{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reconstruction using GraphEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected time to run through: 5 hrs**\n",
    "\n",
    "This tutorial demonstrates how to get a reconstruction using GraphEM, leveraging HadCRUT4 and PAGES2k."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data preparation\n",
    "\n",
    "To go through this tutorial, please prepare test data following the steps:\n",
    "\n",
    "Download the test case named “PAGES2k_HadCRUT” with this [link](https://drive.google.com/drive/folders/12V0Ny4eO2HKU40-Iy5Jiw2SAfVNzXXcd?usp=sharing).\n",
    "Create a directory named “testcases” in the same directory where this notebook sits.\n",
    "Put the unzipped direcotry “PAGES2k_HadCRUT” into “testcases”.\n",
    "\n",
    "Below, we first load some useful packages, including our `GraphEM`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import LMRt\n",
    "import GraphEM\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as  plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Low-level workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = GraphEM.ReconJob()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.load_configs() >>> loading reconstruction configurations from: ./testcases/PAGES2k_HadCRUT/configs.yml\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_configs() >>> job.configs created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.load_configs() >>> job.configs[\"job_dirpath\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_configs() >>> /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon created\u001b[0m\n",
      "{'anom_period': [1951, 1980],\n",
      " 'calib_period': [1930, 2000],\n",
      " 'job_dirpath': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon',\n",
      " 'job_id': 'GraphEM_tutorial',\n",
      " 'obs_path': {'tas': './data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc'},\n",
      " 'obs_varname': {'lat': 'latitude', 'lon': 'longitude', 'tas': 'tas_mean'},\n",
      " 'proxydb_path': './data/proxy/pages2k_dataset.pkl',\n",
      " 'ptype_list': ['coral.d18O',\n",
      "                'coral.SrCa',\n",
      "                'coral.calc',\n",
      "                'tree.TRW',\n",
      "                'tree.MXD'],\n",
      " 'recon_period': [1500, 2000]}\n"
     ]
    }
   ],
   "source": [
    "job.load_configs('./testcases/PAGES2k_HadCRUT/configs.yml', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.load_proxydb() >>> job.configs[\"proxydb_path\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/proxy/pages2k_dataset.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_proxydb() >>> 692 records loaded\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_proxydb() >>> job.proxydb created\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.load_proxydb(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.filter_proxydb() >>> filtering proxy records according to: ['coral.d18O', 'coral.SrCa', 'coral.calc', 'tree.TRW', 'tree.MXD']\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.filter_proxydb() >>> 519 records remaining\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.filter_proxydb(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_proxydb() >>> job.configs[\"ptype_season\"] = {'coral.d18O': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.SrCa': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.calc': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.TRW': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.MXD': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_proxydb() >>> seasonalizing proxy records according to: {'coral.d18O': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.SrCa': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.calc': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.TRW': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.MXD': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_proxydb() >>> 519 records remaining\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_proxydb() >>> job.proxydb updated\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# ptype_season = {}\n",
    "# for k, v in job.proxydb.type_dict.items():\n",
    "#     ptype_season[k] = list(range(1, 13)) # annual\n",
    "    \n",
    "# job.seasonalize_proxydb(ptype_season, verbose=True)\n",
    "job.seasonalize_proxydb(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.load_obs() >>> loading instrumental observation fields from: {'tas': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc'}\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_obs() >>> job.obs created\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.load_obs(varname_dict={'lat': 'latitude', 'lon': 'longitude', 'tas': 'tas_mean'}, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_obs() >>> job.configs[\"obs_season\"] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n",
      "\u001b[1m\u001b[30mGraphEM: job.seasonalize_obs() >>> seasonalized obs w/ season [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n",
      "Dataset Overview\n",
      "-----------------------\n",
      "\n",
      "     Name:  tas\n",
      "   Source:  /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc\n",
      "    Shape:  time:171, lat:36, lon:72\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_obs() >>> job.obs updated\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fzhu/Github/LMRt/LMRt/utils.py:261: RuntimeWarning: Mean of empty slice\n",
      "  tmp = np.nanmean(var[inds, ...], axis=0)\n"
