04_Scenario_Distributions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "13689494-c391-4712-83ee-a8428e2b7248",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import dataframe_image as dfi\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0342dc4-3b00-434c-b900-d167774aed21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reformat_prepped(prepped,seq_len,partition,llo_groups,scenario='Baseline'):\n",
    "    x_data = prepped[f'x_{partition}'][:,-seq_len:,:]\n",
    "    shape_x = x_data.shape\n",
    "    x_data = x_data.reshape(shape_x[0]*shape_x[1],shape_x[2])\n",
    "    x_data = x_data*prepped['x_std']+prepped['x_mean']\n",
    "    dates= prepped[f'times_{partition}'][:,-seq_len:,:].reshape(shape_x[0]*shape_x[1])\n",
    "    seg_ids = prepped[f'ids_{partition}'][:,-seq_len:,:].reshape(shape_x[0]*shape_x[1])\n",
    "    x_vars = prepped['x_vars']\n",
    "    df = pd.DataFrame(columns=x_vars, data=x_data)\n",
    "    df['date']=pd.to_datetime(dates)\n",
    "    df['month']=df['date'].dt.month\n",
    "    df['sg_d_nt']=seg_ids\n",
    "    if np.isin(scenario, ['Coastal','Headwaters','Appalachians']):\n",
    "        if np.isin(partition,['trn','val']):\n",
    "            df = df[~df.sg_d_nt.isin(llo_groups.seg_id_nat[llo_groups.test_group == scenario])]\n",
    "        elif partition == 'tst':\n",
    "            df = df[df.sg_d_nt.isin(llo_groups.seg_id_nat[llo_groups.test_group == scenario])]\n",
    "    return df\n",
    "\n",
    "def calc_diff_temp(tst,trn):\n",
    "    return tst['seg_tave_air'][tst.month.isin([6,7,8])].median()-trn['seg_tave_air'][trn.month.isin([6,7,8])].median()\n",
    "\n",
    "def calc_diff_precip(tst,trn):\n",
    "    return tst['seg_rain'].median()-trn['seg_rain'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ac44354-14ac-4957-964d-03f568c8bb72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Coastal', 'Appalachians', 'Headwaters'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llo_groups = pd.read_csv('data_DRB/DRB_spatial/llo_groups.csv')\n",
    "llo_groups.loc[llo_groups.test_group == 'Piedmont', 'test_group']='Headwaters'\n",
    "llo_groups.loc[llo_groups.test_group == 'Coastal_Plains', 'test_group']='Coastal'\n",
    "llo_groups['test_group'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67668095-3489-42d4-b5f5-67675c6090bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_trn = reformat_prepped(np.load(\"results/baseline/GWN/prepped.npz\"),15,'trn',llo_groups)\n",
    "lto_max_trn = reformat_prepped(np.load(\"results/LTO/max/GWN/prepped.npz\"),15,'trn', llo_groups)\n",
    "drought_trn = reformat_prepped(np.load(\"results/Drought/GWN/prepped.npz\"),15,'trn', llo_groups)\n",
    "app_trn = reformat_prepped(np.load(\"results/LLO/appalachians/GWN/prepped.npz\"),15,'trn',llo_groups, 'Appalachians')\n",
    "head_trn = reformat_prepped(np.load(\"results/LLO/piedmont/GWN/prepped.npz\"),15,'trn',llo_groups, 'Headwaters') #Piedmont misslabled in prep script, need to correct\n",
    "coast_trn = reformat_prepped(np.load(\"results/LLO/coastal/GWN/prepped.npz\"),15,'trn',llo_groups, 'Coastal')\n",
    "\n",
    "baseline_val = reformat_prepped(np.load(\"results/baseline/GWN/prepped.npz\"),15,'val', llo_groups)\n",
    "lto_max_val = reformat_prepped(np.load(\"results/LTO/max/GWN/prepped.npz\"),15,'val', llo_groups)\n",
    "drought_val = reformat_prepped(np.load(\"results/Drought/GWN/prepped.npz\"),15,'val', llo_groups)\n",
    "app_val = reformat_prepped(np.load(\"results/LLO/appalachians/GWN/prepped.npz\"),15,'val', llo_groups, 'Appalachians')\n",
    "head_val = reformat_prepped(np.load(\"results/LLO/piedmont/GWN/prepped.npz\"),15,'val', 'Headwaters') #Piedmont misslabled in prep script, need to correct\n",
    "coast_val = reformat_prepped(np.load(\"results/LLO/coastal/GWN/prepped.npz\"),15,'val', 'Coastal')\n",
    "\n",
    "baseline_tst = reformat_prepped(np.load(\"results/baseline/GWN/prepped.npz\"),15,'tst', llo_groups)\n",
    "lto_max_tst = reformat_prepped(np.load(\"results/LTO/max/GWN/prepped.npz\"),15,'tst', llo_groups)\n",
    "drought_tst = reformat_prepped(np.load(\"results/Drought/GWN/prepped.npz\"),15,'tst', llo_groups)\n",
    "app_tst = reformat_prepped(np.load(\"results/LLO/appalachians/GWN/prepped.npz\"),15,'tst',llo_groups, 'Appalachians')\n",
    "head_tst = reformat_prepped(np.load(\"results/LLO/piedmont/GWN/prepped.npz\"),15,'tst',llo_groups, 'Headwaters') #Piedmont misslabled in prep script, need to correct\n",
    "coast_tst = reformat_prepped(np.load(\"results/LLO/coastal/GWN/prepped.npz\"),15,'tst', llo_groups, 'Coastal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1e74e906-d995-40c0-9855-0eb1b9d95dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_diff = calc_diff_temp(baseline_tst,baseline_trn)\n",
    "max_diff= calc_diff_temp(lto_max_tst,lto_max_trn)\n",
    "drought_diff = calc_diff_temp(drought_tst,drought_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc65520f-2977-4963-88c0-0b1184145f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.style.use('seaborn-colorblind')\n",
    "\n",
    "pal = list(sns.color_palette('colorblind',4))\n",
    "\n",
    "fig, ax = plt.subplots(2,1,figsize=(4,3.5), constrained_layout=True, dpi=200,sharey=True,sharex=True)\n",
    "\n",
    "#sns.kdeplot(lto_min['seg_tave_air'][lto_min.month.isin([6,7,8])],ax=ax[0],label=f'Test',color = pal[0])\n",
    "#sns.kdeplot(lto_min_trn['seg_tave_air'][lto_min_trn.month.isin([6,7,8])],ax=ax[0],label=f'Train',color = pal[1])\n",
    "#ax[0].axvline(lto_min['seg_tave_air'][lto_min.month.isin([6,7,8])].median(),color=pal[0], alpha = .5)\n",
    "#ax[0].axvline(lto_min_trn['seg_tave_air'][lto_min_trn.month.isin([6,7,8])].median(),color = pal[1],alpha=.5)\n",
    "#ax[0].set_title('Train Hot/Test Cold')\n",
    "#ax[0].set_ylabel('')\n",
    "\n",
    "sns.kdeplot(lto_max_tst['seg_tave_air'][lto_max_tst.month.isin([6,7,8])],ax=ax[0],label=f'Test',color = pal[0])\n",
    "sns.kdeplot(lto_max_trn['seg_tave_air'][lto_max_trn.month.isin([6,7,8])],ax=ax[0],label=f'Train',color = pal[1])\n",
    "#ax[0].set_title('')\n",
    "ax[0].axvline(lto_max_tst['seg_tave_air'][lto_max_tst.month.isin([6,7,8])].median(),color=pal[0], alpha = .5)\n",
    "ax[0].axvline(lto_max_trn['seg_tave_air'][lto_max_trn.month.isin([6,7,8])].median(),color = pal[1],alpha=.5)\n",
    "ax[0].set_ylabel('Warming\\nScenario')\n",
    "\n",
    "sns.kdeplot(baseline_tst['seg_tave_air'][baseline_tst.month.isin([6,7,8])],ax=ax[1],label=f'Test',color = pal[0])\n",
    "sns.kdeplot(baseline_trn['seg_tave_air'][baseline_trn.month.isin([6,7,8])],ax=ax[1],label=f'Train',color = pal[1])\n",
    "#ax[1].set_title('')\n",
    "ax[1].axvline(baseline_tst['seg_tave_air'][baseline_tst.month.isin([6,7,8])].median(),color=pal[0], alpha = .5)\n",
    "ax[1].axvline(baseline_trn['seg_tave_air'][baseline_trn.month.isin([6,7,8])].median(),color = pal[1],alpha=.5)\n",
    "ax[1].set_ylabel('Baseline\\nScenario')\n",
    "\n",
    "ax[1].set_xlabel('Air Temp °C')\n",
    "ax[1].annotate(f'Δ°C={base_diff:.2f}', xy=(3,.05))\n",
    "ax[0].annotate(f'Δ°C={max_diff:.2f}', xy=(3,.05))\n",
    "#ax[0].annotate(f'Δ°C={min_diff:.2f}', xy=(3,.05))\n",
    "\n",
    "plt.yticks([])\n",
    "ax[0].legend(loc='upper right')\n",
    "plt.savefig('results/figures/temp_distributions_warming_baseline.png',dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad42e9a8-0691-497c-844f-08ed97df8898",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reformat_precip(scenario1,scenario2,name1,name2):\n",
    "    scenario1['run']=name1\n",
    "    scenario2['run']=name2\n",
    "    df = pd.concat([scenario1,scenario2],ignore_index=True)\n",
    "    df['year'] = temp_df.date.dt.year.where(temp_df.date.dt.month < 10,temp_df.date.dt.year + 1)\n",
    "    df = drought_df.groupby(['run','year','date']).mean().groupby(['run','year']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60dec981-a0fd-4483-86b4-b89c220bc0f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def reformat_precip(tst,trn):\n",
    "    tst['Partition']='Test'\n",
    "    trn['Partition']='Train'\n",
    "    df = pd.concat([tst,trn],ignore_index=True)\n",
    "    df['year']=df.date.dt.year.where(df.date.dt.month < 10,df.date.dt.year + 1)\n",
    "    df = df.groupby(['Partition','year','date']).mean().groupby(['Partition','year']).sum()\n",
    "    return df.reset_index()\n",
    "\n",
    "   \n",
    "fig, ax = plt.subplots(1,2,figsize=(4,3),dpi=200,constrained_layout=True,sharey=True,sharex=True)\n",
    "sns.boxplot(data=reformat_precip(drought_tst,drought_trn), x='Partition', y='seg_rain',ax=ax[0],order=['Train','Test'], palette=[pal[1],pal[0]])#,label=f'Drought',color = pal[0])\n",
    "ax[0].set_ylabel('Annual Rainfall (m)')\n",
    "ax[0].set_xlabel('Drought Scenario')\n",
    "\n",
    "sns.boxplot(data=reformat_precip(baseline_tst,baseline_trn), x='Partition', y='seg_rain',ax=ax[1],order=['Train','Test'], palette=[pal[1],pal[0]])\n",
    "ax[1].set_ylabel('')\n",
    "ax[1].set_xlabel('Baseline')\n",
    "plt.savefig('results/figures/precip_distributions_warming_baseline.png',dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c9854067-365f-40c0-960c-32286b2921c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "precip_df = reformat_precip(drought_tst,drought_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "025e4136-04c2-4e89-9d03-29db41c0ed26",
   "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>year</th>\n",
       "      <th>seg_slope</th>\n",
       "      <th>seg_elev</th>\n",
       "      <th>seg_width_mean</th>\n",
       "      <th>seg_tave_air</th>\n",
       "      <th>seginc_swrad</th>\n",
       "      <th>seg_rain</th>\n",
       "      <th>seginc_potet</th>\n",
       "      <th>month</th>\n",
       "      <th>sg_d_nt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partition</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>Test</th>\n",
       "      <td>1990.5</td>\n",
       "      <td>1.803479</td>\n",
       "      <td>54072.800659</td>\n",
       "      <td>11372.766421</td>\n",
       "      <td>3707.604290</td>\n",
