analysis_basin_split.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Metrics Switch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean flag to signal whether it's necessary to load all of hte results\n",
    "# and calculate new performance statistics.\n",
    "# This takes several hours.\n",
    "CALCULATE_NEW_METRICS = True\n",
    "\n",
    "# If we calculate new performance statistics, they are saved in this file.\n",
    "# If we do not calculate new performance statistics, old ones are loaded\n",
    "# from this file.\n",
    "pub_stats_filename = 'results/pub_statistics.pkl'\n",
    "\n",
    "\n",
    "# We also need the time split stats for plotting.\n",
    "time_split_stats_filename = 'results/time_split_statistics.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import pickle as pkl\n",
    "\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from typing import Sequence\n",
    "\n",
    "# The neuralhydroloy repository must exist in the current working directory.\n",
    "from neuralhydrology.evaluation import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The available metrics are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['NSE',\n",
       " 'MSE',\n",
       " 'RMSE',\n",
       " 'KGE',\n",
       " 'Alpha-NSE',\n",
       " 'Pearson-r',\n",
       " 'Beta-KGE',\n",
       " 'Beta-NSE',\n",
       " 'FHV',\n",
       " 'FMS',\n",
       " 'FLV',\n",
       " 'Peak-Timing',\n",
       " 'Missed-Peaks']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a list of all available performance metrics in NeuralHydrology\n",
    "metrics_list = metrics.get_available_metrics()\n",
    "print('The available metrics are:')\n",
    "metrics_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The metrics that we are going to calculate are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['NSE',\n",
       " 'KGE',\n",
       " 'Alpha-NSE',\n",
       " 'Pearson-r',\n",
       " 'Beta-NSE',\n",
       " 'Peak-Timing',\n",
       " 'Missed-Peaks']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove any metrics that you don't care about. \n",
    "# Feel free to change this.\n",
    "metrics_list.remove('MSE')\n",
    "metrics_list.remove('RMSE')\n",
    "metrics_list.remove('FHV')\n",
    "metrics_list.remove('FMS')\n",
    "metrics_list.remove('FLV')\n",
    "metrics_list.remove('Beta-KGE')\n",
    "\n",
    "print('The metrics that we are going to calculate are:')\n",
    "metrics_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 531 basins.\n"
     ]
    }
   ],
   "source": [
    "basin_file = '531_basin_list.txt'\n",
    "with Path(basin_file).open('r') as fp:\n",
    "    basins = sorted(basin.strip() for basin in fp if basin.strip())\n",
    "print(f\"There are {len(basins)} basins.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths to results from different models. \n",
    "# Note that these must match what is in the config files.\n",
    "autoregression_dir = 'runs/pub/autoregression'\n",
    "assimilation_dir = 'runs/pub/assimilation'\n",
    "simulation_dir = 'runs/pub/simulation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10 simulation runs.\n"
     ]
    }
   ],
   "source": [
