{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "182f3f4c-5323-4418-85db-2d99a11de09a",
   "metadata": {},
   "source": [
    "# Reconstruct a cropped domain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf48cf47-c86d-4240-ba05-f3dacf8b89cf",
   "metadata": {},
   "source": [
    "**Expected time to run through: 10 mins**\n",
    "\n",
    "This tutorial demonstrates how to reconstruct a cropped domain to save running time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a854ccd3-b948-46f1-8a7f-cb63637addcd",
   "metadata": {},
   "source": [
    "## Test data preparation\n",
    "\n",
    "To go through this tutorial, please prepare test data following the steps:\n",
    "1. Download the test case named \"PAGES2k_CCSM4_GISTEMP\" with this [link](https://drive.google.com/drive/folders/1UGn-LNd_tGSjPUKa52E6ffEM-ms2VD-N?usp=sharing).\n",
    "2. Create a directory named \"testcases\" in the same directory where this notebook sits.\n",
    "3. Put the unzipped direcotry \"PAGES2k_CCSM4_GISTEMP\" into \"testcases\".\n",
    "\n",
    "Below, we first load some useful packages, including our `LMRt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "608d8ed6-1310-4fa7-a319-7e76c6f95019",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import LMRt\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3584453-df31-401b-a51a-6a2bfcd00c10",
   "metadata": {},
   "source": [
    "## Load and modify configurations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a8fcc0-d9e4-438b-8454-d583140b94c8",
   "metadata": {},
   "source": [
    "We will first load the given example YAML file, which is set to reconstruct the temperature field only.\n",
    "We then modify specific items to set it to reconstruct both the temperature and sea level pressure fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bfeeed4-4c78-4406-ae27-bf28f4cbb42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.load_configs() >>> loading reconstruction configurations from: ./testcases/PAGES2k_CCSM4_GISTEMP/configs.yml\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_configs() >>> job.configs created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.load_configs() >>> job.configs[\"job_dirpath\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_configs() >>> /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon created\u001b[0m\n",
      "{'anom_period': [1951, 1980],\n",
      " 'job_dirpath': '/Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon',\n",
      " 'job_id': 'LMRt_quickstart',\n",
      " 'obs_path': {'tas': './data/obs/gistemp1200_ERSSTv4.nc'},\n",
      " 'obs_varname': {'tas': 'tempanomaly'},\n",
      " 'prior_path': {'tas': './data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc'},\n",
      " 'prior_regrid_ntrunc': 42,\n",
      " 'prior_season': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
      " 'prior_varname': {'tas': 'TREFHT'},\n",
      " 'proxy_frac': 0.75,\n",
      " 'proxydb_path': './data/proxy/pages2k_dataset.pkl',\n",
      " 'psm_calib_period': [1850, 2015],\n",
      " 'ptype_psm': {'coral.SrCa': 'linear',\n",
      "               'coral.calc': 'linear',\n",
      "               'coral.d18O': 'linear'},\n",
      " 'ptype_season': {'coral.SrCa': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
      "                  'coral.calc': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
      "                  'coral.d18O': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]},\n",
      " 'recon_loc_rad': 25000,\n",
      " 'recon_nens': 100,\n",
      " 'recon_period': [0, 2000],\n",
      " 'recon_seeds': [0,\n",
      "                 1,\n",
      "                 2,\n",
      "                 3,\n",
      "                 4,\n",
      "                 5,\n",
      "                 6,\n",
      "                 7,\n",
      "                 8,\n",
      "                 9,\n",
      "                 10,\n",
      "                 11,\n",
      "                 12,\n",
      "                 13,\n",
      "                 14,\n",
      "                 15,\n",
      "                 16,\n",
      "                 17,\n",
      "                 18,\n",
      "                 19],\n",
      " 'recon_timescale': 1,\n",
      " 'recon_vars': 'tas'}\n"
     ]
    }
   ],
   "source": [
    "job = LMRt.ReconJob()\n",
    "job.load_configs(cfg_path='./testcases/PAGES2k_CCSM4_GISTEMP/configs.yml', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9fd3af8-ea7e-49f4-b4cc-de7cc746e16e",
   "metadata": {},
   "source": [
