e5da0-e547-4d07-b841-d5a096180db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6aa0bdb9-c4a3-4278-8a19-9487722a01ee' class='xr-var-data-in' type='checkbox'><label for='data-6aa0bdb9-c4a3-4278-8a19-9487722a01ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([1000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d62e82c3-1f74-43ef-8598-214fd92f8bf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d62e82c3-1f74-43ef-8598-214fd92f8bf0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31, 1, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-0b70b1e1-54cf-4d55-afcc-a7db8b9dc564' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b70b1e1-54cf-4d55-afcc-a7db8b9dc564' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2a10f9b-8b44-4459-8fdb-c3a07354d4f0' class='xr-var-data-in' type='checkbox'><label for='data-f2a10f9b-8b44-4459-8fdb-c3a07354d4f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 52.79 kiB </td>\n",
       "                        <td> 124 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13514, 1, 1, 1) </td>\n",
       "                        <td> (31, 1, 1, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 444 chunks in 889 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "               &#x27;1979-01-05&#x27;, &#x27;1979-01-06&#x27;, &#x27;1979-01-07&#x27;, &#x27;1979-01-08&#x27;,\n",
       "               &#x27;1979-01-09&#x27;, &#x27;1979-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2015-12-22&#x27;, &#x27;2015-12-23&#x27;, &#x27;2015-12-24&#x27;, &#x27;2015-12-25&#x27;,\n",
       "               &#x27;2015-12-26&#x27;, &#x27;2015-12-27&#x27;, &#x27;2015-12-28&#x27;, &#x27;2015-12-29&#x27;,\n",
       "               &#x27;2015-12-30&#x27;, &#x27;2015-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=13514, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea33b783-2fc3-44a7-8656-985fc79ab2d6' class='xr-index-data-in' type='checkbox'/><label for='index-ea33b783-2fc3-44a7-8656-985fc79ab2d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-581ea393-eeb6-4fd1-b723-ddcdbd1602e4' class='xr-index-data-in' type='checkbox'/><label for='index-581ea393-eeb6-4fd1-b723-ddcdbd1602e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([59.999969762058264], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c894cca8-0336-47a8-90bc-d9ea9dbc5dc0' class='xr-index-data-in' type='checkbox'/><label for='index-c894cca8-0336-47a8-90bc-d9ea9dbc5dc0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9367c136-eca2-418d-ac08-aa12ba02af29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9367c136-eca2-418d-ac08-aa12ba02af29' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.6 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sellonlatbox,0.,359.7,59.75,60.25 /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc /work/bd1022/from_Mistral/bd1022/b381258/ERA5/E5pl00_1D_1979-01_131.nc\n",
       "Sat Apr 29 21:20:52 2023: cdo -s -f nc4 -t ecmwf setgridtype,regular /pool/data/ERA5/E5/pl/an/1D/131/E5pl00_1D_1979-01_131.grb /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.6 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 13514, lon: 1, lat: 1, plev: 1)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2015-12-31\n",
       "  * lon      (lon) float64 0.0\n",
       "  * lat      (lat) float64 60.0\n",
       "  * plev     (plev) float64 1e+03\n",
       "Data variables:\n",
       "    U        (time, plev, lat, lon) float32 dask.array<chunksize=(31, 1, 1, 1), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_dir=''\n",
    "variable=131\n",
    "infiles = f'{root_path}{sub_dir}E5pl00_1D_*{variable}.nc'\n",
    "print(infiles)\n",
    "\n",
    "kwargs = dict(decode_times = False) \n",
    "\n",
    "ds = xr.open_mfdataset(\n",
    "    infiles, \n",
    "    parallel = True,\n",
    "    concat_dim = 'time',\n",
    "    combine='nested',\n",
    "    data_vars='minimal', coords='minimal', \n",
    "    **kwargs \n",
    "    #chunks = {\"time\": 10}\n",
    ")#.chunk({'time': 30})\n",
    "ds['time'] = pd.date_range('1979-01-01', '2015-12-31', freq = '1D')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35d602e3-1403-4b14-86c9-31d85df44feb",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 6525)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-12-31\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Data variables:\n",
       "    U        (time) float32 51.74 51.39 51.69 52.7 ... 37.39 35.08 31.1 23.28\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-36f35332-7489-4304-863a-0834f2757bed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36f35332-7489-4304-863a-0834f2757bed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6525</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1cecafa0-36d7-43cf-aa90-8b7acabe44c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1cecafa0-36d7-43cf-aa90-8b7acabe44c8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2014-12-31</div><input id='attrs-11191f91-de77-4e3b-8e64-b8c90cec68b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11191f91-de77-4e3b-8e64-b8c90cec68b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ca7dabc-63b7-4e1c-b4bd-e6644e42e293' class='xr-var-data-in' type='checkbox'><label for='data-3ca7dabc-63b7-4e1c-b4bd-e6644e42e293' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2014-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-30T00:00:00.000000000&#x27;, &#x27;2014-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-27285203-2a62-4a55-b9c8-eb3e549b303e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27285203-2a62-4a55-b9c8-eb3e549b303e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e466231f-24df-4e1f-86b3-d231254af111' class='xr-var-data-in' type='checkbox'><label for='data-e466231f-24df-4e1f-86b3-d231254af111' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-16ae9afa-f895-433c-a54c-527a6dfa44fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16ae9afa-f895-433c-a54c-527a6dfa44fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9aa196c-ccc6-4bff-8b38-bdc2fa628212' class='xr-var-data-in' type='checkbox'><label for='data-b9aa196c-ccc6-4bff-8b38-bdc2fa628212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(59.99996976)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-4b7d7cc8-2407-43f1-a757-b3bfb3b15e4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b7d7cc8-2407-43f1-a757-b3bfb3b15e4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95848d25-9e0b-4a91-9db7-a083f298858e' class='xr-var-data-in' type='checkbox'><label for='data-95848d25-9e0b-4a91-9db7-a083f298858e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(1000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d9b696f-715c-48b6-a10f-e0082941c724' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d9b696f-715c-48b6-a10f-e0082941c724' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>51.74 51.39 51.69 ... 31.1 23.28</div><input id='attrs-1393ff92-f2df-47f7-80c8-7501c368a401' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1393ff92-f2df-47f7-80c8-7501c368a401' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b75d0d47-2b7c-47c7-9ab3-d32e38bd3143' class='xr-var-data-in' type='checkbox'><label for='data-b75d0d47-2b7c-47c7-9ab3-d32e38bd3143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div><div class='xr-var-data'><pre>array([51.743317, 51.39232 , 51.69148 , ..., 35.079605, 31.104052,\n",
       "       23.280987], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-33ed26a3-13a2-43fc-895d-2b4239d4c697' class='xr-section-summary-in' type='checkbox'  ><label for='section-33ed26a3-13a2-43fc-895d-2b4239d4c697' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b4a77d59-4718-4220-93b3-4bb87ca04692' class='xr-index-data-in' type='checkbox'/><label for='index-b4a77d59-4718-4220-93b3-4bb87ca04692' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1979-01-01&#x27;, &#x27;1979-01-02&#x27;, &#x27;1979-01-03&#x27;, &#x27;1979-01-04&#x27;,\n",
       "               &#x27;1979-01-05&#x27;, &#x27;1979-01-06&#x27;, &#x27;1979-01-07&#x27;, &#x27;1979-01-08&#x27;,\n",
       "               &#x27;1979-01-09&#x27;, &#x27;1979-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;, &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;,\n",
       "               &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;, &#x27;2014-12-29&#x27;,\n",
       "               &#x27;2014-12-30&#x27;, &#x27;2014-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6525, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b521977-28a7-466a-af34-7f427eb03b2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b521977-28a7-466a-af34-7f427eb03b2e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.6 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sellonlatbox,0.,359.7,59.75,60.25 /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc /work/bd1022/from_Mistral/bd1022/b381258/ERA5/E5pl00_1D_1979-01_131.nc\n",
       "Sat Apr 29 21:20:52 2023: cdo -s -f nc4 -t ecmwf setgridtype,regular /pool/data/ERA5/E5/pl/an/1D/131/E5pl00_1D_1979-01_131.grb /work/bd1022/from_Mistral/bd1022/b381258/ERA5/temp.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.6 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 6525)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-12-31\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Data variables:\n",
       "    U        (time) float32 51.74 51.39 51.69 52.7 ... 37.39 35.08 31.1 23.28\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.6 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Sat Apr 29 21:21:10 2023: cdo -zonmean -sellevel,1000 -sell...\n",
       "    CDO:          Climate Data Operators version 2.0.6 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winter_month_ls = [11,12,1,2,3,4]\n",
    "\n",
    "ds_sel = ds.sel(\n",
    "    time = np.isin(\n",
    "        ds.time.dt.month, \n",
    "        winter_month_ls\n",
    "    )\n",
    ").squeeze().sel(time = slice(None,'2014')).load()\n",
    "ds_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04d15541-f0a5-4d63-8d92-196bd2780327",
   "metadata": {},
   "outputs": [
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       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (time: 6525)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 1979-01-01 ... 2014-12-31\n",
       "    plev                float64 ...\n",
       "Data variables:\n",
       "    angle               (time) float64 ...\n",
       "    aspect_ratio        (time) float64 ...\n",
       "    objective_area      (time) float64 ...\n",
       "    kurtosis            (time) float64 ...\n",
       "    centroid_latitude   (time) float64 ...\n",