       "      <td>53501.858438</td>\n",
       "      <td>0.958978</td>\n",
       "      <td>32.002281</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>739019.10989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>1.803479</td>\n",
       "      <td>54072.800659</td>\n",
       "      <td>11372.766421</td>\n",
       "      <td>3747.586891</td>\n",
       "      <td>52970.176983</td>\n",
       "      <td>1.147006</td>\n",
       "      <td>31.816809</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>739019.10989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year  seg_slope      seg_elev  seg_width_mean  seg_tave_air  \\\n",
       "Partition                                                                  \n",
       "Test       1990.5   1.803479  54072.800659    11372.766421   3707.604290   \n",
       "Train      1999.0   1.803479  54072.800659    11372.766421   3747.586891   \n",
       "\n",
       "           seginc_swrad  seg_rain  seginc_potet   month       sg_d_nt  \n",
       "Partition                                                              \n",
       "Test       53501.858438  0.958978     32.002281  2382.0  739019.10989  \n",
       "Train      52970.176983  1.147006     31.816809  2382.0  739019.10989  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precip_df.groupby(['Partition']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a7d8314-cf49-4156-a4f0-309f36a26c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_styler_correct(s):\n",
    "    if s.name[1]=='Test':\n",
    "        return [\"background-color: #AAAAAA\"] * len(s)\n",
    "    elif s.name[1]=='Train':\n",
    "        return [\"background-color: #CCCCCC\"] * len(s)\n",
    "    else:\n",
    "        return [\"background-color: #EEEEEE\"] * len(s)\n",
    "    \n",
    "def format_run_tables(trn,val,tst,run):\n",
    "    ## Train\n",
    "    trn = (trn.describe()[['seg_slope','seg_elev','seg_width_mean','seg_tave_air','seginc_swrad','seg_rain','seginc_potet']].filter(items=['min','25%','50%','75%','max'],axis=0)\n",
    "           .assign(Partition='Train'))\n",
    "    \n",
    "    tst = (tst.describe()[['seg_slope','seg_elev','seg_width_mean','seg_tave_air','seginc_swrad','seg_rain','seginc_potet']].filter(items=['min','25%','50%','75%','max'],axis=0)\n",
    "           .assign(Partition='Test'))\n",
    "    \n",
    "    val = (val.describe()[['seg_slope','seg_elev','seg_width_mean','seg_tave_air','seginc_swrad','seg_rain','seginc_potet']].filter(items=['min','25%','50%','75%','max'],axis=0)\n",
    "           .assign(Partition='Val'))\n",
    "\n",
    "    table_out = (pd.concat([trn,tst,val])\n",
    "                     .rename(columns={'seg_slope':'Slope (m/km)','seg_elev':'Elevation (m)','seg_width_mean':'Mean Width (m)',\n",
    "                          'seg_tave_air':'Air Temp. (°C)','seginc_swrad':'SW Rad. (W/sq.m)','seg_rain':'Precip. (cm)','seginc_potet':'Potential ET (cm)'})\n",
    "                     .assign(**{'Precip. (cm)':(lambda x: x['Precip. (cm)']*100)},\n",
    "                             **{'Potential ET (cm)':(lambda x: x['Potential ET (cm)']*100)})\n",
    "                     .round(3)\n",
    "                     .reset_index()\n",
    "                     .assign(Percentile=lambda x:pd.Categorical(x['index'],ordered=True, categories = ['min','25%','50%','75%','max']))\n",
    "                     .drop(columns=['index'])\n",
    "                     .pivot(columns=['Partition'],index=['Percentile']))\n",
    "    \n",
    "    table_styled = table_out.style.apply(my_styler_correct).format(precision=2).set_caption(run).set_table_styles([{\n",
    "    'selector': 'caption',\n",
    "    'props': [\n",
    "        ('weight', 'bold'),\n",
    "        ('font-size', '18px')\n",
    "        ]\n",
    "    }])\n",
    "\n",
    "    #dfi.export(table_styled, f'results/figures/{run}_summary.png', table_conversion='matplotlib')\n",
    "    return table_styled\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d1eabf9d-e0fa-44b9-a3d0-6e7c234d6056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_39b7e caption {\n",
       "  weight: bold;\n",
       "  font-size: 18px;\n",
       "}\n",
       "#T_39b7e_row0_col0, #T_39b7e_row0_col3, #T_39b7e_row0_col6, #T_39b7e_row0_col9, #T_39b7e_row0_col12, #T_39b7e_row0_col15, #T_39b7e_row0_col18, #T_39b7e_row1_col0, #T_39b7e_row1_col3, #T_39b7e_row1_col6, #T_39b7e_row1_col9, #T_39b7e_row1_col12, #T_39b7e_row1_col15, #T_39b7e_row1_col18, #T_39b7e_row2_col0, #T_39b7e_row2_col3, #T_39b7e_row2_col6, #T_39b7e_row2_col9, #T_39b7e_row2_col12, #T_39b7e_row2_col15, #T_39b7e_row2_col18, #T_39b7e_row3_col0, #T_39b7e_row3_col3, #T_39b7e_row3_col6, #T_39b7e_row3_col9, #T_39b7e_row3_col12, #T_39b7e_row3_col15, #T_39b7e_row3_col18, #T_39b7e_row4_col0, #T_39b7e_row4_col3, #T_39b7e_row4_col6, #T_39b7e_row4_col9, #T_39b7e_row4_col12, #T_39b7e_row4_col15, #T_39b7e_row4_col18 {\n",
       "  background-color: #AAAAAA;\n",
       "}\n",
       "#T_39b7e_row0_col1, #T_39b7e_row0_col4, #T_39b7e_row0_col7, #T_39b7e_row0_col10, #T_39b7e_row0_col13, #T_39b7e_row0_col16, #T_39b7e_row0_col19, #T_39b7e_row1_col1, #T_39b7e_row1_col4, #T_39b7e_row1_col7, #T_39b7e_row1_col10, #T_39b7e_row1_col13, #T_39b7e_row1_col16, #T_39b7e_row1_col19, #T_39b7e_row2_col1, #T_39b7e_row2_col4, #T_39b7e_row2_col7, #T_39b7e_row2_col10, #T_39b7e_row2_col13, #T_39b7e_row2_col16, #T_39b7e_row2_col19, #T_39b7e_row3_col1, #T_39b7e_row3_col4, #T_39b7e_row3_col7, #T_39b7e_row3_col10, #T_39b7e_row3_col13, #T_39b7e_row3_col16, #T_39b7e_row3_col19, #T_39b7e_row4_col1, #T_39b7e_row4_col4, #T_39b7e_row4_col7, #T_39b7e_row4_col10, #T_39b7e_row4_col13, #T_39b7e_row4_col16, #T_39b7e_row4_col19 {\n",
       "  background-color: #CCCCCC;\n",
       "}\n",
       "#T_39b7e_row0_col2, #T_39b7e_row0_col5, #T_39b7e_row0_col8, #T_39b7e_row0_col11, #T_39b7e_row0_col14, #T_39b7e_row0_col17, #T_39b7e_row0_col20, #T_39b7e_row1_col2, #T_39b7e_row1_col5, #T_39b7e_row1_col8, #T_39b7e_row1_col11, #T_39b7e_row1_col14, #T_39b7e_row1_col17, #T_39b7e_row1_col20, #T_39b7e_row2_col2, #T_39b7e_row2_col5, #T_39b7e_row2_col8, #T_39b7e_row2_col11, #T_39b7e_row2_col14, #T_39b7e_row2_col17, #T_39b7e_row2_col20, #T_39b7e_row3_col2, #T_39b7e_row3_col5, #T_39b7e_row3_col8, #T_39b7e_row3_col11, #T_39b7e_row3_col14, #T_39b7e_row3_col17, #T_39b7e_row3_col20, #T_39b7e_row4_col2, #T_39b7e_row4_col5, #T_39b7e_row4_col8, #T_39b7e_row4_col11, #T_39b7e_row4_col14, #T_39b7e_row4_col17, #T_39b7e_row4_col20 {\n",
       "  background-color: #EEEEEE;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_39b7e\">\n",
       "  <caption>Baseline</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_39b7e_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_39b7e_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_39b7e_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_39b7e_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_39b7e_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_39b7e_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_39b7e_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
       "      <th id=\"T_39b7e_level1_col0\" class=\"col_heading level1 col0\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
       "      <th id=\"T_39b7e_level1_col18\" class=\"col_heading level1 col18\" >Test</th>\n",
       "      <th id=\"T_39b7e_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_39b7e_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_39b7e_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_39b7e_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col3\" class=\"data row0 col3\" >-1.20</td>\n",
       "      <td id=\"T_39b7e_row0_col4\" class=\"data row0 col4\" >-1.20</td>\n",
       "      <td id=\"T_39b7e_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_39b7e_row0_col6\" class=\"data row0 col6\" >8.80</td>\n",
       "      <td id=\"T_39b7e_row0_col7\" class=\"data row0 col7\" >8.80</td>\n",
       "      <td id=\"T_39b7e_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_39b7e_row0_col9\" class=\"data row0 col9\" >-24.33</td>\n",
       "      <td id=\"T_39b7e_row0_col10\" class=\"data row0 col10\" >-26.15</td>\n",
       "      <td id=\"T_39b7e_row0_col11\" class=\"data row0 col11\" >-19.17</td>\n",
       "      <td id=\"T_39b7e_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39b7e_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_39b7e_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col3\" class=\"data row1 col3\" >12.44</td>\n",
       "      <td id=\"T_39b7e_row1_col4\" class=\"data row1 col4\" >12.44</td>\n",
       "      <td id=\"T_39b7e_row1_col5\" class=\"data row1 col5\" >12.44</td>\n",
       "      <td id=\"T_39b7e_row1_col6\" class=\"data row1 col6\" >21.32</td>\n",
       "      <td id=\"T_39b7e_row1_col7\" class=\"data row1 col7\" >21.32</td>\n",
       "      <td id=\"T_39b7e_row1_col8\" class=\"data row1 col8\" >21.32</td>\n",
       "      <td id=\"T_39b7e_row1_col9\" class=\"data row1 col9\" >2.76</td>\n",
       "      <td id=\"T_39b7e_row1_col10\" class=\"data row1 col10\" >2.71</td>\n",
       "      <td id=\"T_39b7e_row1_col11\" class=\"data row1 col11\" >2.61</td>\n",
       "      <td id=\"T_39b7e_row1_col12\" class=\"data row1 col12\" >77.68</td>\n",
       "      <td id=\"T_39b7e_row1_col13\" class=\"data row1 col13\" >77.63</td>\n",
       "      <td id=\"T_39b7e_row1_col14\" class=\"data row1 col14\" >79.76</td>\n",
       "      <td id=\"T_39b7e_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row1_col18\" class=\"data row1 col18\" >2.82</td>\n",
       "      <td id=\"T_39b7e_row1_col19\" class=\"data row1 col19\" >2.81</td>\n",
       "      <td id=\"T_39b7e_row1_col20\" class=\"data row1 col20\" >2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39b7e_level0_row2\" class=\"row_heading level0 row2\" >50%</th>\n",
       "      <td id=\"T_39b7e_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col3\" class=\"data row2 col3\" >86.44</td>\n",
       "      <td id=\"T_39b7e_row2_col4\" class=\"data row2 col4\" >86.44</td>\n",
       "      <td id=\"T_39b7e_row2_col5\" class=\"data row2 col5\" >86.44</td>\n",
