{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d52e464a3b45c1ba",
   "metadata": {},
   "source": [
    "# Read CMIP-PPE data and emulate with Gaussian Processor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f45a45c-df17-4238-b264-8b7a9aec1953",
   "metadata": {},
   "source": [
    "\n",
    "**Adjusted for the eScience course from Duncan Watson-Parris' example here: [gist.github.com/duncanwp](https://gist.github.com/duncanwp/89175a17b7221e4d3639765621c7f7f9)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58e60b8-51f6-4034-8bf3-526d5da67751",
   "metadata": {},
   "source": [
    "You have to use the env:ml-notebook to run this example.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d98ac0ce-2f20-4e3d-8620-450325c76c25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from esem import gp_model\n",
    "from esem.utils import validation_plot, get_param_mask\n",
    "from pathlib import Path\n",
    "xr.set_options(display_style='html')\n",
    "import intake\n",
    "import cftime\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import datetime\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82ad6e05-9c2c-4c63-bc19-91eedd386cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def global_mean(ds):\n",
    "    weights = np.cos(np.deg2rad(ds.lat))\n",
    "    return ds.weighted(weights).mean(['lat', 'lon'])\n",
    "\n",
    "def get_ensemble_member(ds):\n",
    "    fname = ds.encoding['source']\n",
    "    member = int(fname.split('.')[-4])\n",
    "    return ds.assign_coords(member=member).expand_dims('member')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2babd9-e578-4f7e-84e7-0b663f382b5c",
   "metadata": {},
   "source": [
    "## Open the overview over the parameters in the CAM6 CESM PPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "92417e17-40ad-4f5b-82fb-3eb2f7939743",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('~/shared-craas1-ns9989k-ns9560k/CAM6_CESM_PPE/')\n",
    "\n",
    "params = (xr.open_dataset(data_path / \"parameter_262_w_control.nc\")\n",
    "          .to_pandas()\n",
    "          .drop(columns = ['Sample_nmb'])\n",
    "         )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad02937f-4434-47c3-b722-7b4fd03bc5c5",
   "metadata": {},
   "source": [
    "### Open CMIP6 online catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "263acb2b-8f49-48ed-acc7-072f91c4edfb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>cesm-ppe catalog with 2 dataset(s) from 32571 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble</th>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_levels</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_range</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>32571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_url = '/mnt/craas1-ns9989k-geo4992/data/catalogs/cesm-ppe.json'\n",
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16661a25-147f-40e3-b96a-a3c170b3ffc9",
   "metadata": {},
   "source": [
    "\n",
    "### Search corresponding data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94ff4c4-8f03-46db-b19c-be4abe6d6b29",
   "metadata": {},
   "source": [
    "Please check [here](https://pangeo-data.github.io/escience-2022/pangeo101/data_discovery.html?highlight=cmip6) for info about CMIP and variables :) \n",
    "\n",
    "Particularly useful is maybe the variable search which you find here: https://clipc-services.ceda.ac.uk/dreq/mipVars.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2549450a-4a44-4e04-bfda-1b8eeade4f27",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>ensemble</th>\n",
       "      <th>frequency</th>\n",
       "      <th>variable</th>\n",
       "      <th>units</th>\n",
       "      <th>long_name</th>\n",
       "      <th>vertical_levels</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>time_range</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>present-day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>present-day</td>\n",
       "      <td>1.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>present-day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>present-day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>present-day</td>\n",
       "      <td>4.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>present-day</td>\n",
       "      <td>258.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>present-day</td>\n",
       "      <td>259.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>present-day</td>\n",
       "      <td>260.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>present-day</td>\n",
       "      <td>261.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>present-day</td>\n",
       "      <td>262.0</td>\n",
       "      <td>monthly</td>\n",
       "      <td>SWCF</td>\n",
       "      <td>W/m2</td>\n",
       "      <td>Shortwave cloud forcing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0001-01-16</td>\n",
       "      <td>0003-12-16</td>\n",
       "      <td>0001-01-16-0003-12-16</td>\n",
       "      <td>/mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      experiment  ensemble frequency variable units                long_name  \\\n",
       "0    present-day       0.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "1    present-day       1.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "2    present-day       2.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "3    present-day       3.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "4    present-day       4.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "..           ...       ...       ...      ...   ...                      ...   \n",
       "257  present-day     258.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "258  present-day     259.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "259  present-day     260.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "260  present-day     261.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "261  present-day     262.0   monthly     SWCF  W/m2  Shortwave cloud forcing   \n",
       "\n",
       "     vertical_levels  start_time    end_time             time_range  \\\n",
       "0                1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "1                1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "2                1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "3                1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "4                1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "..               ...         ...         ...                    ...   \n",
       "257              1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "258              1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "259              1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "260              1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "261              1.0  0001-01-16  0003-12-16  0001-01-16-0003-12-16   \n",
       "\n",
       "                                                  path  \n",
       "0    /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "1    /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "2    /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "3    /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "4    /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "..                                                 ...  \n",
       "257  /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "258  /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "259  /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "260  /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "261  /mnt/craas1-ns9989k-ns9560k/CAM6_CESM_PPE/PD/m...  \n",
       "\n",
       "[262 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = col.search(\n",
    "    experiment=['present-day'], \n",
    "    variable = ['SWCF'], \n",
    "    frequency='monthly'\n",
    ")\n",
    "\n",
    "cat.df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4befa75c-7f95-4e1d-b5a2-f604fe54d12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SWCF'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.df['variable'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7858ef-8c0b-4d9d-9127-d1cef642b7ec",
   "metadata": {},
   "source": [
    "### Create dictionary from the list of datasets we found\n",