    "We need to modify the `prior_path` and the `prior_varname` in the configurations.\n",
    "We also modify the `job_dirpath` to save the results to a different location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a21a1ca9-2b8d-4bbf-ad1c-acd14500fc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "job.configs['job_dirpath'] = './testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain'\n",
    "\n",
    "job.configs['prior_path'] = {\n",
    "    'tas': './data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc',\n",
    "}\n",
    "\n",
    "# prior_varname indicates the variable names in the given netCDF files\n",
    "job.configs['prior_varname'] = {\n",
    "    'tas': 'TREFHT',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a18ba1-9791-41f0-ab7a-9f2f34f8ac79",
   "metadata": {},
   "source": [
    "## Run the reconstruction using the high-level workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07cc27e2-fa4e-4c35-b995-4c820db4faff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.load_proxydb() >>> job.configs[\"proxydb_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/proxy/pages2k_dataset.pkl\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_proxydb() >>> 692 records loaded\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_proxydb() >>> job.proxydb created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.filter_proxydb() >>> filtering proxy records according to: ['coral.d18O', 'coral.SrCa', 'coral.calc']\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.filter_proxydb() >>> 95 records remaining\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: 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]}\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.seasonalize_proxydb() >>> 95 records remaining\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.seasonalize_proxydb() >>> job.proxydb updated\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.load_prior() >>> loading model prior fields from: {'tas': '/Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc'}\u001b[0m\n",
      "Time axis not overlap with the reference period [1951, 1980]; use its own time period as reference [850.08, 1850.00].\n",
      "\u001b[1m\u001b[30mLMRt: job.load_prior() >>> raw prior\u001b[0m\n",
      "Dataset Overview\n",
      "-----------------------\n",
      "\n",
      "     Name:  tas\n",
      "   Source:  /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc\n",
      "    Shape:  time:12000, lat:96, lon:144\n",
      "\u001b[1m\u001b[32mLMRt: job.load_prior() >>> job.prior created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.load_obs() >>> loading instrumental observation fields from: {'tas': '/Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/obs/gistemp1200_ERSSTv4.nc'}\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.load_obs() >>> job.obs created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> job.configs[\"precalc\"][\"seasonalized_prior_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/seasonalized_prior.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> job.configs[\"precalc\"][\"seasonalized_obs_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/seasonalized_obs.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> job.configs[\"precalc\"][\"prior_loc_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/prior_loc.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> job.configs[\"precalc\"][\"obs_loc_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/obs_loc.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> job.configs[\"precalc\"][\"calibed_psm_path\"] = /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/calibed_psm.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.seasonalize_ds_for_psm() >>> 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]}\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.seasonalize_ds_for_psm() >>> Seasonalizing variables from prior with season: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Searching nearest location:   8%|▊         | 8/95 [00:00<00:01, 79.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mLMRt: job.seasonalize_ds_for_psm() >>> job.seasonalized_prior created\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Searching nearest location: 100%|██████████| 95/95 [00:01<00:00, 93.82it/s]\n",
      "/Users/fzhu/Github/LMRt/LMRt/utils.py:261: RuntimeWarning: Mean of empty slice\n",