    "# Get paths to all simulation results.\n",
    "simulation_run_dirs = glob.glob(simulation_dir + '/pub_simulation_*')\n",
    "for i, run_dir in enumerate(simulation_run_dirs):\n",
    "    simulation_run_dirs[i] = run_dir.split('/')[-1]\n",
    "print(f\"There are {len(simulation_run_dirs)} simulation runs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10 assimilation runs.\n"
     ]
    }
   ],
   "source": [
    "# Get paths to all assimilation results.\n",
    "assimilation_run_dirs = glob.glob(assimilation_dir + '/**')\n",
    "for i, run_dir in enumerate(assimilation_run_dirs):\n",
    "    assimilation_run_dirs[i] = run_dir.split('/')[-1]\n",
    "print(f\"There are {len(assimilation_run_dirs)} assimilation runs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10 autoregression runs.\n"
     ]
    }
   ],
   "source": [
    "# Get paths to all autoregression results.\n",
    "autoregression_run_dirs = glob.glob(autoregression_dir + '/**')\n",
    "for i, run_dir in enumerate(autoregression_run_dirs):\n",
    "    autoregression_run_dirs[i] = run_dir.split('/')[-1]\n",
    "print(f\"There are {len(autoregression_run_dirs)} autoregression runs.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer Experiment Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensembles = set()\n",
    "for run in autoregression_run_dirs:\n",
    "    ensembles.add(int(run.split('seed_')[-1].split('_')[0]))\n",
    "ensembles = sorted(list(ensembles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoregression_statistics = {}\n",
    "assimilation_statistics = {}\n",
    "simulation_statistics = {}\n",
    "\n",
    "for metric in metrics_list:\n",
    "    autoregression_statistics[metric] = pd.DataFrame(index=basins, columns=ensembles, dtype=np.float64)\n",
    "    assimilation_statistics[metric] = pd.DataFrame(index=basins, columns=ensembles, dtype=np.float64)\n",
    "    simulation_statistics[metric] = pd.DataFrame(index=basins, columns=ensembles, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Obs Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the saved metrics. \n",
    "# Even if we just calcuated them, might as well make sure the file works.\n",
    "# You need to change this path manually because of the timestamp. It must\n",
    "# point to a time_split directory and not to a pub directory, in order to \n",
    "# have all of the basins.\n",
    "with open('runs/time_split/simulation/simulation_seed_0_1304_224615/test/model_epoch030/test_results.p', 'rb') as f:\n",
    "    data_xr = pkl.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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    {
     "data": {
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    {
     "data": {
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   ],
   "source": [
    "if CALCULATE_NEW_METRICS:\n",
    "    for run in simulation_run_dirs:\n",
    "        ensemble = int(run.split('seed_')[-1].split('_')[0])\n",
    "\n",
    "        with open(simulation_dir + '/' + run + '/test/model_epoch030/test_results.p', 'rb') as f:\n",
    "            run_data = pkl.load(f)\n",
    "\n",
    "        for basin in tqdm(run_data):\n",
