4Redo_Notebook4_proxies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook #4: The proxy database\n",
    "\n",
    "In this script, we'll take a look at one of the LMR inputs: the proxy database.\n",
    "\n",
    "One of the initial steps of running the LMR is to preprocess the proxy network.  This step puts the data into a standard format and calculates annual-mean values, among other things.  Let's take a look at these standard files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A non-python command to make sure all figures are plotted on this page.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary python packages \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cartopy.crs as ccrs\n",
    "import seaborn as sns\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the proxy data and metadata using the pandas library.\n",
    "proxies = pd.read_pickle('../LMR_data/data/proxies/Pages2kv1_Proxies.df.pckl')\n",
    "metadata = pd.read_pickle('../LMR_data/data/proxies/Pages2kv1_Metadata.df.pckl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(2512, 577)\n"
     ]
    }
   ],
   "source": [
    "# The \"type\" and \"shape\" commands can be used to learn more about the data set.\n",
    "# The proxy data:\n",
    "print(type(proxies))\n",
    "print(proxies.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(522, 15)\n"
     ]
    }
   ],
   "source": [
    "# The proxy metadata:\n",
    "print(type(metadata))\n",
    "print(metadata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Ant_01   Ant_02 Ant_03 Ant_04 Ant_05 Ant_06 Ant_07 Ant_08 Ant_09  \\\n",
      "Year C.E.                                                                    \n",
      "-500         NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-499         NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-498         NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-497         NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-496         NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "...          ...      ...    ...    ...    ...    ...    ...    ...    ...   \n",
      " 2007        NaN -21.6681    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2008        NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2009        NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2010        NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2011        NaN      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "\n",
      "          Ant_10  ... SAm_14  SAm_15 SAm_16 SAm_17 SAm_18 SAm_19 SAm_20  \\\n",
      "Year C.E.         ...                                                     \n",
      "-500         NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-499         NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-498         NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-497         NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "-496         NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "...          ...  ...    ...     ...    ...    ...    ...    ...    ...   \n",
      " 2007     -158.1  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2008        NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2009        NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2010        NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      " 2011        NaN  ...    NaN     NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "\n",
      "          SAm_21 SAm_22 SAm_23  \n",
      "Year C.E.                       \n",
      "-500         NaN    NaN    NaN  \n",
      "-499         NaN    NaN    NaN  \n",
      "-498         NaN    NaN    NaN  \n",
      "-497         NaN    NaN    NaN  \n",
      "-496         NaN    NaN    NaN  \n",
      "...          ...    ...    ...  \n",
      " 2007        NaN    NaN    NaN  \n",
      " 2008        NaN    NaN    NaN  \n",
      " 2009        NaN    NaN    NaN  \n",
      " 2010        NaN    NaN    NaN  \n",
      " 2011        NaN    NaN    NaN  \n",
      "\n",
      "[2512 rows x 577 columns]\n"
     ]
    }
   ],
   "source": [
    "# Let's look at the contents of the proxy file.\n",
    "print(proxies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Ant_01', 'Ant_02', 'Ant_03', 'Ant_04', 'Ant_05', 'Ant_06', 'Ant_07',\n",
      "       'Ant_08', 'Ant_09', 'Ant_10',\n",
      "       ...\n",
      "       'SAm_14', 'SAm_15', 'SAm_16', 'SAm_17', 'SAm_18', 'SAm_19', 'SAm_20',\n",
      "       'SAm_21', 'SAm_22', 'SAm_23'],\n",
      "      dtype='object', length=577)\n"
     ]
    }
   ],
   "source": [
    "# The \"keys\" command shows all of the keys for this dataset.\n",
    "print(proxies.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PAGES 2k Region', 'Proxy ID', 'Country/Region', 'Area', 'Site',\n",
      "       'Lat (N)', 'Lon (E)', 'Archive type', 'Proxy measurement',\n",
      "       'Proxy other info', 'Sign relation to temp', 'Oldest (C.E.)',\n",
      "       'Youngest (C.E.)', 'Resolution (yr)', 'Reference'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Display all of the fields in the metadata file.\n",
    "print(metadata.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            PAGES 2k Region :                 Asia\n",
      "                   Proxy ID :              Asi_020\n",
      "             Country/Region : Altai Mountains (AT)\n",
      "                       Area :              RUSS139\n",
      "                       Site :               UKHLWW\n",
      "                    Lat (N) :                50.15\n",