    "- This step may take several minutes so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "66132009-2292-4985-ad47-edf940999491",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'experiment.frequency'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1/1 00:25&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset_dict = cat.to_dataset_dict()#preprocess = get_ensemble_member,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b469a9fd-6e3f-4880-b5c3-4857147a9833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:   (lat: 192, lon: 288, time: 36, ensemble: 262)\n",
       "Coordinates:\n",
       "  * lat       (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon       (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * time      (time) object 288B 0001-01-16 12:00:00 ... 0003-12-16 12:00:00\n",
       "  * ensemble  (ensemble) float64 2kB 0.0 1.0 2.0 3.0 ... 259.0 260.0 261.0 262.0\n",
       "Data variables:\n",
       "    SWCF      (ensemble, time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 36, 192, 288), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    intake_esm_vars:                   [&#x27;SWCF&#x27;]\n",
       "    intake_esm_attrs:experiment:       present-day\n",
       "    intake_esm_attrs:frequency:        monthly\n",
       "    intake_esm_attrs:variable:         SWCF\n",
       "    intake_esm_attrs:units:            W/m2\n",
       "    intake_esm_attrs:long_name:        Shortwave cloud forcing\n",
       "    intake_esm_attrs:vertical_levels:  1.0\n",
       "    intake_esm_attrs:start_time:       0001-01-16\n",
       "    intake_esm_attrs:end_time:         0003-12-16\n",
       "    intake_esm_attrs:time_range:       0001-01-16-0003-12-16\n",
       "    intake_esm_attrs:_data_format_:    netcdf\n",
       "    intake_esm_dataset_key:            present-day.monthly</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f1a5a669-4dbe-41b1-acbf-f0ee5636629f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1a5a669-4dbe-41b1-acbf-f0ee5636629f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 36</li><li><span class='xr-has-index'>ensemble</span>: 262</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db802896-9e4f-4572-88f1-aa056600e08c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db802896-9e4f-4572-88f1-aa056600e08c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-d857b20b-a79e-48a0-aaf6-7f2f82c413ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d857b20b-a79e-48a0-aaf6-7f2f82c413ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc7591f1-afcc-475a-bca9-c7ed6ba643f1' class='xr-var-data-in' type='checkbox'><label for='data-bc7591f1-afcc-475a-bca9-c7ed6ba643f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-48a97b1c-dd36-4e99-b2ee-a8f7f576b83c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48a97b1c-dd36-4e99-b2ee-a8f7f576b83c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-686eea7a-f761-46db-89a2-f8bbd88340be' class='xr-var-data-in' type='checkbox'><label for='data-686eea7a-f761-46db-89a2-f8bbd88340be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0001-01-16 12:00:00 ... 0003-12-...</div><input id='attrs-ca7ebc0e-dd2f-4a73-8efc-62d177e12f83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca7ebc0e-dd2f-4a73-8efc-62d177e12f83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80b5396a-5183-4094-b868-7622177a359a' class='xr-var-data-in' type='checkbox'><label for='data-80b5396a-5183-4094-b868-7622177a359a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(3, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble</span></div><div class='xr-var-dims'>(ensemble)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 260.0 261.0 262.0</div><input id='attrs-5d10cacc-473f-4224-905d-65aa1a79e1d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d10cacc-473f-4224-905d-65aa1a79e1d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-646a8622-171c-44c8-ac14-f12b9d744a8b' class='xr-var-data-in' type='checkbox'><label for='data-646a8622-171c-44c8-ac14-f12b9d744a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 260., 261., 262.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1eee8562-354c-4e5b-b21b-7b960a8f6bee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1eee8562-354c-4e5b-b21b-7b960a8f6bee' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SWCF</span></div><div class='xr-var-dims'>(ensemble, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 36, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-26881819-91be-49c3-ad83-04ca43a05dfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26881819-91be-49c3-ad83-04ca43a05dfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4218c2d1-731b-4f57-93a7-c148eaf3e26a' class='xr-var-data-in' type='checkbox'><label for='data-4218c2d1-731b-4f57-93a7-c148eaf3e26a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>Sampling_Sequence :</span></dt><dd>rad_lwsw</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>long_name :</span></dt><dd>Shortwave cloud forcing</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.94 GiB </td>\n",
       "                        <td> 7.59 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (262, 36, 192, 288) </td>\n",
       "                        <td> (1, 36, 192, 288) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 262 chunks in 787 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"551\" height=\"153\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"109\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"109\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"13\" y1=\"0\" x2=\"13\" y2=\"25\" />\n",
       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"25\" />\n",
       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
       "  <line x1=\"47\" y1=\"0\" x2=\"47\" y2=\"25\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
       "  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"25\" />\n",
       "  <line x1=\"57\" y1=\"0\" x2=\"57\" y2=\"25\" />\n",
       "  <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
       "  <line x1=\"64\" y1=\"0\" x2=\"64\" y2=\"25\" />\n",
       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
       "  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"25\" />\n",
       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
       "  <line x1=\"92\" y1=\"0\" x2=\"92\" y2=\"25\" />\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"25\" />\n",
       "  <line x1=\"98\" y1=\"0\" x2=\"98\" y2=\"25\" />\n",
       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 109.16666666666666,0.0 109.16666666666666,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"54.583333\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >262</text>\n",
       "  <text x=\"129.166667\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,129.166667,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"179\" y1=\"0\" x2=\"202\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"179\" y1=\"80\" x2=\"202\" y2=\"103\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"179\" y1=\"0\" x2=\"179\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"202\" y1=\"23\" x2=\"202\" y2=\"103\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"179.0,0.0 202.40819709107552,23.408197091075515 202.40819709107552,103.40819709107552 179.0,80.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"179\" y1=\"0\" x2=\"299\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"202\" y1=\"23\" x2=\"322\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"179\" y1=\"0\" x2=\"202\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"299\" y1=\"0\" x2=\"322\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"179.0,0.0 299.0,0.0 322.4081970910755,23.408197091075515 202.40819709107552,23.408197091075515\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"202\" y1=\"23\" x2=\"322\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"202\" y1=\"103\" x2=\"322\" y2=\"103\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"202\" y1=\"23\" x2=\"202\" y2=\"103\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"322\" y1=\"23\" x2=\"322\" y2=\"103\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"202.40819709107552,23.408197091075515 322.4081970910755,23.408197091075515 322.4081970910755,103.40819709107552 202.40819709107552,103.40819709107552\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"262.408197\" y=\"123.408197\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"342.408197\" y=\"63.408197\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,342.408197,63.408197)\">192</text>\n",