    "\n",
    "            sim = run_data[basin]['1D']['xr'].stack(datetime=['date', 'time_step'])['QObs(mm/d)_sim']\n",
    "            sim['datetime'] = sim.coords['date']# + sim.coords['time_step']\n",
    "            obs = data_xr[basin]['1D']['xr']['QObs(mm/d)_obs'].stack(datetime=['date', 'time_step'])\n",
    "            obs['datetime'] = obs.coords['date']# + obs.coords['time_step']\n",
    "            basin_metrics = metrics.calculate_metrics(obs=obs, sim=sim, metrics=metrics_list)\n",
    "\n",
    "            for metric in metrics_list:\n",
    "                simulation_statistics[metric].loc[basin, ensemble] = basin_metrics[metric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.702759\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_statistics['NSE'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assimilation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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    {
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    {
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   ],
   "source": [
    "if CALCULATE_NEW_METRICS:\n",
    "    for run in tqdm(assimilation_run_dirs):\n",
    "        hf = float(run.split('holdout_')[-1].split('_')[0])   \n",
    "        lead_time = int(run.split('lead_')[-1].split('_')[0])\n",
    "        ensemble = int(run.split('seed_')[-1].split('_')[0])\n",
    "\n",
    "        with open(assimilation_dir + '/' + run + '/test/model_epoch030/test_results_data_assimilation.p', 'rb') as f:\n",
    "            run_data = pkl.load(f)\n",
    "\n",
    "        for basin in tqdm(run_data):\n",
    "            sim = run_data[basin]['1D']['xr'].stack(datetime=['date', 'time_step'])['QObs(mm/d)_sim']\n",
    "            sim['datetime'] = sim.coords['date']# + sim.coords['time_step']\n",
    "            obs = data_xr[basin]['1D']['xr']['QObs(mm/d)_obs'].stack(datetime=['date', 'time_step'])\n",
    "            obs['datetime'] = obs.coords['date']# + obs.coords['time_step']\n",
    "            basin_metrics = metrics.calculate_metrics(obs=obs, sim=sim, metrics=metrics_list)\n",
    "\n",
    "            for metric in metrics_list:\n",
    "                assimilation_statistics[metric].loc[basin, ensemble] = basin_metrics[metric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.771882\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assimilation_statistics['NSE'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoregression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "version_major": 2,
       "version_minor": 0
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    {
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    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "if CALCULATE_NEW_METRICS:\n",
    "    for run in tqdm(autoregression_run_dirs):\n",
    "        ensemble = int(run.split('seed_')[-1].split('_')[0])\n",
    "\n",
    "        with open(autoregression_dir + '/' + run + '/test/model_epoch030/test_results.p', 'rb') as f:\n",
    "            run_data = pkl.load(f)\n",
    "\n",
    "        for basin in tqdm(run_data):\n",
    "            sim = run_data[basin]['1D']['xr'].stack(datetime=['date', 'time_step'])['QObs(mm/d)_sim']\n",