      "                    Lon (E) :     85.3666666666667\n",
      "               Archive type :            Tree ring\n",
      "          Proxy measurement :       Latewood width\n",
      "           Proxy other info :                  nan\n",
      "      Sign relation to temp :             Positive\n",
      "              Oldest (C.E.) :               1581.0\n",
      "            Youngest (C.E.) :               1994.0\n",
      "            Resolution (yr) :                  1.0\n",
      "                  Reference :    Schweingruber, FH\n"
     ]
    }
   ],
   "source": [
    "# As an example, plot the metadata of the first record.\n",
    "# Loop through every element of the metadata and print it to screen.\n",
    "for key in metadata.keys():\n",
    "    print('%27s : %20s' % (key, metadata.loc[100][key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index:                 Site:       Country/Region:         Archive type:\n",
      "   81:               AAT2LS, Altai Mountains (AT),            Tree ring\n",
      "   82:               DJAZLS, Altai Mountains (AT),            Tree ring\n",
      "   83:               JABELS, Altai Mountains (AT),            Tree ring\n",
      "   84:               KUR2LS, Altai Mountains (AT),            Tree ring\n",
      "   85:               KUR3LS, Altai Mountains (AT),            Tree ring\n",
      "   86:               MSALLS, Altai Mountains (AT),            Tree ring\n",
      "   87:               NCHALS, Altai Mountains (AT),            Tree ring\n",
      "   88:               TUT2LS, Altai Mountains (AT),            Tree ring\n",
      "   89:               UGLALS, Altai Mountains (AT),            Tree ring\n",
      "   90:               ULAGLS, Altai Mountains (AT),            Tree ring\n",
      "   91:               UULTRW, Altai Mountains (AT),            Tree ring\n",
      "   92:               UULEWW, Altai Mountains (AT),            Tree ring\n",
      "   93:               UULLWW, Altai Mountains (AT),            Tree ring\n",
      "   94:               UULEWD, Altai Mountains (AT),            Tree ring\n",
      "   95:               UULLWD, Altai Mountains (AT),            Tree ring\n",
      "   96:               UULMND, Altai Mountains (AT),            Tree ring\n",
      "   97:               UULMXD, Altai Mountains (AT),            Tree ring\n",
      "   98:               UKHTRW, Altai Mountains (AT),            Tree ring\n",
      "   99:               UKHEWW, Altai Mountains (AT),            Tree ring\n",
      "  100:               UKHLWW, Altai Mountains (AT),            Tree ring\n",
      "  101:               UKHEWD, Altai Mountains (AT),            Tree ring\n",
      "  102:               UKHLWD, Altai Mountains (AT),            Tree ring\n",
      "  103:               UKHMND, Altai Mountains (AT),            Tree ring\n",
      "  104:               UKHMXD, Altai Mountains (AT),            Tree ring\n",
      "  105:               GAYTTD,          Bhutan (BT),            Tree ring\n",
      "  106:               NASPTD,          Bhutan (BT),            Tree ring\n",
      "  107:               PHUMTD,          Bhutan (BT),            Tree ring\n",
      "  108:               ZACHTD,          Bhutan (BT),            Tree ring\n",
      "  109:               BHUTTD,          Bhutan (BT),            Tree ring\n",
      "  110:               CAMGSP,          Bhutan (BT),            Tree ring\n",
      "  111:               DHURPS,          Bhutan (BT),            Tree ring\n",
      "  112:               GEYZSP,          Bhutan (BT),            Tree ring\n",
      "  113:               PEMBSP,          Bhutan (BT),            Tree ring\n",
      "  114:               BHUTSP,          Bhutan (BT),            Tree ring\n",
      "  115:               LAYAJR,          Bhutan (BT),            Tree ring\n",
      "  116:               TSHEPS,          Bhutan (BT),            Tree ring\n",
      "  117:               TSHETD,          Bhutan (BT),            Tree ring\n",
      "  118:               TANSIC,           China (CH),            Tree ring\n",
      "  119:               XCHSIC,           China (CH),            Tree ring\n",
      "  120:               DAOSIC,           China (CH),            Tree ring\n",
      "  121:               MAXSIC,           China (CH),            Tree ring\n",
      "  122:               WXIYUN,           China (CH),            Tree ring\n",
      "  123:               DEZQIN,           China (CH),            Tree ring\n",
      "  124:               GOUQIN,           China (CH),            Tree ring\n",
      "  125:               HEBQIN,           China (CH),            Tree ring\n",
      "  126:               LAJQIN,           China (CH),            Tree ring\n",
      "  127:               GHEGAN,           China (CH),            Tree ring\n",
      "  128:               DULAJP,           China (CH),            Tree ring\n",
      "  129:               SHENJP,           China (CH),            Tree ring\n",
      "  130:               DUSHJP,           China (CH),            Tree ring\n",
      "  131:               MQAXJP,           China (CH),            Tree ring\n",
      "  132:               MQBXJP,           China (CH),            Tree ring\n",
      "  133:               MQDXJP,           China (CH),            Tree ring\n",
      "  134:               MQFXJP,           China (CH),            Tree ring\n",
      "  135:               MQRXJP,           China (CH),            Tree ring\n",