       "  <text x=\"180.704099\" y=\"111.704099\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,180.704099,111.704099)\">36</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-71dc6447-d787-49f0-ac3f-7b419d3ffa6e' class='xr-section-summary-in' type='checkbox'  ><label for='section-71dc6447-d787-49f0-ac3f-7b419d3ffa6e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a8412e8f-b5c6-4022-b7bc-3c7f3e4b2196' class='xr-index-data-in' type='checkbox'/><label for='index-a8412e8f-b5c6-4022-b7bc-3c7f3e4b2196' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4021374c-4ce4-4dc7-9eff-34e18b3532ee' class='xr-index-data-in' type='checkbox'/><label for='index-4021374c-4ce4-4dc7-9eff-34e18b3532ee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-857cd3d6-2e2a-46dc-b4f6-7b0a25722615' class='xr-index-data-in' type='checkbox'/><label for='index-857cd3d6-2e2a-46dc-b4f6-7b0a25722615' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0001-01-16 12:00:00, 0001-02-15 00:00:00, 0001-03-16 12:00:00,\n",
       "             0001-04-16 00:00:00, 0001-05-16 12:00:00, 0001-06-16 00:00:00,\n",
       "             0001-07-16 12:00:00, 0001-08-16 12:00:00, 0001-09-16 00:00:00,\n",
       "             0001-10-16 12:00:00, 0001-11-16 00:00:00, 0001-12-16 12:00:00,\n",
       "             0002-01-16 12:00:00, 0002-02-15 00:00:00, 0002-03-16 12:00:00,\n",
       "             0002-04-16 00:00:00, 0002-05-16 12:00:00, 0002-06-16 00:00:00,\n",
       "             0002-07-16 12:00:00, 0002-08-16 12:00:00, 0002-09-16 00:00:00,\n",
       "             0002-10-16 12:00:00, 0002-11-16 00:00:00, 0002-12-16 12:00:00,\n",
       "             0003-01-16 12:00:00, 0003-02-15 00:00:00, 0003-03-16 12:00:00,\n",
       "             0003-04-16 00:00:00, 0003-05-16 12:00:00, 0003-06-16 00:00:00,\n",
       "             0003-07-16 12:00:00, 0003-08-16 12:00:00, 0003-09-16 00:00:00,\n",
       "             0003-10-16 12:00:00, 0003-11-16 00:00:00, 0003-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=36, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ensemble</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fea19419-8362-4a51-af36-27ffa7045061' class='xr-index-data-in' type='checkbox'/><label for='index-fea19419-8362-4a51-af36-27ffa7045061' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
       "       ...\n",
       "       253.0, 254.0, 255.0, 256.0, 257.0, 258.0, 259.0, 260.0, 261.0, 262.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;ensemble&#x27;, length=262))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-258adc77-0003-46d5-b5d3-49ed310dde51' class='xr-section-summary-in' type='checkbox'  ><label for='section-258adc77-0003-46d5-b5d3-49ed310dde51' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;SWCF&#x27;]</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>present-day</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>monthly</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>SWCF</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>W/m2</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>Shortwave cloud forcing</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>1.0</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>0001-01-16</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>0003-12-16</dd><dt><span>intake_esm_attrs:time_range :</span></dt><dd>0001-01-16-0003-12-16</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>present-day.monthly</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:   (lat: 192, lon: 288, time: 36, ensemble: 262)\n",
       "Coordinates:\n",
       "  * lat       (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon       (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * time      (time) object 288B 0001-01-16 12:00:00 ... 0003-12-16 12:00:00\n",
       "  * ensemble  (ensemble) float64 2kB 0.0 1.0 2.0 3.0 ... 259.0 260.0 261.0 262.0\n",
       "Data variables:\n",
       "    SWCF      (ensemble, time, lat, lon) float32 2GB dask.array<chunksize=(1, 36, 192, 288), meta=np.ndarray>\n",
       "Attributes:\n",
       "    intake_esm_vars:                   ['SWCF']\n",
       "    intake_esm_attrs:experiment:       present-day\n",
       "    intake_esm_attrs:frequency:        monthly\n",
       "    intake_esm_attrs:variable:         SWCF\n",
       "    intake_esm_attrs:units:            W/m2\n",
       "    intake_esm_attrs:long_name:        Shortwave cloud forcing\n",
       "    intake_esm_attrs:vertical_levels:  1.0\n",
       "    intake_esm_attrs:start_time:       0001-01-16\n",
       "    intake_esm_attrs:end_time:         0003-12-16\n",
       "    intake_esm_attrs:time_range:       0001-01-16-0003-12-16\n",
       "    intake_esm_attrs:_data_format_:    netcdf\n",
       "    intake_esm_dataset_key:            present-day.monthly"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_dict['present-day.monthly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ede46016-c40e-41e5-ab92-f75e9e96b9d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = dset_dict['present-day.monthly']\n",
    "SWCF = global_mean(ds['SWCF']).mean('time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fea91992-0e87-4560-a526-8bfb18b1df3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some of the PPE ensemble members are missing data so just select the params we actually have\n",
    "sub_params = params.iloc[SWCF.ensemble.values]\n",
    "# Unit normalise all the parameters\n",
    "ppe_params = (sub_params - sub_params.min()) / (sub_params.max() - sub_params.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "df911922-a001-4ad2-8e25-eb19f5ae97c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 9 iterations, i.e. alpha=4.397e-01, with an active set of 9 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 20 iterations, i.e. alpha=1.975e-01, with an active set of 20 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 22 iterations, i.e. alpha=1.077e-01, with an active set of 22 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 35 iterations, i.e. alpha=2.229e-02, with an active set of 35 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 39 iterations, i.e. alpha=4.559e-03, with an active set of 39 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 41 iterations, i.e. alpha=2.061e-03, with an active set of 41 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 42 iterations, i.e. alpha=5.161e-04, with an active set of 42 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 42 iterations, i.e. alpha=1.493e-04, with an active set of 42 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/ml-notebook/lib/python3.11/site-packages/sklearn/linear_model/_least_angle.py:688: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 43 iterations, i.e. alpha=6.496e-05, with an active set of 43 regressors, and the smallest cholesky pivot element being 5.960e-08. Reduce max_iter or increase eps parameters.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['micro_mg_autocon_nd_exp', 'micro_mg_dcs', 'cldfrc_dp1', 'cldfrc_dp2',\n",
       "       'clubb_C6thlb', 'clubb_C8', 'clubb_c1', 'clubb_c11', 'clubb_c14',\n",
       "       'dust_emis_fact', 'micro_mg_accre_enhan_fact', 'micro_mg_autocon_fact',\n",
       "       'micro_mg_autocon_lwp_exp', 'micro_mg_berg_eff_factor',\n",
       "       'micro_mg_homog_size', 'micro_mg_iaccr_factor', 'micro_mg_vtrmi_factor',\n",
       "       'microp_aero_wsub_scale', 'microp_aero_wsubi_scale',\n",
       "       'seasalt_emis_scale', 'zmconv_capelmt', 'zmconv_ke',\n",
       "       'zmconv_tiedke_add'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use an information criterion to choose the best parameters automatically:\n",
    "best_params = ppe_params[ppe_params.columns[get_param_mask(ppe_params, SWCF)]]\n",
    "best_params.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "22a8ba21-2ad6-4ee4-9e28-7f9cb07a56ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_test = 25\n",
    "\n",
    "X_test, X_train = best_params[:n_test], best_params[n_test:]\n",
    "Y_test, Y_train = SWCF[:n_test], SWCF[n_test:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733d85db-b666-42be-8db5-196f978670b4",
   "metadata": {},
   "source": [
    "## Global mean GP Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "268df2dc-a02d-4559-932b-4a6caa152747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can try different kernels here\n",