       "    centroid_longitude  (time) float64 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-995101fb-6a98-459d-b2d3-14883008ef3b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-995101fb-6a98-459d-b2d3-14883008ef3b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6525</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ea58f3a-e9b9-4b25-a259-8acb25b44c29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ea58f3a-e9b9-4b25-a259-8acb25b44c29' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2014-12-31</div><input id='attrs-5bc60c34-338c-47f5-b8e7-bfbfa0c6d355' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bc60c34-338c-47f5-b8e7-bfbfa0c6d355' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21aa51c8-84e0-45db-96e3-5bc865851379' class='xr-var-data-in' type='checkbox'><label for='data-21aa51c8-84e0-45db-96e3-5bc865851379' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2014-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-30T00:00:00.000000000&#x27;, &#x27;2014-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0268eda5-3082-4b0a-9a5f-fa5daa6ad17a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0268eda5-3082-4b0a-9a5f-fa5daa6ad17a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01dbcb2c-aa7f-4ac7-8700-634f0f9f749f' class='xr-var-data-in' type='checkbox'><label for='data-01dbcb2c-aa7f-4ac7-8700-634f0f9f749f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-affe4db9-4dd8-4df7-a8dc-f1eb4ac494b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-affe4db9-4dd8-4df7-a8dc-f1eb4ac494b1' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d2cb408-8527-4401-9ef0-6e690b6b3ff1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d2cb408-8527-4401-9ef0-6e690b6b3ff1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77dc8da-6e00-442e-aef4-4ae52db40f74' class='xr-var-data-in' type='checkbox'><label for='data-d77dc8da-6e00-442e-aef4-4ae52db40f74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aspect_ratio</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6d267af-16f3-4b20-9f63-21c8f4d01934' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6d267af-16f3-4b20-9f63-21c8f4d01934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4bc298a-c01d-4851-841a-c4b912a51c96' class='xr-var-data-in' type='checkbox'><label for='data-f4bc298a-c01d-4851-841a-c4b912a51c96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>objective_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-736625bc-31d8-4752-90a1-d2cdd8751e03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-736625bc-31d8-4752-90a1-d2cdd8751e03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c3db17b-aae2-468c-bc23-0acb89ae35cc' class='xr-var-data-in' type='checkbox'><label for='data-4c3db17b-aae2-468c-bc23-0acb89ae35cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kurtosis</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-125a52d7-c644-4140-9036-c2a11247bdb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-125a52d7-c644-4140-9036-c2a11247bdb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55718649-f5af-4547-bfbf-06944ba9e2a8' class='xr-var-data-in' type='checkbox'><label for='data-55718649-f5af-4547-bfbf-06944ba9e2a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d247c9e2-0e79-4793-8c6e-ff944ad2010f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d247c9e2-0e79-4793-8c6e-ff944ad2010f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31c75b96-331c-4b6f-9652-cca884ad74c1' class='xr-var-data-in' type='checkbox'><label for='data-31c75b96-331c-4b6f-9652-cca884ad74c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f21fc9fb-024a-431d-a148-42a99fd3d5b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f21fc9fb-024a-431d-a148-42a99fd3d5b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9dd9a7c-f681-456a-b226-815d6002d482' class='xr-var-data-in' type='checkbox'><label for='data-c9dd9a7c-f681-456a-b226-815d6002d482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[6525 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af29b28a-27c4-4d20-80ea-72e1b0bd4419' class='xr-section-summary-in' type='checkbox'  ><label for='section-af29b28a-27c4-4d20-80ea-72e1b0bd4419' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cbeacfe9-f459-43f9-ba70-b66a4e91bca4' class='xr-index-data-in' type='checkbox'/><label for='index-cbeacfe9-f459-43f9-ba70-b66a4e91bca4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1979-01-01&#x27;, &#x27;1979-01-02&#x27;, &#x27;1979-01-03&#x27;, &#x27;1979-01-04&#x27;,\n",
       "               &#x27;1979-01-05&#x27;, &#x27;1979-01-06&#x27;, &#x27;1979-01-07&#x27;, &#x27;1979-01-08&#x27;,\n",
       "               &#x27;1979-01-09&#x27;, &#x27;1979-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;, &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;,\n",
       "               &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;, &#x27;2014-12-29&#x27;,\n",
       "               &#x27;2014-12-30&#x27;, &#x27;2014-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6525, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8b0b72f-0a08-4243-abc1-77df3f582e91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b8b0b72f-0a08-4243-abc1-77df3f582e91' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (time: 6525)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 1979-01-01 ... 2014-12-31\n",
       "    plev                float64 ...\n",
       "Data variables:\n",
       "    angle               (time) float64 ...\n",
       "    aspect_ratio        (time) float64 ...\n",
       "    objective_area      (time) float64 ...\n",
       "    kurtosis            (time) float64 ...\n",
       "    centroid_latitude   (time) float64 ...\n",
       "    centroid_longitude  (time) float64 ..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filename2 = f'{root_path}{sub_dir}ERA5_moments@10and50and100hPa_NHonly.nc'\n",
    "filename2 = f'/work/bd1022/b381258/ERA5/ERA5_moments@10hPa_NHonly_includingApril.nc'\n",
    "moments = xr.open_dataset(filename2).sel(time = slice(None,'2014'))#.sel(plev = 1000)\n",
    "#moments = moments.convert_calendar('noleap')\n",
    "moments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0af253c1-a9ec-40b5-ba2a-f264c3da2a9c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "30+31+31+28+31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4faee0e3-d1ae-48f1-8656-d277b55a8b99",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (time: 29)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 2000-02-01 ... 2000-02-29\n",
       "    plev                float64 ...\n",
       "Data variables:\n",
       "    angle               (time) float64 ...\n",
       "    aspect_ratio        (time) float64 ...\n",
       "    objective_area      (time) float64 ...\n",
       "    kurtosis            (time) float64 ...\n",
       "    centroid_latitude   (time) float64 ...\n",
       "    centroid_longitude  (time) float64 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-71a5e7dc-9c80-4264-8807-4e8a2e7320a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71a5e7dc-9c80-4264-8807-4e8a2e7320a9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 29</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5f9aaa10-e3cd-478e-aa2f-49af8c2e830e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f9aaa10-e3cd-478e-aa2f-49af8c2e830e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-02-01 ... 2000-02-29</div><input id='attrs-fdc78ee7-28b1-4ded-8368-fb16f0a1fa6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdc78ee7-28b1-4ded-8368-fb16f0a1fa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287e34e3-7a5b-4425-b231-227d863a9aae' class='xr-var-data-in' type='checkbox'><label for='data-287e34e3-7a5b-4425-b231-227d863a9aae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-02-01T00:00:00.000000000&#x27;, &#x27;2000-02-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-03T00:00:00.000000000&#x27;, &#x27;2000-02-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-05T00:00:00.000000000&#x27;, &#x27;2000-02-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-07T00:00:00.000000000&#x27;, &#x27;2000-02-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-09T00:00:00.000000000&#x27;, &#x27;2000-02-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-11T00:00:00.000000000&#x27;, &#x27;2000-02-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-13T00:00:00.000000000&#x27;, &#x27;2000-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-15T00:00:00.000000000&#x27;, &#x27;2000-02-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-17T00:00:00.000000000&#x27;, &#x27;2000-02-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-19T00:00:00.000000000&#x27;, &#x27;2000-02-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-21T00:00:00.000000000&#x27;, &#x27;2000-02-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-23T00:00:00.000000000&#x27;, &#x27;2000-02-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-25T00:00:00.000000000&#x27;, &#x27;2000-02-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-27T00:00:00.000000000&#x27;, &#x27;2000-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-02-29T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-00e5009a-94ac-40aa-99b3-3560eea6751d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00e5009a-94ac-40aa-99b3-3560eea6751d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a49f5db1-3031-42c4-baba-24f95d55b782' class='xr-var-data-in' type='checkbox'><label for='data-a49f5db1-3031-42c4-baba-24f95d55b782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f798a607-eea1-48b3-a95a-be7f276fa5e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f798a607-eea1-48b3-a95a-be7f276fa5e3' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b097aa36-edfa-44e2-b969-f6702a4eea3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b097aa36-edfa-44e2-b969-f6702a4eea3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96ef9aa7-057c-40f3-99d5-681bc97f90b0' class='xr-var-data-in' type='checkbox'><label for='data-96ef9aa7-057c-40f3-99d5-681bc97f90b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aspect_ratio</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65866ac9-c8da-42c7-b3ea-2ae98deac2d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65866ac9-c8da-42c7-b3ea-2ae98deac2d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a6a556-73fa-45b0-b544-888a6b0bcbd5' class='xr-var-data-in' type='checkbox'><label for='data-b5a6a556-73fa-45b0-b544-888a6b0bcbd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>objective_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48bcc5a3-8c2d-4a59-aed4-6b9860672bc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48bcc5a3-8c2d-4a59-aed4-6b9860672bc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1845c8e3-dd5f-4add-9e17-24f2c31a6af5' class='xr-var-data-in' type='checkbox'><label for='data-1845c8e3-dd5f-4add-9e17-24f2c31a6af5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kurtosis</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a9f4781-f010-46b2-9fb9-0065decc82a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a9f4781-f010-46b2-9fb9-0065decc82a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a54c3ad7-f360-410a-8706-88b3d7fbd5dd' class='xr-var-data-in' type='checkbox'><label for='data-a54c3ad7-f360-410a-8706-88b3d7fbd5dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-76f3dd05-d8fa-49f6-930a-f7c89ccd6119' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76f3dd05-d8fa-49f6-930a-f7c89ccd6119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4406e3b9-e481-42ac-9cad-2dfef11849ed' class='xr-var-data-in' type='checkbox'><label for='data-4406e3b9-e481-42ac-9cad-2dfef11849ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f582adf5-e709-4393-8d52-5b45b1dc523d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f582adf5-e709-4393-8d52-5b45b1dc523d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a785fd4a-f3ab-436d-ad29-7e40fcf35cce' class='xr-var-data-in' type='checkbox'><label for='data-a785fd4a-f3ab-436d-ad29-7e40fcf35cce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[29 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a2c0946-b8f0-482b-a74c-696226eb2993' class='xr-section-summary-in' type='checkbox'  ><label for='section-9a2c0946-b8f0-482b-a74c-696226eb2993' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9aac85da-31c2-4127-869b-412cf71217e4' class='xr-index-data-in' type='checkbox'/><label for='index-9aac85da-31c2-4127-869b-412cf71217e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-02-01&#x27;, &#x27;2000-02-02&#x27;, &#x27;2000-02-03&#x27;, &#x27;2000-02-04&#x27;,\n",