      "  136:               TDAXJP,           China (CH),            Tree ring\n",
      "  137:               TDBXJP,           China (CH),            Tree ring\n",
      "  138:               HBLXJP,           China (CH),            Tree ring\n",
      "  139:               HBMXJP,           China (CH),            Tree ring\n",
      "  140:               HBHXJP,           China (CH),            Tree ring\n",
      "  141:               BARELC,           China (CH),            Tree ring\n",
      "  142:               HUASPI,           China (CH),            Tree ring\n",
      "  143:               SANGTS,           China (CH),            Tree ring\n",
      "  144:               BLKALS,           China (CH),            Tree ring\n",
      "  145:               TIANMU,           China (CH),            Tree ring\n",
      "  146:               WULANJ,           China (CH),            Tree ring\n",
      "  147:               QUMAJP,           China (CH),            Tree ring\n",
      "  148:               ZHIDJP,           China (CH),            Tree ring\n",
      "  149:               ZHANGX,           China (CH),            Tree ring\n",
      "  150:               XIANGC,           China (CH),            Tree ring\n",
      "  151:               BAWSIC,           China (CH),            Tree ring\n",
      "  152:               PTCYUN,           China (CH),            Tree ring\n",
      "  153:               WEXYUN,           China (CH),            Tree ring\n",
      "  154:               HXBURU,           China (CH),            Tree ring\n",
      "  155:               DQHZHO,           China (CH),            Tree ring\n",
      "  156:               SMASLY,           China (CH),            Tree ring\n",
      "  157:               HYGJUP,           China (CH),            Tree ring\n",
      "  158:               MQACJP,           China (CH),            Tree ring\n",
      "  159:               10GK_P,           China (CH),            Tree ring\n",
      "  160:               11YC_T,           China (CH),            Tree ring\n",
      "  161:               12YM_A,           China (CH),            Tree ring\n",
      "  162:               13YE_T,           China (CH),            Tree ring\n",
      "  163:               22DL_T,           China (CH),            Tree ring\n",
      "  164:                 DLH1,           China (CH),            Tree ring\n",
      "  165:                 DLH2,           China (CH),            Tree ring\n",
      "  166:                 DLH3,           China (CH),            Tree ring\n",
      "  167:                 DLH4,           China (CH),            Tree ring\n",
      "  168:                 DLH5,           China (CH),            Tree ring\n",
      "  169:                 DLH6,           China (CH),            Tree ring\n",
      "  170:                  HSE,           China (CH),            Tree ring\n",
      "  171:                  HSS,           China (CH),            Tree ring\n",
      "  172:                  HSW,           China (CH),            Tree ring\n",
      "  173:                  QML,           China (CH),            Tree ring\n",
      "  174:                  TJ1,           China (CH),            Tree ring\n",
      "  175:                  WL1,           China (CH),            Tree ring\n",
      "  176:                  WL2,           China (CH),            Tree ring\n",
      "  177:                  WL3,           China (CH),            Tree ring\n",
      "  178:                  WL4,           China (CH),            Tree ring\n",
      "  179:                 ZD31,           China (CH),            Tree ring\n",
      "  180:                  ZHD,           China (CH),            Tree ring\n",
      "  181:               MUNATG,       Indonesia (ID),            Tree ring\n",
      "  182:               GANGCD,           India (IN),            Tree ring\n",
      "  183:               RANGCD,           India (IN),            Tree ring\n",
      "  184:               NARKAB,           India (IN),            Tree ring\n",
      "  185:               NARANG,           India (IN),            Tree ring\n",
      "  186:               KERALA,           India (IN),            Tree ring\n",
      "  187:                JANCD,           India (IN),            Tree ring\n",
      "  188:               MTASSP,           Japan (JP),            Tree ring\n",
      "  189:               MTASMD,           Japan (JP),            Tree ring\n",
      "  190:               KUNASH,           Japan (JP),            Tree ring\n",
      "  191:                  YKS,           Japan (JP),            Tree ring\n",
      "  192:                  MSR,           Japan (JP),            Tree ring\n",
      "  193:                  ONB,           Japan (JP),            Tree ring\n",
      "  194:                  SJT,           Japan (JP),            Tree ring\n",
      "  195:                  TKC,           Japan (JP),            Tree ring\n",
      "  196:                  TSO,           Japan (JP),            Tree ring\n",
      "  197:                YKCOM,           Japan (JP),            Tree ring\n",
      "  198:                  YKA,           Japan (JP),            Tree ring\n",
      "  199:                  YKC,           Japan (JP),            Tree ring\n",
      "  200:                  KMD,           Japan (JP),            Tree ring\n",
      "  201:               BONKJT,       Kyrgystan (KG),            Tree ring\n",
      "  202:               GEFKJT,       Kyrgystan (KG),            Tree ring\n",