       "      <td id=\"T_39b7e_row2_col6\" class=\"data row2 col6\" >25.54</td>\n",
       "      <td id=\"T_39b7e_row2_col7\" class=\"data row2 col7\" >25.54</td>\n",
       "      <td id=\"T_39b7e_row2_col8\" class=\"data row2 col8\" >25.54</td>\n",
       "      <td id=\"T_39b7e_row2_col9\" class=\"data row2 col9\" >11.34</td>\n",
       "      <td id=\"T_39b7e_row2_col10\" class=\"data row2 col10\" >10.83</td>\n",
       "      <td id=\"T_39b7e_row2_col11\" class=\"data row2 col11\" >11.40</td>\n",
       "      <td id=\"T_39b7e_row2_col12\" class=\"data row2 col12\" >146.20</td>\n",
       "      <td id=\"T_39b7e_row2_col13\" class=\"data row2 col13\" >135.10</td>\n",
       "      <td id=\"T_39b7e_row2_col14\" class=\"data row2 col14\" >141.21</td>\n",
       "      <td id=\"T_39b7e_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_39b7e_row2_col18\" class=\"data row2 col18\" >7.81</td>\n",
       "      <td id=\"T_39b7e_row2_col19\" class=\"data row2 col19\" >7.03</td>\n",
       "      <td id=\"T_39b7e_row2_col20\" class=\"data row2 col20\" >7.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39b7e_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_39b7e_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
       "      <td id=\"T_39b7e_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
       "      <td id=\"T_39b7e_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_39b7e_row3_col3\" class=\"data row3 col3\" >249.81</td>\n",
       "      <td id=\"T_39b7e_row3_col4\" class=\"data row3 col4\" >249.81</td>\n",
       "      <td id=\"T_39b7e_row3_col5\" class=\"data row3 col5\" >249.81</td>\n",
       "      <td id=\"T_39b7e_row3_col6\" class=\"data row3 col6\" >34.10</td>\n",
       "      <td id=\"T_39b7e_row3_col7\" class=\"data row3 col7\" >34.10</td>\n",
       "      <td id=\"T_39b7e_row3_col8\" class=\"data row3 col8\" >34.10</td>\n",
       "      <td id=\"T_39b7e_row3_col9\" class=\"data row3 col9\" >19.45</td>\n",
       "      <td id=\"T_39b7e_row3_col10\" class=\"data row3 col10\" >18.94</td>\n",
       "      <td id=\"T_39b7e_row3_col11\" class=\"data row3 col11\" >19.44</td>\n",
       "      <td id=\"T_39b7e_row3_col12\" class=\"data row3 col12\" >227.66</td>\n",
       "      <td id=\"T_39b7e_row3_col13\" class=\"data row3 col13\" >221.12</td>\n",
       "      <td id=\"T_39b7e_row3_col14\" class=\"data row3 col14\" >226.54</td>\n",
       "      <td id=\"T_39b7e_row3_col15\" class=\"data row3 col15\" >0.20</td>\n",
       "      <td id=\"T_39b7e_row3_col16\" class=\"data row3 col16\" >0.23</td>\n",
       "      <td id=\"T_39b7e_row3_col17\" class=\"data row3 col17\" >0.18</td>\n",
       "      <td id=\"T_39b7e_row3_col18\" class=\"data row3 col18\" >14.85</td>\n",
       "      <td id=\"T_39b7e_row3_col19\" class=\"data row3 col19\" >14.25</td>\n",
       "      <td id=\"T_39b7e_row3_col20\" class=\"data row3 col20\" >14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39b7e_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_39b7e_row4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "      <td id=\"T_39b7e_row4_col1\" class=\"data row4 col1\" >0.19</td>\n",
       "      <td id=\"T_39b7e_row4_col2\" class=\"data row4 col2\" >0.19</td>\n",
       "      <td id=\"T_39b7e_row4_col3\" class=\"data row4 col3\" >787.33</td>\n",
       "      <td id=\"T_39b7e_row4_col4\" class=\"data row4 col4\" >787.33</td>\n",
       "      <td id=\"T_39b7e_row4_col5\" class=\"data row4 col5\" >787.33</td>\n",
       "      <td id=\"T_39b7e_row4_col6\" class=\"data row4 col6\" >80.44</td>\n",
       "      <td id=\"T_39b7e_row4_col7\" class=\"data row4 col7\" >80.44</td>\n",
       "      <td id=\"T_39b7e_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_39b7e_row4_col9\" class=\"data row4 col9\" >33.61</td>\n",
       "      <td id=\"T_39b7e_row4_col10\" class=\"data row4 col10\" >32.59</td>\n",
       "      <td id=\"T_39b7e_row4_col11\" class=\"data row4 col11\" >33.42</td>\n",
       "      <td id=\"T_39b7e_row4_col12\" class=\"data row4 col12\" >341.44</td>\n",
       "      <td id=\"T_39b7e_row4_col13\" class=\"data row4 col13\" >340.66</td>\n",
       "      <td id=\"T_39b7e_row4_col14\" class=\"data row4 col14\" >344.35</td>\n",
       "      <td id=\"T_39b7e_row4_col15\" class=\"data row4 col15\" >20.75</td>\n",
       "      <td id=\"T_39b7e_row4_col16\" class=\"data row4 col16\" >19.90</td>\n",
       "      <td id=\"T_39b7e_row4_col17\" class=\"data row4 col17\" >11.51</td>\n",
       "      <td id=\"T_39b7e_row4_col18\" class=\"data row4 col18\" >35.37</td>\n",
       "      <td id=\"T_39b7e_row4_col19\" class=\"data row4 col19\" >35.65</td>\n",
       "      <td id=\"T_39b7e_row4_col20\" class=\"data row4 col20\" >34.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab02e8f1c0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(baseline_trn,baseline_val,baseline_tst, 'Baseline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a1674ea-29ad-4931-a9e1-bb4f14e59e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "</style>\n",
       "<table id=\"T_23729\">\n",
       "  <caption>Warming</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_23729_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_23729_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_23729_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_23729_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_23729_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_23729_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_23729_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
       "      <th id=\"T_23729_level1_col0\" class=\"col_heading level1 col0\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
       "      <th id=\"T_23729_level1_col18\" class=\"col_heading level1 col18\" >Test</th>\n",
       "      <th id=\"T_23729_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_23729_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_23729_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_23729_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col3\" class=\"data row0 col3\" >-1.20</td>\n",
       "      <td id=\"T_23729_row0_col4\" class=\"data row0 col4\" >-1.20</td>\n",
       "      <td id=\"T_23729_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_23729_row0_col6\" class=\"data row0 col6\" >8.80</td>\n",
       "      <td id=\"T_23729_row0_col7\" class=\"data row0 col7\" >8.80</td>\n",
       "      <td id=\"T_23729_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_23729_row0_col9\" class=\"data row0 col9\" >-23.80</td>\n",
       "      <td id=\"T_23729_row0_col10\" class=\"data row0 col10\" >-26.15</td>\n",
       "      <td id=\"T_23729_row0_col11\" class=\"data row0 col11\" >-22.31</td>\n",
       "      <td id=\"T_23729_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_23729_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23729_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_23729_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col3\" class=\"data row1 col3\" >12.44</td>\n",
       "      <td id=\"T_23729_row1_col4\" class=\"data row1 col4\" >12.44</td>\n",
       "      <td id=\"T_23729_row1_col5\" class=\"data row1 col5\" >12.44</td>\n",
       "      <td id=\"T_23729_row1_col6\" class=\"data row1 col6\" >21.32</td>\n",
       "      <td id=\"T_23729_row1_col7\" class=\"data row1 col7\" >21.32</td>\n",
       "      <td id=\"T_23729_row1_col8\" class=\"data row1 col8\" >21.32</td>\n",
       "      <td id=\"T_23729_row1_col9\" class=\"data row1 col9\" >3.42</td>\n",
       "      <td id=\"T_23729_row1_col10\" class=\"data row1 col10\" >2.56</td>\n",
       "      <td id=\"T_23729_row1_col11\" class=\"data row1 col11\" >1.97</td>\n",
       "      <td id=\"T_23729_row1_col12\" class=\"data row1 col12\" >86.74</td>\n",
       "      <td id=\"T_23729_row1_col13\" class=\"data row1 col13\" >75.50</td>\n",
       "      <td id=\"T_23729_row1_col14\" class=\"data row1 col14\" >73.61</td>\n",
       "      <td id=\"T_23729_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_23729_row1_col18\" class=\"data row1 col18\" >3.20</td>\n",
       "      <td id=\"T_23729_row1_col19\" class=\"data row1 col19\" >2.71</td>\n",
       "      <td id=\"T_23729_row1_col20\" class=\"data row1 col20\" >2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23729_level0_row2\" class=\"row_heading level0 row2\" >50%</th>\n",
       "      <td id=\"T_23729_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col3\" class=\"data row2 col3\" >86.44</td>\n",
       "      <td id=\"T_23729_row2_col4\" class=\"data row2 col4\" >86.44</td>\n",
       "      <td id=\"T_23729_row2_col5\" class=\"data row2 col5\" >86.44</td>\n",
       "      <td id=\"T_23729_row2_col6\" class=\"data row2 col6\" >25.54</td>\n",
       "      <td id=\"T_23729_row2_col7\" class=\"data row2 col7\" >25.54</td>\n",
       "      <td id=\"T_23729_row2_col8\" class=\"data row2 col8\" >25.54</td>\n",
       "      <td id=\"T_23729_row2_col9\" class=\"data row2 col9\" >10.86</td>\n",
       "      <td id=\"T_23729_row2_col10\" class=\"data row2 col10\" >11.08</td>\n",
       "      <td id=\"T_23729_row2_col11\" class=\"data row2 col11\" >11.03</td>\n",
       "      <td id=\"T_23729_row2_col12\" class=\"data row2 col12\" >146.30</td>\n",
       "      <td id=\"T_23729_row2_col13\" class=\"data row2 col13\" >136.17</td>\n",
       "      <td id=\"T_23729_row2_col14\" class=\"data row2 col14\" >131.56</td>\n",
       "      <td id=\"T_23729_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_23729_row2_col18\" class=\"data row2 col18\" >7.77</td>\n",
       "      <td id=\"T_23729_row2_col19\" class=\"data row2 col19\" >7.17</td>\n",
       "      <td id=\"T_23729_row2_col20\" class=\"data row2 col20\" >6.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23729_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_23729_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
       "      <td id=\"T_23729_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