    "            sim['datetime'] = sim.coords['date']# + sim.coords['time_step']\n",
    "            obs = data_xr[basin]['1D']['xr']['QObs(mm/d)_obs'].stack(datetime=['date', 'time_step'])\n",
    "            obs['datetime'] = obs.coords['date']# + obs.coords['time_step']\n",
    "            basin_metrics = metrics.calculate_metrics(obs=obs, sim=sim, metrics=metrics_list)\n",
    "\n",
    "            for metric in metrics_list:\n",
    "                autoregression_statistics[metric].loc[basin, ensemble] = basin_metrics[metric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.806685\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoregression_statistics['NSE'].median().dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save & Load Precaculated Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we calculated new metrics, save these over any old ones that might exist.\n",
    "if CALCULATE_NEW_METRICS:\n",
    "    with open(pub_stats_filename, 'wb') as f:\n",
    "        pkl.dump([\n",
    "            simulation_statistics,\n",
    "            autoregression_statistics,\n",
    "            assimilation_statistics\n",
    "        ], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the saved metrics. \n",
    "# Even if we just calcuated them, might as well make sure the file works.\n",
    "with open(pub_stats_filename, 'rb') as f:\n",
    "    pub_sim, pub_ar, pub_da = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the saved metrics. \n",
    "# Even if we just calcuated them, might as well make sure the file works.\n",
    "with open(time_split_stats_filename, 'rb') as f:\n",
    "    time_sim, time_ar, time_da = pkl.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to choose plotting colors (by data split type).\n",
    "def _model_to_color(model: str) -> str:\n",
    "    colors = {}\n",
    "    colors['Basin-Split'] = '#5D3A9B'\n",
    "    colors['Time-Split'] = '#E66100'\n",
    "    if 'Basin-Split' in model:\n",
    "        return colors['Basin-Split']\n",
    "    elif 'Time-Split' in model:\n",
    "        return colors['Time-Split']\n",
    "    else:\n",
    "        raise ValueError(f'Model string {model} not recognized.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to choose plotting linestyles (by model type).\n",
    "def _model_to_linestyle(model: str) -> str:\n",
    "    ls = {}\n",
    "    ls['Sim'] = '-'\n",
    "    ls['AR'] = '--'\n",
    "    ls['DA'] = ':'\n",
    "    if 'Sim' in model:\n",
    "        return ls['Sim']\n",
    "    elif 'AR' in model:\n",
    "        return ls['AR']\n",
    "    elif 'DA' in model:\n",
    "        return ls['DA']\n",
    "    else:\n",
    "        raise ValueError(f'Model string {model} not recognized.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _plot_cdf(\n",
    "    scores: np.ndarray, \n",
    "    ax: plt.Axes, \n",
    "    label: str, \n",
    "    xlabel:str, \n",
    "    xlim:Sequence[float]=None,\n",
    "    lw: float=2,\n",
    "    color: str=None,\n",
    "    ls: str='-',\n",
    "):\n",
    "    \n",
    "    # Plot the CDF\n",
    "    y_data = np.array(range(len(scores))) / len(scores)\n",