    "gp = gp_model(X_train, Y_train, kernel=['Linear', 'RBF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f6ee5f93-0fa2-412f-a540-943cd13cee21",
   "metadata": {},
   "outputs": [],
   "source": [
    "gp.train()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "da3f2342-ef8c-4684-a28d-205222695a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "m, v = gp.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b22deab-48de-4ac1-9834-392b616a0646",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "42f0c7e2-bfef-4f65-b663-97e7fa2e55c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of 'Bad' estimates : 8.00%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAFzCAYAAAD/rTTeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIOElEQVR4nO3dd1hTZ/8/8HcSJAICIjiggqho69aqVWqt66naOlq1w6pUHDgp7kHLHu6NtWJdtbWOVltbtcq3rR3+XHXvjYLgBGRqIOH+/eFDHiP7kJAE3q/rynXljJzzyanNm3Puc+5bJoQQICIikkBu7AKIiMh8MUSIiEgyhggREUnGECEiIskYIkREJBlDhIiIJGOIEBGRZAwRIiKSzMLYBZia3NxcJCYmwtbWFjKZzNjlEBGVmRAC6enpcHFxgVyu33MHhsgLEhMT4erqauwyiIj0Lj4+HnXr1tXrNhkiL7C1tQXw7GDb2dkZuRoi48rMzISLiwuAZ39g2djYGLkikiItLQ2urq7a3zd9Yoi8IO8Slp2dHUOEKj2FQqF9b2dnxxAxc4a4RM+GdSIikowhQkREkvFyFhFRBZWZmYlq1aoZdB88EyEiIskYIkREJBlDhIiIJGOIEBGRZAwRIiKSjCFCRESSMUSIiEgyhggREUnGECGicpeZmQmZTAaZTIbMzExjl0NlwBAhIiLJGCJERCQZQ4SIiCRjiBARkWRmESK3bt3CqFGjUL9+fVhZWaFhw4YIDg5Gdna2znpxcXHo168fbGxs4OTkBD8/v3zrEBGR/phFV/CXL19Gbm4uoqOj4eHhgfPnz8PHxweZmZlYtGgRAECj0aBPnz6oWbMmDh48iKSkJAwfPhxCCERFRRn5GxARVUwyIYQwdhFSLFy4EF9++SVu3rwJAPj111/Rt29fxMfHa8eE3rp1K7y9vfHgwYMSD3WblpYGe3t7pKamcnhcqvSeH48iIyNDb8PjGmq7pOuXX35B//79tdOG+F0zizORgqSmpqJGjRra6cOHD6N58+baAAGAXr16QaVS4cSJE+jWrVuB21GpVFCpVNrptLQ0wxVNRFQO1Go1Vq9eje3btxt8X2bRJvKiGzduICoqCuPGjdPOu3fvHmrXrq2znoODAywtLXHv3r1CtzV37lzY29trX66urgarm4jI0FJSUjB9+nTs2LGjXPZn1BAJCQnRPrVa2Ov48eM6n0lMTETv3r3xwQcfYPTo0TrLZDJZvn0IIQqcn8ff3x+pqanaV3x8vH6+HBFRObt8+TLGjBmDM2fOwNraGoGBgQbfp1EvZ/n6+mLw4MFFruPu7q59n5iYiG7dusHT0xNr1qzRWa9OnTo4evSozryUlBTk5OTkO0N5nlKphFKpLH3xREQmZM+ePVi2bBnUajXc3NwQHh4OR0dHg+/XqCHi5OQEJyenEq2bkJCAbt26oW3bttiwYQPkct2TKE9PT0RGRuLu3btwdnYGAMTExECpVKJt27Z6r52IyBTk5ORgxYoV2L17NwCgc+fOmD17NqytrculXzKzaFhPTExE165d4ebmhkWLFuHhw4faZXXq1AEA9OzZE02bNoWXlxcWLlyI5ORkTJ8+HT4+PrzLiogqpIcPHyI4OBiXLl2CTCbDqFGjMGTIkCIv4eubWYRITEwMrl+/juvXr6Nu3bo6y/LuUFYoFNizZw8mTJiATp06wcrKCkOGDNE+R0JEVJGcPXsWwcHBePz4MWxtbREQEIDXXnut3Osw2+dEDIXPiRD9D58TMT1CCPz4449YtWoVNBoNGjZsiPDwcO1l/Oc9f5wBPidCRFSpqVQqLF68GP/3f/8HAOjRowdmzJhh1JuDGCJERGbg7t27CAoKwvXr1yGXyzF+/HgMGjSoXNs/CsIQISIyccePH0dYWBjS09NRvXp1BAcHo3Xr1sYuCwBDhIjIZAkhsGXLFqxduxZCCLzyyisICwtDzZo1jV2aFkOEiMgEZWVlYf78+fj7778BAH369IGfnx8sLS2NXJkuhggRkYmJj49HYGAgbt++DQsLC0yaNAl9+/Y1dlkFYogQEZmQQ4cOITIyEllZWXB0dERYWBiaNm1q7LIKxRAhIjIBQghs3LgRmzZtAgC0bNkSISEhcHBwMHJlRWOIEBEZWXp6OiIjI7WdyA4cOBDjx4+HhYXp/0SbfoVERBXYzZs3ERgYiMTERFhaWmL69Ol46623jF1WiTFEiIiM5MCBA5g/fz5UKhXq1KmDsLAwNGrUyNhllQpDhIionGk0GqxZs0Y7fG27du0QGBholv31MUSIiMrR48ePERYWhlOnTgEAhgwZglGjRuUbI8lcMESICAB71i0PV65cQVBQEB48eICqVavC398fb775prHLKhOGCBFROfj111+xdOlS5OTkoG7duoiIiEC9evWMXVaZMUSIiAxIrVZj5cqV2LVrFwDg9ddfx2effVZhzvQYIkREBpKUlITg4GBcuHABMpkM3t7e8PLyMnr37frEECEiMoDz588jODgYycnJsLGxQWBgIDp06GDssvSOIUJEpEdCCOzatQsrV66ERqNB/fr1ER4ejpdeesnYpRkEQ4SISE9UKhWWLl2K/fv3AwC6du2KmTNnwsrKysiVGQ5DhIhID+7fv4/AwEBcu3YNMpkMY8eOxYcfflih2j8KwhAhIiqjkydPIjQ0FGlpabC3t0dwcDDatGlj7LLKBUOEiEgiIQS2bduGNWvWQAiBxo0bIzw8HLVq1TJ2aeWGIUJEJMGTJ0+wYMEC/PnnnwCA3r17Y/LkyVAqlcYtrJwxRIiISikhIQGBgYGIjY2FhYUFfH190b9//wrf/lEQhggRUSkcOXIEERERyMzMRI0aNRAWFoZmzZoZuyyjYYgQEZWAEAKbNm3Cxo0bAQDNmjVDaGgoHB0djVuYkTFEiIiKkZmZiTlz5uDQoUMAgHfffRe+vr5mMXytofEIEBEV4datWwgMDMSdO3dQpUoVTJ06Fb179zZ2WSaDIUJEVIi//voL8+bNw9OnT1GrVi2Eh4ejcePGxi7LpDBEiIhekJubi7Vr12LLli0AgDZt2iAoKAjVq1c3bmEmiCFCRPSctLQ0hIWF4cSJEwCAjz76CD4+PlAoFEauzDQxRIiI/uvatWsIDAzE/fv3oVQqMWvWLHTr1s3YZZk0hggREYCYmBgsXrwY2dnZcHFxQUREBOrXr2/sskweQ4SIKjW1Wo1Vq1bhxx9/BAB06NABAQEBqFatmpErMw8MESITlJmZqf0Ry8jIqDDjcZua5ORkhISE4Ny5cwCATz75BN7e3pWy+xKp5MYugIhM06NHj7Bw4ULtdN++ffHDDz8gNzfXiFXpz4ULFzBmzBicO3cO1tbWiIyMxIgRIxggpcQzESLK58qVK3j77bfx8OFD7by///4bf/75JwYMGIDt27eb7dPaQgjs3r0bK1asgFqtRr169RAREYG6desauzSzxDMRIsrnww8/RFJSEoQQ2nl5ZyA//fQTFixYIGm7GRkZiIiIgIeHh3bemDFjcPHixbIVXELZ2dlYtGgRlixZArVajS5dumDVqlUMkDKQief/lZB2ZLLU1FTY2dkZuxyqpIzRJvL8PotTq1YtJCQklOpsJDU1FV26dMG5c+d0LokpFApUqVIFMTEx6Ny5c6nrLqkHDx4gODgYly9fhkwmg4+PDwYPHlyhL1+9+N/UEL9rPBMhonyKe7DuwYMHuHHjRqm2GRgYiPPnz+drU9FoNMjOzsYHH3yAnJycUtdaEqdPn8bYsWNx+fJl2NraYsGCBfj4448rdIAAz9q1DI0hQkSSlOYHODMzE+vWrYNGoylweW5uLu7fv49ffvlFX+UBeNb+8f3332PatGl4/PgxPDw8EB0djXbt2ul1P6ZGrVZjypQpOpcNDYUhQkT5FPZjn8fFxQUNGzYs8fZiY2ORlZVV5DpVqlTB6dOnS7zN4jx9+hSRkZFYtWoVcnNz8dZbb2HlypVwdnbW2z5Mla+vL5YvXw61Wm3wfZnn7RVElVB5tpM0adIE165dK/BHSCaTYcqUKaXqS6ok447n5uaiatWqpaqzMImJiQgMDMTNmzehUCgwYcIEDBgwoMJfvgKAGzduIDo6utz2xzMRIspn+/btqFOnToHLhg0bhqlTp5Zqex4eHmjYsGGRP+IajQZ9+/Yt1XYLcuzYMYwdOxY3b96Eg4MDlixZgoEDB1aKAAGA7777rlw7i2SIEFE+9evXx4ULF3QeNgSAH3/8EV9//TXk8tL9dMhkMgQEBKCwm0EVCgV69uyJli1baudlZmZCJpNBJpMhMzOz2H0IIfDtt99i9uzZyMjIQNOmTbFmzRqdbVYGjx49KvV/n7JgiBBRgezs7DB+/HideW+99Zbkv+i9vb0RGhoKmUyW70euY8eO2LZtm+Ras7KyEBQUhHXr1kEIgX79+mHZsmVwcnKSvE1z5ebmVmyblj4xRIgqidL+ZW8IQUFBuHbtGqZMmaKd9/PPP+Pvv/+WPODT7du3MW7cOBw8eBAWFhaYMWMGpk6diipVquipavMydOhQnokUpH///nBzc0PVqlXh7OwMLy8vJCYm6qwTFxeHfv36wcbGBk5OTvDz80N2draRKiaigjRs2BChoaHa6e7du0v+0fvnn38wfvx4xMfHo2bNmoiKisI777yjr1LNUp06dRAeHl5u+zObEOnWrRu2b9+OK1euYMeOHbhx4wbef/997XKNRoM+ffogMzMTBw8exNatW7Fjxw5MmzbNiFUTFc4UzgzMVd7wtUFBQXjy5AlatWqFNWvW4JVXXjF2aSZh9uzZiI6OLvTmCL0SZmrXrl1CJpOJ7OxsIYQQe/fuFXK5XCQkJGjX2bJli1AqlSI1NbXE201NTRUASvUZIikyMjIEAAFAZGRkFLusqPXLur+S7FPqfqXUUtQ6aWlpYsaMGaJr166ia9euYuXKlSInJ0cvdVU0eb9neS9D/K6Z5XMiycnJ2Lx5M15//XXtdc/Dhw+jefPmcHFx0a7Xq1cvqFQqnDhxotAhLlUqFVQqlXY6LS3NsMUTkWQ3btxAYGAg7t69C6VSiRkzZqBHjx7GLstklcetvmZzOQsAZs2aBRsbGzg6OiIuLg67du3SLrt37x5q166ts76DgwMsLS1x7969Qrc5d+5c2Nvba1+urq4Gq5+IpPv9998xceJE3L17F87Ozvjiiy8YICbAqCESEhKivSZc2Ov48ePa9WfMmIFTp04hJiYGCoUCn3zyic595wXdeiiEKPKWRH9/f6Smpmpf8fHx+v2SRFRm0dHRiIiIgEqlQvv27REdHV2qblfIcIx6OcvX1xeDBw8uch13d3fteycnJzg5OaFx48Zo0qQJXF1dceTIEXh6eqJOnTo4evSozmdTUlKQk5OT7wzleUqlskRdMhCR8fz0009QKBQYNmwYRowYUa63sFLRjBoieaEgRd4ZSF57hqenJyIjI7WnugAQExMDpVKJtm3b6qdgIio3V65c0b63srJCQECAQccbIWnMomH92LFjOHbsGN544w04ODjg5s2bCAoKQsOGDeHp6QkA6NmzJ5o2bQovLy8sXLgQycnJmD59Onx8fDi4FJmE0gz6VNnt2bMHixcv1k4vX76ct++aKLMIESsrK+zcuRPBwcHIzMyEs7Mzevfuja1bt2ovRSkUCuzZswcTJkxAp06dYGVlhSFDhmDRokVGrp6ISionJwcrVqzA7t27dbru4A0vpsssQqRFixb4448/il3Pzc0Nu3fvLoeKiPQrMzOzXIbANWWPHj1CcHAwLl68CJlMhuHDh+Off/4xdllUDLZOEZHRnT17FmPGjMHFixdha2uLefPmFXvTDZkGszgTIaKKa9euXVi/fj00Gg0aNGiAiIgIODs7sysYM8EQISKjWr16NRQKBXr06IHp06frbXRDKh8MESIqd8/3IiGXyzFx4kQMGjSo0ow+WJEwRIioXB0/fhxBQUHa6blz52pv1SfzwxAhonIhhMCWLVuwdu1aqNVq7fzKNnxtRcO7s4gMxFzGC8nKysLKlSvRoUMH7bwFCxYgOTk537pSv0dWVhZCQkLw1VdfQQiBXr16Sa6XTAtDhKgSe/z4MTp16gQ/Pz9cuHBBOz88PBytWrXSS4ekd+7cwYQJE/D333/DwsIC06ZNw6RJk8q8XTINvJxFVIlNnjwZ586d0+kNG3h26enevXsYMWJEmbZ/6NAhREZGIisrC46OjggLC0PTpk1N+syMSochQlRJPXr0CJs3b9bpXuR5arUaR44ckbRtIQQ2btyITZs2AXjW7hEcHIwaNWpIrpdME0OEqJI6deqUTgO3vqSnpyMyMlI7NMPAgQMxfvx4WFjw56Yi4n9VokrKEEOn3rx5E4GBgUhMTISlpSWmTZuGnj176n0/ZDoYIkSV1GuvvQYbGxu9tU8cOHAA8+fPh0qlQu3atREeHo5GjRrpZdtkunh3FlElVa1aNUyYMKHQp8QVCgX69etX7HY0Gg2+/PJLhIWFQaVSoW3btlizZg0DpJJgiBBVYhEREejfvz8A5Btytm3btvjyyy+L/Pzjx48xY8YMbN++HQDw8ccfY8GCBRwIrhJhiFCFYS4P95kSS0tL7Ny5E/v378d7772nnf/NN9/g4MGDqF69eqGfvXr1KsaOHYtTp06hatWqCA0NxZgxYzj+eSXD/9pElZxcLkfPnj21t+MCwIABA1ClSpVCP7Nv3z74+vriwYMHqFu3LlavXo0333yzPMolE8OGdSIqMbVajWXLlmHXrl0AgNdffx2fffZZpR+VsTJjiBBRiQUEBCA2NhYymQze3t7w8vJi9+2VHEOEiErsypUrcHBwQEBAADp27GjscsgEMESIqFAv9qnl6uqKRYsWoW7dukaqiEwNG9bJ4HjXlHlSqVRYunSpzrz58+czQEgHz0SIKJ/79+9j/vz5uHTpks58KysrI1VEpoohQkT5+Pn5ITMzE7a2tsYuhUwcL2cREQDd9o+0tDQ0btwYUVFRRqyIzAFDhIjw5MkTzJ07Vzv9TpcuiF6zBrUbNjRiVWQOGCJElVxCQgImTpyIf/75RzvP19fXiBWROWGbCFElduTIEURERCAzMxMODg7a+XyAkEqKZyJkNnirsP4IIbBp0yZ89tlnyMzMRLNmzdj+QZLwTISoEgoNDcXx48cBAO+++y58fX2hUqmMXBWZI56JEFVCR48eRZUqVTBr1ixMnjy5xOOfd+/eHevXr4dGozFwhWQuGCJUqZjLJbHc3Fzt+6ysLL1s8/mG85o1ayIqKgq9e/cu1TYuXLiAUaNG4aOPPmKQEACGCJFJEUJgzZo1aN68uXaeu7s7pkyZgoyMDEnbzM3NxZo1azBnzhztvBUrVuDll18u9DMpKSlFbnPnzp2Ijo6WVA9VLAwRIhPy+eefY+zYsYiLi9POy8rKQlRUFN566y3tvM2bNyM9Pb3Y7aWlpWHmzJnYsmWLzvyiRiwEgG3bthW77eXLlxe7DlV8DBEyS9WqVTPpy1FSXLx4UeeBv+dpNBqcO3dOOz127FjUqVOnyDHQr127hjFjxuDEiRNQKpWYPXt2iWs5c+ZMkcuFELh69SqePHlS4m1SxcS7s4hMxNq1a2FhYQG1Wl2i9bOysjBhwgRYW1tj+PDhOstiYmKwePFiZGdnw8XFBeHh4ahdu3aJaylqaNw8MpmsxA3yVHGV+kxEo9Hgr7/+KvaaKRGVzrVr10ocIM/7/PPPtY3carUaUVFRmDt3LrKzs9GhQwesXr0aDRo0KNU2e/ToUeRyhUKBHj16lChsqGIrdYgoFAr06tULjx8/NkA5RJVX9erVoVAoSv25hIQEHD58GMnJyZg2bRp27twJAPjkk08wd+5c2NraQgiBCxcuaD/z4mBTL3rnnXeKXK7RaDBz5sxS11oaOTk52vfx8fEG3RdJJ6lNpEWLFrh586a+ayGq1D788EPJt82eOXMGY8aMwdmzZ2FtbY