       "      <td id=\"T_23729_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_23729_row3_col3\" class=\"data row3 col3\" >249.81</td>\n",
       "      <td id=\"T_23729_row3_col4\" class=\"data row3 col4\" >249.81</td>\n",
       "      <td id=\"T_23729_row3_col5\" class=\"data row3 col5\" >249.81</td>\n",
       "      <td id=\"T_23729_row3_col6\" class=\"data row3 col6\" >34.10</td>\n",
       "      <td id=\"T_23729_row3_col7\" class=\"data row3 col7\" >34.10</td>\n",
       "      <td id=\"T_23729_row3_col8\" class=\"data row3 col8\" >34.10</td>\n",
       "      <td id=\"T_23729_row3_col9\" class=\"data row3 col9\" >19.55</td>\n",
       "      <td id=\"T_23729_row3_col10\" class=\"data row3 col10\" >19.01</td>\n",
       "      <td id=\"T_23729_row3_col11\" class=\"data row3 col11\" >19.14</td>\n",
       "      <td id=\"T_23729_row3_col12\" class=\"data row3 col12\" >232.12</td>\n",
       "      <td id=\"T_23729_row3_col13\" class=\"data row3 col13\" >220.82</td>\n",
       "      <td id=\"T_23729_row3_col14\" class=\"data row3 col14\" >220.34</td>\n",
       "      <td id=\"T_23729_row3_col15\" class=\"data row3 col15\" >0.18</td>\n",
       "      <td id=\"T_23729_row3_col16\" class=\"data row3 col16\" >0.24</td>\n",
       "      <td id=\"T_23729_row3_col17\" class=\"data row3 col17\" >0.20</td>\n",
       "      <td id=\"T_23729_row3_col18\" class=\"data row3 col18\" >15.14</td>\n",
       "      <td id=\"T_23729_row3_col19\" class=\"data row3 col19\" >14.25</td>\n",
       "      <td id=\"T_23729_row3_col20\" class=\"data row3 col20\" >14.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23729_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_23729_row4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "      <td id=\"T_23729_row4_col1\" class=\"data row4 col1\" >0.19</td>\n",
       "      <td id=\"T_23729_row4_col2\" class=\"data row4 col2\" >0.19</td>\n",
       "      <td id=\"T_23729_row4_col3\" class=\"data row4 col3\" >787.33</td>\n",
       "      <td id=\"T_23729_row4_col4\" class=\"data row4 col4\" >787.33</td>\n",
       "      <td id=\"T_23729_row4_col5\" class=\"data row4 col5\" >787.33</td>\n",
       "      <td id=\"T_23729_row4_col6\" class=\"data row4 col6\" >80.44</td>\n",
       "      <td id=\"T_23729_row4_col7\" class=\"data row4 col7\" >80.44</td>\n",
       "      <td id=\"T_23729_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_23729_row4_col9\" class=\"data row4 col9\" >33.61</td>\n",
       "      <td id=\"T_23729_row4_col10\" class=\"data row4 col10\" >32.10</td>\n",
       "      <td id=\"T_23729_row4_col11\" class=\"data row4 col11\" >32.25</td>\n",
       "      <td id=\"T_23729_row4_col12\" class=\"data row4 col12\" >344.35</td>\n",
       "      <td id=\"T_23729_row4_col13\" class=\"data row4 col13\" >340.66</td>\n",
       "      <td id=\"T_23729_row4_col14\" class=\"data row4 col14\" >339.69</td>\n",
       "      <td id=\"T_23729_row4_col15\" class=\"data row4 col15\" >20.75</td>\n",
       "      <td id=\"T_23729_row4_col16\" class=\"data row4 col16\" >19.90</td>\n",
       "      <td id=\"T_23729_row4_col17\" class=\"data row4 col17\" >18.86</td>\n",
       "      <td id=\"T_23729_row4_col18\" class=\"data row4 col18\" >35.37</td>\n",
       "      <td id=\"T_23729_row4_col19\" class=\"data row4 col19\" >35.65</td>\n",
       "      <td id=\"T_23729_row4_col20\" class=\"data row4 col20\" >34.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab02e98310>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(lto_max_trn,lto_max_val,lto_max_tst,'Warming')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "693a3f4f-0456-4173-a38d-09308f05e6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table id=\"T_ebbd5\">\n",
       "  <caption>Drought</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ebbd5_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_ebbd5_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_ebbd5_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_ebbd5_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_ebbd5_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_ebbd5_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_ebbd5_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
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       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
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       "      <th id=\"T_ebbd5_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_ebbd5_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_ebbd5_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_ebbd5_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_ebbd5_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_ebbd5_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_ebbd5_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_ebbd5_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_ebbd5_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_ebbd5_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_ebbd5_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
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       "      <th id=\"T_ebbd5_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_ebbd5_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ebbd5_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_ebbd5_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col3\" class=\"data row0 col3\" >-1.20</td>\n",
       "      <td id=\"T_ebbd5_row0_col4\" class=\"data row0 col4\" >-1.20</td>\n",
       "      <td id=\"T_ebbd5_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_ebbd5_row0_col6\" class=\"data row0 col6\" >8.80</td>\n",
       "      <td id=\"T_ebbd5_row0_col7\" class=\"data row0 col7\" >8.80</td>\n",
       "      <td id=\"T_ebbd5_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_ebbd5_row0_col9\" class=\"data row0 col9\" >-24.33</td>\n",
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       "      <td id=\"T_ebbd5_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_ebbd5_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_ebbd5_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col3\" class=\"data row1 col3\" >12.44</td>\n",
       "      <td id=\"T_ebbd5_row1_col4\" class=\"data row1 col4\" >12.44</td>\n",
       "      <td id=\"T_ebbd5_row1_col5\" class=\"data row1 col5\" >12.44</td>\n",
       "      <td id=\"T_ebbd5_row1_col6\" class=\"data row1 col6\" >21.32</td>\n",
       "      <td id=\"T_ebbd5_row1_col7\" class=\"data row1 col7\" >21.32</td>\n",
       "      <td id=\"T_ebbd5_row1_col8\" class=\"data row1 col8\" >21.32</td>\n",
       "      <td id=\"T_ebbd5_row1_col9\" class=\"data row1 col9\" >2.86</td>\n",
       "      <td id=\"T_ebbd5_row1_col10\" class=\"data row1 col10\" >2.66</td>\n",
       "      <td id=\"T_ebbd5_row1_col11\" class=\"data row1 col11\" >2.77</td>\n",
       "      <td id=\"T_ebbd5_row1_col12\" class=\"data row1 col12\" >79.62</td>\n",
       "      <td id=\"T_ebbd5_row1_col13\" class=\"data row1 col13\" >76.86</td>\n",
       "      <td id=\"T_ebbd5_row1_col14\" class=\"data row1 col14\" >80.54</td>\n",
       "      <td id=\"T_ebbd5_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row1_col18\" class=\"data row1 col18\" >2.92</td>\n",
       "      <td id=\"T_ebbd5_row1_col19\" class=\"data row1 col19\" >2.77</td>\n",
       "      <td id=\"T_ebbd5_row1_col20\" class=\"data row1 col20\" >2.85</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_ebbd5_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row2_col3\" class=\"data row2 col3\" >86.44</td>\n",
       "      <td id=\"T_ebbd5_row2_col4\" class=\"data row2 col4\" >86.44</td>\n",
       "      <td id=\"T_ebbd5_row2_col5\" class=\"data row2 col5\" >86.44</td>\n",
       "      <td id=\"T_ebbd5_row2_col6\" class=\"data row2 col6\" >25.54</td>\n",
       "      <td id=\"T_ebbd5_row2_col7\" class=\"data row2 col7\" >25.54</td>\n",
       "      <td id=\"T_ebbd5_row2_col8\" class=\"data row2 col8\" >25.54</td>\n",
       "      <td id=\"T_ebbd5_row2_col9\" class=\"data row2 col9\" >11.48</td>\n",
       "      <td id=\"T_ebbd5_row2_col10\" class=\"data row2 col10\" >10.81</td>\n",
       "      <td id=\"T_ebbd5_row2_col11\" class=\"data row2 col11\" >11.31</td>\n",
       "      <td id=\"T_ebbd5_row2_col12\" class=\"data row2 col12\" >144.12</td>\n",
       "      <td id=\"T_ebbd5_row2_col13\" class=\"data row2 col13\" >135.73</td>\n",
       "      <td id=\"T_ebbd5_row2_col14\" class=\"data row2 col14\" >140.48</td>\n",
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       "      <td id=\"T_ebbd5_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_ebbd5_row2_col18\" class=\"data row2 col18\" >7.85</td>\n",
       "      <td id=\"T_ebbd5_row2_col19\" class=\"data row2 col19\" >7.07</td>\n",
       "      <td id=\"T_ebbd5_row2_col20\" class=\"data row2 col20\" >7.29</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_ebbd5_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_ebbd5_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
       "      <td id=\"T_ebbd5_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
       "      <td id=\"T_ebbd5_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_ebbd5_row3_col3\" class=\"data row3 col3\" >249.81</td>\n",
       "      <td id=\"T_ebbd5_row3_col4\" class=\"data row3 col4\" >249.81</td>\n",
       "      <td id=\"T_ebbd5_row3_col5\" class=\"data row3 col5\" >249.81</td>\n",
       "      <td id=\"T_ebbd5_row3_col6\" class=\"data row3 col6\" >34.10</td>\n",
       "      <td id=\"T_ebbd5_row3_col7\" class=\"data row3 col7\" >34.10</td>\n",
       "      <td id=\"T_ebbd5_row3_col8\" class=\"data row3 col8\" >34.10</td>\n",
       "      <td id=\"T_ebbd5_row3_col9\" class=\"data row3 col9\" >19.14</td>\n",
       "      <td id=\"T_ebbd5_row3_col10\" class=\"data row3 col10\" >19.08</td>\n",
       "      <td id=\"T_ebbd5_row3_col11\" class=\"data row3 col11\" >19.47</td>\n",
       "      <td id=\"T_ebbd5_row3_col12\" class=\"data row3 col12\" >225.91</td>\n",
       "      <td id=\"T_ebbd5_row3_col13\" class=\"data row3 col13\" >221.94</td>\n",
       "      <td id=\"T_ebbd5_row3_col14\" class=\"data row3 col14\" >224.80</td>\n",