      "  203:               GRAKJT,       Kyrgystan (KG),            Tree ring\n",
      "  204:               HOCKJT,       Kyrgystan (KG),            Tree ring\n",
      "  205:               MURKJT,       Kyrgystan (KG),            Tree ring\n",
      "  206:               AJTMJT,       Kyrgystan (KG),            Tree ring\n",
      "  207:               KJTRJT,       Kyrgystan (KG),            Tree ring\n",
      "  208:               MJTQJT,       Kyrgystan (KG),            Tree ring\n",
      "  209:               XJTCJT,       Kyrgystan (KG),            Tree ring\n",
      "  210:               SHIYAT,       Kyrgystan (KG),            Tree ring\n",
      "  211:               ESPERG,       Kyrgystan (KG),            Tree ring\n",
      "  212:               SHIESP,       Kyrgystan (KG),            Tree ring\n",
      "  213:               KRNKAM,       Kamchatka (KM),            Tree ring\n",
      "  214:               ULKEPS,      Kazakhstan (KZ),            Tree ring\n",
      "  215:               KOELIU,      Kazakhstan (KZ),            Tree ring\n",
      "  216:               ENGILC,      Kazakhstan (KZ),            Tree ring\n",
      "  217:               KOKKIY,      Kazakhstan (KZ),            Tree ring\n",
      "  218:               NANENG,      Kazakhstan (KZ),            Tree ring\n",
      "  219:               ANKHLS,        Mongolia (MG),            Tree ring\n",
      "  220:               BAYHLS,        Mongolia (MG),            Tree ring\n",
      "  221:               BIGELS,        Mongolia (MG),            Tree ring\n",
      "  222:               BIARLS,        Mongolia (MG),            Tree ring\n",
      "  223:               BULGLS,        Mongolia (MG),            Tree ring\n",
      "  224:               DAYNLS,        Mongolia (MG),            Tree ring\n",
      "  225:               HENTLS,        Mongolia (MG),            Tree ring\n",
      "  226:               HORBLS,        Mongolia (MG),            Tree ring\n",
      "  227:               KHELLS,        Mongolia (MG),            Tree ring\n",
      "  228:               KHOGLS,        Mongolia (MG),            Tree ring\n",
      "  229:               KHOTLS,        Mongolia (MG),            Tree ring\n",
      "  230:               KHOVLS,        Mongolia (MG),            Tree ring\n",
      "  231:               MANDLS,        Mongolia (MG),            Tree ring\n",
      "  232:               MANHPS,        Mongolia (MG),            Tree ring\n",
      "  233:               ONONLS,        Mongolia (MG),            Tree ring\n",
      "  234:               OVOOLS,        Mongolia (MG),            Tree ring\n",
      "  235:               SODAPS,        Mongolia (MG),            Tree ring\n",
      "  236:               SPKRLS,        Mongolia (MG),            Tree ring\n",
      "  237:               SUBALS,        Mongolia (MG),            Tree ring\n",
      "  238:               SUMELS,        Mongolia (MG),            Tree ring\n",
      "  239:               TUCHLS,        Mongolia (MG),            Tree ring\n",
      "  240:               UNDULS,        Mongolia (MG),            Tree ring\n",
      "  241:               ZMGLPS,        Mongolia (MG),            Tree ring\n",
      "  242:               ZNMDLS,        Mongolia (MG),            Tree ring\n",
      "  243:               ZSMDLS,        Mongolia (MG),            Tree ring\n",
      "  244:               ZURTLS,        Mongolia (MG),            Tree ring\n",
      "  245:               Hushre,        Mongolia (MG),            Tree ring\n",
      "  246:               TERELJ,        Mongolia (MG),            Tree ring\n",
      "  247:               AGHERI,           Nepal (NP),            Tree ring\n",
      "  248:               BHULJR,           Nepal (NP),            Tree ring\n",
      "  249:               CHDAND,           Nepal (NP),            Tree ring\n",
      "  250:               DEORLA,           Nepal (NP),            Tree ring\n",
      "  251:               DDANJR,           Nepal (NP),            Tree ring\n",
      "  252:               GHURCH,           Nepal (NP),            Tree ring\n",
      "  253:               KATKUW,           Nepal (NP),            Tree ring\n",
      "  254:               LANGTD,           Nepal (NP),            Tree ring\n",
      "  255:               LUKUTD,           Nepal (NP),            Tree ring\n",
      "  256:               MUMBAS,           Nepal (NP),            Tree ring\n",
      "  257:               RACHAS,           Nepal (NP),            Tree ring\n",
      "  258:               BURGPW,           Nepal (NP),            Tree ring\n",
      "  259:               GHUNAB,           Nepal (NP),            Tree ring\n",
      "  260:               LAMIAB,           Nepal (NP),            Tree ring\n",
      "  261:               YAKHTD,           Nepal (NP),            Tree ring\n",
      "  262:               ENEPAB,           Nepal (NP),            Tree ring\n",
      "  263:               ESPPAK,        Pakistan (PK),            Tree ring\n",
      "  264:               ASTABP,        Pakistan (PK),            Tree ring\n",
      "  265:               ASTPIW,        Pakistan (PK),            Tree ring\n",
      "  266:               BAG1JU,        Pakistan (PK),            Tree ring\n",
      "  267:               BAG2JU,        Pakistan (PK),            Tree ring\n",
      "  268:               BAG4JU,        Pakistan (PK),            Tree ring\n",
      "  269:               BAG5JU,        Pakistan (PK),            Tree ring\n",