3IyEiMGDECMpkMf/75J9q0aYMOHTpo1+/YsSN+//33QrdX2BmGTCaDXC7HqlWrdBr69UkIgaioKDRq1Eg7r0mTJnjnnXcQGxtrkH1SGQgJ9u/fL1q3bi1++eUXkZiYKFJTU3Ve5iw1NVUAMPvvYUoyMjIEAAFAZGRk6GU7BW2rJPvRVy2lrTcjIyNf/c+/7t+/L9RqtWjXrp1QKBSFrlfYq2PHjqJr165i+PDhIi4uTlvDb7/9JiwsLIRcLtdZXyaTCYVCIfbt21dwvffvCwGIjBf24+fnJ2JjY/V+bJ4XGBhY4HdUKBSiZs2aOt+PivbivzlD/K5JChGZTKZ9yeVy7Stv2pwxRPSPIVKyEBFCiEePHonu3buXODxkMpmwtbUVXbt2FUFBQSIzM1O7/9zcXNGoUSMhk8kK/Wz9+vWFRqPJX28hIZJXp76PTZ7bt28XWi8AYWFhIcaMGaOXGiqD8ggRSbdWHDhwQMrHiKgYjo6O+P3333H48GG8/vrrAJ41nM+fPx8ajUanLSPvyfvGjRvDx8cHH3/8sU7vu0ePHsW1a9cK3ZcQArGxsTh48CDefPNNw32pUti0aRPkcnmhl/XUajU2bdqEFStWQKlUlnN1VBBJIdKlSxd910FEz2nZsqX2vb+/P3r16oUZM2bg6NGj2vnVq1dHixYtsHjxYrRr1y7fNp5/YLEoJV1Pn2xsbAps3I+Pjy+2G/qnT58iOTkZzs7OhiqPSkHyTd6PHz/GunXrcOnSJchkMjRt2hQjR46Evb29PusjIgCdO3fGb7/9ph3zvH379mjVqhXCwsIK/TF1cnIq0bZr1qyptzrLqiS1WFhY8HfGhEi6O+v48eNo2LAhli5diuTkZDx69AhLlixBw4YNcfLkSX3XSFThLV++HHfu3Cl0+dOnT7FgwQLt9DvvvIOVK1fmD5DMTEAmA2QyvNm2LerUqVPkfp2cnNC9e/cy1a5PQ4cOLfJZGQsLCwwaNAjW1tblWBUVRVKITJkyBf3798etW7ewc+dO/Pjjj4iNjUXfvn0xefJkPZdIVPHNnTsX9erVQ2BgYL7LPHfv3sXEiRPx559/audNnz692DYBCwsLneApyPz5803qgcEmTZrA29u7wEtaCoUCVapUQWBgoBEqo8JIPhOZNWuWTpcHFhYWmDlzpnagGyIqOSEEcnNzERERgWXLluks+/TTT3Hz5k2dSzglHb7Wy8sL69aty9fhop2dHaKjozFy5Miylq53a9aswaeffprvwcsGDRrgzz//RLNmzYxUGRVEUojY2dkV2BgXHx+vvWZL5sFcxteoTCIiInRGGczMzESTJk2wcuVKSdsbOXIk7t69i2+//VY778aNGxgzZkyZazWEKlWqYPny5bh+/bp23q+//oorV67gtddeM2JlVBBJIfLRRx9h1KhR2LZtG+Lj43Hnzh1s3boVo0ePxscff6zvGnWoVCq0bt0aMpkMp0+f1lkWFxeHfv36wcbGBk5OTvDz80N2drZB6yHSt+TkZEycOFE73bt3byxfvrzEDeUFqVq1Kt577z3ttJWVVVlKLBfPN7J37ty5xGdfVL4k3Z21aNEiyGQyfPLJJ9pGsCpVqmD8+PGYN2+eXgt80cyZM+Hi4pKvq2qNRoM+ffqgZs2aOHjwIJKSkjB8+HBtFwpE5uTs2bPa95MmTUKVKlX4BxGZJEkhYmlpieXLl2Pu3Lm4ceMGhBDw8PAw+B0Tv/76K2JiYrBjxw78+uuvOstiYmJw8eJFxMfHw8XFBQCwePFieHt7IzIyEnZ2dgatjeh5mZmZqFatmuTPOzs74+LFi3qsiMgwJF3OGjlyJNLT02FtbY0WLVqgZcuWsLa2RmZmpsEa6u7fvw8fHx988803BYbV4cOH0bx5c22AAECvXr2gUqlw4sSJQrerUqmQlpam8yIyppdeegkbN240dhlEJSIpRL7++usCRzR78uQJNm3aVOaiXiSEgLe3N8aNG1fgk7kAcO/evXyD7jg4OMDS0hL37t0rdNtz586Fvb299uXq6qrX2olKSiaTwdraGvv374eDg4OxyyEqkVKFSFpaGlJTUyGEQHp6us5f7ykpKdi7dy9q1apV4u2FhIRo7wwq7HX8+HFERUUhLS0N/v7+RW6voIY3IUSRDXL+/v5ITU3VvjhuARnK+fPni1zeqVMnnD9/3qRuYbUBcJ/DPlARStUmUr16dZ1O314kk8kQGhpa4u35+vpi8ODBRa7j7u6OiIgIHDlyJN/DVe3atcPQoUPx9ddfo06dOjr9CgFASkoKcnJyihwWVKlUsiM3MjghBEaPHl3kOpaWlqhfv345VUSkH6UKkQMHDkAIge7du2PHjh2oUaOGdpmlpSXq1aun0yZRHCcnpxLdtrhixQpERERopxMTE9GrVy9s27ZNO9COp6cnIiMjcffuXW1XEDExMVAqlWjbtm2JayIyhJMnTxZ7JvLHH3/g+vXr8PDwKKeqiMquVCGS13tvbGwsXF1dIZdLalIpNTc3N53pvLteGjZsiLp16wIAevbsiaZNm8LLywsLFy5EcnIypk+fDh8fH96ZRUY3ZMiQEq138eJFhgiZFUm3+NarVw8AkJWVhbi4uHz3rz/fjXV5USgU2LNnDyZMmIBOnTrBysoKQ4YMwaJFi8q9FqIXJSQklGg9c3gIkMxHXpf7aWlpBuv5WFKIPHz4ECNGjMj3rEYeqeNEl5S7u3uBYxG4ublh9+7dBt03UXEePHiAqVOnlvpz9vb26Ny5swEqIjIcSdejJk+ejJSUFBw5cgRWVlbYt28fvv76azRq1Ag///yzvmskMhuPHj2Cp6cntm7dWurPzpw5E1WrVjVAVWWT9dzt/E+fPjViJWSKJIXIH3/8gaVLl6J9+/aQy+WoV68ehg0bhgULFmDu3Ln6rpHIbERGRuL27dulPhvv3r07pk2bZqCqyua19u2175s3b47AwEDk5OQYsSIyJZJCJDMzU/s8SI0aNfDw4UMAQIsWLTgoFVVaOTk5WLt2raTLuX/88Qfq16+Pf//91wCVld7zA0OlZ2X97316OiIjI/Hhhx8iNzfXGKWRiZEUIi+//DKuXLkCAGjdujWio6ORkJCA1atXc9xjqrQePXqEjIwMyZ9/8OABevTogdu3b+uxKmmKuiwthMBPP/3E9kcCUIY2kbt37wIAgoODsW/fPri5uWHFihWYM2eOXgskMgcqlQpr1qwp0zY0Gg2ysrJMotfp4rovUigU+Oqrr8qpGjJlku7OGjp0qPZ9mzZtcOvWLVy+fBlubm5lGvOAyBzdu3cPgYGBuH79OhwdHZGSkiL5Uo9Go8F3331Xqp4fDOFmMV2daDQaXLt2rZyqIVOml6cFra2t8eqrrzJAqNI5fvw4xowZg+vXr6N69epYtmxZmR/CLcslMX1xdHQscrlMJuP/7wSgFGcipbnvfcmSJZKKITIXQghs3boVX331FYQQeOWVVxAaGopatWqhZs2aGDJkCJKTk3U+M3DgQOzcubPI7crlcrz88suGLL1EPvjgA6CYsyEvL69yqoZMWYlD5NSpUyVaj0NYUkWXlZWF+fPn4++//wYAvPPOO5g0aRIsLS0BPBvH5vr16zp9y50+fRoeHh7Fhkhubq7O0LjGMnTo0EJDxMLCAu7u7hg2bFg5V0WmqMQhcuDAAUPWQWQW7ty5g4CAANy+fRsWFhbw8/ND37598/3xlBcoeUrSH5ZMJkPv3r0xbNgwqFQqvdZdEnldZAAAMjMLXa99+/b4/vvvYWNjU06VkSkrnx4UiSqAQ4cOYezYsbh9+zYcHR2xbNky9OvXTy9n305OTggLC8NPP/0ECwtJ97sYzI87dmjfx8TE4NChQ3jppZeMWBGZEkn/Wrt161bk/zh//PGH5IKo4rl//76xSygTIQQ2btyove21RYsWCAkJ0blcVVZnzpwp1TAK5enVNm2071u1amXESsgUSToTad26NVq1aqV9NW3aFNnZ2Th58iRatGih7xrJTF29ehX9+/dHw4YNtfM6deqE/fv3G7Gq0snIyIC/v782QAYMGIAlS5boNUAAmNzZB1FJSfqXu3Tp0gLnh4SEmMTtiWR8V65cQceOHZGenq4z/+zZs3j77bexfft2vP/++3rd55kzZ7Tv//33X3Tp0qVMl5piY2MREBCAxMREWFpaYtq0aejZs6c+SiWqMPTaJjJs2DCsX79en5skMzVt2jSkp6fn60dKCAEhBMaMGaO3xuPExER07twZnTp10s7r1q0b2rdvj1u3bkna5oEDBzB+/HgkJiaidu3aWLlyJQOEqAB6DZHDhw+bZFfWVL4SExOxd+/eIjsiTElJ0cuwAZmZmejatSuOHDmSb9mZM2fw5ptvIiUlpcTb02g0+PLLLxEWFgaVSoW2bdtizZo1aNSoUZlrJaqIJF3OGjhwoM60EAJ3797F8ePHERgYqJfCyHzdunWrwEHDnmdhYYEbN26UeV/ffvstrl+/XuD+1Go1EhISsG7dOkyfPr3YbT1+/BhhYWHaZ6I+/vhjjB49utyGgSYyR5JC5MVhFvOesg0LC+MpP8HBwaHYdTQaTYnWK863335b5PLc3Fxs2rSp2BC5evUqAgMD8eDBA1StWhWzZ89Gly5dylwfUUUnKUQ2bNig7zqoAnnllVfQrFkzXLx4sdAzEoVCgQEDBpR5X48ePSr2rCcpKanI5fv27cOSJUuQk5ODunXrIjw8HO7u7mWujagyKPN5ekZGBtLS0nReVLnJZDLMmTOn0B93mUyGSZMmaQc2KwsPDw8oFIpCl8vlcp1bjJ+nVquxbNkyzJ8/Hzk5OfD09MSXX37JACEqBUkhEhsbiz59+sDGxgb29vZwcHCAg4MDqlevrpdLFGT++vfvj2+//Ra2trY68xUKBaZMmYL58+eXepsJCQn55vn4+BTZgJ+bm4uxY8cWuGzWrFnYtWsXAMDb2xuRkZGoVq1aqesiqszKNJ7I+vXrUbt2bXa6SAUaOnQoBgwYgC1btmD06NEAnrU9NGjQoFTbSU1NxdixY7F9+3ad+R9++CHWr1+Pd999Fz///HO+Mx+5XI7u3bvjo48+KnC7Fy9ehJ2dHT7//HN4enqWqqYK779DXud7T/QiIYGNjY24fPmylI+avNTUVAFApKamGruUcpGRkSEACAAiIyPD5PahUqnEa6+9JhQKhXYbeS+5XC6aNm0qUlJSREBAgLCzs9Mus7GxEdOnTxdPnjzRbis3N1ds3bpVu87QoUNFfHy8vr+uEEL3O+d97xfnPf+6f/9+oZ/PO2YlOo4ZGUIAz14vrFOiz6enCzFihBAKhXY794uo05DK499mZWHI3zVJZyLt27dHfHy8SYx7QBXb999/j2PHjhW4LDc3F5cuXcJ3332H8PBwTJ48WTtQUmxsLGrWrKldV6VSYdmyZdizZ4923rJlyziw0vNycoDevYEjR4DCLhE+fVq+NZHJkxQia9euxbhx45CQkIDmzZujSpUqOstbtmypl+KINm7cCLlcXuRws+vWrcOECRN0HnS1trbWvr9//z6CgoJw9epVnUuvVlZWhinaXP3wA/D//l/R6+zYAUyZUj71kFmQFCIPHz7EjRs3MGLECO08mUwGIQRkMlmRDZ1EpZGQkFBkgIj/PuhamFOnTiE0NBSpqamws7PDzJkz8cYbbxiiVPO3bh2gUBR+FgIA33zDECEdkkJk5MiRaNOmDbZs2cKGdTIoV1dXXL16tdA/TGQyGerWrZtvvhAC27ZtQ3R0NIQQaNSoEcLDw3n3VVHi44sOEABITCyfWshsSAqR27dv4+effy7RaG1EZTFy5EjExMQUuU7enV/PmzdvHv7ffy/N9OrVC1OmTIFSqURmESP2VXouLsD160ARZ36oXbv86iGzIOk5ke7du+t0u01kKIMGDULnzp0LfKBQLpejVatW8PLyyrfs77//hkKhwKRJkzBr1iwolcryKNe8jRhRdIAAwH9v7yfKI+lMpF+/fpgyZQrOnTuHFi1a5GtY79+/v16KI7KwsMDevXsxbtw4fPfddzrPgtSoUQOhoaHaBvLn7+JycHBAZGQkmjdvXu41m62PPgJWrABOny78staHH5ZrSWT6JIXIuHHjAABhYWH5lrFhnfRNpVIVeJtvUlIS3nvvPWzZsgUqlQrr1q3TLouKioKbm5vO+sePH8eqVau007t27cKHH37IUQXzKJXAb78BPj7P7tQqyHN3vREBEi9n5ebmFvpigJC+hYSE4ObNm/meSM+b9vLywldffaWz3NHRUWe9iRMnon379tphboFnT9S/9tprxXbQWKlUrw58/z1w+bJ2ls2///7vvY2NEYoiU1aqEHnnnXeQmpqqnY6MjMTjx4+100lJSWjatKneiiN68uQJ1q9fX+gfJ0II5OTkICkpCVMKufV02bJl2jOQF7dz9uzZQrtFqdSev+PthTM6oueVKkT279+vM6Tp/PnzkZycrJ1Wq9W4cuWK/qqjSi8xMRFZWVlFriOTydChQ4cCx7JRq9VYuHBhoZ/VaDT4/fffcfbs2TLXSlQZlSpECrucQGQoJXmuQy6X52v/yHPp0qUiH0YEnvUsvH//fkn1lScbGxvtGPW8rESmguN+kkmrXbs2PD09ixyiVqPRYNCgQQUuy8nJKXYfMpmsROsRUX6lChGZTJbv6XQ+rU6GFhwcXOQIif369Su0v7aXX35Zpx+tgqjVarRv377MdRJVRqW6t1EIAW9vb+2DW0+fPsW4ceO0p9bPt5eQ6bt9+za++uor7fT58+fRoUMHI1ZUMJlMhmbNmuHChQv5wuTtt9/Gd999V+hnbWxsMHr0aHzxxRcFNs4rFAq4u7ujR48eeq+bqDIo1ZnI8OHDUatWLdjb28Pe3h7Dhg2Di4uLdrpWrVr45JNPDFVruarI3WMIIRAYGIj69etj7ty52vkdO3bEsGHDkJ2dbcTq/ketViMqKgpz586Fk5MTJkyYoLP80KFD+OWXX4ptN4mMjETbtm3znTXL5XLY2tpix44dRV4uMwZDtH+wTYUMQu8jlJi5vMFbEhMTjV2KwaxYsaLQwZFkMpmYMGGCXvcnZXChpKQk4efnJ7p27Sq6du0q1q9fL9LT0/MN9FTS/WRlZYlly5aJxo0ba9fx8/MTt2/f1tv3LKyWvHpKMyhVGXZc6KBUUreTcf++UQaH4qBU+mPIQalkQvAWq+elpaXB3t4eiYmJcHZ2NnY5epeTk4O6deviwYMHha5jYWGBO3fuoLaeOtvLzMzUni1kZGQU+1fwxYsXERQUhKSkJFhbW+Ozzz5Dp06ddLZT0LZKsp/S1iJVQbUChd9tdv/+fdSqVUsfOwby9pGRAUj9fs9tJ/P+fVT7778FQx4zMpy837W8IRH0ybTO4cng/v333yIDBHh2GWnv3r3lVJGu3bt3Y9KkSUhKSkK9evWwevVqdOrUySi1mL20NGNXQJUAQ6SSKe7BPeBZQ3ZJ1tOn7OxsLFq0CIsXL4ZarUbnzp2xatUquLq6lmsdZu2334Du3f837eYGeHsDCQlGK4kqPvY8V8m88sor2lEoCyOEQLNmzcqtpgcPHiA4OBiXL1+GTCbD6NGj8fHHH/P28dL4/vtnvfA+T60GNm8GYmKAY8d0uzIh0hOeiVQydevWRZ8+fQocnwN4dsurh4cHunTpUi71nD59GmPHjsXly5dha2uLBQsWYMiQIQyQ0sjKAvIG5nrxjwO1Gnj4EPD3L/+6qFJgiFRCX3zxBWrWrJmvC3S5XA6lUonNmzcb/EdcCIHvv/8e06ZNw+PHj+Hh4YHo6Gi0a9fOoPutkHbseNb+UdjZpVoNbN0KPNdZKpG+MEQqITc3N5w4cQJjxoxB1apVtfMHDRqEf//9F6+99ppB969SqRAZGYlVq1YhNzcXb731FlauXFkh74YrF1evAi8MDJePWg3ExZVPPVSpmE2IuLu7a7tdyXvNnj1bZ524uDj069cPNjY2cHJygp+fn8k8OGdqXFxc8MUXX+h0TrhhwwaDd+V/7949TJgwAb///jvkcjk+/fRT+Pv7c/jasrC3L3wkwufp+dZOIsDMGtbDwsLg4+OjnX7+nnuNRoM+ffqgZs2aOHjwIJKSkjB8+HAIIRAVFWWMcs3Ci0MbG9qnn36KJ0+eoHr16ggNDS20zysqhYEDgZkzC18ulwOtWwPu7uVVEVUiZhUitra2qFOnToHLYmJicPHiRcTHx8PFxQUAsHjxYnh7eyMyMlLvD9hQyT1/J1hGRgaaN2+O0NBQ1KxZ04hVmZYyPcDXoAEwfDiwaROQm5t/uRBAAUNZE+mD2VzOAp4NguXo6IjWrVsjMjJS51LV4cOH0bx5c22AAECvXr2gUqlw4sQJY5RLePZcSnh4uHa6d+/eWL58OQNE31avBoYNyz/f2hrYuBHo06fcS6LKwWzORCZNmoRXX30VDg4OOHbsGPz9/REbG4u1a9cCeHat/cVuOhwcHGBpaYl79+