       "               &#x27;2000-02-05&#x27;, &#x27;2000-02-06&#x27;, &#x27;2000-02-07&#x27;, &#x27;2000-02-08&#x27;,\n",
       "               &#x27;2000-02-09&#x27;, &#x27;2000-02-10&#x27;, &#x27;2000-02-11&#x27;, &#x27;2000-02-12&#x27;,\n",
       "               &#x27;2000-02-13&#x27;, &#x27;2000-02-14&#x27;, &#x27;2000-02-15&#x27;, &#x27;2000-02-16&#x27;,\n",
       "               &#x27;2000-02-17&#x27;, &#x27;2000-02-18&#x27;, &#x27;2000-02-19&#x27;, &#x27;2000-02-20&#x27;,\n",
       "               &#x27;2000-02-21&#x27;, &#x27;2000-02-22&#x27;, &#x27;2000-02-23&#x27;, &#x27;2000-02-24&#x27;,\n",
       "               &#x27;2000-02-25&#x27;, &#x27;2000-02-26&#x27;, &#x27;2000-02-27&#x27;, &#x27;2000-02-28&#x27;,\n",
       "               &#x27;2000-02-29&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1990e38a-5ac1-44d4-95e8-058783dc525a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1990e38a-5ac1-44d4-95e8-058783dc525a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (time: 29)\n",
       "Coordinates:\n",
       "  * time                (time) datetime64[ns] 2000-02-01 ... 2000-02-29\n",
       "    plev                float64 ...\n",
       "Data variables:\n",
       "    angle               (time) float64 ...\n",
       "    aspect_ratio        (time) float64 ...\n",
       "    objective_area      (time) float64 ...\n",
       "    kurtosis            (time) float64 ...\n",
       "    centroid_latitude   (time) float64 ...\n",
       "    centroid_longitude  (time) float64 ..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moments.sel(time = '2000-02')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "05241cf6-0b67-4a2c-8adc-a94fbb075972",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7ffe957b62f0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_sel = pd.to_datetime('1985-03-24')\n",
    "td = pd.Timedelta('40D')\n",
    "date_start = date_sel -td\n",
    "date_end = date_sel+td\n",
    "moments[['centroid_latitude','aspect_ratio']].sel(time = slice(date_start,date_end)).to_array().plot(x = 'time', row = 'variable', sharey = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "db330831-caa4-40f9-9a36-b6cc7fb3171d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffe95b9e5f0>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_sel['U'].sel(time = slice(date_start,date_end)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c9c5109-ff84-4f08-981d-ef9d717d3aa8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       ".xr-text-repr-fallback {\n",
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       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;U&#x27; (time: 151)&gt;\n",
       "dask.array&lt;getitem, shape=(151,), dtype=float32, chunksize=(31,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2014-01-01 2014-01-02 ... 2014-12-31\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'U'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bbd1c1af-8d98-43a7-be3f-2830ec9ca310' class='xr-array-in' type='checkbox' checked><label for='section-bbd1c1af-8d98-43a7-be3f-2830ec9ca310' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 604 B </td>\n",
       "                        <td> 124 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (151,) </td>\n",
       "                        <td> (31,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 893 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
       "  <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"25\" />\n",
       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >151</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-602fd56e-db70-45a6-b0c8-9735d6328752' class='xr-section-summary-in' type='checkbox'  checked><label for='section-602fd56e-db70-45a6-b0c8-9735d6328752' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2014-01-01 ... 2014-12-31</div><input id='attrs-724ab7ed-6d57-475f-b01f-dba45c6cbb28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-724ab7ed-6d57-475f-b01f-dba45c6cbb28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a4f87e-5b0e-4329-b4b7-342a7794ea24' class='xr-var-data-in' type='checkbox'><label for='data-44a4f87e-5b0e-4329-b4b7-342a7794ea24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-02T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2014-12-17T00:00:00.000000000&#x27;, &#x27;2014-12-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-19T00:00:00.000000000&#x27;, &#x27;2014-12-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-21T00:00:00.000000000&#x27;, &#x27;2014-12-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-23T00:00:00.000000000&#x27;, &#x27;2014-12-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-25T00:00:00.000000000&#x27;, &#x27;2014-12-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-27T00:00:00.000000000&#x27;, &#x27;2014-12-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-29T00:00:00.000000000&#x27;, &#x27;2014-12-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-eea3b830-92f0-42e1-8312-de3139860543' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eea3b830-92f0-42e1-8312-de3139860543' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ebf4227-b4f8-4ccc-9f40-c67db7d63666' class='xr-var-data-in' type='checkbox'><label for='data-7ebf4227-b4f8-4ccc-9f40-c67db7d63666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-f4912db9-f7fb-4769-b1bc-87923b5ea0be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4912db9-f7fb-4769-b1bc-87923b5ea0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe78b749-99c9-43f0-8bf4-91278e0586d6' class='xr-var-data-in' type='checkbox'><label for='data-fe78b749-99c9-43f0-8bf4-91278e0586d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(59.99996976)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-5c9e6680-53ad-4423-8b59-e1afdc82b683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c9e6680-53ad-4423-8b59-e1afdc82b683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4e6f0bf-9a3b-4ed9-a79b-72fcb857e629' class='xr-var-data-in' type='checkbox'><label for='data-d4e6f0bf-9a3b-4ed9-a79b-72fcb857e629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(1000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf2dd0bd-8c44-45ab-a0bb-1169c567716c' class='xr-section-summary-in' type='checkbox'  ><label for='section-bf2dd0bd-8c44-45ab-a0bb-1169c567716c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-993ab873-fb13-4e68-9b9b-8f7839871ac0' class='xr-index-data-in' type='checkbox'/><label for='index-993ab873-fb13-4e68-9b9b-8f7839871ac0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2014-01-01&#x27;, &#x27;2014-01-02&#x27;, &#x27;2014-01-03&#x27;, &#x27;2014-01-04&#x27;,\n",
       "               &#x27;2014-01-05&#x27;, &#x27;2014-01-06&#x27;, &#x27;2014-01-07&#x27;, &#x27;2014-01-08&#x27;,\n",
       "               &#x27;2014-01-09&#x27;, &#x27;2014-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;, &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;,\n",
       "               &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;, &#x27;2014-12-29&#x27;,\n",
       "               &#x27;2014-12-30&#x27;, &#x27;2014-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80a44def-074e-4e0d-b5d2-27d837cbf6c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80a44def-074e-4e0d-b5d2-27d837cbf6c3' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'U' (time: 151)>\n",
       "dask.array<getitem, shape=(151,), dtype=float32, chunksize=(31,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2014-01-01 2014-01-02 ... 2014-12-31\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_sel"
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  },
  {
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       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;U&#x27; (time: 151)&gt;\n",
       "dask.array&lt;concatenate, shape=(151,), dtype=float32, chunksize=(31,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2012-12-01 2012-12-02 ... 2013-04-30\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'U'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-18b85cae-0bff-49f4-95c1-ce256dde72e0' class='xr-array-in' type='checkbox' checked><label for='section-18b85cae-0bff-49f4-95c1-ce256dde72e0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(31,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 604 B </td>\n",
       "                        <td> 124 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (151,) </td>\n",
       "                        <td> (31,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 895 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >151</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-a47959d3-1105-4ff2-8f14-0468181b1bc2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a47959d3-1105-4ff2-8f14-0468181b1bc2' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-12-01 ... 2013-04-30</div><input id='attrs-4a952f6a-7e7f-4f00-8110-783d58cee56a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a952f6a-7e7f-4f00-8110-783d58cee56a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cd6d350-b44f-43d1-ad05-9abb614e170d' class='xr-var-data-in' type='checkbox'><label for='data-4cd6d350-b44f-43d1-ad05-9abb614e170d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2012-12-02T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2013-01-06T00:00:00.000000000&#x27;, &#x27;2013-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-08T00:00:00.000000000&#x27;, &#x27;2013-01-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-10T00:00:00.000000000&#x27;, &#x27;2013-01-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-12T00:00:00.000000000&#x27;, &#x27;2013-01-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-14T00:00:00.000000000&#x27;, &#x27;2013-01-15T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2013-01-18T00:00:00.000000000&#x27;, &#x27;2013-01-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-20T00:00:00.000000000&#x27;, &#x27;2013-01-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-22T00:00:00.000000000&#x27;, &#x27;2013-01-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-24T00:00:00.000000000&#x27;, &#x27;2013-01-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-26T00:00:00.000000000&#x27;, &#x27;2013-01-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-28T00:00:00.000000000&#x27;, &#x27;2013-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-30T00:00:00.000000000&#x27;, &#x27;2013-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-02-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-03T00:00:00.000000000&#x27;, &#x27;2013-02-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-05T00:00:00.000000000&#x27;, &#x27;2013-02-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-07T00:00:00.000000000&#x27;, &#x27;2013-02-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-09T00:00:00.000000000&#x27;, &#x27;2013-02-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-11T00:00:00.000000000&#x27;, &#x27;2013-02-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-13T00:00:00.000000000&#x27;, &#x27;2013-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-15T00:00:00.000000000&#x27;, &#x27;2013-02-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-17T00:00:00.000000000&#x27;, &#x27;2013-02-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-19T00:00:00.000000000&#x27;, &#x27;2013-02-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-21T00:00:00.000000000&#x27;, &#x27;2013-02-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-23T00:00:00.000000000&#x27;, &#x27;2013-02-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-25T00:00:00.000000000