    "    x_data = np.sort(np.squeeze(scores))\n",
    "    if color is not None:\n",
    "        ax.plot(x_data, y_data, label=label, lw=lw, c=color, ls=ls)\n",
    "    else:\n",
    "        ax.plot(x_data, y_data, label=label, lw=lw, ls=ls)\n",
    "\n",
    "    # Aesthetics\n",
    "    ax.legend(fontsize=14)\n",
    "    ax.set_ylabel('Fraction of Basins')\n",
    "    ax.set_xlabel(xlabel, fontsize=20)\n",
    "\n",
    "    if xlim is not None:\n",
    "        ax.set_xlim(xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 1\n",
    "\n",
    "Median NSE scores of PUB and time-split models with a lag of 1 day and no data withheld during inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib\n",
    "# matplotlib.use('TkAgg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gsnearing/anaconda3/envs/neuralhydrology/lib/python3.7/site-packages/ipykernel_launcher.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = 'NSE'\n",
    "comparison_index = ['Basin-Split Sim', 'Basin-Split DA', 'Basin-Split AR', 'Time-Split Sim', 'Time-Split DA', 'Time-Split AR']\n",
    "\n",
    "plotdata = pd.DataFrame(index=basins, columns=comparison_index)\n",
    "plotdata['Basin-Split Sim'] = pub_sim[metric]\n",
    "plotdata['Basin-Split DA'] = pub_da[metric]\n",
    "plotdata['Basin-Split AR'] = pub_ar[metric]\n",
    "plotdata['Time-Split Sim'] = time_sim[metric]\n",
    "plotdata['Time-Split DA'] = time_da[metric][(0.00, 1, 0)]\n",
    "plotdata['Time-Split AR'] = time_ar[metric][(0.00, 0.00, 1, 0)]\n",
    "\n",
    "color_mapping = [_model_to_color(model) for model in comparison_index]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n",
    "\n",
    "axes[0].bar(plotdata.columns, plotdata.median().values, color=color_mapping)\n",
    "axes[0].vlines(plotdata.columns, plotdata.quantile(0.10), plotdata.quantile(0.90), color='grey', lw=5)\n",
    "axes[0].grid()\n",
    "axes[0].set_ylabel(f'Median {metric} over 531 Basins', fontsize=20)\n",
    "axes[0].set_xticklabels(plotdata.columns, rotation=90, fontsize=20)\n",
    "\n",
    "for col in plotdata.columns:\n",
    "    _plot_cdf(scores=plotdata[col], ax=axes[1], label=col, \n",
    "    xlabel=metric, xlim=[0,1], color=_model_to_color(col), ls=_model_to_linestyle(col))\n",
    "axes[1].grid()\n",
    "axes[1].set_ylabel(f'Fraction of Basins', fontsize=20)\n",
    "\n",
    "plot_file_name = f\"./results/plots/figure4_pub_nse.png\"\n",
    "plt.savefig(plot_file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7Kl1uX5fbBt1s39q1azvZrkltaV+/K5DrgLXAs4Fnccc5QEzxcOGMJH0buMc0m946YGzTzTkyWdT/OPCucvldwLHAYdP9EdvLgGUAixcv9igGWFu+fPl667o0sFuX29fltkH32zep64MptqV9/brxLpnLH7b9+Jm2SfqtpB3Kq48dmL4ovwq4Z8/yQuCq8m//tudvfQr4xlxijZiU4cAjBte3BjKVpGW2l1Zw3FMoxtc6uvz99Wneczawq6SdKZ6Gfz5wSBnHDpO3wIBnABdVEFNEhgOPmIVZJRBgcUXHPRr4kqSXAL8CngMgaUeK7roH2l4n6QjgmxTdeI+3/bNy//dK2pPiFtYK4GUVxRUREQOabQKZ0/Mfk2z/AVjvQUTbV1HMOTK5fCqwXhdd2y+qIo6IiBhev7Gw1mN7/7oCiYiI8bLBKxBJ21M8o7EbPXOh2+7bCysiIrptkCuQfwcuBnYGjqKoOZxdY0wRETEGBkkgf2X7MxTzoy+3fRjFmFgRETGPDVJEv7n8vVrSkymexVhYX0gRETEOBkkg/yzpLsDrgA8D2wCvrjOoiIhov0ESyJ9sXwNcA+wHIOlRtUYVERGtN0gN5MMDrouIiHmk32i8jwAeCWwv6bU9m7aheDI8IiLmsX63sO5EMZHUJsDWPevXUIzQGxER81