     ]
    }
   ],
   "source": [
    "job.seasonalize_obs(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.recon_time created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.calib_time created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.calib_idx created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.temp created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.df_proxy created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.proxy created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.lonlat created\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.prep_data(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>0.837</td>\n",
       "      <td>1.644</td>\n",
       "      <td>9.078696</td>\n",
       "      <td>1.152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 519 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAm_153  NAm_165  Asi_178  Asi_174  Asi_198  NAm_145  Arc_071  \\\n",
       "1500.0      NaN      NaN    0.994      NaN      NaN      NaN     1.06   \n",
       "1501.0      NaN      NaN    1.131      NaN      NaN      NaN     0.70   \n",
       "1502.0      NaN      NaN    0.898      NaN      NaN      NaN     1.31   \n",
       "1503.0      NaN      NaN    1.032      NaN      NaN      NaN     0.70   \n",
       "1504.0      NaN      NaN    0.832      NaN      NaN      NaN    -0.43   \n",
       "...         ...      ...      ...      ...      ...      ...      ...   \n",
       "1996.0    1.346      NaN    1.028      NaN    1.647    1.146     1.36   \n",
       "1997.0      NaN      NaN      NaN      NaN    1.751    1.263     2.30   \n",
       "1998.0      NaN      NaN      NaN      NaN    1.499    1.054     0.67   \n",
       "1999.0      NaN      NaN      NaN      NaN    1.054    1.067     1.47   \n",
       "2000.0      NaN      NaN      NaN      NaN    0.842    0.994     1.97   \n",
       "\n",
       "        NAm_102  NAm_046  Ocn_065  ...  NAm_143  Asi_208  Asi_119   Ocn_153  \\\n",
       "1500.0      NaN    1.026      NaN  ...      NaN    0.805    0.849       NaN   \n",
       "1501.0      NaN    1.058      NaN  ...      NaN    0.694    0.882       NaN   \n",
       "1502.0      NaN    1.088      NaN  ...      NaN    0.742    0.620       NaN   \n",
       "1503.0      NaN    0.875      NaN  ...      NaN    0.677    0.413       NaN   \n",
       "1504.0      NaN    1.139      NaN  ...      NaN    0.730    0.551       NaN   \n",
       "...         ...      ...      ...  ...      ...      ...      ...       ...   \n",
       "1996.0    1.033    1.373      NaN  ...      NaN    1.490    0.813  9.099227   \n",
       "1997.0    1.037    1.153      NaN  ...      NaN    1.278    0.864  9.192620   \n",
       "1998.0    1.171    1.369      NaN  ...      NaN    1.332    0.743  9.124461   \n",
       "1999.0      NaN    1.502      NaN  ...      NaN    1.161    1.269  9.073438   \n",
       "2000.0      NaN    1.674      NaN  ...      NaN    0.837    1.644  9.078696   \n",
       "\n",
       "        Asi_026  Ocn_169  Asi_201  Asi_179   Ocn_071   Ocn_072  \n",
       "1500.0    0.710      NaN      NaN      NaN       NaN       NaN  \n",
       "1501.0    0.759      NaN      NaN      NaN       NaN       NaN  \n",
       "1502.0    0.944      NaN      NaN      NaN       NaN       NaN  \n",
       "1503.0    0.845      NaN      NaN      NaN       NaN       NaN  \n",
       "1504.0    0.702      NaN      NaN      NaN       NaN       NaN  \n",
       "...         ...      ...      ...      ...       ...       ...  \n",
       "1996.0    0.922      NaN    1.070    1.044 -4.114561  8.819362  \n",
       "1997.0    1.112      NaN    1.007    1.166 -4.231963  8.767808  \n",
       "1998.0    1.260      NaN    1.111    1.161 -4.427678  8.751082  \n",
       "1999.0    0.980      NaN    1.402    0.851 -4.081667  8.825333  \n",
       "2000.0    1.152      NaN    0.930      NaN       NaN       NaN  \n",
       "\n",
       "[501 rows x 519 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.df_proxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(501, 2592)\n",
      "(501, 519)\n",
      "(3111, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(job.temp))\n",
    "print(np.shape(job.proxy))\n",
    "print(np.shape(job.lonlat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.save_job() >>> Prepration data saved to: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/job.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.save_job() >>> job.configs[\"prep_savepath\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/job.pkl\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.save(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating graph using neighborhood method\n",
      "Running GraphEM:\n",
      "\n",
      "Iter     dXmis     rdXmis\n",
      "\n",
      "001     0.3304     0.0900\n",
      "002     0.3269     0.0914\n",
      "003     0.1768     0.0516\n",
      "004     0.1376     0.0413\n",
      "005     0.1085     0.0331\n",
      "006     0.1010     0.0307\n",
      "007     0.1025     0.0309\n",
      "008     0.1075     0.0318\n",
      "009     0.1023     0.0297\n",
      "010     0.0868     0.0247\n",
      "011     0.0694     0.0194\n",
      "012     0.0563     0.0156\n",
      "013     0.0486     0.0134\n",
      "014     0.0469     0.0128\n",
      "015     0.0476     0.0130\n",
      "016     0.0478     0.0130\n",
      "017     0.0497     0.0135\n",
      "018     0.0498     0.0135\n",
      "019     0.0498     0.0135\n",
      "020     0.0507     0.0137\n",
      "021     0.0496     0.0134\n",