4Vul2VSqXT+3Aan/LVm7i4OAQGBiI2NlY7b9KkSeV+Ca1SUCqBr78Gpk0DWrV6Nm/lSuCTTwBbW+PWRhWaUc9EQkJC8jWWv/g6fvw4AGDKlCno0qULWrZsidGjR2P16tVYt24dkpKStNsr6LZUIUSRt6vOnTtX2wuxvb09n5DWk4MHD2L8+PGIi4uDo6OjscupPBo2/N97b28GCBmcUc9EfH19MXjw4CLXcS+kMbBjx44AgOvXr8PR0RF16tTB0aNHddZJSUlBTk5OkR0J+vv7Y+rUqdrptLQ0BkkZ5ObmYv369di8eTMAoGXLlpgxYwZ++ukn4xZWjjQaDX799VedecX9MUNkrowaIk5OTnBycpL02VOnTgGA9tkCT09PREZG4u7du9p5MTExUCqVaNu2baHbUSqVvL1UT9LT0xEREYFjx44BAN5//32MHTu2Ug1WduLECQwYMADx8fE683v06IEtW7YYqSoiwzGLNpHDhw/jyJEj6NatG+zt7fHvv/9iypQp6N+/P9zc3AAAPXv2RNOmTeHl5YWFCxciOTkZ06dPh4+PD+/MKgc3btxAYGAg7t69C6VSienTp+M///kPgMoz4uWtW7fQvXv3Agc0O3HiBPqwcZsqILMIEaVSiW3btiE0NBQqlQr16tWDj48PZj53b7xCocCePXswYcIEdOrUCVZWVhgyZAgWLVpkxMorh99//x0LFy6ESqWCs7MzwsLC4OHhYeyyyt3y5cuRlZVV4DC8Go0Gly9fNkJVRIZlFiHy6quv4siRI8Wu5+bmht27d5dDRQQ8G3ckOjoaP/zwAwCgXbt2CAoKgm0lbcz97rvvoFarC11eXO/JRObILEKETM/jx48REhKCM2fOAACGDh2KkSNHmtxY5eWpuNvDX3zoEih8pEMic8EQoVK7fPkygoKC8PDhQ1hZWcHf3x+dO3c2dllG5+HhgQsXLhR6tqFQKAq81EVkzirvn40kyd69e/Hpp5/i4cOHcHV1xZdffskA+a/x48cXuZwBQhURQ4RKJCcnB0uWLMHChQuhVqvRqVMnrF69GvXq1TN2aSZj1KhReOONNwq9pDdx4sRyrojI8BgiVKxHjx5h8uTJ+OWXXyCTyTBy5EiEh4fD2tra2KWZFKVSif3792PGjBk6t5W7urpi1apVmDdvnhGrIzIMtolQkc6ePYuQkBCkpKSgWrVqCAgIQIcOHYxdlsmysrLCvHnzMHPmTG13LxcuXICtrW2Bz48QmTuGCBVICIEff/wRq1atgkajQYMGDRAeHq7TSzIV7vleEMz9jjUbGxvemkyFYohQPiqVCkuWLEFMTAwAoHv37pgxY4bOULpUei/+GPPMhCoChgjpuHfvHgIDA3H9+nXI5XKMGzcO77//PjsPJKICMURI6+TJk1i4cCHS09NRvXp1BAUFoU2bNsYui4hMGEOEtAICAiCXy/Hyyy8jLCwMtWrVMnZJRGTiGCKVXFZWlva9EAJvv/02Jk+eDEtLSyNWRUTmgiFSid25cwezZs3STvv6+uKDDz5g+wcRlZh533tIkh06dAhjx47VGTypT58+DBAiKhWGSCUjhMCGDRvw+eefIysrC02bNjX4PvNubRVCwMbGxuD7I6LywxCpRDIyMuDv749NmzYBAAYMGID58+cbuarKi+FKFQHbRCqJ2NhYBAQEIDExEZaWlpg2bRp69uzJB96IqEwYIpXAn3/+iXnz5kGlUqF27doIDw9Ho0aNjF0WEVUADJEKTKPR4KuvvsK2bdsAAG3btkVgYCDs7e2NXBkRVRQMkQoqNTUVYWFhOHnyJABg8ODBGD16NBQKhZErI6KKhCFSAV29ehWBgYF48OABqlatilmzZqFr167GLouIKiCGSAWzb98+LFmyBDk5OXjppZcQHh6O+vXrG7ssIqqgGCIVhFqtxsqVK7Fr1y4AgKenJz777DNUq1bNyJURUUXGEKkAkpKSEBISgvPnzwMAvL298cknn1S6p885eBJR+WOImLkLFy4gODgYSUlJsLGxweeffw5PT09jl2UQNjY2yMjI4NkVkQlhiJgpIQR+/vlnrFy5Emq1Gu7u7ggPD0fdunWNXRoRVSIMETOUnZ2NpUuXYt++fQCArl27YubMmbCysjJyZURU2TBEzMyDBw8QGBiIq1evQiaTYcyYMfjoo48qXfsHEZkGhogZOXXqFEJDQ5Gamgo7OzsEBQWhbdu2xi6LiCoxhogZEEJg+/btiI6OhhACjRo1QlhYGOrUqWPs0qiisrEBeKcblQBDxMQ9ffoUCxYswIEDBwAAPXv2xNSpU6FUKo1cGRERQ8SkJSQkIDAwELGxsVAoFPD19cW7777L9g8iMhkMERN19OhRREREICMjAzVq1EBISAhatGhh7LKIiHQwREyMEALffPMNNm7cCCEEmjVrhtDQUDg6Ohq7NCKifBgiJiQzMxNz5szBoUOHAAD9+/fHp59+CgsL/mciItPEX6dCaDSact3f7du3ERAQgDt37qBKlSqYMmUK3n777XKtwRywfywi0yI3dgGmql27dli0aFG5hMlff/2FcePG4c6dO6hVqxZWrFjBACEis8AQKcT9+/cxc+ZMDB06FLm5uQbZR25uLtasWYOQkBA8ffoUrVu3RnR0NF555RWD7I+ISN8YIkUQQmDbtm34+eef9b7ttLQ0zJo1C1u2bAEAfPjhh1i0aBGqV6+u930RERkKQ6QYCoUCq1at0us2r127hrFjx+L48eNQKpUIDAzE+PHjjTL+eV4bgxACNjY25b5/IjJvbFgvhkajwaVLl/S2vf/7v//DokWLkJ2dDWdnZ0RERKBBgwZ62z4VjQ3zRPrFECkBW1vbMm9DrVbjyy+/xM6dOwEAHTp0wOeff66XbRMRGQtDpBhyuRxDhgwp0zaSk5MRGhqKs2fPAgC8vLzg7e0NuZxXE4nIvDFEiqBQKFC9enWMGTNG8jYuXryIoKAgJCUlwdraGv7+/njjjTf0WCURkfEwRIpQt25d7N69G7Vq1ZL0+d27d2P58uVQq9Vwc3NDeHg43Nzc9FwlEZHxMEQKsXHjRgwbNkzSHVPZ2dlYsWIF9uzZAwDo3LkzZs+eDWtra32XSURkVAyRQvTs2VNSgDx48ADBwcG4fPkyZDIZRo8ejY8//pjdtxNRhcQQ0aMzZ84gJCQEjx8/hq2tLYKCgtCuXTtjl0VEZDBmdXvQnj170KFDB1hZWcHJyQkDBw7UWR4XF4d+/frBxsYGTk5O8PPzQ3Z2tsHrEkLghx9+wNSpU/H48WM0bNgQ0dHRDBAiqvDM5kxkx44d8PHxwZw5c9C9e3cIIXDu3Dntco1Ggz59+qBmzZo4ePAgkpKSMHz4cAghEBUVZbC6VCoVFi5ciN9//x0A8J///AfTp0/n8LVEVCnIhBk8vqtWq+Hu7o7Q0FCMGjWqwHV+/fVX9O3bF/Hx8XBxcQEAbN26Fd7e3njw4AHs7OxKtK+0tDTY29sjMTERzs7ORa579+5dBAQE4ObNm5DL5ZgwYQIGDhzI9g9CZmYmqlWrBgDIyMhglzJkVHm/a6mpqSX+LSwps7icdfLkSSQkJEAul6NNmzZwdnbG22+/jQsXLmjXOXz4MJo3b64NEADo1asXVCoVTpw4ofeajh07hrFjx+LmzZuoXr06lixZgkGDBjFAiKhSMYsQuXnzJgAgJCQEAQEB2L17NxwcHNClSxckJycDAO7du4fatWvrfM7BwQGWlpa4d+9eodtWqVRIS0vTeRVFCIHNmzdj9uzZSE9PR5MmTbBmzRq0atWqjN+SiMj8GDVEQkJCIJPJinwdP35cO57H559/jkGDBqFt27bYsGEDZDIZvv/+e+32CjoLEEIUeXYwd+5c2Nvba1+urq6FrpuVlYXg4GCsXbsWQgj07dsXy5cvR82aNctwFIiIzJdRG9Z9fX0xePDgItdxd3dHeno6AKBp06ba+UqlEg0aNEBcXBwAoE6dOjh69KjOZ1NSUpCTk5PvDOV5/v7+mDp1qnY6LS2twCCJi4tDYGAg4uLiYGFhgUmTJqFv377Ff0kiogrMqCHi5OQEJyenYtdr27YtlEolrly5ou13KicnB7du3UK9evUAAJ6enoiMjMTdu3e1DeIxMTFQKpVo27ZtodtWKpXF3kl18OBBzJ07F1lZWXByckJYWBiaNGlS0q9JRFRhmcUtvnZ2dhg3bhyCg4Ph6uqKevXqYeHChQCADz74AMCzJ8ybNm0KLy8vLFy4EMnJyZg+fTp8fHwk342Qm5uL9evXY/PmzQCAli1bIiQkBA4ODvr5YkREZs4sQgQAFi5cCAsLC3h5eeHJkyfo0KED/vjjD+0PukKhwJ49ezBhwgR06tQJVlZWGDJkCBYtWiRpfxkZGVi2bBmOHTsGABg0aBDGjRsHCwuzOWRERAZnFs+JlKe8+6nfe+89PH78GEqlEtOmTcNbb71l7NLIjPA5ETIlhnxOhH9WF+L+/fuoV68ewsPD4eHhYexyiIhMEkOkEC1btsSCBQv0ntpERBWJWTxsaAyBgYEMECKiYjBECsHxz4mIisdfSiIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJ+JwIkQHY2NiAnUFQZcAzESIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJGCJERCQZQ4SIiCRjiBARkWQMESIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJGCJERCQZQ4SIiCRjiBARkWQMESIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJGCJERCQZQ4SIiCRjiBARkWQMESIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJGCJERCQZQ4SIiCRjiBARkWQWxi7A1AghAAAajQZpaWlGroaIqOzyfsvyft/0iSHygvT0dACAq6urkSshItKv9PR02Nvb63WbMmGIaDJjubm5SExMhK2tLWQyWaHrpaWlwdXVFfHx8bCzsyvHCs0Lj1PJ8ViVDI9TyeUdq7i4OMhkMri4uEAu128rBs9EXiCXy1G3bt0Sr29nZ8d/yCXA41RyPFYlw+NUcvb29gY7VmxYJyIiyRgiREQkGUNEIqVSieDgYCiVSmOXYtJ4nEqOx6pkeJxKrjyOFRvWiYhIMp6JEBGRZAwRIiKSjCFCRESSMUSIiEgyhohEe/bsQYcOHWBlZQUnJycMHDhQZ3lcXBz69esHGxsbODk5wc/PD9nZ2Uaq1jjc3d0hk8l0XrNnz9ZZh8dJl0qlQuvWrSGTyXD69GmdZTxWQP/+/eHm5oaqVavC2dkZXl5eSExM1FmHxwm4desWRo0ahfr168PKygoNGzZEcHBwvuOgj2PFJ9Yl2LFjB3x8fDBnzhx0794dQgicO3dOu1yj0aBPnz6oWbMmDh48iKSkJAwfPhxCCERFRRmx8vIXFhYGHx8f7XS1atW073mc8ps5cyZcXFxw5swZnfk8Vs9069YNn332GZydnZGQkIDp06fj/fffx6FDhwDwOOW5fPkycnNzER0dDQ8PD5w/fx4+Pj7IzMzEokWLAOjxWAkqlZycHPHSSy+JtWvXFrrO3r17hVwuFwkJCdp5W7ZsEUqlUqSmppZHmSahXr16YunSpYUu53HStXfvXvHKK6+ICxcuCADi1KlTOst4rPLbtWuXkMlkIjs7WwjB41SUBQsWiPr162un9XWseDmrlE6ePImEhATI5XK0adMGzs7OePvtt3HhwgXtOocPH0bz5s3h4uKinderVy+oVCqcOHHCGGUbzfz58+Ho6IjWrVsjMjJS51SZx+l/7t+/Dx8fH3zzzTewtrbOt5zHKr/k5GRs3rwZr7/+OqpUqQKAx6koqampqFGjhnZaX8eKIVJKN2/eBACEhIQgICAAu3fvhoODA7p06YLk5GQAwL1791C7dm2dzzk4OMDS0hL37t0r95qNZdKkSdi6dSsOHDgAX19fLFu2DBMmTNAu53F6RggBb29vjBs3Du3atStwHR6r/5k1axZsbGzg6OiIuLg47Nq1S7uMx6lgN27cQFRUFMaNG6edp69jxRD5r5CQkHyNwC++jh8/jtzcXADA559/jkGDBqFt27bYsGEDZDIZvv/+e+32CupGXghRZPfy5qCkxwkApkyZgi5duqBly5YYPXo0Vq9ejXXr1iEpKUm7vYp6nICSH6uoqCikpaXB39+/yO1V1GNVmn9TADBjxgycOnUKMTExUCgU+OSTT3QGW6qoxwko/bECgMTERPTu3RsffPABRo8erbNMH8eKDev/5evri8GDBxe5jru7u3bQqqZNm2rnK5VKNGjQAHFxcQCAOnXq4OjRozqfTUlJQU5OTr7kNzclPU4F6dixIwDg+vXrcHR0rNDHCSj5sYqIiMCRI0fy9W/Url07DB06FF9//XWFPlal/Tfl5OQEJycnNG7cGE2aNIGrqyuOHDkCT0/PCn2cgNIfq8TERHTr1g2enp5Ys2aNznp6O1b6abKpPFJTU4VSqdRpWM/Ozha1atUS0dHRQoj/NVglJiZq19m6dWulb9z75ZdfBABx+/ZtIQSPU57bt2+Lc+fOaV/79+8XAMQPP/wg4uPjhRA8VoWJi4sTAMSBAweEEDxOz7tz545o1KiRGDx4sFCr1fmW6+tYMUQkmDRpknjppZfE/v37xeXLl8WoUaNErVq1RHJyshBCCLVaLZo3by569OghTp48KX777TdRt25d4evra+TKy8+hQ4fEkiVLxKlTp8TNmzfFtm3bhIuLi+jfv792HR6ngsXGxua7O4vHSoijR4+KqKgocerUKXHr1i3xxx9/iDfeeEM0bNhQPH36VAjB45QnISFBeHh4iO7du4s7d+6Iu3fval959HWsGCISZGdni2nTpolatWoJW1tb8Z///EecP39eZ53bt2+LPn36CCsrK1GjRg3h6+ur/YdeGZw4cUJ06NBB2Nvbi6pVq4qXX35ZBAcHi8zMTJ31KvtxKkhBISIEj9XZs2dFt27dRI0aNYRSqRTu7u5i3Lhx4s6dOzrrVfbjJIQQGzZsEAAKfD1PH8eKXcETEZFkvDuLiIgkY4gQEZFkDBEiIpKMIUJERJIxRIiISDKGCBERScYQISIiyRgiRCbkzz//hEwmw+PHj0v8GXd3dyxbtsxgNREVhSFCVAre3t6QyWQ6XWrnmTBhAmQyGby9vcu/MCIjYYgQlZKrqyu2bt2KJ0+eaOc9ffoUW7ZsgZubmxErIyp/DBGiUnr11Vfh5uaGnTt3auft3LkTrq6uaNOmjXaeSqWCn58fatWqhapVq+KNN97Av//+q7OtvXv3onHjxrCyskK3bt1w69atfPs7dOgQ3nzzTVhZWcHV1RV+fn7IzMw02PcjKg2GCJEEI0aMwIYNG7TT69evx8iRI3XWmTlzJnbs2IGvv/4aJ0+ehIeHB3r16qUdATM+Ph4DBw7EO++8g9OnT2P06NGYPXu2zjbOnTuHXr16YeDAgTh79iy2bduGgwcPwtfX1/Bfkqgk9NdvJFHFN3z4cPHuu++Khw8fCqVSKWJjY8WtW7dE1apVxcOHD8W7774rhg8fLjIyMkSVKlXE5s2btZ/Nzs4WLi4uYsGCBUIIIfz9/UWTJk1Ebm6udp1Zs2YJACIlJUUIIYSXl5cYM2aMTg3//POPkMvl4smTJ0IIIerVqyeWLl1q2C9OVAiObEgkgZOTE/r06YOvv/4aQgj06dMHTk5O2uU3btxATk4OOnXqpJ1XpUoVvPbaa7h06RIA4NKlS+jYsaPOUKSenp46+zlx4gSuX7+OzZs3a+cJIZCbm4vY2Fg0adLEUF+RqEQYIkQSjRw5UntZ6YsvvtBZJv47wsKLY1WL58avFiUYhSE3Nxdjx46Fn59fvmVsxCdTwDYRIol69+6N7OxsZGdno1evXjrLPDw8YGlpiYMHD2rn5eTk4Pjx49qzh6ZNm+LIkSM6n3tx+tVXX8WFCxfg4eGR72VpaWmgb0ZUcgwRIokUCgUuXbqES5cuQaFQ6CyzsbHB+PHjMWPGDOzbtw8XL16Ej48PsrKyMGrUKADAuHHjcOPGDUydOhVXrlzBd999h40bN+psZ9asWTh8+DAmTpyI06dP49q1a/j555/x6aefltfXJCoSQ4SoDOzs7GBnZ1fgsnnz5mHQoEHw8vLCq6++iuvXr2P//v1wcHAA8Oxy1I4dO/DLL7+gVatWWL16NebMmaOzjZYtW+Kvv/7CtWvX0LlzZ7Rp0waBgYFwdnY2+HcjKgkOj0tERJLxTISIiCRjiBARkWQMESIikowhQkREkjFEiIhIMoYIERFJxhAhIiLJGCJERCQZQ4SIiCRjiBARkWQMESIikowhQkREkv1/Xw1Y5o9jIJEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_plot(Y_test.data, m, v, figsize=(4,4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "922c6192-05da-485b-9f12-ef4a6c52d739",