&#x27;, &#x27;2013-02-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-27T00:00:00.000000000&#x27;, &#x27;2013-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-01T00:00:00.000000000&#x27;, &#x27;2013-03-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-03T00:00:00.000000000&#x27;, &#x27;2013-03-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-05T00:00:00.000000000&#x27;, &#x27;2013-03-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-07T00:00:00.000000000&#x27;, &#x27;2013-03-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-09T00:00:00.000000000&#x27;, &#x27;2013-03-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-11T00:00:00.000000000&#x27;, &#x27;2013-03-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-13T00:00:00.000000000&#x27;, &#x27;2013-03-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-15T00:00:00.000000000&#x27;, &#x27;2013-03-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-17T00:00:00.000000000&#x27;, &#x27;2013-03-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-19T00:00:00.000000000&#x27;, &#x27;2013-03-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-21T00:00:00.000000000&#x27;, &#x27;2013-03-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-23T00:00:00.000000000&#x27;, &#x27;2013-03-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-25T00:00:00.000000000&#x27;, &#x27;2013-03-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-27T00:00:00.000000000&#x27;, &#x27;2013-03-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-29T00:00:00.000000000&#x27;, &#x27;2013-03-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-03-31T00:00:00.000000000&#x27;, &#x27;2013-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-02T00:00:00.000000000&#x27;, &#x27;2013-04-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-04T00:00:00.000000000&#x27;, &#x27;2013-04-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-06T00:00:00.000000000&#x27;, &#x27;2013-04-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-08T00:00:00.000000000&#x27;, &#x27;2013-04-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-10T00:00:00.000000000&#x27;, &#x27;2013-04-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-12T00:00:00.000000000&#x27;, &#x27;2013-04-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-14T00:00:00.000000000&#x27;, &#x27;2013-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-16T00:00:00.000000000&#x27;, &#x27;2013-04-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-18T00:00:00.000000000&#x27;, &#x27;2013-04-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-20T00:00:00.000000000&#x27;, &#x27;2013-04-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-22T00:00:00.000000000&#x27;, &#x27;2013-04-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-24T00:00:00.000000000&#x27;, &#x27;2013-04-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-26T00:00:00.000000000&#x27;, &#x27;2013-04-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-28T00:00:00.000000000&#x27;, &#x27;2013-04-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-30T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-9a441b25-4ae1-42ff-a26c-4aecc240da4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a441b25-4ae1-42ff-a26c-4aecc240da4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e97c0f18-ebfe-4df0-bf7a-7d04fbf5bc35' class='xr-var-data-in' type='checkbox'><label for='data-e97c0f18-ebfe-4df0-bf7a-7d04fbf5bc35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-934fd588-0561-4ece-b692-59fd07f53337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-934fd588-0561-4ece-b692-59fd07f53337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-626056ba-77d2-45f5-b1af-3b5bae0316a7' class='xr-var-data-in' type='checkbox'><label for='data-626056ba-77d2-45f5-b1af-3b5bae0316a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(59.99996976)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-eff7e0d0-3536-437e-9789-f0ddeeee61da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eff7e0d0-3536-437e-9789-f0ddeeee61da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84679fee-e010-4de3-a649-a7f996be7770' class='xr-var-data-in' type='checkbox'><label for='data-84679fee-e010-4de3-a649-a7f996be7770' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(1000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d941d6b7-446d-45c4-bc66-edb89a75f136' class='xr-section-summary-in' type='checkbox'  ><label for='section-d941d6b7-446d-45c4-bc66-edb89a75f136' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2593f047-bf1d-4b60-bcbc-9c9418f1e539' class='xr-index-data-in' type='checkbox'/><label for='index-2593f047-bf1d-4b60-bcbc-9c9418f1e539' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2012-12-01&#x27;, &#x27;2012-12-02&#x27;, &#x27;2012-12-03&#x27;, &#x27;2012-12-04&#x27;,\n",
       "               &#x27;2012-12-05&#x27;, &#x27;2012-12-06&#x27;, &#x27;2012-12-07&#x27;, &#x27;2012-12-08&#x27;,\n",
       "               &#x27;2012-12-09&#x27;, &#x27;2012-12-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2013-04-21&#x27;, &#x27;2013-04-22&#x27;, &#x27;2013-04-23&#x27;, &#x27;2013-04-24&#x27;,\n",
       "               &#x27;2013-04-25&#x27;, &#x27;2013-04-26&#x27;, &#x27;2013-04-27&#x27;, &#x27;2013-04-28&#x27;,\n",
       "               &#x27;2013-04-29&#x27;, &#x27;2013-04-30&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c64765d-54cb-4b9f-bf53-8faeafed61c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c64765d-54cb-4b9f-bf53-8faeafed61c8' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'U' (time: 151)>\n",
       "dask.array<concatenate, shape=(151,), dtype=float32, chunksize=(31,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2012-12-01 2012-12-02 ... 2013-04-30\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_sel"
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  },
  {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;U&#x27; (label: 36, time: 5379)&gt;\n",
       "array([[ 51.743317,  51.39232 ,  51.69148 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ..., -10.007022, -10.045717,\n",
       "         -9.552811]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-04-30\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "  * label    (label) &lt;U11 &#x27;NDJFMA_1979&#x27; &#x27;NDJFMA_1980&#x27; ... &#x27;NDJFMA_2014&#x27;\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'U'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 36</li><li><span class='xr-has-index'>time</span>: 5379</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-be9988ed-cfb3-4eed-b598-c5927c0bd9b3' class='xr-array-in' type='checkbox' checked><label for='section-be9988ed-cfb3-4eed-b598-c5927c0bd9b3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>51.74 51.39 51.69 52.7 51.83 ... -10.28 -10.17 -10.01 -10.05 -9.553</span></div><div class='xr-array-data'><pre>array([[ 51.743317,  51.39232 ,  51.69148 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ..., -10.007022, -10.045717,\n",
       "         -9.552811]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e8fa2d2d-b575-42a2-a1f8-a5d8b1cb972a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8fa2d2d-b575-42a2-a1f8-a5d8b1cb972a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2014-04-30</div><input id='attrs-c8e5bdf6-6ce0-4686-b446-913297bc6f1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8e5bdf6-6ce0-4686-b446-913297bc6f1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-779feda8-78ef-4e7c-9e16-40d78c30f0d3' class='xr-var-data-in' type='checkbox'><label for='data-779feda8-78ef-4e7c-9e16-40d78c30f0d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2014-04-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-04-29T00:00:00.000000000&#x27;, &#x27;2014-04-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-9ee9fbd0-f7d3-4e6d-b3cd-131597d3b759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ee9fbd0-f7d3-4e6d-b3cd-131597d3b759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31640e5e-3cb5-4b1e-9bf4-1c808157571a' class='xr-var-data-in' type='checkbox'><label for='data-31640e5e-3cb5-4b1e-9bf4-1c808157571a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-7e6ec7e3-3076-4f02-8dc3-ac09ba4932e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e6ec7e3-3076-4f02-8dc3-ac09ba4932e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7305b0e-6830-4c76-844a-2040d1aa1596' class='xr-var-data-in' type='checkbox'><label for='data-e7305b0e-6830-4c76-844a-2040d1aa1596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(59.99996976)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-9363a098-c6af-4578-aa13-7ed5342993c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9363a098-c6af-4578-aa13-7ed5342993c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-190b8a80-88db-420e-ab8b-d0748e53914d' class='xr-var-data-in' type='checkbox'><label for='data-190b8a80-88db-420e-ab8b-d0748e53914d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(1000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;NDJFMA_1979&#x27; ... &#x27;NDJFMA_2014&#x27;</div><input id='attrs-0c32e7da-a5cc-4b40-9a68-3e00b4cb5b18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c32e7da-a5cc-4b40-9a68-3e00b4cb5b18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51476018-69c5-4114-8af5-948ea8b2f940' class='xr-var-data-in' type='checkbox'><label for='data-51476018-69c5-4114-8af5-948ea8b2f940' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;NDJFMA_1979&#x27;, &#x27;NDJFMA_1980&#x27;, &#x27;NDJFMA_1981&#x27;, &#x27;NDJFMA_1982&#x27;,\n",
       "       &#x27;NDJFMA_1983&#x27;, &#x27;NDJFMA_1984&#x27;, &#x27;NDJFMA_1985&#x27;, &#x27;NDJFMA_1986&#x27;,\n",
       "       &#x27;NDJFMA_1987&#x27;, &#x27;NDJFMA_1988&#x27;, &#x27;NDJFMA_1989&#x27;, &#x27;NDJFMA_1990&#x27;,\n",
       "       &#x27;NDJFMA_1991&#x27;, &#x27;NDJFMA_1992&#x27;, &#x27;NDJFMA_1993&#x27;, &#x27;NDJFMA_1994&#x27;,\n",
       "       &#x27;NDJFMA_1995&#x27;, &#x27;NDJFMA_1996&#x27;, &#x27;NDJFMA_1997&#x27;, &#x27;NDJFMA_1998&#x27;,\n",
       "       &#x27;NDJFMA_1999&#x27;, &#x27;NDJFMA_2000&#x27;, &#x27;NDJFMA_2001&#x27;, &#x27;NDJFMA_2002&#x27;,\n",
       "       &#x27;NDJFMA_2003&#x27;, &#x27;NDJFMA_2004&#x27;, &#x27;NDJFMA_2005&#x27;, &#x27;NDJFMA_2006&#x27;,\n",
       "       &#x27;NDJFMA_2007&#x27;, &#x27;NDJFMA_2008&#x27;, &#x27;NDJFMA_2009&#x27;, &#x27;NDJFMA_2010&#x27;,\n",
       "       &#x27;NDJFMA_2011&#x27;, &#x27;NDJFMA_2012&#x27;, &#x27;NDJFMA_2013&#x27;, &#x27;NDJFMA_2014&#x27;],\n",
       "      dtype=&#x27;&lt;U11&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9f6cbcb-2211-4ec6-9ad3-6d57de64f3b1' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9f6cbcb-2211-4ec6-9ad3-6d57de64f3b1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08cb774c-d5e4-4104-915a-7dc945c59e07' class='xr-index-data-in' type='checkbox'/><label for='index-08cb774c-d5e4-4104-915a-7dc945c59e07' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1979-01-01&#x27;, &#x27;1979-01-02&#x27;, &#x27;1979-01-03&#x27;, &#x27;1979-01-04&#x27;,\n",
       "               &#x27;1979-01-05&#x27;, &#x27;1979-01-06&#x27;, &#x27;1979-01-07&#x27;, &#x27;1979-01-08&#x27;,\n",
       "               &#x27;1979-01-09&#x27;, &#x27;1979-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-04-21&#x27;, &#x27;2014-04-22&#x27;, &#x27;2014-04-23&#x27;, &#x27;2014-04-24&#x27;,\n",
       "               &#x27;2014-04-25&#x27;, &#x27;2014-04-26&#x27;, &#x27;2014-04-27&#x27;, &#x27;2014-04-28&#x27;,\n",