i/0XiXA8slnWB7paQtbV83wtgiIqLFBimi7yjpNIqrkXtJ2gN4me1/qDe0aKsMeR4RMFgC+SDwJIoh2LF9gaTH1BlUtFuGPI8IGHAwRdtXTll1Sw2xRETEGBnkCuRKSY8ELOlOwCspxsaKiIh5bJArkMOBlwM7UUwzu2e5HBER89gGr0Bs/x54wQhiiYiIMTLjFYikO0s6VNLTVHijpG9I+pCk7UYZZEREtE+/W1gnAU8EDgMmgHsDHwGuBU6oO7CIiGi3frewdrP9IEmbAKts71uuP13SBSOILSIiWqzfFchNALbXUcwB0ivdeCMi5rl+VyALJR0HqOc15fJOtUcWERGt1i+BvKHn9TlTtk1djoiIeabfYIonjjKQiIgYLwMNZRIRETFVEkhERAylkQQiaVtJZ0i6rPx9txned7ykqyVdNMz+ERFRnw0mEEnbS3qLpGXlCf14ScfP8bhvBs60vStwZrk8nROA/eewf0RE1GSQ0Xi/DvwP8G2qe/7jIGBJ+fpEiifd3zT1Tba/J2nRsPtHRER9BkkgW9iu+uS8wPZqANurJd29rv0lLQWWAixYsICJiYkhQ56bpo5bp7Vr13ayXdDttk3VxXZ2/d+vLe0bJIF8Q9KBtk+dzR+W9G3gHtNseuts/s5c2V4GLANYvHixRzGD3vLly9db18WZ+7o8I2GX2wZ3nI64i+3s+r9fW9o3SAJ5FfAWSTcCN1M8iW7b2/TbyfbjZ9om6beSdiivHnYArp5N0MBc94+YlzIdcVRpg0V021vb3sj25ra3KZf7Jo8BnAIcWr4+lKLOMsr9IyJijgbqxivpbpL2lvSYyZ85Hvdo4AmSLgOeUC4jaUdJt90qk3Qy8APg/pJWSXpJv/0jImJ0NngLS9LfUdzGWgicD+xDcVJ/7LAHtf0H4HHTrL8KOLBn+eDZ7B8REaMzyBXIq4CHAStt7wfsBfyu1qgiIqL1BkkgN9i+AUDSZrYvAe5fb1gREdF2g/TCWiXprsDXgDMk/Yn1J5iKiIh5ZoMJxPYzypdHSvoucBfg9FqjioiI1psxgUjaxvYaSdv2rP5p+Xsr4I+1RhYREa3W7wrk88BTgHMBUzxAOMnALjXGFRERLddvRsKnlL93Hl04ERExLvrdwnpIvx1tn1d9OBERMS763cI6tvx9Z2AxcAHFbazdgR8Bj643tIiIaLMZnwOxvV/54OBK4CG2F9t+KMWDhJePKsCIiGinQR4kfIDtyd5X2L4I2LO2iCIiYiwM8iDhxZI+DXyOovfVC4GLa40qIiJab5AE8rfA31OMiQXwPeDjtUUUERFjYZAn0W+Q9AngVNuXjiCmiIgYAxusgUh6GsUw7qeXy3tKOqXmuCIiouUGKaK/A9gb+DOA7fOBRbVFFBERY2GQBLLO9jW1RxIREWNlkCL6RZIOATaWtCvwSuCsesOKiIi2G+QK5BXAXwM3AicDa4BX1xhTRESMgUF6YV0PvLX8iYiIAPoPpti3p5Xtp1UfTkREjIt+VyCPAK6kuG31I+44H0hERMxz/RLIPYAnAAcDhwD/DZxs+2ejCCwiItqt32i8t9g+3fahwD4UI/BOSHrFyKKLiIjW6ltEl7QZ8GSKq5BFwHHAV+oPKyIi2q5fEf1E4EHAacBR5TDuERERQP/nQF4E3I9iFN6zJK0pf66VtGYuB5W0raQzJF1W/r7bDO87XtLVki6asv5ISb+WdH75c+Bc4omIiNnrVwPZyPbW5c82PT9b295mjsd9M3Cm7V2BM8vl6ZwA7D/Dtg/Y3rP8OXWO8URExCwN8iR6HQ4CTixfnwg8fbo32f4e8McRxRQREbMwyFhYdVhgezWA7dWS7j7E3zhC0ouBc4DX2f7TdG+StBRYCrBgwQImJiaGDHlumjpundauXdvJdkG32wZp37hrS/tqSyCSvk3xLMlUVQyJ8nHgXRRT7L4LOBY4bLo32l4GLANYvHixlyxZUsHh+1u+fPl660Zx3FGbmJjoZLug222DtG/ctaV9tSUQ24+faZuk30raobz62AG4epZ/+7c9f+tTwDeGjzQiIobRVA3kFODQ8vWhwNdns3OZdCY9A0gX44iIEWsqgRwNPEHSZRTDpRwNIGlHSbf1qJJ0MvAD4P6SVkl6SbnpvZJ+KulCYD/gNaMNPyIiGimi2/4D8Lhp1l8FHNizfPAM+7+ovugiImIQTV2BRETEmEsCiYiIoSSBRETEUJJAIiJiKEkgERExlKaGMum0fffdF4AVK1awaNGiZoOJiKhJEkgNJocYaMtwAxERdcgtrIiIGEoSSEREDCUJJCIihpIEEhERQ0kCiYiIoSSBRETEUJJAIiJiKEkgERExFNluOoaRkfQ7YOUID7kd8PsRHm/Uuty+LrcN0r5xN+r23dv29lNXzqsEMmqSzrG9uOk46tLl9nW5bZD2jbu2tC+3sCIiYihJIBERMZQkkHotazqAmnW5fV1uG6R9464V7UsNJCIihpIrkIiIGEoSSEREDCUJJCI6R9I9Jb2h6Ti6LjMSVkzS7sAiev7b2v5KYwHVSNKjgENsv7zpWKom6f7A622/tOlYqiLpbsA9ueNn87zmIqqWpO2A5wAHAzsBX202ovpIuivwctvvbjKOJJAKSToe2B34GXBrudpAZxKIpD2BQ4DnAr9kzNtWJvxjgB2BrwEfBj4GPBw4trnIqiXpXcDfAL+g+ExS/n5sUzFVQdLWwDMoPpP3o0gau9he2GhgFZF0T+CfuP3z+XngXcCLgJObi6yQBFKtfWzv1nQQVZN0P+D5FN/s/gB8kaIH336NBlaNTwEfB34A7A+cR/E/6Qts39BkYBV7LnAf2zc1HUjFrgZ+DLwN+F/blvSMhmOq0knAcuDLFJ/PH1J8Qd3d9m+aDAzSjbdSkj4DHGv7503HUiVJtwL/A7zE9uXluits79JsZHMn6Xzbe/YsXwkssn1Lc1FVT9KXgb+3fXXTsVRJ0msovtxsSZH4vwic0YXPJoCkC2zv0bP8W+Betm9sMKzb5AqkWicCP5D0G+BGQIBt795sWHP2LIr/Sb8r6XTgCxRt64I7S9qL29uzFthdkqBTNYJ/BX4i6SKKzyYAtp/WXEhzZ/sDwAck7UJxhfw1YEdJbwK+avv/moyvCmXtavLz+RtgC0lbAtj+Y2OBkSuQSkm6HHgt8FNur4Fge5QjANem/NA+neJ/1MdSJMyv2v5Wk3HNhaTv9tls22NdI5gk6WfAJ1n/s7m8saBqIunBlHU62/dpOp65kLSC4t9rui9sbvpKKwmkQpK+05UTzoZI2paix8vzutpmSZvavrnpOKogabntfZuOoy5l0nhAuXix7YuajGe+SAKpkKSPAXcF/os73iYY655KAJI2AQ6g539S4HTb65qLqnrlrav9KL7BPtX2goZDqoSk91N8Jk/hjp/Nsb5FJ+kuwNcpuidfSPFN/cEU8/483faaBsOrhaT7UHZqsf2gRmNJAqmOpM9Os9q2Dxt5MBWStCPwXWA18BOK/0n3Au4BLLG9usHwKiHp4RRJ4xnAtsDLgVNs/6nRwCoyw626sb9FJ+k44CbgjbZvLddtBBwNbG77FU3GVxVJOwDPo/iM7k5R0/qK7Z82GlcSSGyIpBOA821/cMr6VwIPtX1oE3FVQdK7Kbq4/oqiX/1XgXNs79xoYDEQST+n6NK6bsr6TYCf2n5gM5FVQ9JLKWqOC4EvlT9fb8vnM72wKiDpjbbfK+nD3P6Q1m1sv7KBsKq0j+2/mbrS9nGSLm0gniotBS6leBbkG7ZvkNSZb1WSXmj7c5JeO9122+8fdUwVu2m626i210lqRVfXOfooxTNKh9g+B6BNn88kkGpcXP4+p9Eo6vOXPtuuH1kU9bgH8ESKb3kfLG/1bC5pk47Ud7Ysf2/daBT1mdoNe5KAzRqIp2o7UnRWeb+kBRRXIJs2G9LtcgurJmXf7T+7A/+BJV0BvH66TcB7x72r5CRJdwaeQpFMHg2cafuQZqOKfjbQDZuOjJYAgKSF3D4ixBYUXejf0mhMHTi/NU7S24Ev2b5E0mbAacCewDqKS89vNxnfXM3QOeA2tv92VLGMiqRtgGfYPrHpWOaivIc+YfuysofZZygeDF0JHGr7J40GGEMpB/t8vu2jGo0jCWTuyoe0HlSOw7OU4hvC4ykGdzvR9t6NBhjzVvnk+V62b5Z0CPA6ilt2ewHvsP3/Gg0wxlrmA6nGTT23qp4EfMH2LbYvJnWmaNa6nochnwKcZPsP5VXxln32i9igJJBq3CjpQZK2p3gIrXdojy0aiilmobz1uMF1Y+hWSTuU9Z3HAb23UzdvKKboiCSQarwK+E/gEuADtn8JIOlAigfvOqHDJ1koukoOsm7cvJ2id+AKigcjfwYgaV/gigbjqpSkMwdZN67a2r7cXqmA7R9x+xAfvetPBU4dfUS1+QHwkAHWjQ1J96CYvW7zKd1Bt6EDV4+2vyHp3sDWU56qP4fiyeaxVl5ZbQFsN2XU2m0ousCOtba3LwkkNqjjJ9knUczUtxDofajuWqDRLpJVKZ9n+dOUddc1FE7VXga8muJk2juu1xqKh/DGXavbl15YsUGSDqU4yS7mjg9LXguc0JHBIp9l+8tNxxHDkfQK2x9uOo66tLV9SSAVkrTZ1JnCpls3rrp4ku0Z6uN1TD8MzbgP9dFpkh5r+zuSnjnd9nH/ctP29uUWVrU6VyOA20+ywKLpxlQa85PsZFfWrRqNomaSzrT9uA2tG0P7At8BnjrNNgNjnUBoefuSQCrQ8RoBdPgka/uT5e9Gn+itS9uLsHNl+x3l786NhgDtb19uYVVgPtQIuqqcT2JG4z6SsqRXcXsR9qqeTWuAT9n+SBNxVWWmUYYnjfnVcevblyuQCpTjJZ3YxRoBdP4ke27TAdTJ9oeAD7W1CFuBro4yPKnV7csVSAW6Xogtr7BmNO4DDvYqB1G07WubjqUKbS/CxnjLFUg1OlsjgPUTRNdOsgCSFgOfpfjGJ0l/Bg6zPe5XKK0uwlZF0i7Ah4B9KNr1A+A1tjvxtH1b25crkBjY1JMs8Ge6cZJF0oXAy23/T7n8aOBjtndvNrIYhKQfUjxYd3K56vnAK2w/vLmoqtPW9iWBVKDjNYLbdPkkK+n7th+1oXXjpu1F2KpI+tHUk6mkH9rep6mYqtTW9uUWVjXG/hv4gK6dTB4Atv9XUlduY/1Y0icpvuGZYpyoCUkPAbB9Xr+dW6zVRdgKfVfSm4EvcPu/339L2hbA9h+bDK4CrWxfrkBq0MUaAYCkD1A8U9B7kv0T8GUY65PshqZGte3HjiyYmDVJv+yz2bZ3GVkwNWhr+5JAKtTlGgHkJDvO2lqEjfGWBFKhLtcIukrSU4ELba8sl9/O7XOGv2pybpdx19Yi7FxJehhwpe3flMsv5vZ/vyPH/dZV29uXCaWqtV6NgOJp9LEm6anlnBKTy2+XdIGkUyTt3GRsFXg38DsASU8BXggcBpwCfKLBuKom2/9me1358zmmeWZpDH0SuAlA0mOAo4GTgGuAZQ3GVZVWty9XIBXqao2gvLLax/b15Un2/cDBwF7Ac2w/qdEA50DSBbb3KF8fD1xq+z3l8nm2x3ogzEmSjqa4pdpbhN2Mck6Jpr/JDmvKv99Hgd/ZPrJcPt/2ng2GN2dtb196YVVrz/L3O6asfyTF/7TjWiOw7evL188EPlPWdc6V9A8NxlUFSdoKuJ5izvCP9Wy7czMh1WJy9sGXTVl/GMVnc1yLzBtL2qScNOtxwNKebV04v7W6fY0H0CW292s6hpp0+ST7QeB8isEFL7Z9DkA5qvLq5sKqlu1xv9U4k5OB5ZJ+D/wFmKw/3pfiNs+4a3X7cgurAl0vxEo6jGJ61zXA1bb3L9fvBRwz7nNKSNoJuDtwge1by3U7AJva/lWjwc1R24uwVZC0D7AD8C2XU/VKuh+w1bjeNu7V5vYlgVSgyzWCSV0+yU4l6cjJ+8zjTtJ5wONt/7Eswn4BeAXF7dYH2n52k/HVQdJS240XmOvSpvalF1Y1pq0R2P40sH2DcVXG9q9t/6QneRxpe3XXkkfpaU0HUKGNe64yngcss/1l2/8E3LfBuOp0eNMB1Kw17UsCqYYkbSVpI4oawZk928a9RjCTLp1kp9KG3zI2NpY0Wet8HMXIvJO6WgPt0r/fdFrTvq5+gEbtg8yDQuwUrfkQ1+ChTQdQoVYXYWsy3dD1XdKa9qUGUpH5VCMAkLTRZDu7QNL2wEuBRfR8sbJ9WFMxVaXNRdiqSFoA/Auwo+0DJO0GPML2ZxoOrRJtbV8SSE26VIid1PGT7FkU387PBW6ZXO9uTlHcmiJsVSSdRjEO3Vtt71HetvuJ7Qc3HFol2tq+JJCadOkp5kldPsm24aneUenoZ/Ns2w+T9BPbe5XrOvNv