   "metadata": {},
   "source": [
    "## Calibrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "624c3fae-0ec3-498b-b8f1-f60adc2d4cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from esem.utils import get_random_params\n",
    "from esem.abc_sampler import ABCSampler, constrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a59b9590-cb49-41e2-834d-ac96512d3ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>micro_mg_autocon_nd_exp</th>\n",
       "      <th>micro_mg_dcs</th>\n",
       "      <th>cldfrc_dp1</th>\n",
       "      <th>cldfrc_dp2</th>\n",
       "      <th>clubb_C6thlb</th>\n",
       "      <th>clubb_C8</th>\n",
       "      <th>clubb_c1</th>\n",
       "      <th>clubb_c11</th>\n",
       "      <th>clubb_c14</th>\n",
       "      <th>dust_emis_fact</th>\n",
       "      <th>...</th>\n",
       "      <th>micro_mg_berg_eff_factor</th>\n",
       "      <th>micro_mg_homog_size</th>\n",
       "      <th>micro_mg_iaccr_factor</th>\n",
       "      <th>micro_mg_vtrmi_factor</th>\n",
       "      <th>microp_aero_wsub_scale</th>\n",
       "      <th>microp_aero_wsubi_scale</th>\n",
       "      <th>seasalt_emis_scale</th>\n",
       "      <th>zmconv_capelmt</th>\n",
       "      <th>zmconv_ke</th>\n",
       "      <th>zmconv_tiedke_add</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmb_sim</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.971500</td>\n",
       "      <td>0.805847</td>\n",
       "      <td>0.206424</td>\n",
       "      <td>0.671471</td>\n",
       "      <td>0.684229</td>\n",
       "      <td>0.793950</td>\n",
       "      <td>0.779754</td>\n",
       "      <td>0.227661</td>\n",
       "      <td>0.300534</td>\n",
       "      <td>0.974161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106340</td>\n",
       "      <td>0.556251</td>\n",
       "      <td>0.469144</td>\n",
       "      <td>0.505458</td>\n",
       "      <td>0.931798</td>\n",
       "      <td>0.049484</td>\n",
       "      <td>0.955314</td>\n",
       "      <td>0.074489</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>0.634364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.649866</td>\n",
       "      <td>0.894649</td>\n",
       "      <td>0.618148</td>\n",
       "      <td>0.938361</td>\n",
       "      <td>0.518324</td>\n",
       "      <td>0.538249</td>\n",
       "      <td>0.665506</td>\n",
       "      <td>0.812990</td>\n",
       "      <td>0.567041</td>\n",
       "      <td>0.865573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032305</td>\n",
       "      <td>0.301909</td>\n",
       "      <td>0.499515</td>\n",
       "      <td>0.088459</td>\n",
       "      <td>0.731500</td>\n",
       "      <td>0.092761</td>\n",
       "      <td>0.432079</td>\n",
       "      <td>0.058586</td>\n",
       "      <td>0.084965</td>\n",
       "      <td>0.833772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.703768</td>\n",
       "      <td>0.767592</td>\n",
       "      <td>0.051199</td>\n",
       "      <td>0.348967</td>\n",
       "      <td>0.086444</td>\n",
       "      <td>0.549257</td>\n",
       "      <td>0.273296</td>\n",
       "      <td>0.979376</td>\n",
       "      <td>0.715680</td>\n",
       "      <td>0.107133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783043</td>\n",
       "      <td>0.644865</td>\n",
       "      <td>0.811843</td>\n",
       "      <td>0.905836</td>\n",
       "      <td>0.991289</td>\n",
       "      <td>0.268722</td>\n",
       "      <td>0.468469</td>\n",
       "      <td>0.823717</td>\n",
       "      <td>0.698678</td>\n",
       "      <td>0.404756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.948312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888282</td>\n",
       "      <td>0.481579</td>\n",
       "      <td>0.058440</td>\n",
       "      <td>0.964224</td>\n",
       "      <td>0.379381</td>\n",
       "      <td>0.530253</td>\n",
       "      <td>0.069777</td>\n",
       "      <td>0.730504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756368</td>\n",
       "      <td>0.749765</td>\n",
       "      <td>0.772850</td>\n",
       "      <td>0.655938</td>\n",
       "      <td>0.489364</td>\n",
       "      <td>0.415544</td>\n",
       "      <td>0.174367</td>\n",
       "      <td>0.469552</td>\n",
       "      <td>0.591195</td>\n",
       "      <td>0.371451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.123560</td>\n",
       "      <td>0.365255</td>\n",
       "      <td>0.674939</td>\n",
       "      <td>0.979595</td>\n",
       "      <td>0.072070</td>\n",
       "      <td>0.271905</td>\n",
       "      <td>0.706585</td>\n",
       "      <td>0.654203</td>\n",
       "      <td>0.923545</td>\n",
       "      <td>0.517474</td>\n",
       "      <td>...</td>\n",
       "      <td>0.887158</td>\n",
       "      <td>0.438671</td>\n",
       "      <td>0.960535</td>\n",
       "      <td>0.433522</td>\n",
       "      <td>0.682204</td>\n",
       "      <td>0.255607</td>\n",
       "      <td>0.757154</td>\n",
       "      <td>0.702068</td>\n",
       "      <td>0.835647</td>\n",
       "      <td>0.854877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0.725604</td>\n",
       "      <td>0.924849</td>\n",
       "      <td>0.092477</td>\n",
       "      <td>0.631673</td>\n",
       "      <td>0.705486</td>\n",
       "      <td>0.747839</td>\n",
       "      <td>0.605231</td>\n",
       "      <td>0.586143</td>\n",
       "      <td>0.377838</td>\n",
       "      <td>0.898157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.357568</td>\n",
       "      <td>0.320785</td>\n",
       "      <td>0.139384</td>\n",
       "      <td>0.989630</td>\n",
       "      <td>0.379718</td>\n",
       "      <td>0.667079</td>\n",
       "      <td>0.751509</td>\n",
       "      <td>0.923623</td>\n",
       "      <td>0.522542</td>\n",
       "      <td>0.081568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>0.633606</td>\n",
       "      <td>0.341112</td>\n",
       "      <td>0.076788</td>\n",
       "      <td>0.264890</td>\n",
       "      <td>0.977408</td>\n",
       "      <td>0.668325</td>\n",
       "      <td>0.017842</td>\n",
       "      <td>0.878602</td>\n",
       "      <td>0.976971</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971503</td>\n",
       "      <td>0.063019</td>\n",
       "      <td>0.132357</td>\n",
       "      <td>0.034985</td>\n",
       "      <td>0.057183</td>\n",
       "      <td>0.494946</td>\n",
       "      <td>0.397352</td>\n",
       "      <td>0.818635</td>\n",
       "      <td>0.491236</td>\n",
       "      <td>0.056177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.619860</td>\n",
       "      <td>0.568149</td>\n",
       "      <td>0.765996</td>\n",
       "      <td>0.915039</td>\n",
       "      <td>0.338742</td>\n",
       "      <td>0.983273</td>\n",
       "      <td>0.243526</td>\n",
       "      <td>0.099371</td>\n",
       "      <td>0.314178</td>\n",
       "      <td>0.179294</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693411</td>\n",
       "      <td>0.448854</td>\n",
       "      <td>0.205011</td>\n",
       "      <td>0.371056</td>\n",
       "      <td>0.273651</td>\n",
       "      <td>0.185371</td>\n",
       "      <td>0.772156</td>\n",
       "      <td>0.323095</td>\n",
       "      <td>0.959486</td>\n",
       "      <td>0.484692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0.992654</td>\n",
       "      <td>0.126066</td>\n",
       "      <td>0.255441</td>\n",
       "      <td>0.698665</td>\n",
       "      <td>0.249564</td>\n",
       "      <td>0.683599</td>\n",
       "      <td>0.718731</td>\n",
       "      <td>0.917675</td>\n",
       "      <td>0.627753</td>\n",
       "      <td>0.204757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.416335</td>\n",
       "      <td>0.667422</td>\n",
       "      <td>0.226351</td>\n",
       "      <td>0.178084</td>\n",
       "      <td>0.944702</td>\n",
       "      <td>0.452394</td>\n",
       "      <td>0.803122</td>\n",
       "      <td>0.049163</td>\n",
       "      <td>0.247609</td>\n",
       "      <td>0.997869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0.297873</td>\n",
       "      <td>0.868720</td>\n",
       "      <td>0.830021</td>\n",