      "022     0.0489     0.0132\n",
      "023     0.0483     0.0131\n",
      "024     0.0480     0.0130\n",
      "025     0.0481     0.0130\n",
      "026     0.0490     0.0133\n",
      "027     0.0495     0.0134\n",
      "028     0.0500     0.0135\n",
      "029     0.0502     0.0136\n",
      "030     0.0503     0.0136\n",
      "031     0.0500     0.0135\n",
      "032     0.0495     0.0134\n",
      "033     0.0488     0.0132\n",
      "034     0.0468     0.0126\n",
      "035     0.0458     0.0124\n",
      "036     0.0444     0.0120\n",
      "037     0.0430     0.0116\n",
      "038     0.0416     0.0112\n",
      "039     0.0403     0.0108\n",
      "040     0.0390     0.0104\n",
      "041     0.0378     0.0101\n",
      "042     0.0377     0.0100\n",
      "043     0.0352     0.0094\n",
      "044     0.0341     0.0090\n",
      "045     0.0331     0.0087\n",
      "046     0.0322     0.0085\n",
      "047     0.0313     0.0082\n",
      "048     0.0305     0.0080\n",
      "049     0.0298     0.0078\n",
      "050     0.0291     0.0076\n",
      "051     0.0284     0.0074\n",
      "052     0.0277     0.0072\n",
      "053     0.0271     0.0070\n",
      "054     0.0265     0.0068\n",
      "055     0.0259     0.0067\n",
      "056     0.0253     0.0065\n",
      "057     0.0248     0.0064\n",
      "058     0.0243     0.0062\n",
      "059     0.0239     0.0061\n",
      "060     0.0235     0.0060\n",
      "061     0.0231     0.0059\n",
      "062     0.0227     0.0058\n",
      "063     0.0224     0.0057\n",
      "064     0.0221     0.0056\n",
      "065     0.0218     0.0055\n",
      "066     0.0216     0.0054\n",
      "067     0.0214     0.0054\n",
      "068     0.0212     0.0053\n",
      "069     0.0210     0.0053\n",
      "070     0.0208     0.0052\n",
      "071     0.0207     0.0052\n",
      "072     0.0328     0.0082\n",
      "073     0.0239     0.0059\n",
      "074     0.0217     0.0054\n",
      "075     0.0207     0.0051\n",
      "076     0.0201     0.0050\n",
      "\u001b[1m\u001b[32mGraphEM: job.run_solver() >>> job.G created and saved to: ./testcases/PAGES2k_HadCRUT/recon/G.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.run_solver() >>> job.recon created\u001b[0m\n",
      "CPU times: user 1d 5h 1min 54s, sys: 1h 51min 56s, total: 1d 6h 53min 50s\n",
      "Wall time: 4h 38min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "save_path = './testcases/PAGES2k_HadCRUT/recon/G.pkl'\n",
    "job.run_solver(save_path=save_path, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(501, 36, 72)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(job.recon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.save_recon() >>> Reconstruction saved to: ./testcases/PAGES2k_HadCRUT/recon/recon.nc\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.save_recon(f'./testcases/PAGES2k_HadCRUT/recon/recon.nc', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top-level workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mGraphEM: job.load_configs() >>> loading reconstruction configurations from: ./testcases/PAGES2k_HadCRUT/configs.yml\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_configs() >>> job.configs created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.load_configs() >>> job.configs[\"job_dirpath\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_configs() >>> /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon created\u001b[0m\n",
      "{'anom_period': [1951, 1980],\n",
      " 'calib_period': [1970, 2000],\n",
      " 'job_dirpath': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon',\n",
      " 'job_id': 'LMRt_quickstart',\n",
      " 'obs_path': {'tas': './data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc'},\n",
      " 'obs_varname': {'lat': 'latitude', 'lon': 'longitude', 'tas': 'tas_mean'},\n",
      " 'proxydb_path': './data/proxy/pages2k_dataset.pkl',\n",
      " 'ptype_list': ['coral.d18O',\n",
      "                'coral.SrCa',\n",
      "                'coral.calc',\n",
      "                'tree.TRW',\n",
      "                'tree.MXD'],\n",
      " 'recon_period': [1500, 2000]}\n",
      "\u001b[1m\u001b[36mLMRt: job.load_configs() >>> job.configs[\"job_dirpath\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_configs() >>> /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.load_proxydb() >>> job.configs[\"proxydb_path\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/proxy/pages2k_dataset.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_proxydb() >>> 692 records loaded\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_proxydb() >>> job.proxydb created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_proxydb() >>> job.configs[\"ptype_season\"] = {'coral.d18O': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.SrCa': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.calc': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.TRW': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.MXD': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_proxydb() >>> seasonalizing proxy records according to: {'coral.d18O': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.SrCa': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'coral.calc': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.TRW': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'tree.MXD': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_proxydb() >>> 692 records remaining\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_proxydb() >>> job.proxydb updated\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.load_obs() >>> loading instrumental observation fields from: {'tas': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc'}\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.load_obs() >>> job.obs created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.seasonalize_obs() >>> job.configs[\"obs_season\"] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n",