       "               &#x27;2014-04-29&#x27;, &#x27;2014-04-30&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=5379, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0485bba-560d-4da0-a024-1831765ac1cd' class='xr-index-data-in' type='checkbox'/><label for='index-b0485bba-560d-4da0-a024-1831765ac1cd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;NDJFMA_1979&#x27;, &#x27;NDJFMA_1980&#x27;, &#x27;NDJFMA_1981&#x27;, &#x27;NDJFMA_1982&#x27;,\n",
       "       &#x27;NDJFMA_1983&#x27;, &#x27;NDJFMA_1984&#x27;, &#x27;NDJFMA_1985&#x27;, &#x27;NDJFMA_1986&#x27;,\n",
       "       &#x27;NDJFMA_1987&#x27;, &#x27;NDJFMA_1988&#x27;, &#x27;NDJFMA_1989&#x27;, &#x27;NDJFMA_1990&#x27;,\n",
       "       &#x27;NDJFMA_1991&#x27;, &#x27;NDJFMA_1992&#x27;, &#x27;NDJFMA_1993&#x27;, &#x27;NDJFMA_1994&#x27;,\n",
       "       &#x27;NDJFMA_1995&#x27;, &#x27;NDJFMA_1996&#x27;, &#x27;NDJFMA_1997&#x27;, &#x27;NDJFMA_1998&#x27;,\n",
       "       &#x27;NDJFMA_1999&#x27;, &#x27;NDJFMA_2000&#x27;, &#x27;NDJFMA_2001&#x27;, &#x27;NDJFMA_2002&#x27;,\n",
       "       &#x27;NDJFMA_2003&#x27;, &#x27;NDJFMA_2004&#x27;, &#x27;NDJFMA_2005&#x27;, &#x27;NDJFMA_2006&#x27;,\n",
       "       &#x27;NDJFMA_2007&#x27;, &#x27;NDJFMA_2008&#x27;, &#x27;NDJFMA_2009&#x27;, &#x27;NDJFMA_2010&#x27;,\n",
       "       &#x27;NDJFMA_2011&#x27;, &#x27;NDJFMA_2012&#x27;, &#x27;NDJFMA_2013&#x27;, &#x27;NDJFMA_2014&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fc147e3-7f1a-4873-86dc-6e7a383a4b04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fc147e3-7f1a-4873-86dc-6e7a383a4b04' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>code :</span></dt><dd>131</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>320</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'U' (label: 36, time: 5379)>\n",
       "array([[ 51.743317,  51.39232 ,  51.69148 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ..., -10.007022, -10.045717,\n",
       "         -9.552811]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2014-04-30\n",
       "    lon      float64 0.0\n",
       "    lat      float64 60.0\n",
       "    plev     float64 1e+03\n",
       "  * label    (label) <U11 'NDJFMA_1979' 'NDJFMA_1980' ... 'NDJFMA_2014'\n",
       "Attributes:\n",
       "    long_name:         U velocity\n",
       "    units:             m s**-1\n",
       "    code:              131\n",
       "    table:             128\n",
       "    CDI_grid_type:     gaussian\n",
       "    CDI_grid_num_LPE:  320"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = ds_sel['U']\n",
    "\n",
    "temp_sel_ls = []\n",
    "for year in range(1979,2015):\n",
    "    #print(year)\n",
    "    JFMA = temp.sel(time = slice(f'{year}-01',f'{year}-4'))\n",
    "    try:\n",
    "        ND = temp.sel(time = f'{year-1}-11')\n",
    "        temp_sel = xr.concat([ND,JFMA], dim = 'time')\n",
    "    except:\n",
    "        temp_sel = xr.concat([JFMA], dim = 'time')\n",
    "    temp_sel['label'] = f'NDJFMA_{year}'\n",
    "    temp_sel_ls.append(temp_sel)\n",
    "        \n",
    "        \n",
    "temp_sel_all = xr.concat(temp_sel_ls, dim = 'label')    \n",
    "temp_sel_all\n",
    "    \n",
    "    #print(temp_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7828ffa9-6200-4140-ad12-ac992bff0391",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(15.875111, dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_sel_ls[0].mean().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "066b6883-ab54-456b-a14b-662c40c54db3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(15.875111, dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_sel_all.mean('time')[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8ad1995-82c0-43eb-a759-825ffc51dd15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  35],\n",
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       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_sel_all.mean('time').plot()\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb76f7e6-164f-4878-9c3b-cfe0cde61418",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979 120\n",
      "to remove=  []\n",
      "onset dates = ['1979-02-22T00:00:00.000000000']\n",
      "52\n",
      "[1.49774967 1.56507011 1.70444281 1.76719279 1.88457011 2.18835329\n",
      " 2.60775255 2.9444468  2.96638119 2.80251611 2.94875588 3.21317788\n",
      " 3.85224407 5.17138084 4.97973701 3.94692904 3.69822215 1.73598128\n",
      " 1.59323635 1.77405752 2.14866065]\n",
      "[74.71257708 75.38153443 76.8264879  78.51427054 80.03990578 80.49603519\n",
      " 79.87192227 77.20745806 73.04162077 71.73440803 71.93842477 72.97872797\n",
      " 75.90846553 81.63424273 80.54549254 75.29988913 70.12922907 64.30465778\n",
      " 66.59582726 72.5038648  80.63690343]\n",
      "days above aspect threshold= 11\n",
      "days below latcent threshold= 1\n",
      "1980 182\n",
      "to remove=  [1, 2]\n",
      "onset dates = ['1980-02-29T00:00:00.000000000']\n",
      "120\n",
      "[2.17512015 1.82633444 1.63261471 1.50514285 1.37000842 1.30342233\n",
      " 1.36586141 1.42576734 1.31134876 1.10082705 1.28359842 1.78627211\n",
      " 2.02951948 1.75553718 1.57087394 1.40294791 1.39957426 1.48146232\n",
      " 1.63817498 1.65993295 1.55932735]\n",
      "[75.07634476 73.74867533 73.11658462 72.96989265 71.95998002 69.88175576\n",
      " 68.47565641 68.20200467 67.26766912 64.14929818 60.22953732 59.14968769\n",
      " 60.95082382 63.46552154 65.5664518  66.57706839 67.85952067 69.13898247\n",
      " 70.60019466 72.00353523 73.53684646]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 6\n",
      "1981 181\n",
      "to remove=  []\n",
      "onset dates = ['1981-03-04T00:00:00.000000000']\n",
      "123\n",
      "[1.12162171 1.3225121  1.37188329 1.28172635 1.29189019 1.37703614\n",
      " 1.54359155 1.731698   1.83882213 1.92937334 1.91509627 1.72039329\n",
      " 1.60130271 1.57748842 1.55787456 1.26381748 1.12105941 1.03177428\n",
      " 1.12054758 1.83707919 2.85409079]\n",
      "[82.34694251 81.16632278 81.27099787 80.30186212 76.81820663 72.33568364\n",
      " 68.4623276  65.90478383 64.27772685 62.89062753 62.08017165 62.81622313\n",
      " 64.99138825 66.72490354 66.6223773  64.70398241 63.09783607 62.41606465\n",
      " 62.56388445 64.72224548 69.32553458]\n",
      "days above aspect threshold= 1\n",
      "days below latcent threshold= 11\n",
      "1982 181\n",
      "to remove=  []\n",
      "onset dates = ['1981-12-04T00:00:00.000000000']\n",
      "33\n",
      "[1.20121611 1.22146184 1.24787447 1.29531618 1.36762776 1.4450217\n",
      " 1.57523029 1.64024287 1.66214245 1.73024117 1.86045728 2.01033979\n",
      " 1.92897281 1.70835898 1.5616265  1.56744537 1.67952622 1.75575498\n",
      " 1.72264186 1.45660032 1.22703431]\n",
      "[74.25325759 75.0711782  75.92418601 76.78929005 76.74786615 76.09571388\n",
      " 75.31190391 73.62073449 70.90557265 68.37355613 67.11598487 67.16129585\n",
      " 67.41763416 67.72563496 68.69146071 70.10711865 71.64059116 72.72356265\n",
      " 73.28096719 73.14689102 72.84115987]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 0\n",
      "1983 181\n",
      "no events\n",
      "1984 182\n",
      "to remove=  [1]\n",
      "onset dates = ['1984-02-23T00:00:00.000000000']\n",
      "114\n",
      "[1.39660783 1.37398937 1.38697826 1.5176447  1.70465388 1.78044288\n",
      " 1.82899442 1.99018655 2.16291776 2.4464319  2.75145996 3.04871541\n",
      " 3.07573711 2.68684849 2.13145683 1.91556711 1.76291093 1.45241794\n",
      " 1.21461898 1.10869967 1.20827374]\n",
      "[78.20295306 77.07637332 75.86804536 74.34369545 73.26100264 72.9157049\n",
      " 72.21141136 71.31486594 70.20107095 69.37604639 68.26689454 66.78932559\n",
      " 65.40707648 64.89715245 65.25990683 65.66056797 63.6931608  62.200929\n",
      " 61.27886074 59.66953441 59.21725706]\n",
      "days above aspect threshold= 5\n",
      "days below latcent threshold= 9\n",
      "1985 181\n",
      "to remove=  []\n",
      "onset dates = ['1985-01-01T00:00:00.000000000']\n",
      "61\n",
      "[1.98914199 2.05475578 2.2105019  2.4487358  2.7482198  3.13852989\n",
      " 3.3443903  3.12251767 3.11939335 3.15406245 2.83256164 2.67888198\n",
      " 2.70134254 2.90292409 2.7223878  2.43504601 2.09585704 2.47298104\n",
      " 3.10576694 3.45634912 3.55340665]\n",
      "[81.38535765 78.43181829 75.58411562 75.08688234 76.55221142 79.84256802\n",
      " 83.44921392 85.89980422 85.49274959 81.71077071 78.15817634 77.43517227\n",
      " 78.33268606 75.16052177 68.92603691 66.0084247  67.36122691 69.70320485\n",
      " 71.76528911 74.71516986 77.11563296]\n",
      "days above aspect threshold= 17\n",
      "days below latcent threshold= 0\n",
      "1986 181\n",
      "no events\n",
      "1987 181\n",
      "to remove=  [1]\n",
      "onset dates = ['1987-01-23T00:00:00.000000000']\n",
      "83\n",
      "[1.24885427 1.23961044 1.20865132 1.23688537 1.35077452 1.55516919\n",
      " 1.61412538 1.44414514 1.25752708 1.52601015 1.84467762 2.18899995\n",
      " 2.00988285 2.00574638 1.74075112 1.5313865  1.11114463 1.2276904\n",
      " 1.23727753 1.23166837 1.25210826]\n",
      "[72.87755098 72.51903585 71.9816026  71.22035851 69.7207461  67.79370862\n",
      " 66.42056522 65.87521454 65.87284777 64.84730629 63.50040197 61.5528528\n",
      " 58.12414989 56.31118422 57.6121273  59.28850928 60.99704401 60.61921641\n",
      " 59.07052406 58.34854306 59.71378452]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 14\n",
      "1988 182\n",
      "to remove=  [2]\n",
      "onset dates = ['1987-12-08T00:00:00.000000000' '1988-03-14T00:00:00.000000000']\n",
      "37\n",
      "[1.53630154 1.39679584 1.29509544 1.31171523 1.4213852  1.48684036\n",
      " 1.39772835 1.28268537 1.21532701 1.24776705 1.49208708 2.05348331\n",
      " 2.73419438 3.10696777 3.44471396 3.42422351 3.49574441 3.68031298\n",
      " 3.48642295 2.41089763 1.58774485]\n",
      "[72.01549174 69.61185912 68.408279   67.97548953 68.17234014 68.36721723\n",
      " 68.41196913 68.67290065 67.2018583  64.41137715 61.33507383 60.27849202\n",
      " 61.27061934 64.20156609 67.00918149 68.74526468 72.98714482 78.3090931\n",
      " 85.16890761 78.00941216 72.88008226]\n",
      "days above aspect threshold= 8\n",
      "days below latcent threshold= 5\n",
      "134\n",
      "[2.19850367 2.29464327 2.0306344  1.76539161 1.7156651  1.8350276\n",
      " 2.31649325 3.06097836 4.37828021 5.59698387 6.39849613 6.75775499\n",
      " 5.3554343  1.75176161 1.75475257 1.77640852 2.08483743 1.74828919\n",
      " 1.40481051 1.2739294  1.32107726]\n",
      "[78.59790913 81.99328812 83.93540789 83.3410822  80.50362236 77.47780496\n",
      " 75.59550752 73.53800052 71.54688108 71.16500015 70.86756891 73.49477479\n",
      " 80.09690098 85.84742206 87.30463795 85.12142396 84.11416131 81.66015808\n",
      " 78.48286071 77.84864188 77.98991993]\n",
      "days above aspect threshold= 6\n",
      "days below latcent threshold= 0\n",
      "1989 181\n",
      "to remove=  [1]\n",
      "onset dates = ['1989-02-21T00:00:00.000000000']\n",
      "112\n",
      "[2.51309879 2.14387171 1.83690293 1.76690925 1.783694   1.79786474\n",
      " 2.07144774 1.74047145 2.33090779 3.27678093 5.5148413  6.16234136\n",
      " 4.86229432 1.12982198 1.22621674 1.33369939 1.04297372 1.44590403\n",
      " 2.85762436 1.51194301 1.33258868]\n",
      "[76.53560666 73.93130825 73.36249191 72.70380938 71.86366028 71.03607436\n",
      " 67.89337419 61.64071613 64.28062943 71.40781803 83.06890264 84.2079108\n",
      " 69.22643689 60.51817188 60.42266618 62.55541065 67.26749672 72.28908205\n",
      " 77.88210587 80.46701526 77.49920838]\n",