      "  270:               BUKCDD,        Pakistan (PK),            Tree ring\n",
      "  271:               BUKPIG,        Pakistan (PK),            Tree ring\n",
      "  272:               CGPCDD,        Pakistan (PK),            Tree ring\n",
      "  273:               CGPPIG,        Pakistan (PK),            Tree ring\n",
      "  274:               CHEPCS,        Pakistan (PK),            Tree ring\n",
      "  275:               CHP2JU,        Pakistan (PK),            Tree ring\n",
      "  276:               CHP3JU,        Pakistan (PK),            Tree ring\n",
      "  277:               DskJUE,        Pakistan (PK),            Tree ring\n",
      "  278:               ISBCDD,        Pakistan (PK),            Tree ring\n",
      "  279:               JOTPIG,        Pakistan (PK),            Tree ring\n",
      "  280:               JUTPCS,        Pakistan (PK),            Tree ring\n",
      "  281:               KARPCS,        Pakistan (PK),            Tree ring\n",
      "  282:               MOR1JU,        Pakistan (PK),            Tree ring\n",
      "  283:               MOR2JU,        Pakistan (PK),            Tree ring\n",
      "  284:               MOR3JU,        Pakistan (PK),            Tree ring\n",
      "  285:               MOR4JU,        Pakistan (PK),            Tree ring\n",
      "  286:               MSFCDD,        Pakistan (PK),            Tree ring\n",
      "  287:               MUSPIG,        Pakistan (PK),            Tree ring\n",
      "  288:               NLTJUE,        Pakistan (PK),            Tree ring\n",
      "  289:               NLTPCS,        Pakistan (PK),            Tree ring\n",
      "  290:               SAT1JU,        Pakistan (PK),            Tree ring\n",
      "  291:               SAT2JU,        Pakistan (PK),            Tree ring\n",
      "  292:               SAT3JU,        Pakistan (PK),            Tree ring\n",
      "  293:               TKACDD,        Pakistan (PK),            Tree ring\n",
      "  294:               ZACCDD,        Pakistan (PK),            Tree ring\n",
      "  295:               HRPCSM,        Pakistan (PK),            Tree ring\n",
      "  296:               NOGSAK, Sakhalin Island (SK),            Tree ring\n",
      "  297:               CHOLPB,           Tibet (TB),            Tree ring\n",
      "  298:               QAMDPB,           Tibet (TB),            Tree ring\n",
      "  299:               RIWOPB,           Tibet (TB),            Tree ring\n",
      "  300:               DENSJT,           Tibet (TB),            Tree ring\n",
      "  301:               QAMDJT,           Tibet (TB),            Tree ring\n",
      "  302:               MAINJT,           Tibet (TB),            Tree ring\n",
      "  303:               NYINJT,           Tibet (TB),            Tree ring\n",
      "  304:               RETIJT,           Tibet (TB),            Tree ring\n",
      "  305:               CENTIB,           Tibet (TB),            Tree ring\n",
      "  306:               MHGSTG,        Thailand (TH),            Tree ring\n",
      "  307:               CILAUP,          Taiwan (TW),            Tree ring\n",
      "  308:               MCCHFH,         Vietnam (VM),            Tree ring\n",
      "  309:               BDFLON,         Vietnam (VM),            Tree ring\n",
      "  310:              Mt Read,            Australia,            Tree ring\n",
      "  311:                Oroko,          New Zealand,            Tree ring\n",
      "  313: Celery Top Pine East,            Australia,            Tree ring\n",
      "  314:            Pink Pine,          New Zealand,            Tree ring\n",
      "  315:              Urewera,          New Zealand,            Tree ring\n",
      "  316:     Buckley's Chance,            Australia,            Tree ring\n",
      "  317: North Island_LIBI_Composite_1,          New Zealand,            Tree ring\n",
      "  318: Celery Top Pine West,            Australia,            Tree ring\n",
      "  319:           Mangawhero,          New Zealand,            Tree ring\n",
      "  320:                Kauri,          New Zealand,            Tree ring\n",
      "  322:  NI_LIBI_Composite_2,          New Zealand,            Tree ring\n",
      "  324: Stewart_Island_HABI_composite,          New Zealand,            Tree ring\n",
      "  329:                  Moa,          New Zealand,            Tree ring\n",
      "  334:               BawBaw,            Australia,            Tree ring\n",
      "[ 81.  82.  83.  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.\n",
      "  95.  96.  97.  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108.\n",
      " 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122.\n",
      " 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136.\n",
      " 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150.\n",
      " 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164.\n",
      " 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178.\n",
      " 179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192.\n",
      " 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206.\n",
      " 207. 208. 209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220.\n",
      " 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234.\n",
      " 235. 236. 237. 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248.\n",
      " 249. 250. 251. 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262.\n",
      " 263. 264. 265. 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276.\n",