       "      <td id=\"T_ebbd5_row3_col15\" class=\"data row3 col15\" >0.19</td>\n",
       "      <td id=\"T_ebbd5_row3_col16\" class=\"data row3 col16\" >0.23</td>\n",
       "      <td id=\"T_ebbd5_row3_col17\" class=\"data row3 col17\" >0.19</td>\n",
       "      <td id=\"T_ebbd5_row3_col18\" class=\"data row3 col18\" >14.54</td>\n",
       "      <td id=\"T_ebbd5_row3_col19\" class=\"data row3 col19\" >14.33</td>\n",
       "      <td id=\"T_ebbd5_row3_col20\" class=\"data row3 col20\" >14.77</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_ebbd5_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_ebbd5_row4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "      <td id=\"T_ebbd5_row4_col1\" class=\"data row4 col1\" >0.19</td>\n",
       "      <td id=\"T_ebbd5_row4_col2\" class=\"data row4 col2\" >0.19</td>\n",
       "      <td id=\"T_ebbd5_row4_col3\" class=\"data row4 col3\" >787.33</td>\n",
       "      <td id=\"T_ebbd5_row4_col4\" class=\"data row4 col4\" >787.33</td>\n",
       "      <td id=\"T_ebbd5_row4_col5\" class=\"data row4 col5\" >787.33</td>\n",
       "      <td id=\"T_ebbd5_row4_col6\" class=\"data row4 col6\" >80.44</td>\n",
       "      <td id=\"T_ebbd5_row4_col7\" class=\"data row4 col7\" >80.44</td>\n",
       "      <td id=\"T_ebbd5_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_ebbd5_row4_col9\" class=\"data row4 col9\" >32.59</td>\n",
       "      <td id=\"T_ebbd5_row4_col10\" class=\"data row4 col10\" >33.61</td>\n",
       "      <td id=\"T_ebbd5_row4_col11\" class=\"data row4 col11\" >31.78</td>\n",
       "      <td id=\"T_ebbd5_row4_col12\" class=\"data row4 col12\" >335.48</td>\n",
       "      <td id=\"T_ebbd5_row4_col13\" class=\"data row4 col13\" >344.35</td>\n",
       "      <td id=\"T_ebbd5_row4_col14\" class=\"data row4 col14\" >339.69</td>\n",
       "      <td id=\"T_ebbd5_row4_col15\" class=\"data row4 col15\" >19.90</td>\n",
       "      <td id=\"T_ebbd5_row4_col16\" class=\"data row4 col16\" >20.75</td>\n",
       "      <td id=\"T_ebbd5_row4_col17\" class=\"data row4 col17\" >18.86</td>\n",
       "      <td id=\"T_ebbd5_row4_col18\" class=\"data row4 col18\" >34.12</td>\n",
       "      <td id=\"T_ebbd5_row4_col19\" class=\"data row4 col19\" >35.65</td>\n",
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       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab03e49160>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(drought_trn,drought_val,drought_tst,'Drought')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "489fa0c0-818d-48f5-88af-daa75baaf2f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ce1d1 caption {\n",
       "  weight: bold;\n",
       "  font-size: 18px;\n",
       "}\n",
       "#T_ce1d1_row0_col0, #T_ce1d1_row0_col3, #T_ce1d1_row0_col6, #T_ce1d1_row0_col9, #T_ce1d1_row0_col12, #T_ce1d1_row0_col15, #T_ce1d1_row0_col18, #T_ce1d1_row1_col0, #T_ce1d1_row1_col3, #T_ce1d1_row1_col6, #T_ce1d1_row1_col9, #T_ce1d1_row1_col12, #T_ce1d1_row1_col15, #T_ce1d1_row1_col18, #T_ce1d1_row2_col0, #T_ce1d1_row2_col3, #T_ce1d1_row2_col6, #T_ce1d1_row2_col9, #T_ce1d1_row2_col12, #T_ce1d1_row2_col15, #T_ce1d1_row2_col18, #T_ce1d1_row3_col0, #T_ce1d1_row3_col3, #T_ce1d1_row3_col6, #T_ce1d1_row3_col9, #T_ce1d1_row3_col12, #T_ce1d1_row3_col15, #T_ce1d1_row3_col18, #T_ce1d1_row4_col0, #T_ce1d1_row4_col3, #T_ce1d1_row4_col6, #T_ce1d1_row4_col9, #T_ce1d1_row4_col12, #T_ce1d1_row4_col15, #T_ce1d1_row4_col18 {\n",
       "  background-color: #AAAAAA;\n",
       "}\n",
       "#T_ce1d1_row0_col1, #T_ce1d1_row0_col4, #T_ce1d1_row0_col7, #T_ce1d1_row0_col10, #T_ce1d1_row0_col13, #T_ce1d1_row0_col16, #T_ce1d1_row0_col19, #T_ce1d1_row1_col1, #T_ce1d1_row1_col4, #T_ce1d1_row1_col7, #T_ce1d1_row1_col10, #T_ce1d1_row1_col13, #T_ce1d1_row1_col16, #T_ce1d1_row1_col19, #T_ce1d1_row2_col1, #T_ce1d1_row2_col4, #T_ce1d1_row2_col7, #T_ce1d1_row2_col10, #T_ce1d1_row2_col13, #T_ce1d1_row2_col16, #T_ce1d1_row2_col19, #T_ce1d1_row3_col1, #T_ce1d1_row3_col4, #T_ce1d1_row3_col7, #T_ce1d1_row3_col10, #T_ce1d1_row3_col13, #T_ce1d1_row3_col16, #T_ce1d1_row3_col19, #T_ce1d1_row4_col1, #T_ce1d1_row4_col4, #T_ce1d1_row4_col7, #T_ce1d1_row4_col10, #T_ce1d1_row4_col13, #T_ce1d1_row4_col16, #T_ce1d1_row4_col19 {\n",
       "  background-color: #CCCCCC;\n",
       "}\n",
       "#T_ce1d1_row0_col2, #T_ce1d1_row0_col5, #T_ce1d1_row0_col8, #T_ce1d1_row0_col11, #T_ce1d1_row0_col14, #T_ce1d1_row0_col17, #T_ce1d1_row0_col20, #T_ce1d1_row1_col2, #T_ce1d1_row1_col5, #T_ce1d1_row1_col8, #T_ce1d1_row1_col11, #T_ce1d1_row1_col14, #T_ce1d1_row1_col17, #T_ce1d1_row1_col20, #T_ce1d1_row2_col2, #T_ce1d1_row2_col5, #T_ce1d1_row2_col8, #T_ce1d1_row2_col11, #T_ce1d1_row2_col14, #T_ce1d1_row2_col17, #T_ce1d1_row2_col20, #T_ce1d1_row3_col2, #T_ce1d1_row3_col5, #T_ce1d1_row3_col8, #T_ce1d1_row3_col11, #T_ce1d1_row3_col14, #T_ce1d1_row3_col17, #T_ce1d1_row3_col20, #T_ce1d1_row4_col2, #T_ce1d1_row4_col5, #T_ce1d1_row4_col8, #T_ce1d1_row4_col11, #T_ce1d1_row4_col14, #T_ce1d1_row4_col17, #T_ce1d1_row4_col20 {\n",
       "  background-color: #EEEEEE;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ce1d1\">\n",
       "  <caption>Appalachians</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ce1d1_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_ce1d1_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_ce1d1_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_ce1d1_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_ce1d1_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_ce1d1_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_ce1d1_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
       "      <th id=\"T_ce1d1_level1_col0\" class=\"col_heading level1 col0\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
       "      <th id=\"T_ce1d1_level1_col18\" class=\"col_heading level1 col18\" >Test</th>\n",
       "      <th id=\"T_ce1d1_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_ce1d1_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce1d1_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_ce1d1_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col3\" class=\"data row0 col3\" >39.49</td>\n",
       "      <td id=\"T_ce1d1_row0_col4\" class=\"data row0 col4\" >-1.20</td>\n",
       "      <td id=\"T_ce1d1_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_ce1d1_row0_col6\" class=\"data row0 col6\" >12.71</td>\n",
       "      <td id=\"T_ce1d1_row0_col7\" class=\"data row0 col7\" >8.80</td>\n",
       "      <td id=\"T_ce1d1_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_ce1d1_row0_col9\" class=\"data row0 col9\" >-26.15</td>\n",
       "      <td id=\"T_ce1d1_row0_col10\" class=\"data row0 col10\" >-25.41</td>\n",
       "      <td id=\"T_ce1d1_row0_col11\" class=\"data row0 col11\" >-18.83</td>\n",
       "      <td id=\"T_ce1d1_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce1d1_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_ce1d1_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col3\" class=\"data row1 col3\" >97.25</td>\n",
       "      <td id=\"T_ce1d1_row1_col4\" class=\"data row1 col4\" >0.71</td>\n",
       "      <td id=\"T_ce1d1_row1_col5\" class=\"data row1 col5\" >0.71</td>\n",
       "      <td id=\"T_ce1d1_row1_col6\" class=\"data row1 col6\" >22.84</td>\n",
       "      <td id=\"T_ce1d1_row1_col7\" class=\"data row1 col7\" >20.33</td>\n",
       "      <td id=\"T_ce1d1_row1_col8\" class=\"data row1 col8\" >20.33</td>\n",
       "      <td id=\"T_ce1d1_row1_col9\" class=\"data row1 col9\" >1.36</td>\n",
       "      <td id=\"T_ce1d1_row1_col10\" class=\"data row1 col10\" >3.47</td>\n",
       "      <td id=\"T_ce1d1_row1_col11\" class=\"data row1 col11\" >3.32</td>\n",
       "      <td id=\"T_ce1d1_row1_col12\" class=\"data row1 col12\" >68.91</td>\n",
       "      <td id=\"T_ce1d1_row1_col13\" class=\"data row1 col13\" >82.23</td>\n",
       "      <td id=\"T_ce1d1_row1_col14\" class=\"data row1 col14\" >83.16</td>\n",
       "      <td id=\"T_ce1d1_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row1_col18\" class=\"data row1 col18\" >2.21</td>\n",
       "      <td id=\"T_ce1d1_row1_col19\" class=\"data row1 col19\" >3.17</td>\n",
       "      <td id=\"T_ce1d1_row1_col20\" class=\"data row1 col20\" >3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce1d1_level0_row2\" class=\"row_heading level0 row2\" >50%</th>\n",
       "      <td id=\"T_ce1d1_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col3\" class=\"data row2 col3\" >146.47</td>\n",
       "      <td id=\"T_ce1d1_row2_col4\" class=\"data row2 col4\" >29.71</td>\n",
       "      <td id=\"T_ce1d1_row2_col5\" class=\"data row2 col5\" >29.71</td>\n",
       "      <td id=\"T_ce1d1_row2_col6\" class=\"data row2 col6\" >27.92</td>\n",
       "      <td id=\"T_ce1d1_row2_col7\" class=\"data row2 col7\" >24.15</td>\n",
       "      <td id=\"T_ce1d1_row2_col8\" class=\"data row2 col8\" >24.15</td>\n",
       "      <td id=\"T_ce1d1_row2_col9\" class=\"data row2 col9\" >9.78</td>\n",
       "      <td id=\"T_ce1d1_row2_col10\" class=\"data row2 col10\" >11.61</td>\n",
       "      <td id=\"T_ce1d1_row2_col11\" class=\"data row2 col11\" >11.97</td>\n",
       "      <td id=\"T_ce1d1_row2_col12\" class=\"data row2 col12\" >127.30</td>\n",
       "      <td id=\"T_ce1d1_row2_col13\" class=\"data row2 col13\" >143.15</td>\n",
       "      <td id=\"T_ce1d1_row2_col14\" class=\"data row2 col14\" >147.36</td>\n",
       "      <td id=\"T_ce1d1_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row2_col18\" class=\"data row2 col18\" >6.44</td>\n",
       "      <td id=\"T_ce1d1_row2_col19\" class=\"data row2 col19\" >7.66</td>\n",
       "      <td id=\"T_ce1d1_row2_col20\" class=\"data row2 col20\" >7.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce1d1_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_ce1d1_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
       "      <td id=\"T_ce1d1_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
       "      <td id=\"T_ce1d1_row3_col3\" class=\"data row3 col3\" >279.02</td>\n",