      "days above aspect threshold= 6\n",
      "days below latcent threshold= 5\n",
      "1990 181\n",
      "no events\n",
      "1991 181\n",
      "no events\n",
      "1992 182\n",
      "no events\n",
      "to remove=  []\n",
      "no events\n",
      "no events\n",
      "onset dates = []\n",
      "1993 181\n",
      "no events\n",
      "1994 181\n",
      "no events\n",
      "1995 181\n",
      "no events\n",
      "1996 182\n",
      "no events\n",
      "1997 181\n",
      "no events\n",
      "1998 181\n",
      "to remove=  []\n",
      "onset dates = []\n",
      "1999 181\n",
      "to remove=  []\n",
      "onset dates = ['1998-12-15T00:00:00.000000000' '1999-02-25T00:00:00.000000000']\n",
      "44\n",
      "[1.52701019 1.56024366 1.6248063  1.69039288 1.74271408 1.65844614\n",
      " 1.50234078 1.39960888 1.21095728 1.37097301 1.70767934 1.73527396\n",
      " 1.65429578 1.62631546 1.63158773 1.17450801 1.44212515 2.16463302\n",
      " 2.11094196 2.28633006 2.70012596]\n",
      "[73.43189199 74.21035946 74.85082546 75.421564   76.56128003 76.77217946\n",
      " 75.79315997 74.33581503 71.45997006 68.39719658 64.96681904 61.14394683\n",
      " 57.61591716 55.71936287 56.55704669 59.36349141 68.62774339 82.11468543\n",
      " 86.14800843 85.78358495 85.00332238]\n",
      "days above aspect threshold= 1\n",
      "days below latcent threshold= 6\n",
      "116\n",
      "[1.47697529 1.49294684 1.49903547 1.48068618 1.37541794 1.22532735\n",
      " 1.28663828 1.56217661 1.98087708 2.8566381  3.94800493 4.42164986\n",
      " 4.7078269  5.27782392 5.63298805 5.1295413  4.96366387 5.22778851\n",
      " 4.838644   3.97771486 4.30716651]\n",
      "[76.76070572 76.56817048 76.71173293 76.96723882 76.76634729 76.1183687\n",
      " 75.35199443 74.82309314 73.36759349 72.55957391 72.96122498 74.22248194\n",
      " 76.153114   78.67409    80.42609693 75.56317369 71.20137325 67.57159096\n",
      " 62.09646284 45.82877364 46.38206252]\n",
      "days above aspect threshold= 12\n",
      "days below latcent threshold= 3\n",
      "2000 182\n",
      "to remove=  []\n",
      "onset dates = ['2000-03-20T00:00:00.000000000']\n",
      "140\n",
      "[1.15814939 1.14783378 1.19812034 1.37101096 1.59939663 1.95034686\n",
      " 2.05877942 1.38379003 1.15311752 1.26451385 1.27699913 1.17580637\n",
      " 1.13253008 1.27410782 1.31625836 1.1936845  1.14464746 1.20606841\n",
      " 1.25172749 1.29481039 1.20938258]\n",
      "[70.146869   70.17271746 71.69725558 74.60677527 76.96368556 78.07919096\n",
      " 75.53773138 68.59689089 64.95162967 64.58235435 65.65663185 66.80096394\n",
      " 68.02602536 70.63559903 73.63775566 76.31428008 78.35911496 79.65207188\n",
      " 81.09608988 84.21504896 85.16830526]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 3\n",
      "2001 181\n",
      "to remove=  []\n",
      "onset dates = ['2001-02-11T00:00:00.000000000']\n",
      "102\n",
      "[2.53979665 2.43980184 2.16036263 1.89611399 1.88080891 2.16428473\n",
      " 2.59662167 2.86987909 3.07647343 3.18336377 2.79337972 2.57177272\n",
      " 2.71474052 2.49279081 1.86243438 1.67167297 1.59983026 2.3473497\n",
      " 7.93871103 1.62521682 1.7970152 ]\n",
      "[74.96921266 73.01507896 71.52056235 70.18870159 69.18928448 68.1249933\n",
      " 67.3558166  66.81840031 67.20662816 67.77137157 66.74222829 65.22635241\n",
      " 63.65598316 60.02061094 55.08113026 53.29014534 53.58647893 56.82276391\n",
      " 57.17592977 45.36053955 76.57184874]\n",
      "days above aspect threshold= 11\n",
      "days below latcent threshold= 9\n",
      "2002 181\n",
      "to remove=  []\n",
      "onset dates = ['2001-12-30T00:00:00.000000000' '2002-02-17T00:00:00.000000000']\n",
      "59\n",
      "[1.03740784 1.06698788 1.10930047 1.06372285 1.10520472 1.17426247\n",
      " 1.15823619 1.26526155 1.5002354  1.68087807 1.95714642 2.33884226\n",
      " 2.6107165  2.61352796 2.13201278 1.68977889 1.33689666 1.14273357\n",
      " 1.01910298 1.14929202 1.14676317]\n",
      "[73.31326114 73.39234174 71.62807994 68.97688043 66.90327081 66.17802955\n",
      " 65.95467331 65.58771155 65.29028555 65.06079924 64.83063941 63.90122637\n",
      " 64.42381676 65.42493899 66.04108631 65.97732233 65.79932085 66.13730968\n",
      " 67.56017001 69.56489437 71.19684357]\n",
      "days above aspect threshold= 2\n",
      "days below latcent threshold= 10\n",
      "108\n",
      "[1.15485736 1.12823188 1.1426005  1.18339565 1.25260368 1.26209855\n",
      " 1.28877866 1.39528914 1.62252052 1.82554005 1.91612171 1.93464399\n",
      " 1.90814268 1.83355428 1.60594575 1.22156404 1.14630889 1.25195834\n",
      " 1.29429051 1.28973539 1.29869171]\n",
      "[75.05391159 73.51022294 71.94500969 71.57113327 72.03260691 72.09916606\n",
      " 71.43307916 70.69927686 69.52424813 67.89292878 66.55350036 66.23722176\n",
      " 66.65233466 67.23852624 67.92070916 68.24491776 67.66082241 66.6577791\n",
      " 65.52664936 64.93054639 64.4117121 ]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 3\n",
      "2003 181\n",
      "to remove=  []\n",
      "onset dates = ['2003-01-18T00:00:00.000000000']\n",
      "78\n",
      "[1.73198929 1.67654968 1.66763627 1.67174159 1.7088614  1.49636768\n",
      " 1.25613596 1.34763835 1.85819648 2.73144404 3.30956242 3.33853692\n",
      " 3.15585076 2.93112412 3.21024095 3.27898935 2.81217612 2.03468398\n",
      " 1.95371896 2.70259658 3.35358376]\n",
      "[73.96452329 73.43471636 72.98203326 73.44344872 74.08959823 73.02736319\n",
      " 71.92354036 71.48468844 71.57676693 72.54765517 73.46154237 75.73693535\n",
      " 77.7527301  78.51740173 78.54975842 77.21338715 72.30382372 68.73434689\n",
      " 70.25312363 75.72827035 83.63819609]\n",
      "days above aspect threshold= 10\n",
      "days below latcent threshold= 0\n",
      "2004 182\n",
      "to remove=  []\n",
      "onset dates = ['2004-01-05T00:00:00.000000000']\n",
      "65\n",
      "[1.45000784 1.13879337 1.32535316 1.58146609 1.58891518 1.44565009\n",
      " 1.47088998 1.52479683 1.62547428 1.65806431 1.70637012 1.65595286\n",
      " 1.699409   1.92175523 1.68686746 1.5010897  1.31519379 1.11796715\n",
      " 1.04707059 1.04303089 1.07950524]\n",
      "[70.95193475 69.11708183 67.59474184 67.21758942 68.1675086  68.20833588\n",
      " 66.60797331 63.92441042 61.31887145 59.27675025 58.03215098 57.09344256\n",
      " 55.09704892 53.77850413 53.3382863  54.63446401 57.47513556 59.90630112\n",
      " 60.78734563 61.47161648 62.75474482]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 14\n",
      "2005 181\n",
      "to remove=  []\n",
      "onset dates = []\n",
      "2006 181\n",
      "to remove=  [1]\n",
      "onset dates = ['2006-01-21T00:00:00.000000000']\n",
      "81\n",
      "[1.58332513 1.75724798 1.89718359 2.02920158 2.21224025 2.30252677\n",
      " 2.1420898  2.01163604 1.88493671 1.59332277 1.24972014 1.33846125\n",
      " 1.57415436 1.87276518 2.04849555 2.07258387 2.11644385 2.03650592\n",
      " 2.0131035  1.85091873 1.80991199]\n",
      "[68.8099806  69.12960851 69.48673588 69.13684876 68.39678646 67.13960965\n",
      " 65.60722787 64.49323151 62.44239    61.48216535 59.4033129  57.58934677\n",
      " 56.37677214 55.47706124 54.79218019 54.30053025 55.73176418 58.10372643\n",
      " 59.32483324 58.51911036 59.40980281]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 15\n",
      "2007 181\n",
      "to remove=  []\n",
      "onset dates = ['2007-02-24T00:00:00.000000000']\n",
      "115\n",
      "[1.76209622 1.86692068 2.07527349 2.21509192 2.30864019 2.18519199\n",
      " 1.99430838 1.84540917 1.73272533 1.7435371  1.93211331 2.2221505\n",
      " 2.41663031 2.24451271 1.75171622 1.32461487 1.41684835 1.73716465\n",
      " 2.21926077 2.1651686  1.6071913 ]\n",
      "[79.16586155 82.15276547 84.80229965 86.13376866 83.45622445 79.84239918\n",
      " 76.69162639 72.50043755 68.69464487 65.99528208 64.60998817 64.14340773\n",
      " 64.35390466 65.08471325 67.22993154 70.77812012 73.93328866 74.99560144\n",
      " 74.8954241  75.42094373 74.89609792]\n",
      "days above aspect threshold= 1\n",
      "days below latcent threshold= 5\n",
      "2008 182\n",
      "to remove=  [1, 2]\n",
      "onset dates = ['2008-02-22T00:00:00.000000000']\n",
      "113\n",
      "[1.44343957 1.61939568 1.65922173 1.87074991 1.95378527 1.74662768\n",
      " 1.59137208 1.47228155 1.34331194 1.20977401 1.4164234  1.77610606\n",
      " 1.71617138 1.77330649 1.70213709 1.28723113 1.28960087 1.25130807\n",
      " 1.09286389 1.02802083 1.07379849]\n",
      "[71.15392677 69.84991249 70.48144303 72.29816948 71.25515036 67.06884155\n",
      " 64.30274934 63.89868468 64.24540693 64.12016258 61.92075938 59.21613298\n",
      " 57.51170014 58.97065063 61.89726042 63.74866318 63.73744767 63.60981136\n",
      " 64.96972621 67.40726306 69.00497416]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 13\n",
      "2009 181\n",
      "to remove=  []\n",
      "onset dates = ['2009-01-24T00:00:00.000000000']\n",
      "84\n",
      "[1.92779692 2.05999108 2.10079414 2.29691268 2.68173912 3.17634445\n",
      " 3.60645755 4.06227398 4.59724892 4.71074854 4.33122364 3.98327677\n",
      " 3.7650635  3.61962973 3.20804784 2.83279173 3.14067197 3.31470646\n",
      " 7.49060129 5.17133249 4.52599442]\n",
      "[85.07306191 83.96106445 85.67621626 88.5108665  88.78078131 87.93974316\n",
      " 87.1739414  86.03073512 85.59856108 84.23074672 79.07371378 74.52557892\n",
      " 74.4581238  72.54422471 67.29908175 63.65637226 63.23529465 62.87681109\n",
      " 75.57496484 67.07304745 58.73339394]\n",
      "days above aspect threshold= 17\n",
      "days below latcent threshold= 4\n",
      "2010 181\n",
      "to remove=  [1]\n",
      "onset dates = ['2010-02-09T00:00:00.000000000' '2010-03-24T00:00:00.000000000']\n",
      "100\n",
      "[2.45299116 2.37127055 2.20129316 1.7280191  1.53312052 1.68445528\n",
      " 2.00251688 1.83319129 1.80013133 2.74538822 2.95648229 2.65867563\n",
      " 2.4686316  2.10733073 1.83175624 2.00933871 2.48663155 3.31069575\n",
      " 3.84219569 3.82063216 2.87610327]\n",
      "[63.54723109 62.09164627 62.39718734 62.66605636 63.22581896 64.68886455\n",
      " 67.32928184 67.08677205 65.69919219 68.44424143 68.42786451 66.56551441\n",
      " 65.72711723 64.64829392 62.49673404 62.4230264  65.49479995 69.20044503\n",
      " 70.58177458 69.6667421  67.02307228]\n",
      "days above aspect threshold= 10\n",
      "days below latcent threshold= 12\n",
      "143\n",
      "[1.19070521 1.28113474 1.28649143 1.14764884 1.1972902  1.43406102\n",
      " 1.58206548 1.6494598  1.63828489 1.53042273 1.38561055 1.24914248\n",
      " 1.27983842 1.37342461 1.49279892 1.61380823 1.58944865 1.59779079\n",
      " 1.58091385 1.43090969 1.37434647]\n",
      "[72.02281752 72.78372039 73.72742442 74.46793945 74.01754379 72.24388529\n",
      " 69.94354355 67.69217693 65.90413585 64.70887297 64.37309778 64.57952699\n",
      " 64.64414961 65.40896695 66.57548496 67.36534051 67.58742247 67.71499854\n",
      " 67.79387095 67.70801746 67.84128449]\n",
      "days above aspect threshold= 0\n",
      "days below latcent threshold= 6\n",
      "2011 181\n",
      "no events\n",
      "2012 182\n",
      "no events\n",
      "2013 181\n",
      "to remove=  []\n",
      "onset dates = ['2013-01-06T00:00:00.000000000']\n",
      "66\n",
      "[1.56288797 1.472995   1.39537763 1.29970723 1.24322422 1.22475671\n",
      " 1.14170406 1.28147426 1.65019573 2.34751239 3.01888237 3.1449477\n",