      " 277. 278. 279. 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290.\n",
      " 291. 292. 293. 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304.\n",
      " 305. 306. 307. 308. 309. 310. 311. 313. 314. 315. 316. 317. 318. 319.\n",
      " 320. 322. 324. 329. 334.]\n"
     ]
    }
   ],
   "source": [
    "# We can write some code to search the metadata for a proxy we're interested in.\n",
    "#search_string = 'Europe'\n",
    "#field_to_search = 'PAGES 2k Region'\n",
    "\n",
    "#search_string = 'lake'\n",
    "#field_to_search = 'Archive type'\n",
    "\n",
    "# Loop through every key.  If part of the key matches the the search string, print the index and the key.\n",
    "#n_proxies = metadata.shape[0]\n",
    "#print('%5s: %20s: %20s: %20s:' % ('index','Site','Country/Region','Archive type'))\n",
    "#for i in range(n_proxies):\n",
    "    #if isinstance(metadata.loc[i][field_to_search], basestring):\n",
    "        #if search_string.lower() in metadata.loc[i][field_to_search].lower():\n",
    "            #print('%5s: %20s, %20s, %20s' % (i, metadata.loc[i]['Site'], metadata.loc[i]['Country/Region'],\\\n",
    "                                              #metadata.loc[i]['Archive type']))\n",
    "                        \n",
    "search_string = 'Asia'\n",
    "field_to_search = 'PAGES 2k Region'\n",
    "\n",
    "search_string2 = 'Tree ring'\n",
    "field_to_search2 = 'Archive type'\n",
    "\n",
    "# Loop through every key.  If part of the key matches the the search string, print the index and the key.\n",
    "\n",
    "n_proxies = metadata.shape[0]\n",
    "print('%5s: %20s: %20s: %20s:' % ('index','Site','Country/Region','Archive type'))\n",
    "\n",
    "Asia_index = []\n",
    "count = 0\n",
    "\n",
    "#calibration to modern observation part of record \n",
    "#proxies = proxies.truncate(before=1850, after=2000, axis=\"index\")\n",
    "\n",
    "for i in range(n_proxies):\n",
    "    if isinstance(metadata.loc[i][field_to_search], str):\n",
    "        if search_string.lower() in metadata.loc[i][field_to_search].lower() and search_string2.lower() in metadata.loc[i][field_to_search2].lower():\n",
    "       # if search_string.lower() in metadata.loc[i][field_to_search].lower():\n",
    "            print('%5s: %20s, %20s, %20s' % (i, metadata.loc[i]['Site'], metadata.loc[i]['Country/Region'],\\\n",
    "                                              metadata.loc[i]['Archive type']))\n",
    "            \n",
    "            count = count+1\n",
    "            Asia_index = np.append(Asia_index, i)\n",
    "print(Asia_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the index of a record you're interested in.\n",
    "index_selected = Asia_index\n",
    "metadata_sub=metadata.iloc[index_selected]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            PAGES 2k Region : 81.0            Asia\n",
      "82.0            Asia\n",
      "83.0            Asia\n",
      "84.0            Asia\n",
      "85.0            Asia\n",
      "            ...     \n",
      "320.0    Australasia\n",
      "322.0    Australasia\n",
      "324.0    Australasia\n",
      "329.0    Australasia\n",
      "334.0    Australasia\n",
      "Name: PAGES 2k Region, Length: 243, dtype: object\n",
      "                   Proxy ID : 81.0     Asi_001\n",
      "82.0     Asi_002\n",
      "83.0     Asi_003\n",
      "84.0     Asi_004\n",
      "85.0     Asi_005\n",
      "          ...   \n",
      "320.0     Aus_11\n",
      "322.0     Aus_13\n",
      "324.0     Aus_15\n",
      "329.0     Aus_20\n",
      "334.0     Aus_25\n",
      "Name: Proxy ID, Length: 243, dtype: object\n",
      "             Country/Region : 81.0     Altai Mountains (AT)\n",
      "82.0     Altai Mountains (AT)\n",
      "83.0     Altai Mountains (AT)\n",
      "84.0     Altai Mountains (AT)\n",
      "85.0     Altai Mountains (AT)\n",
      "                 ...         \n",
      "320.0             New Zealand\n",
      "322.0             New Zealand\n",
      "324.0             New Zealand\n",
      "329.0             New Zealand\n",
      "334.0               Australia\n",
      "Name: Country/Region, Length: 243, dtype: object\n",
      "                       Area : 81.0     LDEO\n",
      "82.0     LDEO\n",
      "83.0     LDEO\n",
      "84.0     LDEO\n",
      "85.0     LDEO\n",
      "         ... \n",
      "320.0     NaN\n",
      "322.0     NaN\n",
      "324.0     NaN\n",
      "329.0     NaN\n",
      "334.0     NaN\n",
      "Name: Area, Length: 243, dtype: object\n",
      "                       Site : 81.0                            AAT2LS\n",
      "82.0                            DJAZLS\n",
      "83.0                            JABELS\n",
      "84.0                            KUR2LS\n",
      "85.0                            KUR3LS\n",
      "                     ...              \n",
      "320.0                            Kauri\n",
      "322.0              NI_LIBI_Composite_2\n",
      "324.0    Stewart_Island_HABI_composite\n",
      "329.0                              Moa\n",
      "334.0                           BawBaw\n",
      "Name: Site, Length: 243, dtype: object\n",
      "                    Lat (N) : 81.0     50.0833\n",
      "82.0     49.6167\n",
      "83.0     50.8667\n",
      "84.0        50.3\n",
      "85.0     50.2667\n",
      "          ...   \n",
      "320.0        -36\n",
      "322.0        -39\n",
      "324.0        -47\n",
      "329.0        -41\n",
      "334.0        -36\n",
      "Name: Lat (N), Length: 243, dtype: object\n",