      "  tmp = np.nanmean(var[inds, ...], axis=0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mLMRt: job.proxydb.find_nearest_loc() >>> job.proxydb.prior_lat_idx & job.proxydb.prior_lon_idx created\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.proxydb.get_var_from_ds() >>> job.proxydb.records[pid].prior_time & job.proxydb.records[pid].prior_value created\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.seasonalize_ds_for_psm() >>> 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]}\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.seasonalize_ds_for_psm() >>> Seasonalizing variables from obs with season: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Searching nearest location:   8%|▊         | 8/95 [00:00<00:01, 77.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mLMRt: job.seasonalize_ds_for_psm() >>> job.seasonalized_obs created\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Searching nearest location: 100%|██████████| 95/95 [00:01<00:00, 82.93it/s]\n",
      "Calibrating PSM:   7%|▋         | 7/95 [00:00<00:01, 66.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mLMRt: job.proxydb.find_nearest_loc() >>> job.proxydb.obs_lat_idx & job.proxydb.obs_lon_idx created\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.proxydb.get_var_from_ds() >>> job.proxydb.records[pid].obs_time & job.proxydb.records[pid].obs_value created\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.proxydb.init_psm() >>> job.proxydb.records[pid].psm initialized\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.calibrate_psm() >>> PSM calibration period: [1850, 2015]\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calibrating PSM:  66%|██████▋   | 63/95 [00:00<00:00, 76.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of overlapped data points is 0 < 25. Skipping ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calibrating PSM: 100%|██████████| 95/95 [00:01<00:00, 74.40it/s]\n",
      "Forwarding PSM: 100%|██████████| 95/95 [00:00<00:00, 617.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32mLMRt: job.proxydb.calib_psm() >>> job.proxydb.records[pid].psm calibrated\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.proxydb.calib_psm() >>> job.proxydb.calibed created\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: job.proxydb.forward_psm() >>> job.proxydb.records[pid].psm forwarded\u001b[0m\n",
      "\u001b[1m\u001b[30mLMRt: job.seasonalize_prior() >>> seasonalized prior 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/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc\n",
      "    Shape:  time:999, lat:96, lon:144\n",
      "\u001b[1m\u001b[32mLMRt: job.seasonalize_prior() >>> job.prior updated\u001b[0m\n",
      "\u001b[1m\u001b[30mLMRt: job.regrid_prior() >>> regridded prior\u001b[0m\n",
      "Dataset Overview\n",
      "-----------------------\n",
      "\n",
      "     Name:  tas\n",
      "   Source:  /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc\n",
      "    Shape:  time:999, lat:42, lon:63\n",
      "\u001b[1m\u001b[32mLMRt: job.regrid_prior() >>> job.prior updated\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.prepare() >>> Prepration data saved to: ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job.pkl\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.prepare() >>> job.configs[\"precalc\"][\"prep_savepath\"] = ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job.pkl\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "job.prepare(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c9d1b3-2b00-433c-bcae-a473c5af4f3e",
   "metadata": {},
   "source": [
    "## Crop the domain before running DA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b65d710-6954-48fc-9ab2-e4fdf2c03d79",
   "metadata": {},
   "source": [
    "There are two ways of cropping the domain:\n",
    "+ crop only the latitudes with the input argument `domain_range = [lat_min, lat_max]`\n",
    "+ crop both the latitudes and longitudes the input argument `domain_range = [lat_min, lat_max, lon_min, lon_max]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f89078b-bb60-41de-a55a-ea6d5e86fdb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[30mLMRt: job.crop_prior() >>> cutted prior\u001b[0m\n",
      "Dataset Overview\n",
      "-----------------------\n",
      "\n",
      "     Name:  tas\n",
      "   Source:  /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/prior/b.e11.BLMTRC5CN.f19_g16.001.cam.h0.TREFHT.085001-184912.nc\n",
      "    Shape:  time:999, lat:14, lon:18\n",
      "\u001b[1m\u001b[32mLMRt: job.crop_prior() >>> job.prior updated\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# job.crop_prior([-30, 30], verbose=True)  # crop only the latitudes\n",