      " 2.84213775 3.01482411 2.8521097  2.82651334 2.85557738 2.66440133\n",
      " 1.86993803 1.53855288 1.10204967]\n",
      "[70.56421911 71.1299124  71.96082089 72.6672646  73.20904479 73.85504061\n",
      " 74.69850985 76.22060745 77.00098795 76.8152326  75.33900686 74.88899647\n",
      " 79.04692864 81.14908099 82.43932518 80.7995929  75.77104481 69.45698708\n",
      " 62.89446297 61.40358555 59.20146639]\n",
      "days above aspect threshold= 8\n",
      "days below latcent threshold= 3\n",
      "2014 181\n",
      "no events\n",
      "to remove=  []\n",
      "no events\n",
      "no events\n",
      "onset dates = []\n",
      "SSW dates are: [numpy.datetime64('1979-02-22T00:00:00.000000000'), numpy.datetime64('1980-02-29T00:00:00.000000000'), numpy.datetime64('1981-03-04T00:00:00.000000000'), numpy.datetime64('1981-12-04T00:00:00.000000000'), numpy.datetime64('1984-02-23T00:00:00.000000000'), numpy.datetime64('1985-01-01T00:00:00.000000000'), numpy.datetime64('1987-01-23T00:00:00.000000000'), numpy.datetime64('1987-12-08T00:00:00.000000000'), numpy.datetime64('1988-03-14T00:00:00.000000000'), numpy.datetime64('1989-02-21T00:00:00.000000000'), numpy.datetime64('1998-12-15T00:00:00.000000000'), numpy.datetime64('1999-02-25T00:00:00.000000000'), numpy.datetime64('2000-03-20T00:00:00.000000000'), numpy.datetime64('2001-02-11T00:00:00.000000000'), numpy.datetime64('2001-12-30T00:00:00.000000000'), numpy.datetime64('2002-02-17T00:00:00.000000000'), numpy.datetime64('2003-01-18T00:00:00.000000000'), numpy.datetime64('2004-01-05T00:00:00.000000000'), numpy.datetime64('2006-01-21T00:00:00.000000000'), numpy.datetime64('2007-02-24T00:00:00.000000000'), numpy.datetime64('2008-02-22T00:00:00.000000000'), numpy.datetime64('2009-01-24T00:00:00.000000000'), numpy.datetime64('2010-02-09T00:00:00.000000000'), numpy.datetime64('2010-03-24T00:00:00.000000000'), numpy.datetime64('2013-01-06T00:00:00.000000000')]\n",
      "vortex types: ['split', 'displaced', 'displaced', 'unclassified', 'displaced', 'split', 'displaced', 'split', 'split', 'split', 'displaced', 'split', 'displaced', 'split', 'displaced', 'displaced', 'split', 'displaced', 'displaced', 'displaced', 'displaced', 'split', 'displaced', 'displaced', 'split']\n",
      "25\n",
      "number of displacements:  14\n",
      "number of splits:  10\n",
      "number of unclassified:  1\n"
     ]
    }
   ],
   "source": [
    "model = 'ERA5'\n",
    "temp = ds_sel['U']\n",
    "out_dir = './SSWs_stats/'\n",
    "\n",
    "vortex_class=[]\n",
    "SSW_dates=[]\n",
    "temp_sel_ls = []\n",
    "for year in range(1979,2015):\n",
    "    #print(year)\n",
    "    JFMA_dict = dict(time = slice(f'{year}-01',f'{year}-04'))\n",
    "    ND_dict = dict(time = slice(f'{year-1}-11',f'{year-1}-12'))\n",
    "    \n",
    "    JFMA = temp.sel(**JFMA_dict)\n",
    "    try:\n",
    "        ND = temp.sel(**ND_dict)\n",
    "        temp_sel = xr.concat([ND,JFMA], dim = 'time')\n",
    "    except:\n",
    "        temp_sel = xr.concat([JFMA], dim = 'time')\n",
    "    temp_sel['label'] = f'NDJFMA_{year}'\n",
    "    temp_sel_ls.append(temp_sel)\n",
    "\n",
    "    aspect = np.empty(0)\n",
    "    latcent = np.empty(0)       \n",
    "\n",
    "    print(year,len(temp_sel))   \n",
    "\n",
    "\n",
    "    idx=np.where(temp_sel<0)\n",
    "\n",
    "#group the indices into events\n",
    "    events=[list(group) for group in mit.consecutive_groups(idx[0])]\n",
    "    #print(events)\n",
    "    if not (events):\n",
    "        print ('no events')\n",
    "        continue\n",
    "\n",
    "# check last event is not a final warming. If it is, remove\n",
    "    if events[-1][-1] >len(temp_sel)-10:\n",
    "        del events[-1]\n",
    "    if not (events):\n",
    "        print ('no events')\n",
    "        continue\n",
    "# check that there are at least 10 consecutive days of westerlies at some point after last remaining event\n",
    "    if len(events)==1:\n",
    "        idx_end=np.where(temp_sel[events[0][-1]+1:-1]>0)\n",
    "        events_end=[list(group) for group in mit.consecutive_groups(idx_end[0])]\n",
    "        if len(events_end[0])<10:\n",
    "            del events[0]\n",
    "    else:\n",
    "        for i in range(len(events)-1,0,-1):\n",
    "            idx_end=np.where(temp_sel[events[i][-1]+1:-1]>0)\n",
    "            events_end=[list(group) for group in mit.consecutive_groups(idx_end[0])]\n",
    "            consec_pos=[]\n",
    "            for k in range (0,len(events_end)):\n",
    "                length=len(events_end[k])\n",
    "                consec_pos.append(length)\n",
    "            if all (ele <10 for ele in consec_pos):\n",
    "                del events[i]\n",
    "\n",
    "# delete any events starting in April or November\n",
    "    months=[]\n",
    "    for e in range(0,len(events)):\n",
    "        mon=int(temp_sel.time[events[e][0]].dt.strftime('%m'))\n",
    "        months.append(mon)\n",
    "\n",
    "    remove=[]\n",
    "    for e in range(0,len(events)):\n",
    "        if months[e]==4 or months[e]==11:\n",
    "            remove.append(e)\n",
    "\n",
    "    for index in sorted(remove,reverse=True):\n",
    "        del events[index]\n",
    "    #print(events)\n",
    "\n",
    "    if not (events):\n",
    "        print ('no events')\n",
    "\n",
    "#for  events, check if 20 consecutive days of westerlies before events\n",
    "    remove=[]\n",
    "    for j in np.arange(len(events)):\n",
    "        if np.any(temp_sel[events[j][0]-20:events[j][0]]<0):\n",
    "            remove.append(j)\n",
    "    print('to remove= ', remove)\n",
    "\n",
    "    for index in sorted(remove,reverse=True):\n",
    "        del events[index]\n",
    "    #print(events)\n",
    "\n",
    "    if not (events):\n",
    "        print ('no events')\n",
    "\n",
    "#final check for last event, does it have 10 consecutive days of westerlies afterwards\n",
    "    if len(events)>0:\n",
    "        idx_end=np.where(temp_sel[events[-1][-1]+1:-1]>0)\n",
    "        events_end=[list(group) for group in mit.consecutive_groups(idx_end[0])]\n",
    "        consec_pos=[]\n",
    "        for k in range (0,len(events_end)):\n",
    "            length=len(events_end[k])\n",
    "            consec_pos.append(length)\n",
    "        if all (ele <10 for ele in consec_pos):\n",
    "            del events[-1]\n",
    "\n",
    "    elif not (events):\n",
    "        print ('no events')\n",
    "\n",
    "#extract the onset dates from the events\n",
    "    event_length=[]\n",
    "    start_idx=[]\n",
    "    for i in range(0,len(events)):\n",
    "        start=events[i][0]\n",
    "        length=len(events[i])\n",
    "        start_idx.append(start)\n",
    "        event_length.append(length)\n",
    "\n",
    "    #print('indices of onset dates=',start_idx)\n",
    "    #print('event lengths=',event_length)\n",
    "\n",
    "    onset_dates=temp_sel.time[start_idx].values\n",
    "    print('onset dates =',onset_dates)\n",
    "    #sys.exit()\n",
    "    #using vortex moments classify vortex as split, displacement or unclassifiable\n",
    "    #NB calculate vorTex edge for each model separately and add below\n",
    "    vortex_ID=[]\n",
    "    moments_ens_sel = moments.sel(time = temp_sel.time)\n",
    "    for idx in range(0,len(start_idx)):\n",
    "        date_ind=start_idx[idx]\n",
    "        print(date_ind)\n",
    "        \n",
    "        aspect = np.empty(0)\n",
    "        latcent = np.empty(0) \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "        for iday in range(date_ind-10,date_ind+11):\n",
    "            aspect = np.append(aspect, moments_ens_sel['aspect_ratio'].isel(time = iday))\n",
    "            latcent = np.append(latcent, moments_ens_sel['centroid_latitude'].isel(time = iday))\n",
    "\n",
    "        print(aspect)\n",
    "        print(latcent)\n",
    "        aspect_days=np.count_nonzero(aspect>2.4)\n",
    "        latcent_days=np.count_nonzero(latcent<66)\n",
    "        print('days above aspect threshold=',aspect_days)\n",
    "        print('days below latcent threshold=',latcent_days)\n",
    "        vortex_type=[]\n",
    "        if aspect_days > latcent_days:\n",
    "            vortex_type =\"split\"\n",
    "        elif aspect_days < latcent_days:\n",
    "            vortex_type = \"displaced\"\n",
    "        else:\n",
    "            vortex_type = \"unclassified\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        vortex_ID.append(vortex_type)\n",
    "\n",
    "    SSW_dates.extend(onset_dates)\n",
    "    vortex_class.extend(vortex_ID)\n",
    "\n",
    "    #sys.exit()\n",
    "print('SSW dates are:',SSW_dates)\n",
    "print('vortex types:', vortex_class)\n",
    "print(len(SSW_dates))\n",
    "\n",
    "# make dataframe of results for model\n",
    "model_SSWs=pd.DataFrame()\n",
    "model_SSWs['onset dates']=SSW_dates\n",
    "model_SSWs['vortex type']=vortex_class\n",
    "model_SSWs.to_csv(f'{out_dir}{model}.csv')\n",
    "\n",
    "split_model=model_SSWs[(model_SSWs['vortex type']=='split')]\n",
    "disp_model=model_SSWs[(model_SSWs['vortex type']=='displaced')]\n",
    "unclass_model=model_SSWs[(model_SSWs['vortex type']=='unclassified')]\n",
    "#unavail_model=model_SSWs[(model_SSWs['vortex type']==f'{ens} N/A')]\n",
    "\n",
    "split_dates=np.array(split_model['onset dates'])\n",
    "disp_dates=np.array(disp_model['onset dates'])\n",
    "unclass_dates=np.array(unclass_model['onset dates'])\n",
    "#unavail_dates=np.array(unavail_model['onset dates'])\n",
    "\n",
    "#save SSW dates as .npy files for use in subsequent code.\n",
    "np.save(f'{out_dir}{model}_split_dates_all.npy',split_dates)\n",
    "np.save(f'{out_dir}{model}_disp_dates_all.npy',disp_dates)\n",
    "np.save(f'{out_dir}{model}_unclass_dates_all.npy',unclass_dates)\n",
    "np.save(f'{out_dir}{model}_all_SSW_dates.npy',SSW_dates)\n",
    "\n",
    "print('number of displacements: ',len(disp_dates))\n",
    "print('number of splits: ',len(split_dates))\n",
    "print('number of unclassified: ',len(unclass_dates))\n",
    "#print('number of N/A: ',len(unavail_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93f0369f-8294-4829-912b-90fdc182afc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14/35*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e29d6deb-74e9-4b54-9346-90c64cfe74d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
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     "data": {
      "text/plain": [
       "2.8571428571428568"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10/35*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "709f2a97-c187-4883-ad8e-fcc9def71dbe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "157adf44-e591-4330-b452-e645e9d95f28",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "0.4"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14/35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2abf19ba-ef97-4044-b0f0-1898ffe4b34a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.666666666666666"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "24/36*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1322aa1-28c9-448b-83e8-939e4f7aed7e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "6.944444444444445"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25/36*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c36f2e83-6020-4d50-9c8d-6e72902940ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "date_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "939aa62e-3626-403a-aa15-1ad5400b3ee5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onset dates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vortex type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>displaced</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split</th>\n",