      "                    Lon (E) : 81.0     87.9333\n",
      "82.0        88.1\n",
      "83.0     85.2333\n",
      "84.0     87.8333\n",
      "85.0     87.8333\n",
      "          ...   \n",
      "320.0        174\n",
      "322.0        174\n",
      "324.0        168\n",
      "329.0        173\n",
      "334.0        148\n",
      "Name: Lon (E), Length: 243, dtype: object\n",
      "               Archive type : 81.0     Tree ring\n",
      "82.0     Tree ring\n",
      "83.0     Tree ring\n",
      "84.0     Tree ring\n",
      "85.0     Tree ring\n",
      "           ...    \n",
      "320.0    Tree ring\n",
      "322.0    Tree ring\n",
      "324.0    Tree ring\n",
      "329.0    Tree ring\n",
      "334.0    Tree ring\n",
      "Name: Archive type, Length: 243, dtype: object\n",
      "          Proxy measurement : 81.0     Total ring width\n",
      "82.0     Total ring width\n",
      "83.0     Total ring width\n",
      "84.0     Total ring width\n",
      "85.0     Total ring width\n",
      "               ...       \n",
      "320.0     Tree ring width\n",
      "322.0     Tree ring width\n",
      "324.0     Tree ring width\n",
      "329.0     Tree ring width\n",
      "334.0     Tree ring width\n",
      "Name: Proxy measurement, Length: 243, dtype: object\n",
      "           Proxy other info : 81.0     NaN\n",
      "82.0     NaN\n",
      "83.0     NaN\n",
      "84.0     NaN\n",
      "85.0     NaN\n",
      "        ... \n",
      "320.0    NaN\n",
      "322.0    NaN\n",
      "324.0    NaN\n",
      "329.0    NaN\n",
      "334.0    NaN\n",
      "Name: Proxy other info, Length: 243, dtype: object\n",
      "      Sign relation to temp : 81.0     Positive\n",
      "82.0     Positive\n",
      "83.0     Positive\n",
      "84.0     Positive\n",
      "85.0     Positive\n",
      "           ...   \n",
      "320.0    Positive\n",
      "322.0    Positive\n",
      "324.0    Positive\n",
      "329.0    Positive\n",
      "334.0    Negative\n",
      "Name: Sign relation to temp, Length: 243, dtype: object\n",
      "              Oldest (C.E.) : 81.0     1495.0\n",
      "82.0     1561.0\n",
      "83.0     1568.0\n",
      "84.0     1538.0\n",
      "85.0     1497.0\n",
      "          ...  \n",
      "320.0    1577.0\n",
      "322.0    1651.0\n",
      "324.0    1758.0\n",
      "329.0    1623.0\n",
      "334.0    1818.0\n",
      "Name: Oldest (C.E.), Length: 243, dtype: float64\n",
      "            Youngest (C.E.) : 81.0     1998.0\n",
      "82.0     2000.0\n",
      "83.0     1995.0\n",
      "84.0     2000.0\n",
      "85.0     2000.0\n",
      "          ...  \n",
      "320.0    2002.0\n",
      "322.0    1990.0\n",
      "324.0    1993.0\n",
      "329.0    1991.0\n",
      "334.0    2002.0\n",
      "Name: Youngest (C.E.), Length: 243, dtype: float64\n",
      "            Resolution (yr) : 81.0     1.0\n",
      "82.0     1.0\n",
      "83.0     1.0\n",
      "84.0     1.0\n",
      "85.0     1.0\n",
      "        ... \n",
      "320.0    1.0\n",
      "322.0    1.0\n",
      "324.0    1.0\n",
      "329.0    1.0\n",
      "334.0    1.0\n",
      "Name: Resolution (yr), Length: 243, dtype: float64\n",
      "                  Reference : 81.0                                                   NaN\n",
      "82.0                                                   NaN\n",
      "83.0                                                   NaN\n",
      "84.0                                                   NaN\n",
      "85.0                                                   NaN\n",
      "                               ...                        \n",
      "320.0    Fowler, A., Boswijk, G., Gergis, J. and Lorrey...\n",
      "322.0    Xiong, L. and Palmer, J. (2000). Reconstructio...\n",
      "324.0    D'Arrigo, R., Cook, E., Salinger, M., Palmer, ...\n",
      "329.0    Xiong, L. and Palmer, J. (2000). Reconstructio...\n",
      "334.0    Brookhouse, M., Lindesay, J. and Brack, C. (20...\n",
      "Name: Reference, Length: 243, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Loop through every element of the metadata and print it to screen.\n",
    "for key in metadata.keys():\n",
    "    print('%27s : %20s' % (key, metadata.loc[index_selected][key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.0     Asi_001\n",
      "82.0     Asi_002\n",
      "83.0     Asi_003\n",
      "84.0     Asi_004\n",
      "85.0     Asi_005\n",
      "          ...   \n",
      "320.0     Aus_11\n",
      "322.0     Aus_13\n",
      "324.0     Aus_15\n",
      "329.0     Aus_20\n",
      "334.0     Aus_25\n",
      "Name: Proxy ID, Length: 243, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Get the key of the desired record.\n",
    "proxyID_selected = metadata.loc[index_selected]['Proxy ID']\n",
    "print(proxyID_selected)\n",
    "\n",
    "# Get the data for this record.\n",
    "proxy_data = proxies[proxyID_selected]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save some useful metadata to new variables.\n",
    "reference    = metadata.loc[index_selected]['Reference']\n",
    "site_name    = metadata.loc[index_selected]['Site']\n",
    "lat          = metadata.loc[index_selected]['Lat (N)']\n",
    "lon          = metadata.loc[index_selected]['Lon (E)']\n",
    "archive_type = metadata.loc[index_selected]['Archive type']\n",
    "measurement  = metadata.loc[index_selected]['Proxy measurement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a figure of the proxy record.\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "ax = plt.axes([.1,.1,.8,.8])\n",