    "job.crop_prior([-30, 30, 100, 200], verbose=True)  # crop both latitudes and longitudes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23c6740-4788-4b86-9eb3-14c8a86df21f",
   "metadata": {},
   "source": [
    "## Running DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01a9ac90-5107-4653-8c43-6cf495982ac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KF updating:   6%|▌         | 123/2001 [00:00<00:01, 1223.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.run() >>> job.configs[\"recon_seeds\"] = [0]\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run() >>> job.configs[\"save_settings\"] = {'compress_dict': {'zlib': True, 'least_significant_digit': 1}, 'output_geo_mean': False, 'target_lats': [], 'target_lons': [], 'output_full_ens': False, 'dtype': 32}\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run() >>> job.configs saved to: ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job_configs.yml\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run() >>> seed: 0 | max: 0\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run() >>> randomized indices for prior and proxies saved to: ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job_r00_idx.pkl\u001b[0m\n",
      "Proxy Database Overview\n",
      "-----------------------\n",
      "     Source:        /Users/fzhu/Github/LMRt/docsrc/tutorial/testcases/PAGES2k_CCSM4_GISTEMP/data/proxy/pages2k_dataset.pkl\n",
      "       Size:        70\n",
      "Proxy types:        {'coral.calc': 6, 'coral.SrCa': 19, 'coral.d18O': 45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KF updating: 100%|██████████| 2001/2001 [00:08<00:00, 227.88it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mLMRt: job.save_recon() >>> Reconstructed fields saved to: ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job_r00_recon.nc\u001b[0m\n",
      "\u001b[1m\u001b[36mLMRt: job.run() >>> DONE!\u001b[0m\n",
      "CPU times: user 1min 18s, sys: 2.05 s, total: 1min 20s\n",
      "Wall time: 27.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "job.run(recon_seeds=np.arange(1), verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02440a5d-ddac-4637-b430-f9e219d3c7ef",
   "metadata": {},
   "source": [
    "Once done, we will get the struture below in the \"recon_cropped_domain\" directory:\n",
    "```\n",
    ".\n",
    "├── calibed_psm.pkl\n",
    "├── job_configs.yml\n",
    "├── job_r00_idx.pkl\n",
    "├── job_r00_recon.nc\n",
    "├── job.pkl\n",
    "├── obs_loc.pkl\n",
    "├── prior_loc.pkl\n",
    "├── seasonalized_obs.pkl\n",
    "└── seasonalized_prior.pkl\n",
    "```\n",
    "\n",
    "We now do a quick preview of the results below.\n",
    "For more details of the visualization of the results, please move on to the tutorial regarding visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf87edac-a39d-4021-b7c5-86bf54751406",
   "metadata": {},
   "source": [
    "## A quick preview of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8189d4ea-1259-42d3-a7e8-cec524a72fb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recon_paths: ['./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job_r00_recon.nc']\n",
      "idx_paths: ['./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job_r00_idx.pkl']\n",
      "job_path: ./testcases/PAGES2k_CCSM4_GISTEMP/recon_cropped_domain/job.pkl\n"
     ]
    }
   ],
   "source": [
    "# create the res object for reconstruction results\n",
    "res = LMRt.ReconRes(job.configs['job_dirpath'], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d87c2b0f-b71d-49d5-936d-c588f2be17ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[30mLMRt: res.get_var() >>> loading variable: tas\u001b[0m\n",
      "\u001b[1m\u001b[32mLMRt: res.get_var() >>> res.vars filled w/ varnames: ['tas'] and ['year', 'lat', 'lon']\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# get the varialbes from the recon_paths\n",
    "res.get_vars(['tas'], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5c3f0cf-072b-491c-8daf-1fcac395300d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fzhu/Github/LMRt/LMRt/visual.py:267: MatplotlibDeprecationWarning: The 'extend' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  cbar = fig.colorbar(im, ax=ax, orientation=cbar_orientation, pad=cbar_pad, aspect=cbar_aspect, extend=extend,\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the tas field\n",
    "fig, ax = res.vars['tas'].field_list[0].plot(idx_t=-1)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}