       "      <td>2.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unclassified</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              onset dates\n",
       "vortex type              \n",
       "displaced        4.000000\n",
       "split            2.857143\n",
       "unclassified     0.285714"
      ]
     },
     "execution_count": 76,
     "metadata": {},
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   ],
   "source": [
    "model_SSWs.groupby('vortex type').count()/35*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac8d6c79-f93c-424d-9d23-5861d43e19c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onset dates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vortex type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>displaced</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unclassified</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "              onset dates\n",
       "vortex type              \n",
       "displaced              19\n",
       "split                   9\n",
       "unclassified            3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_SSWs.groupby('vortex type').count()#/35*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e41b6680-ff81-40c5-a4df-7dc1030e4050",
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    {
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       "    <tr>\n",
       "      <th>vortex type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>displaced</th>\n",
       "      <td>5.428571</td>\n",
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       "    <tr>\n",
       "      <th>split</th>\n",
       "      <td>2.571429</td>\n",
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       "      <th>unclassified</th>\n",
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       "              onset dates\n",
       "vortex type              \n",
       "displaced        5.428571\n",
       "split            2.571429\n",
       "unclassified     0.857143"
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "model_SSWs.groupby('vortex type').count()/35*10"
   ]
  },
  {
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   "execution_count": 57,
   "id": "cf966236-15e6-4533-9ddc-c85e1d73ab01",
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   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "052a2bc1-28dc-4e30-869d-b7c51fd6ab00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mode_func(aspect, latcent, printing = False):\n",
    "    #for latcent, cube the values to transform the distribution\n",
    "    latcent3=np.power(latcent,3)\n",
    "    #fit Gaussian distribution and use KS test for fit\n",
    "    dist=getattr(stats,'norm')\n",
    "    parameters=dist.fit(latcent3)\n",
    "    (mean,SD)=parameters\n",
    "    ks_stat,ks_pval=stats.kstest(latcent3,\"norm\",parameters)\n",
    "    # convert latitude back (cube root)\n",
    "    mode=np.power(mean,1/3)\n",
    "    if printing:\n",
    "        print('latcent mode:',mode)\n",
    "        print('norm KS statistic and p value:',ks_stat,ks_pval)\n",
    "\n",
    "    #fit GEV to aspect data\n",
    "    dist2=getattr(stats,'genextreme')\n",
    "    parameters=dist2.fit(aspect)\n",
    "    (shape,location,scale)=parameters\n",
    "    ks_stat_GEV,ks_pval_GEV = stats.kstest(aspect,\"genextreme\",parameters)\n",
    "    if printing:\n",
    "        print('location:', location)\n",
    "        print(' GEV KS statistic and p value:',ks_stat_GEV,ks_pval_GEV)\n",
    "\n",
    "    #aspect mode calculated according to Seviour et al. 2016 (S16)\n",
    "    asp_params=stats.genextreme.fit(aspect)\n",
    "    asp_pdf=stats.genextreme.pdf(aspect,asp_params[0],loc=asp_params[1],scale=asp_params[2])\n",
    "    index=np.argmax(asp_pdf)\n",
    "    max_asp_S16=aspect[index]\n",
    "    if printing:\n",
    "        print ('S16 aspect:',max_asp_S16)\n",
    "\n",
    "    #kde alternative\n",
    "    # aspect\n",
    "    min_val=1\n",
    "    max_val=int(np.round(np.max(aspect))+1)\n",
    "    \n",
    "    divs=(max_val-min_val)*100\n",
    "    \n",
    "    asp_xs=np.linspace(min_val,max_val,divs)\n",
    "    asp_density=stats.gaussian_kde(aspect)\n",
    "    asp_ys=asp_density(asp_xs)\n",
    "    index=np.argmax(asp_ys)\n",
    "    max_asp_kde=asp_xs[index]\n",
    "\n",
    "    #latcent\n",
    "    min1=int(np.round(np.min(latcent))-1)\n",
    "    max1=90\n",
    "    divs1=(max1-min1)*100\n",
    "\n",
    "    lat_xs=np.linspace(min1,max1,divs1)\n",
    "    lat_density=stats.gaussian_kde(latcent)\n",
    "    lat_ys=lat_density(lat_xs)\n",
    "    index1=np.argmax(lat_ys)\n",
    "    max_lat_kde=lat_xs[index1]\n",
    "    if printing:\n",
    "        print ('kde aspect:',max_asp_kde)\n",
    "        print ('kde latcent:',max_lat_kde)\n",
    "    \n",
    "    return max_asp_S16,mode\n",
    "\n",
    "\n",
    "def mode_func_lat(latcent):\n",
    "    latcent3=np.power(latcent,3)\n",
    "    #fit Gaussian distribution and use KS test for fit\n",
    "    dist=getattr(stats,'norm')\n",
    "    parameters=dist.fit(latcent3)\n",
    "    (mean,SD)=parameters\n",
    "    ks_stat,ks_pval=stats.kstest(latcent3,\"norm\",parameters)\n",
    "    # convert latitude back (cube root)\n",
    "    mode=np.power(mean,1/3)\n",
    "    return mode\n",
    "\n",
    "def mode_func_aspect(aspect):\n",
    "    asp_params=stats.genextreme.fit(aspect)\n",
    "    asp_pdf=stats.genextreme.pdf(aspect,asp_params[0],loc=asp_params[1],scale=asp_params[2])\n",
    "    index=np.argmax(asp_pdf)\n",
    "    max_asp_S16=aspect[index]\n",
    "    return max_asp_S16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6c87cfa-3b6a-491a-a2fd-304beea1a7dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latcent mode: 77.26459753570796\n",
      "norm KS statistic and p value: 0.020706282412768895 0.01377348231611845\n",
      "location: 1.3464606615344379\n",
      " GEV KS statistic and p value: 0.021973589569153962 0.007356324027527251\n",
      "S16 aspect: 1.2868586137036173\n",
      "kde aspect: 1.3003003003003002\n",
      "kde latcent: 77.87762306334577\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "aspect = moments['aspect_ratio']\n",
    "latcent =  moments['centroid_latitude']\n",
    "\n",
    "aspect=aspect[np.logical_not(np.isnan(aspect))]\n",
    "aspect=aspect[aspect < float('+inf')]\n",
    "aspect=aspect[aspect < 100.0]\n",
    "latcent=latcent[np.logical_not(np.isnan(latcent))]\n",
    "latcent=latcent[latcent < float('+inf')]        \n",
    "\n",
    "mode_aspect, mode_latcent = mode_func(aspect.values, latcent.values)\n",
    "print()\n",
    "\n",
    "\n",
    "mode_aspect_da = xr.DataArray(mode_aspect)\n",
    "mode_aspect_da.name = 'mode_aspect'\n",
    "mode_latcent_da = xr.DataArray(mode_latcent)\n",
    "mode_latcent_da.name = 'mode_latcent' \n",
    "\n",
    "ds_mode = xr.merge([mode_aspect_da, mode_latcent_da])\n",
    "ds_mode.to_netcdf(f'{out_dir}{model}_modes_nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2cb5a512-84b2-4b40-8134-f424f6e24fde",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latcent mode: 77.26459753570796\n",
      "norm KS statistic and p value: 0.020706282412768895 0.01377348231611845\n",
      "location: 1.3464606615344379\n",
      " GEV KS statistic and p value: 0.021973589569153962 0.007356324027527251\n",
      "S16 aspect: 1.2868586137036173\n",
      "kde aspect: 1.3003003003003002\n",
      "kde latcent: 77.87762306334577\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "aspect = moments['aspect_ratio']\n",
    "latcent =  moments['centroid_latitude']\n",
    "\n",
    "aspect=aspect[np.logical_not(np.isnan(aspect))]\n",
    "aspect=aspect[aspect < float('+inf')]\n",
    "aspect=aspect[aspect < 100.0]\n",
    "latcent=latcent[np.logical_not(np.isnan(latcent))]\n",
    "latcent=latcent[latcent < float('+inf')]        \n",
    "\n",
    "mode_aspect, mode_latcent = mode_func(aspect.values, latcent.values, printing = True)\n",
    "print()\n",
    "\n",
    "\n",
    "mode_aspect_da = xr.DataArray(mode_aspect)\n",
    "mode_aspect_da.name = 'mode_aspect'\n",
    "mode_latcent_da = xr.DataArray(mode_latcent)\n",
    "mode_latcent_da.name = 'mode_latcent' \n",
    "\n",
    "#ds_mode = xr.merge([mode_aspect_da, mode_latcent_da])\n",
    "#ds_mode.to_netcdf(f'{out_dir}{model}_modes_nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87092078-92b3-4752-a484-ccd6fc305b28",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConfidenceInterval(low=77.12569550732209, high=77.39958555069424)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (latcent.values,) # aspect.values,\n",
    "res = stats.bootstrap(data, mode_func_lat, confidence_level=0.68, method = 'basic', vectorized = False, n_resamples = 1000)\n",
    "res.confidence_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8eccc994-95a3-4119-a1d5-b071c4585e2b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 14s, sys: 310 ms, total: 1min 14s\n",
      "Wall time: 1min 14s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ConfidenceInterval(low=77.1846348679789, high=77.34519143540835)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data = (latcent.values,) # aspect.values,\n",
    "res_lat = stats.bootstrap(data, mode_func_lat, confidence_level=0.68, method = 'basic', vectorized = False, n_resamples = 10000)\n",
    "res_lat.confidence_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0183759-2ad6-4912-852a-a81ea82bf74a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 6s, sys: 511 ms, total: 2min 6s\n",
      "Wall time: 2min 7s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ConfidenceInterval(low=77.18387353381073, high=77.34716591012236)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data = (latcent.values,) # aspect.values,\n",
    "res_lat = stats.bootstrap(data, mode_func_lat, confidence_level=0.68, method = 'BCa', vectorized = False, n_resamples = 10_000)\n",
    "res_lat.confidence_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8e22c338-af50-4af0-aafe-26b380217c69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15min 14s, sys: 337 ms, total: 15min 15s\n",
      "Wall time: 15min 19s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ConfidenceInterval(low=1.2820737387226975, high=1.2913301865078577)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data = (aspect.values,)\n",
    "res_aspect = stats.bootstrap(data, mode_func_aspect, confidence_level=0.68, method = 'basic', vectorized = False, n_resamples = 10_000)\n",
    "res_aspect.confidence_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f9cc067a-422c-467b-8017-a2b8528d2b1e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23min 46s, sys: 525 ms, total: 23min 46s\n",
      "Wall time: 23min 54s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ConfidenceInterval(low=1.2819788143383286, high=1.2913357962338932)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data = (aspect.values,)\n",
    "res_aspect = stats.bootstrap(data, mode_func_aspect, confidence_level=0.68, method = 'BCa', vectorized = False, n_resamples = 10_000)\n",
    "res_aspect.confidence_interval"
   ]
  }
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