2tb2pQZSny7WCLaw/aamg6jJNyQdaPvUpgMZgS5+Nq+T9FeU43uVt+26VONpZftyBVKTrtUIACT9M3BWF0+y5cRYWwI3AjdTnGRte5tGA6uBpIW2VzUdR5XKib8+DDwIuIii+/yzbV/YaGAVaWv7kkAq1OUaAcyvk2zXtLUIW6WyLnB/is/lpbZvbjikSrWxfUkgFepyjaCrJD3A9iXlN7z1dKiXUiuLsFWRtDHwZNb/8taVOd9b2b7UQKrVyRpBx0+yr6UY4fTYabaN8wjKU21n+0uS/hHA9jpJt2xopzHyX8ANwE+BTt06LrWyfUkg1epqIbazJ1nbS8vfXR1JeVIri7AVWuhuz/zZyvblFlaFUiMYX5KeA5xu+1pJbwMeArzL9k8aDq0SbS3CVkXSe4AzbX+r6Vjq0Nb2JYHEwLp8kpV0oe3dVcxj/6/AMcBbPOZzhvdqYxG2KpKeAXyOYny/zn15a2v7kkAq0PEawW26fJKdfEBL0r8CP7X9+d6HtsZdW4uwVZF0BfB0in+7zp3U2tq+1ECq0dkawRSTRdcnAx+3/XVJRzYYT5V+LemTwOOB90jajG6NVt3KImyFLgMuatPJtWKtbF+uQGJgkr4B/JriJPtQitFdf2x7j0YDq4CkLYD9Kb7hXVYOhPngtt1zHtbk1WPTcdRF0gkU87qfRlGDBDp1hXUCLWxfl75hNU7ScyRtXb5+m6SvlEO6d8VzgW8C+9v+M7At8IZGI6qI7ettfwW4RtK9gE2BSxoOq0qnSXpi00HU6JcU8/DcCdi656crWtm+XIFUqMs1gl6S7k7PRFldGK5e0tMobkHuCFwN3Au4xPZfNxpYRdpahI3xlhpItbpcI5jxJAt04ST7LmAf4NtlMX0/inntu+JY4BG0rAg7V5I+YvsISf9F+YxLL9tjPXNm29uXK5AKdblGACDpAooOAXc4yU4+jDfOJJ1je3HZxr1s3yrpx7b3bjq2Kkj6JnBABwf4XGN7G0n7Trfd9vJRx1SltrcvVyDVei5FIfYY238uC7GdqBGUbrb9B0kblaMNf7d8wKkL/ixpK+B7wL9LuhpY13BMVVoNTJRjYrWmCFuBX0DzJ9Iatbp9SSAVsn098BVJdy8LsdCtQmyXT7IHUVwxvgZ4AXAX4J2NRlStX5Y/dyp/umJ7Sa+daWMHEmSr25cEUqGO1wigwydZl3OFA7dK+m/gD12qFdg+qukYarIxsBXdnCQLWt6+1EAq1OUawVSStqMDJ9lyUMGjgT9SFNL/DdiOorfSi22f3mB4c9b2IuxcdXF63l5tb1+uQKrVyRpBv5OspHE/yX4EeAvF1dR3KArNP5T0AOBkYJzbBvBi4AiKLuVd1Mpv5hVqdfuSQKrV1RpBl0+ym0w+bS7pnbZ/CFCObdZsZNVodRG2Ao9rOoCatbp9SSDV6mqNoMsn2d5urX+Zsm2sb8+VWl2EnSvbf2w6hjq1vX1JIBXqcCG2yyfZPSStobhVsHn5mnL5zjPvNjZaXYSN8ZYiegXmQSH2FuA6ypMscP3kJuDOtjdtKrbor+1F2BhvuQKpRpdrBNjeuOkYYmi58oja5AqkApLOt71n+fpi2w/s2daZSYli/Ejatu330WN8ZTj3anS5RhBjLMkj6pQrkAqkRhAR81ESSEREDCW3sCIiYihJIBERMZQkkIiIGEoSSEREDCUJJCIihvL/Aawky9WOY8QzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_index = ['Basin-Split Sim', 'Basin-Split DA', 'Basin-Split AR', 'Time-Split Sim', 'Time-Split DA', 'Time-Split AR']\n",
    "\n",
    "for metric in metrics_list:\n",
    "\n",
    "    fig = plt.figure()\n",
    "    \n",
    "    plotdata = pd.DataFrame(index=basins, columns=comparison_index)\n",
    "    plotdata['Basin-Split Sim'] = pub_sim[metric]\n",
    "    plotdata['Basin-Split DA'] = pub_da[metric]\n",
    "    plotdata['Basin-Split AR'] = pub_ar[metric]\n",
    "    plotdata['Time-Split Sim'] = time_sim[metric]\n",
    "    plotdata['Time-Split DA'] = time_da[metric][(0.00, 1, 0)]\n",
    "    plotdata['Time-Split AR'] = time_ar[metric][(0.00, 0.00, 1, 0)]\n",
    "    plotdata.median()\n",
    "\n",
    "    color_mapping = [_model_to_color(model) for model in comparison_index]\n",
    "    plotdata.median().plot.bar(color=color_mapping)\n",
    "    # plt.errorbar(plotdata.columns, plotdata.median(), yerr=plotdata.std(), fmt='.', c='k', label='1 \\sigma')\n",
    "    plt.vlines(plotdata.columns, plotdata.quantile(0.10), plotdata.quantile(0.90), color='grey', lw=5)\n",
    "    plt.grid()\n",
    "    # plt.ylim([0,1])\n",
    "    plt.ylabel(f'Median {metric} over 531 Basins')\n",
    "\n",
    "    plot_file_name = f\"./results/plots/pub_{metric}_comparison.png\"\n",
    "    plt.savefig(plot_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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