       "      <td id=\"T_ce1d1_row3_col4\" class=\"data row3 col4\" >190.12</td>\n",
       "      <td id=\"T_ce1d1_row3_col5\" class=\"data row3 col5\" >190.12</td>\n",
       "      <td id=\"T_ce1d1_row3_col6\" class=\"data row3 col6\" >34.55</td>\n",
       "      <td id=\"T_ce1d1_row3_col7\" class=\"data row3 col7\" >33.75</td>\n",
       "      <td id=\"T_ce1d1_row3_col8\" class=\"data row3 col8\" >33.75</td>\n",
       "      <td id=\"T_ce1d1_row3_col9\" class=\"data row3 col9\" >18.00</td>\n",
       "      <td id=\"T_ce1d1_row3_col10\" class=\"data row3 col10\" >19.72</td>\n",
       "      <td id=\"T_ce1d1_row3_col11\" class=\"data row3 col11\" >20.11</td>\n",
       "      <td id=\"T_ce1d1_row3_col12\" class=\"data row3 col12\" >218.21</td>\n",
       "      <td id=\"T_ce1d1_row3_col13\" class=\"data row3 col13\" >224.70</td>\n",
       "      <td id=\"T_ce1d1_row3_col14\" class=\"data row3 col14\" >229.01</td>\n",
       "      <td id=\"T_ce1d1_row3_col15\" class=\"data row3 col15\" >0.23</td>\n",
       "      <td id=\"T_ce1d1_row3_col16\" class=\"data row3 col16\" >0.23</td>\n",
       "      <td id=\"T_ce1d1_row3_col17\" class=\"data row3 col17\" >0.18</td>\n",
       "      <td id=\"T_ce1d1_row3_col18\" class=\"data row3 col18\" >14.04</td>\n",
       "      <td id=\"T_ce1d1_row3_col19\" class=\"data row3 col19\" >14.54</td>\n",
       "      <td id=\"T_ce1d1_row3_col20\" class=\"data row3 col20\" >14.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce1d1_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_ce1d1_row4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "      <td id=\"T_ce1d1_row4_col1\" class=\"data row4 col1\" >0.10</td>\n",
       "      <td id=\"T_ce1d1_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
       "      <td id=\"T_ce1d1_row4_col3\" class=\"data row4 col3\" >787.33</td>\n",
       "      <td id=\"T_ce1d1_row4_col4\" class=\"data row4 col4\" >577.31</td>\n",
       "      <td id=\"T_ce1d1_row4_col5\" class=\"data row4 col5\" >577.31</td>\n",
       "      <td id=\"T_ce1d1_row4_col6\" class=\"data row4 col6\" >69.22</td>\n",
       "      <td id=\"T_ce1d1_row4_col7\" class=\"data row4 col7\" >80.44</td>\n",
       "      <td id=\"T_ce1d1_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_ce1d1_row4_col9\" class=\"data row4 col9\" >31.98</td>\n",
       "      <td id=\"T_ce1d1_row4_col10\" class=\"data row4 col10\" >33.61</td>\n",
       "      <td id=\"T_ce1d1_row4_col11\" class=\"data row4 col11\" >33.42</td>\n",
       "      <td id=\"T_ce1d1_row4_col12\" class=\"data row4 col12\" >340.66</td>\n",
       "      <td id=\"T_ce1d1_row4_col13\" class=\"data row4 col13\" >341.44</td>\n",
       "      <td id=\"T_ce1d1_row4_col14\" class=\"data row4 col14\" >340.90</td>\n",
       "      <td id=\"T_ce1d1_row4_col15\" class=\"data row4 col15\" >19.60</td>\n",
       "      <td id=\"T_ce1d1_row4_col16\" class=\"data row4 col16\" >20.75</td>\n",
       "      <td id=\"T_ce1d1_row4_col17\" class=\"data row4 col17\" >11.33</td>\n",
       "      <td id=\"T_ce1d1_row4_col18\" class=\"data row4 col18\" >35.65</td>\n",
       "      <td id=\"T_ce1d1_row4_col19\" class=\"data row4 col19\" >31.73</td>\n",
       "      <td id=\"T_ce1d1_row4_col20\" class=\"data row4 col20\" >30.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab02f19100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(app_trn,app_val,app_tst,'Appalachians')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c4ebbaca-a28b-4bd4-be77-4c0914fcc394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e591e caption {\n",
       "  weight: bold;\n",
       "  font-size: 18px;\n",
       "}\n",
       "#T_e591e_row0_col0, #T_e591e_row0_col3, #T_e591e_row0_col6, #T_e591e_row0_col9, #T_e591e_row0_col12, #T_e591e_row0_col15, #T_e591e_row0_col18, #T_e591e_row1_col0, #T_e591e_row1_col3, #T_e591e_row1_col6, #T_e591e_row1_col9, #T_e591e_row1_col12, #T_e591e_row1_col15, #T_e591e_row1_col18, #T_e591e_row2_col0, #T_e591e_row2_col3, #T_e591e_row2_col6, #T_e591e_row2_col9, #T_e591e_row2_col12, #T_e591e_row2_col15, #T_e591e_row2_col18, #T_e591e_row3_col0, #T_e591e_row3_col3, #T_e591e_row3_col6, #T_e591e_row3_col9, #T_e591e_row3_col12, #T_e591e_row3_col15, #T_e591e_row3_col18, #T_e591e_row4_col0, #T_e591e_row4_col3, #T_e591e_row4_col6, #T_e591e_row4_col9, #T_e591e_row4_col12, #T_e591e_row4_col15, #T_e591e_row4_col18 {\n",
       "  background-color: #AAAAAA;\n",
       "}\n",
       "#T_e591e_row0_col1, #T_e591e_row0_col4, #T_e591e_row0_col7, #T_e591e_row0_col10, #T_e591e_row0_col13, #T_e591e_row0_col16, #T_e591e_row0_col19, #T_e591e_row1_col1, #T_e591e_row1_col4, #T_e591e_row1_col7, #T_e591e_row1_col10, #T_e591e_row1_col13, #T_e591e_row1_col16, #T_e591e_row1_col19, #T_e591e_row2_col1, #T_e591e_row2_col4, #T_e591e_row2_col7, #T_e591e_row2_col10, #T_e591e_row2_col13, #T_e591e_row2_col16, #T_e591e_row2_col19, #T_e591e_row3_col1, #T_e591e_row3_col4, #T_e591e_row3_col7, #T_e591e_row3_col10, #T_e591e_row3_col13, #T_e591e_row3_col16, #T_e591e_row3_col19, #T_e591e_row4_col1, #T_e591e_row4_col4, #T_e591e_row4_col7, #T_e591e_row4_col10, #T_e591e_row4_col13, #T_e591e_row4_col16, #T_e591e_row4_col19 {\n",
       "  background-color: #CCCCCC;\n",
       "}\n",
       "#T_e591e_row0_col2, #T_e591e_row0_col5, #T_e591e_row0_col8, #T_e591e_row0_col11, #T_e591e_row0_col14, #T_e591e_row0_col17, #T_e591e_row0_col20, #T_e591e_row1_col2, #T_e591e_row1_col5, #T_e591e_row1_col8, #T_e591e_row1_col11, #T_e591e_row1_col14, #T_e591e_row1_col17, #T_e591e_row1_col20, #T_e591e_row2_col2, #T_e591e_row2_col5, #T_e591e_row2_col8, #T_e591e_row2_col11, #T_e591e_row2_col14, #T_e591e_row2_col17, #T_e591e_row2_col20, #T_e591e_row3_col2, #T_e591e_row3_col5, #T_e591e_row3_col8, #T_e591e_row3_col11, #T_e591e_row3_col14, #T_e591e_row3_col17, #T_e591e_row3_col20, #T_e591e_row4_col2, #T_e591e_row4_col5, #T_e591e_row4_col8, #T_e591e_row4_col11, #T_e591e_row4_col14, #T_e591e_row4_col17, #T_e591e_row4_col20 {\n",
       "  background-color: #EEEEEE;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e591e\">\n",
       "  <caption>Headwaters</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e591e_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_e591e_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_e591e_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_e591e_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_e591e_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_e591e_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_e591e_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
       "      <th id=\"T_e591e_level1_col0\" class=\"col_heading level1 col0\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
       "      <th id=\"T_e591e_level1_col18\" class=\"col_heading level1 col18\" >Test</th>\n",
       "      <th id=\"T_e591e_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_e591e_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e591e_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_e591e_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col3\" class=\"data row0 col3\" >222.67</td>\n",
       "      <td id=\"T_e591e_row0_col4\" class=\"data row0 col4\" >-1.20</td>\n",
       "      <td id=\"T_e591e_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_e591e_row0_col6\" class=\"data row0 col6\" >8.80</td>\n",
       "      <td id=\"T_e591e_row0_col7\" class=\"data row0 col7\" >10.79</td>\n",
       "      <td id=\"T_e591e_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_e591e_row0_col9\" class=\"data row0 col9\" >-25.41</td>\n",
       "      <td id=\"T_e591e_row0_col10\" class=\"data row0 col10\" >-26.15</td>\n",
       "      <td id=\"T_e591e_row0_col11\" class=\"data row0 col11\" >-19.17</td>\n",
       "      <td id=\"T_e591e_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_e591e_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e591e_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_e591e_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col3\" class=\"data row1 col3\" >301.46</td>\n",
       "      <td id=\"T_e591e_row1_col4\" class=\"data row1 col4\" >6.31</td>\n",
       "      <td id=\"T_e591e_row1_col5\" class=\"data row1 col5\" >12.44</td>\n",
       "      <td id=\"T_e591e_row1_col6\" class=\"data row1 col6\" >21.65</td>\n",
       "      <td id=\"T_e591e_row1_col7\" class=\"data row1 col7\" >21.32</td>\n",
       "      <td id=\"T_e591e_row1_col8\" class=\"data row1 col8\" >21.32</td>\n",
       "      <td id=\"T_e591e_row1_col9\" class=\"data row1 col9\" >-0.76</td>\n",
       "      <td id=\"T_e591e_row1_col10\" class=\"data row1 col10\" >3.37</td>\n",
       "      <td id=\"T_e591e_row1_col11\" class=\"data row1 col11\" >2.61</td>\n",
       "      <td id=\"T_e591e_row1_col12\" class=\"data row1 col12\" >67.50</td>\n",
       "      <td id=\"T_e591e_row1_col13\" class=\"data row1 col13\" >79.71</td>\n",
       "      <td id=\"T_e591e_row1_col14\" class=\"data row1 col14\" >79.76</td>\n",
       "      <td id=\"T_e591e_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_e591e_row1_col18\" class=\"data row1 col18\" >2.24</td>\n",
       "      <td id=\"T_e591e_row1_col19\" class=\"data row1 col19\" >2.96</td>\n",
       "      <td id=\"T_e591e_row1_col20\" class=\"data row1 col20\" >2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e591e_level0_row2\" class=\"row_heading level0 row2\" >50%</th>\n",
       "      <td id=\"T_e591e_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col3\" class=\"data row2 col3\" >360.88</td>\n",
       "      <td id=\"T_e591e_row2_col4\" class=\"data row2 col4\" >57.30</td>\n",
       "      <td id=\"T_e591e_row2_col5\" class=\"data row2 col5\" >86.44</td>\n",
       "      <td id=\"T_e591e_row2_col6\" class=\"data row2 col6\" >27.21</td>\n",
       "      <td id=\"T_e591e_row2_col7\" class=\"data row2 col7\" >25.29</td>\n",
       "      <td id=\"T_e591e_row2_col8\" class=\"data row2 col8\" >25.54</td>\n",
       "      <td id=\"T_e591e_row2_col9\" class=\"data row2 col9\" >7.81</td>\n",
       "      <td id=\"T_e591e_row2_col10\" class=\"data row2 col10\" >11.56</td>\n",