    "plt.plot(proxy_data,color='k',marker='o',linewidth=1)\n",
    "#plt.title(str(site_name)+\" (\"+str(archive_type)+\").  Lat: \"+str(lat)+\"$^\\circ$N, Lon: \"+\\\n",
    "          #str(lon)+\"$^\\circ$E\\nReference: \"+reference[0:100])\n",
    "plt.xlabel(\"Year\")\n",
    "#plt.ylabel(measurement)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further exploration\n",
    "\n",
    "Search for a different record and look at the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "Use of data from both proxy records and climate models is crucial to the advancement of paleoclimate research.\n",
    "* How many of you primarily work with proxy records?\n",
    "* How many of you primarily work with model output?\n",
    "* Do you often use data from both areas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a bunch of PAGES2k style settings\n",
    "class PAGES2k(object):\n",
    "    archive_types = ['Bivalve',\n",
    "                    'Borehole',\n",
    "                    'Coral',\n",
    "                    'Documentary',\n",
    "                    'Ice core',\n",
    "                    'Hybrid',\n",
    "                    'Lake/wetland sediments',\n",
    "                    'Lake sediment',\n",
    "                    'Marine sediment',\n",
    "                    'Marine sediments',\n",
    "                    'Sclerosponge',\n",
    "                    'Speleothem',\n",
    "                    'Tree ring',\n",
    "                    'Historic',\n",
    "                    'Instrumental',\n",
    "                    'Hyrax midden']\n",
    "    markers = ['p', 'p', 'o', 'v', 'd', '*', 's', 's', '8','8', 'D', '^','h','1','2','3']\n",
    "    markers_dict = dict(zip(archive_types, markers))\n",
    "    \n",
    "    colors = [np.array([ 1.        ,  0.83984375,  0.        ]),\n",
    "              np.array([ 0.73828125,  0.71484375,  0.41796875]),\n",
    "              np.array([ 1.        ,  0.546875  ,  0.        ]),\n",
    "              np.array([ 0.41015625,  0.41015625,  0.41015625]),\n",
    "              np.array([ 0.52734375,  0.8046875 ,  0.97916667]),\n",
    "              np.array([ 0.        ,  0.74609375,  1.        ]),\n",
    "              np.array([ 0.25390625,  0.41015625,  0.87890625]),\n",
    "              np.array([ 0.25390625,  0.41015625,  0.87890625]),\n",
    "              np.array([ 0.54296875,  0.26953125,  0.07421875]),\n",
    "              np.array([ 0.54296875,  0.26953125,  0.07421875]),\n",
    "              np.array([ 1         ,           0,           0]),\n",
    "              np.array([ 1.        ,  0.078125  ,  0.57421875]),\n",
    "              np.array([ 0.1953125 ,  0.80078125,  0.1953125 ]),\n",
    "              np.array([ 0.1953125 ,  0.80078125,  0.8 ]),\n",
    "              np.array([ 0.2 ,  0.2,  0.8 ]),\n",
    "              np.array([ 0.8 ,  0.80078125,  0.8 ])]\n",
    "    colors_dict = dict(zip(archive_types, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sites(df, title=None, lon_col='Lon (E)', lat_col='Lat (N)', archiveType_col='Archive type',\n",
    "               title_size=20, title_weight='bold', figsize=[10, 8], projection=ccrs.Robinson(), markersize=50,\n",
    "               plot_legend=True, legend_ncol=3, legend_anchor=(0, -0.4), legend_fontsize=15, frameon=False, ax=None):\n",
    "    \n",
    "    ''' Plot the location of the sites on a map\n",
    "\n",
    "    Args:\n",
    "        df (Pandas DataFrame): the Pandas DataFrame\n",
    "\n",
    "    Returns:\n",
    "        ax (Axes): the map plot of the sites\n",
    "\n",
    "    '''\n",
    "    p = PAGES2k()\n",
    "    if ax is None:\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        ax = plt.subplot(projection=projection)\n",
    "\n",
    "    sns.set(style=\"ticks\", font_scale=2)\n",
    "\n",
    "    # plot map\n",
    "    if title:\n",
    "        plt.title(title, fontsize=title_size, fontweight=title_weight)\n",
    "        plt.title(\"Tree Rings in Asia\")\n",
    "    ax.set_global()\n",
    "    ax.add_feature(cfeature.LAND, facecolor='gray', alpha=0.3)\n",
    "    ax.gridlines(edgecolor='gray', linestyle=':')\n",
    "\n",
    "    # plot markers by archive types\n",
    "    s_plots = []\n",
    "    type_names = []\n",
    "    df_archiveType_set = np.unique(df[archiveType_col])\n",
    "    for type_name in df_archiveType_set:\n",
    "        selector = df[archiveType_col] == type_name\n",
    "        type_names.append(f'{type_name} (n={len(df[selector])})')\n",
    "        s_plots.append(\n",
    "            ax.scatter(\n",
    "                df[selector][lon_col], df[selector][lat_col], marker=p.markers_dict[type_name],\n",
    "                c=p.colors_dict[type_name], edgecolor='k', s=markersize, transform=ccrs.PlateCarree()\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # plot legend\n",
    "    if plot_legend:\n",
    "        plt.legend(\n",
    "            s_plots, type_names,\n",
    "            scatterpoints=1,\n",
    "            bbox_to_anchor=legend_anchor,\n",
    "            loc='lower left',\n",
    "            ncol=legend_ncol,\n",
    "            frameon=frameon,\n",
    "            fontsize=legend_fontsize\n",
    "        )\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot map of proxy data\n",
    "ax = plot_sites(metadata_sub, title=f'PAGES2k Network (n={len(metadata)})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}