       "      <td>0.961661</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>0.375101</td>\n",
       "      <td>0.032601</td>\n",
       "      <td>0.586763</td>\n",
       "      <td>0.050706</td>\n",
       "      <td>0.727236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159003</td>\n",
       "      <td>0.856063</td>\n",
       "      <td>0.925182</td>\n",
       "      <td>0.108261</td>\n",
       "      <td>0.105863</td>\n",
       "      <td>0.560436</td>\n",
       "      <td>0.720056</td>\n",
       "      <td>0.649758</td>\n",
       "      <td>0.205458</td>\n",
       "      <td>0.675950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         micro_mg_autocon_nd_exp  micro_mg_dcs  cldfrc_dp1  cldfrc_dp2  \\\n",
       "nmb_sim                                                                  \n",
       "25                      0.971500      0.805847    0.206424    0.671471   \n",
       "26                      0.649866      0.894649    0.618148    0.938361   \n",
       "27                      0.703768      0.767592    0.051199    0.348967   \n",
       "28                      0.948312      0.000000    0.888282    0.481579   \n",
       "29                      0.123560      0.365255    0.674939    0.979595   \n",
       "...                          ...           ...         ...         ...   \n",
       "258                     0.725604      0.924849    0.092477    0.631673   \n",
       "259                     0.633606      0.341112    0.076788    0.264890   \n",
       "260                     0.619860      0.568149    0.765996    0.915039   \n",
       "261                     0.992654      0.126066    0.255441    0.698665   \n",
       "262                     0.297873      0.868720    0.830021    0.961661   \n",
       "\n",
       "         clubb_C6thlb  clubb_C8  clubb_c1  clubb_c11  clubb_c14  \\\n",
       "nmb_sim                                                           \n",
       "25           0.684229  0.793950  0.779754   0.227661   0.300534   \n",
       "26           0.518324  0.538249  0.665506   0.812990   0.567041   \n",
       "27           0.086444  0.549257  0.273296   0.979376   0.715680   \n",
       "28           0.058440  0.964224  0.379381   0.530253   0.069777   \n",
       "29           0.072070  0.271905  0.706585   0.654203   0.923545   \n",
       "...               ...       ...       ...        ...        ...   \n",
       "258          0.705486  0.747839  0.605231   0.586143   0.377838   \n",
       "259          0.977408  0.668325  0.017842   0.878602   0.976971   \n",
       "260          0.338742  0.983273  0.243526   0.099371   0.314178   \n",
       "261          0.249564  0.683599  0.718731   0.917675   0.627753   \n",
       "262          0.997973  0.375101  0.032601   0.586763   0.050706   \n",
       "\n",
       "         dust_emis_fact  ...  micro_mg_berg_eff_factor  micro_mg_homog_size  \\\n",
       "nmb_sim                  ...                                                  \n",
       "25             0.974161  ...                  0.106340             0.556251   \n",
       "26             0.865573  ...                  0.032305             0.301909   \n",
       "27             0.107133  ...                  0.783043             0.644865   \n",
       "28             0.730504  ...                  0.756368             0.749765   \n",
       "29             0.517474  ...                  0.887158             0.438671   \n",
       "...                 ...  ...                       ...                  ...   \n",
       "258            0.898157  ...                  0.357568             0.320785   \n",
       "259            0.009132  ...                  0.971503             0.063019   \n",
       "260            0.179294  ...                  0.693411             0.448854   \n",
       "261            0.204757  ...                  0.416335             0.667422   \n",
       "262            0.727236  ...                  0.159003             0.856063   \n",
       "\n",
       "         micro_mg_iaccr_factor  micro_mg_vtrmi_factor  microp_aero_wsub_scale  \\\n",
       "nmb_sim                                                                         \n",
       "25                    0.469144               0.505458                0.931798   \n",
       "26                    0.499515               0.088459                0.731500   \n",
       "27                    0.811843               0.905836                0.991289   \n",
       "28                    0.772850               0.655938                0.489364   \n",
       "29                    0.960535               0.433522                0.682204   \n",
       "...                        ...                    ...                     ...   \n",
       "258                   0.139384               0.989630                0.379718   \n",
       "259                   0.132357               0.034985                0.057183   \n",
       "260                   0.205011               0.371056                0.273651   \n",
       "261                   0.226351               0.178084                0.944702   \n",
       "262                   0.925182               0.108261                0.105863   \n",
       "\n",
       "         microp_aero_wsubi_scale  seasalt_emis_scale  zmconv_capelmt  \\\n",
       "nmb_sim                                                                \n",
       "25                      0.049484            0.955314        0.074489   \n",
       "26                      0.092761            0.432079        0.058586   \n",
       "27                      0.268722            0.468469        0.823717   \n",
       "28                      0.415544            0.174367        0.469552   \n",
       "29                      0.255607            0.757154        0.702068   \n",
       "...                          ...                 ...             ...   \n",
       "258                     0.667079            0.751509        0.923623   \n",
       "259                     0.494946            0.397352        0.818635   \n",
       "260                     0.185371            0.772156        0.323095   \n",
       "261                     0.452394            0.803122        0.049163   \n",
       "262                     0.560436            0.720056        0.649758   \n",
       "\n",
       "         zmconv_ke  zmconv_tiedke_add  \n",
       "nmb_sim                                \n",
       "25        0.798951           0.634364  \n",
       "26        0.084965           0.833772  \n",
       "27        0.698678           0.404756  \n",
       "28        0.591195           0.371451  \n",
       "29        0.835647           0.854877  \n",
       "...            ...                ...  \n",
       "258       0.522542           0.081568  \n",
       "259       0.491236           0.056177  \n",
       "260       0.959486           0.484692  \n",
       "261       0.247609           0.997869  \n",
       "262       0.205458           0.675950  \n",
       "\n",
       "[237 rows x 23 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bce220b1-d3b8-407b-b04f-282e242dc9db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup sampler with 1 million points\n",
    "sample_points = pd.DataFrame(data=get_random_params(23, int(1e6)), columns=X_train.columns)\n",
    "sampler = ABCSampler(gp, np.asarray([-40.5]), obs_uncertainty=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1612dc29-7e37-4b3e-9c5d-bdfd906bb808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2e1d419a6d54186bca265f5b0417216",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000000 [00:00<?, ?sample/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tqdm.auto.tqdm object at 0x7f8f74ba3150>"
     ]
    }
   ],
   "source": [
    "valid_samples = sampler.batch_constrain(sample_points, batch_size=10000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f576276d-6729-4d04-977a-8d0283aff792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining points: 100\n"
     ]
    }
   ],
   "source": [
    "print(\"Remaining points: {}\".format(valid_samples.sum()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6d556619-d81c-42b3-bf7d-e72b485b3741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(valid_samples)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml-notebook]",
   "language": "python",
   "name": "conda-env-ml-notebook-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}