       "      <td id=\"T_e591e_row2_col11\" class=\"data row2 col11\" >11.40</td>\n",
       "      <td id=\"T_e591e_row2_col12\" class=\"data row2 col12\" >126.48</td>\n",
       "      <td id=\"T_e591e_row2_col13\" class=\"data row2 col13\" >139.61</td>\n",
       "      <td id=\"T_e591e_row2_col14\" class=\"data row2 col14\" >141.21</td>\n",
       "      <td id=\"T_e591e_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_e591e_row2_col18\" class=\"data row2 col18\" >5.76</td>\n",
       "      <td id=\"T_e591e_row2_col19\" class=\"data row2 col19\" >7.50</td>\n",
       "      <td id=\"T_e591e_row2_col20\" class=\"data row2 col20\" >7.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e591e_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_e591e_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
       "      <td id=\"T_e591e_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
       "      <td id=\"T_e591e_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_e591e_row3_col3\" class=\"data row3 col3\" >419.46</td>\n",
       "      <td id=\"T_e591e_row3_col4\" class=\"data row3 col4\" >131.38</td>\n",
       "      <td id=\"T_e591e_row3_col5\" class=\"data row3 col5\" >249.81</td>\n",
       "      <td id=\"T_e591e_row3_col6\" class=\"data row3 col6\" >35.19</td>\n",
       "      <td id=\"T_e591e_row3_col7\" class=\"data row3 col7\" >34.00</td>\n",
       "      <td id=\"T_e591e_row3_col8\" class=\"data row3 col8\" >34.10</td>\n",
       "      <td id=\"T_e591e_row3_col9\" class=\"data row3 col9\" >16.06</td>\n",
       "      <td id=\"T_e591e_row3_col10\" class=\"data row3 col10\" >19.73</td>\n",
       "      <td id=\"T_e591e_row3_col11\" class=\"data row3 col11\" >19.44</td>\n",
       "      <td id=\"T_e591e_row3_col12\" class=\"data row3 col12\" >223.05</td>\n",
       "      <td id=\"T_e591e_row3_col13\" class=\"data row3 col13\" >222.47</td>\n",
       "      <td id=\"T_e591e_row3_col14\" class=\"data row3 col14\" >226.54</td>\n",
       "      <td id=\"T_e591e_row3_col15\" class=\"data row3 col15\" >0.23</td>\n",
       "      <td id=\"T_e591e_row3_col16\" class=\"data row3 col16\" >0.22</td>\n",
       "      <td id=\"T_e591e_row3_col17\" class=\"data row3 col17\" >0.18</td>\n",
       "      <td id=\"T_e591e_row3_col18\" class=\"data row3 col18\" >12.51</td>\n",
       "      <td id=\"T_e591e_row3_col19\" class=\"data row3 col19\" >14.79</td>\n",
       "      <td id=\"T_e591e_row3_col20\" class=\"data row3 col20\" >14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e591e_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_e591e_row4_col0\" class=\"data row4 col0\" >0.05</td>\n",
       "      <td id=\"T_e591e_row4_col1\" class=\"data row4 col1\" >0.19</td>\n",
       "      <td id=\"T_e591e_row4_col2\" class=\"data row4 col2\" >0.19</td>\n",
       "      <td id=\"T_e591e_row4_col3\" class=\"data row4 col3\" >577.31</td>\n",
       "      <td id=\"T_e591e_row4_col4\" class=\"data row4 col4\" >787.33</td>\n",
       "      <td id=\"T_e591e_row4_col5\" class=\"data row4 col5\" >787.33</td>\n",
       "      <td id=\"T_e591e_row4_col6\" class=\"data row4 col6\" >51.18</td>\n",
       "      <td id=\"T_e591e_row4_col7\" class=\"data row4 col7\" >80.44</td>\n",
       "      <td id=\"T_e591e_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_e591e_row4_col9\" class=\"data row4 col9\" >28.52</td>\n",
       "      <td id=\"T_e591e_row4_col10\" class=\"data row4 col10\" >33.61</td>\n",
       "      <td id=\"T_e591e_row4_col11\" class=\"data row4 col11\" >33.42</td>\n",
       "      <td id=\"T_e591e_row4_col12\" class=\"data row4 col12\" >341.44</td>\n",
       "      <td id=\"T_e591e_row4_col13\" class=\"data row4 col13\" >340.66</td>\n",
       "      <td id=\"T_e591e_row4_col14\" class=\"data row4 col14\" >344.35</td>\n",
       "      <td id=\"T_e591e_row4_col15\" class=\"data row4 col15\" >15.52</td>\n",
       "      <td id=\"T_e591e_row4_col16\" class=\"data row4 col16\" >20.75</td>\n",
       "      <td id=\"T_e591e_row4_col17\" class=\"data row4 col17\" >11.51</td>\n",
       "      <td id=\"T_e591e_row4_col18\" class=\"data row4 col18\" >30.52</td>\n",
       "      <td id=\"T_e591e_row4_col19\" class=\"data row4 col19\" >35.65</td>\n",
       "      <td id=\"T_e591e_row4_col20\" class=\"data row4 col20\" >34.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab02e986a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(head_trn,head_val,head_tst, 'Headwaters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0c737ea7-9cd3-491c-9849-c97826b88f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_28c79 caption {\n",
       "  weight: bold;\n",
       "  font-size: 18px;\n",
       "}\n",
       "#T_28c79_row0_col0, #T_28c79_row0_col3, #T_28c79_row0_col6, #T_28c79_row0_col9, #T_28c79_row0_col12, #T_28c79_row0_col15, #T_28c79_row0_col18, #T_28c79_row1_col0, #T_28c79_row1_col3, #T_28c79_row1_col6, #T_28c79_row1_col9, #T_28c79_row1_col12, #T_28c79_row1_col15, #T_28c79_row1_col18, #T_28c79_row2_col0, #T_28c79_row2_col3, #T_28c79_row2_col6, #T_28c79_row2_col9, #T_28c79_row2_col12, #T_28c79_row2_col15, #T_28c79_row2_col18, #T_28c79_row3_col0, #T_28c79_row3_col3, #T_28c79_row3_col6, #T_28c79_row3_col9, #T_28c79_row3_col12, #T_28c79_row3_col15, #T_28c79_row3_col18, #T_28c79_row4_col0, #T_28c79_row4_col3, #T_28c79_row4_col6, #T_28c79_row4_col9, #T_28c79_row4_col12, #T_28c79_row4_col15, #T_28c79_row4_col18 {\n",
       "  background-color: #AAAAAA;\n",
       "}\n",
       "#T_28c79_row0_col1, #T_28c79_row0_col4, #T_28c79_row0_col7, #T_28c79_row0_col10, #T_28c79_row0_col13, #T_28c79_row0_col16, #T_28c79_row0_col19, #T_28c79_row1_col1, #T_28c79_row1_col4, #T_28c79_row1_col7, #T_28c79_row1_col10, #T_28c79_row1_col13, #T_28c79_row1_col16, #T_28c79_row1_col19, #T_28c79_row2_col1, #T_28c79_row2_col4, #T_28c79_row2_col7, #T_28c79_row2_col10, #T_28c79_row2_col13, #T_28c79_row2_col16, #T_28c79_row2_col19, #T_28c79_row3_col1, #T_28c79_row3_col4, #T_28c79_row3_col7, #T_28c79_row3_col10, #T_28c79_row3_col13, #T_28c79_row3_col16, #T_28c79_row3_col19, #T_28c79_row4_col1, #T_28c79_row4_col4, #T_28c79_row4_col7, #T_28c79_row4_col10, #T_28c79_row4_col13, #T_28c79_row4_col16, #T_28c79_row4_col19 {\n",
       "  background-color: #CCCCCC;\n",
       "}\n",
       "#T_28c79_row0_col2, #T_28c79_row0_col5, #T_28c79_row0_col8, #T_28c79_row0_col11, #T_28c79_row0_col14, #T_28c79_row0_col17, #T_28c79_row0_col20, #T_28c79_row1_col2, #T_28c79_row1_col5, #T_28c79_row1_col8, #T_28c79_row1_col11, #T_28c79_row1_col14, #T_28c79_row1_col17, #T_28c79_row1_col20, #T_28c79_row2_col2, #T_28c79_row2_col5, #T_28c79_row2_col8, #T_28c79_row2_col11, #T_28c79_row2_col14, #T_28c79_row2_col17, #T_28c79_row2_col20, #T_28c79_row3_col2, #T_28c79_row3_col5, #T_28c79_row3_col8, #T_28c79_row3_col11, #T_28c79_row3_col14, #T_28c79_row3_col17, #T_28c79_row3_col20, #T_28c79_row4_col2, #T_28c79_row4_col5, #T_28c79_row4_col8, #T_28c79_row4_col11, #T_28c79_row4_col14, #T_28c79_row4_col17, #T_28c79_row4_col20 {\n",
       "  background-color: #EEEEEE;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_28c79\">\n",
       "  <caption>Coastal</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_28c79_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Slope (m/km)</th>\n",
       "      <th id=\"T_28c79_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">Elevation (m)</th>\n",
       "      <th id=\"T_28c79_level0_col6\" class=\"col_heading level0 col6\" colspan=\"3\">Mean Width (m)</th>\n",
       "      <th id=\"T_28c79_level0_col9\" class=\"col_heading level0 col9\" colspan=\"3\">Air Temp. (°C)</th>\n",
       "      <th id=\"T_28c79_level0_col12\" class=\"col_heading level0 col12\" colspan=\"3\">SW Rad. (W/sq.m)</th>\n",
       "      <th id=\"T_28c79_level0_col15\" class=\"col_heading level0 col15\" colspan=\"3\">Precip. (cm)</th>\n",
       "      <th id=\"T_28c79_level0_col18\" class=\"col_heading level0 col18\" colspan=\"3\">Potential ET (cm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Partition</th>\n",
       "      <th id=\"T_28c79_level1_col0\" class=\"col_heading level1 col0\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col1\" class=\"col_heading level1 col1\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col2\" class=\"col_heading level1 col2\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col3\" class=\"col_heading level1 col3\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col4\" class=\"col_heading level1 col4\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col5\" class=\"col_heading level1 col5\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col6\" class=\"col_heading level1 col6\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col7\" class=\"col_heading level1 col7\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col8\" class=\"col_heading level1 col8\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col9\" class=\"col_heading level1 col9\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col10\" class=\"col_heading level1 col10\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col11\" class=\"col_heading level1 col11\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col12\" class=\"col_heading level1 col12\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col13\" class=\"col_heading level1 col13\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col14\" class=\"col_heading level1 col14\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col15\" class=\"col_heading level1 col15\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col16\" class=\"col_heading level1 col16\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col17\" class=\"col_heading level1 col17\" >Val</th>\n",
       "      <th id=\"T_28c79_level1_col18\" class=\"col_heading level1 col18\" >Test</th>\n",
       "      <th id=\"T_28c79_level1_col19\" class=\"col_heading level1 col19\" >Train</th>\n",
       "      <th id=\"T_28c79_level1_col20\" class=\"col_heading level1 col20\" >Val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Percentile</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_28c79_level0_row0\" class=\"row_heading level0 row0\" >min</th>\n",