      "\u001b[1m\u001b[30mGraphEM: job.seasonalize_obs() >>> seasonalized obs w/ season [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n",
      "Dataset Overview\n",
      "-----------------------\n",
      "\n",
      "     Name:  tas\n",
      "   Source:  /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/data/obs/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc\n",
      "    Shape:  time:171, lat:36, lon:72\n",
      "\u001b[1m\u001b[32mGraphEM: job.seasonalize_obs() >>> job.obs updated\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fzhu/Github/LMRt/LMRt/utils.py:258: RuntimeWarning: Mean of empty slice\n",
      "  tmp = np.nanmean(var[inds, ...], axis=0)\n",
      "100%|██████████| 72/72 [00:00<00:00, 22026.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.recon_time created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.calib_time created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.calib_idx created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.temp created\u001b[0m\n",
      "\u001b[1m\u001b[36mGraphEM: job.prep_data() >>> Preparing proxy and lonlat\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.df_proxy created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.proxy created\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.prep_data() >>> job.lonlat created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.save_job() >>> Prepration data saved to: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/job.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.save_job() >>> job.configs[\"prep_savepath\"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/job.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run_cfg() >>> G will be saved to: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/G.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.run_solver() >>> job.G created with the existing result at: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/G.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mGraphEM: job.run_solver() >>> job.recon created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run_cfg() >>> recon. will be saved to: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/recon.nc\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.save_recon() >>> Reconstruction saved to: /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PAGES2k_HadCRUT/recon/recon.nc\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job = GraphEM.ReconJob()\n",
    "job.run_cfg('./testcases/PAGES2k_HadCRUT/configs.yml', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:  (lat: 36, lon: 72, year: 501)\n",
      "Coordinates:\n",
      "  * year     (year) int64 1500 1501 1502 1503 1504 ... 1996 1997 1998 1999 2000\n",
      "  * lat      (lat) float64 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5\n",
      "  * lon      (lon) float64 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5\n",
      "Data variables:\n",
      "    recon    (year, lat, lon) float64 ...\n"
     ]
    }
   ],
   "source": [
    "with xr.open_dataset('./testcases/PAGES2k_HadCRUT/recon/recon.nc') as ds:\n",
    "    print(ds)\n",
    "    recon = ds['recon']\n",
    "    lat = ds['lat']\n",
    "    lon = ds['lon']\n",
    "    year = ds['year']\n",
    "    \n",
    "gm, nhm, shm = LMRt.utils.global_hemispheric_means(recon.values, lat.values)\n",
    "ts_recon = LMRt.Series(time=year, value=gm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(171,)\n",
      "(171, 36, 72)\n",
      "(36,)\n"
     ]
    }
   ],
   "source": [
    "tas = job.obs.fields['tas']\n",
    "gm_hadcrut, nhm_hadcrut, shm_hadcrut = LMRt.utils.global_hemispheric_means(tas.value, tas.lat)\n",
    "print(np.shape(tas.time))\n",
    "print(np.shape(tas.value))\n",
    "print(np.shape(tas.lat))\n",
    "ts_hadcrut = LMRt.Series(\n",
    "    time=tas.time,\n",
    "    value=gm_hadcrut,\n",
    "    label='HadCRUT',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lmr_time, lmr_gmst = pd.read_pickle('https://github.com/LinkedEarth/paleoHackathon/raw/main/data/lmr_gmst.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2001,)\n",
      "(2001, 20, 100)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(lmr_time))\n",
    "print(np.shape(lmr_gmst))\n",
    "lmr_gmst_mean = np.average(np.average(lmr_gmst, axis=-1), axis=-1)\n",
    "ts_lmr = LMRt.Series(time=lmr_time, value=lmr_gmst_mean, label='LMRv2.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot and validate the NINO3.4\n",
    "fig, ax = ts_recon.plot(mute=True, label='recon.')\n",
    "ts_lmr.plot(ax=ax)\n",
    "ax.set_xlim(1500, 2000)\n",
    "ax.set_ylim(-1, 2)\n",
    "ax.set_title('GMST')\n",
    "ts_hadcrut.plot(ax=ax)\n",
    "LMRt.showfig(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  correlation    p-value  signif. (α: 0.05)\n",
      "-------------  ---------  -------------------\n",
      "   -0.0116929       0.97  False\n",
      "\n"
     ]
    }
   ],
   "source": [
    "corr = ts_lmr.slice([1500, 1800]).correlation(ts_recon.slice([1500, 1800]))\n",
    "print(corr)"
   ]
  },
  {
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}