       "      <td id=\"T_28c79_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col3\" class=\"data row0 col3\" >-1.20</td>\n",
       "      <td id=\"T_28c79_row0_col4\" class=\"data row0 col4\" >39.49</td>\n",
       "      <td id=\"T_28c79_row0_col5\" class=\"data row0 col5\" >-1.20</td>\n",
       "      <td id=\"T_28c79_row0_col6\" class=\"data row0 col6\" >10.79</td>\n",
       "      <td id=\"T_28c79_row0_col7\" class=\"data row0 col7\" >8.80</td>\n",
       "      <td id=\"T_28c79_row0_col8\" class=\"data row0 col8\" >8.80</td>\n",
       "      <td id=\"T_28c79_row0_col9\" class=\"data row0 col9\" >-23.83</td>\n",
       "      <td id=\"T_28c79_row0_col10\" class=\"data row0 col10\" >-26.15</td>\n",
       "      <td id=\"T_28c79_row0_col11\" class=\"data row0 col11\" >-19.17</td>\n",
       "      <td id=\"T_28c79_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_28c79_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_28c79_level0_row1\" class=\"row_heading level0 row1\" >25%</th>\n",
       "      <td id=\"T_28c79_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col3\" class=\"data row1 col3\" >0.05</td>\n",
       "      <td id=\"T_28c79_row1_col4\" class=\"data row1 col4\" >120.91</td>\n",
       "      <td id=\"T_28c79_row1_col5\" class=\"data row1 col5\" >12.44</td>\n",
       "      <td id=\"T_28c79_row1_col6\" class=\"data row1 col6\" >19.93</td>\n",
       "      <td id=\"T_28c79_row1_col7\" class=\"data row1 col7\" >22.20</td>\n",
       "      <td id=\"T_28c79_row1_col8\" class=\"data row1 col8\" >21.32</td>\n",
       "      <td id=\"T_28c79_row1_col9\" class=\"data row1 col9\" >4.71</td>\n",
       "      <td id=\"T_28c79_row1_col10\" class=\"data row1 col10\" >0.71</td>\n",
       "      <td id=\"T_28c79_row1_col11\" class=\"data row1 col11\" >2.61</td>\n",
       "      <td id=\"T_28c79_row1_col12\" class=\"data row1 col12\" >87.81</td>\n",
       "      <td id=\"T_28c79_row1_col13\" class=\"data row1 col13\" >68.47</td>\n",
       "      <td id=\"T_28c79_row1_col14\" class=\"data row1 col14\" >79.76</td>\n",
       "      <td id=\"T_28c79_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_28c79_row1_col18\" class=\"data row1 col18\" >3.56</td>\n",
       "      <td id=\"T_28c79_row1_col19\" class=\"data row1 col19\" >2.22</td>\n",
       "      <td id=\"T_28c79_row1_col20\" class=\"data row1 col20\" >2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_28c79_level0_row2\" class=\"row_heading level0 row2\" >50%</th>\n",
       "      <td id=\"T_28c79_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col3\" class=\"data row2 col3\" >11.60</td>\n",
       "      <td id=\"T_28c79_row2_col4\" class=\"data row2 col4\" >241.18</td>\n",
       "      <td id=\"T_28c79_row2_col5\" class=\"data row2 col5\" >86.44</td>\n",
       "      <td id=\"T_28c79_row2_col6\" class=\"data row2 col6\" >23.55</td>\n",
       "      <td id=\"T_28c79_row2_col7\" class=\"data row2 col7\" >27.25</td>\n",
       "      <td id=\"T_28c79_row2_col8\" class=\"data row2 col8\" >25.54</td>\n",
       "      <td id=\"T_28c79_row2_col9\" class=\"data row2 col9\" >12.79</td>\n",
       "      <td id=\"T_28c79_row2_col10\" class=\"data row2 col10\" >9.17</td>\n",
       "      <td id=\"T_28c79_row2_col11\" class=\"data row2 col11\" >11.40</td>\n",
       "      <td id=\"T_28c79_row2_col12\" class=\"data row2 col12\" >147.99</td>\n",
       "      <td id=\"T_28c79_row2_col13\" class=\"data row2 col13\" >127.06</td>\n",
       "      <td id=\"T_28c79_row2_col14\" class=\"data row2 col14\" >141.21</td>\n",
       "      <td id=\"T_28c79_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_28c79_row2_col18\" class=\"data row2 col18\" >8.24</td>\n",
       "      <td id=\"T_28c79_row2_col19\" class=\"data row2 col19\" >6.26</td>\n",
       "      <td id=\"T_28c79_row2_col20\" class=\"data row2 col20\" >7.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_28c79_level0_row3\" class=\"row_heading level0 row3\" >75%</th>\n",
       "      <td id=\"T_28c79_row3_col0\" class=\"data row3 col0\" >0.00</td>\n",
       "      <td id=\"T_28c79_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
       "      <td id=\"T_28c79_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_28c79_row3_col3\" class=\"data row3 col3\" >40.67</td>\n",
       "      <td id=\"T_28c79_row3_col4\" class=\"data row3 col4\" >378.57</td>\n",
       "      <td id=\"T_28c79_row3_col5\" class=\"data row3 col5\" >249.81</td>\n",
       "      <td id=\"T_28c79_row3_col6\" class=\"data row3 col6\" >33.44</td>\n",
       "      <td id=\"T_28c79_row3_col7\" class=\"data row3 col7\" >34.58</td>\n",
       "      <td id=\"T_28c79_row3_col8\" class=\"data row3 col8\" >34.10</td>\n",
       "      <td id=\"T_28c79_row3_col9\" class=\"data row3 col9\" >21.08</td>\n",
       "      <td id=\"T_28c79_row3_col10\" class=\"data row3 col10\" >17.35</td>\n",
       "      <td id=\"T_28c79_row3_col11\" class=\"data row3 col11\" >19.44</td>\n",
       "      <td id=\"T_28c79_row3_col12\" class=\"data row3 col12\" >225.14</td>\n",
       "      <td id=\"T_28c79_row3_col13\" class=\"data row3 col13\" >219.66</td>\n",
       "      <td id=\"T_28c79_row3_col14\" class=\"data row3 col14\" >226.54</td>\n",
       "      <td id=\"T_28c79_row3_col15\" class=\"data row3 col15\" >0.22</td>\n",
       "      <td id=\"T_28c79_row3_col16\" class=\"data row3 col16\" >0.23</td>\n",
       "      <td id=\"T_28c79_row3_col17\" class=\"data row3 col17\" >0.18</td>\n",
       "      <td id=\"T_28c79_row3_col18\" class=\"data row3 col18\" >15.28</td>\n",
       "      <td id=\"T_28c79_row3_col19\" class=\"data row3 col19\" >13.49</td>\n",
       "      <td id=\"T_28c79_row3_col20\" class=\"data row3 col20\" >14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_28c79_level0_row4\" class=\"row_heading level0 row4\" >max</th>\n",
       "      <td id=\"T_28c79_row4_col0\" class=\"data row4 col0\" >0.10</td>\n",
       "      <td id=\"T_28c79_row4_col1\" class=\"data row4 col1\" >0.19</td>\n",
       "      <td id=\"T_28c79_row4_col2\" class=\"data row4 col2\" >0.19</td>\n",
       "      <td id=\"T_28c79_row4_col3\" class=\"data row4 col3\" >190.12</td>\n",
       "      <td id=\"T_28c79_row4_col4\" class=\"data row4 col4\" >787.33</td>\n",
       "      <td id=\"T_28c79_row4_col5\" class=\"data row4 col5\" >787.33</td>\n",
       "      <td id=\"T_28c79_row4_col6\" class=\"data row4 col6\" >80.44</td>\n",
       "      <td id=\"T_28c79_row4_col7\" class=\"data row4 col7\" >69.22</td>\n",
       "      <td id=\"T_28c79_row4_col8\" class=\"data row4 col8\" >80.44</td>\n",
       "      <td id=\"T_28c79_row4_col9\" class=\"data row4 col9\" >33.61</td>\n",
       "      <td id=\"T_28c79_row4_col10\" class=\"data row4 col10\" >31.98</td>\n",
       "      <td id=\"T_28c79_row4_col11\" class=\"data row4 col11\" >33.42</td>\n",
       "      <td id=\"T_28c79_row4_col12\" class=\"data row4 col12\" >338.58</td>\n",
       "      <td id=\"T_28c79_row4_col13\" class=\"data row4 col13\" >341.44</td>\n",
       "      <td id=\"T_28c79_row4_col14\" class=\"data row4 col14\" >344.35</td>\n",
       "      <td id=\"T_28c79_row4_col15\" class=\"data row4 col15\" >20.75</td>\n",
       "      <td id=\"T_28c79_row4_col16\" class=\"data row4 col16\" >19.60</td>\n",
       "      <td id=\"T_28c79_row4_col17\" class=\"data row4 col17\" >11.51</td>\n",
       "      <td id=\"T_28c79_row4_col18\" class=\"data row4 col18\" >31.73</td>\n",
       "      <td id=\"T_28c79_row4_col19\" class=\"data row4 col19\" >35.65</td>\n",
       "      <td id=\"T_28c79_row4_col20\" class=\"data row4 col20\" >34.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aab02e95430>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_run_tables(coast_trn,coast_val,coast_tst,'Coastal')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e943b610-898d-49a6-b458-34d0551952d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1100 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segs = gpd.read_file('data_DRB/DRB_spatial/segs/segs.shp')\n",
    "\n",
    "def calc_diff_seg(tst,trn):\n",
    "    tst_seg = tst[['seg_tave_air','sg_d_nt']].loc[tst.month.isin([6,7,8])].groupby(['sg_d_nt']).median()\n",
    "    trn_seg = trn[['seg_tave_air','sg_d_nt']].loc[trn.month.isin([6,7,8])].groupby(['sg_d_nt']).median()\n",
    "    return tst_seg-trn_seg\n",
    "\n",
    "def calc_diff_seg_drought(tst,trn):\n",
    "    tst['year']=tst.date.dt.year.where(tst.date.dt.month < 10,tst.date.dt.year + 1)\n",
    "    trn['year']=trn.date.dt.year.where(trn.date.dt.month < 10,trn.date.dt.year + 1)\n",
    "    tst_seg = tst[['year','sg_d_nt','seg_rain']].groupby(['year','sg_d_nt']).sum().groupby(['sg_d_nt']).median()\n",
    "    trn_seg = trn[['year','sg_d_nt','seg_rain']].groupby(['year','sg_d_nt']).sum().groupby(['sg_d_nt']).median()\n",
    "    diff = -1*(tst_seg-trn_seg)\n",
    "    return diff\n",
    "\n",
    "temp_diff_seg = calc_diff_seg(lto_max_tst,lto_max_trn)\n",
    "temp_diff_sf = temp_diff_seg.reset_index().merge(segs).set_geometry('geometry')\n",
    "fig, ax = plt.subplots(1,2,figsize=(5,5.5),dpi=200)\n",
    "\n",
    "temp_diff_sf.plot(column='seg_tave_air',ax=ax[0],legend=True, legend_kwds={\"label\":\"°C\", 'location':'bottom','anchor':(0.5,2)})\n",
    "\n",
    "ax[0].set_axis_off()\n",
    "ax[0].set_title('Temperature\\nDifference')\n",
    "\n",
    "drought_diff_seg = calc_diff_seg_drought(drought_tst, drought_trn)\n",
    "drought_diff_sf = drought_diff_seg.reset_index().merge(segs).set_geometry('geometry')\n",
    "\n",
    "drought_diff_sf.plot(column='seg_rain',ax=ax[1],legend=True,legend_kwds={\"label\":\"m/yr\", 'location':'bottom','anchor':(0.5,2)})\n",
    "ax[1].set_axis_off()\n",
    "ax[1].set_title('Precipitation\\nDifference')\n",
    "plt.savefig('results/figures/trn_tst_precip_temp_diff.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "101f7685-9fe3-44a9-a872-555257c6c911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Train'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc9c30f-0d04-488b-ab11-3bc09d6e4832",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}