results-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Results\n",
    "\n",
    "This notebook creates the tables and figures used in the paper. \n",
    "To reproduce the contents of this notebook, you need to download \n",
    "- the models' ensembled predictions (either from [Zenodo (models)](https://doi.org/10.5281/zenodo.4071886) or, apart from NWM, created yourself) into the folder `BASE_DIR`\n",
    "- the [CAMELS dataset](https://ral.ucar.edu/solutions/products/camels) into the folder `DATA_DIR` (more precisely, for this notebook, only the static attributes in `DATA_DIR/camels_attributes_v2.0` are required)\n",
    "\n",
    "`README.md` contains information on where to obtain the required data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from collections import defaultdict\n",
    "from itertools import product\n",
    "from pathlib import Path\n",
    "from typing import List, Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from scipy.stats import wilcoxon, pearsonr\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from neuralhydrology.data.utils import load_camels_us_forcings, load_hourly_us_netcdf, load_camels_us_attributes\n",
    "from neuralhydrology.evaluation.metrics import calculate_metrics\n",
    "from neuralhydrology.evaluation.signatures import calculate_signatures, get_available_signatures\n",
    "\n",
    "rc('font',**{'family':'serif', 'size': 13})\n",
    "rc('text', usetex=True)\n",
    "\n",
    "DATA_DIR = Path('/home/mgauch/mts-lstm/data/datadir/CAMELS_US')\n",
    "BASE_DIR = Path('/home/mgauch/mts-lstm/results')\n",
    "MODELS = ['naive-daily', 'naive-hourly', 'MTS-LSTM', 'sMTS-LSTM', 'NWM']\n",
    "\n",
    "# If set to false, will aggregate NWM predictions, calculate metrics for daily and hourly NWM predictions,\n",
    "# and calculate signatures for all models. The results will be written to BASE_DIR.\n",
    "# If True, will look in BASE_DIR for files `nwm_results.p` and `signatures.p` and load the data from there.\n",
    "# You can either run the notebook once with PRECALCULATED_DATA = False (will take some time) or download the\n",
    "# pre-calculated data from zenodo (https://doi.org/10.5281/zenodo.4071886).\n",
    "PRECALCULATED_DATA = True\n",
    "\n",
    "COLORS = ['#004C71', '#00BABF', '#FFA741', '#CF5000']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation\n",
    "### Load predictions and metrics for each model ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Baselines: naive LSTMs\n",
    "dailylstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_dailylstm.p', 'rb'))\n",
    "hourlylstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_hourlylstm.p', 'rb'))\n",
    "\n",
    "# MTS-LSTM without and with per-frequency input variables\n",
    "mtslstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm.p', 'rb'))\n",
    "mtslstm_multiforcing_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm_multiforcing_validation.p', 'rb'))\n",
    "\n",
    "# sMTS-LSTM with and without cross-frequency regularization\n",
    "smtslstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_smtslstm.p', 'rb'))\n",
    "smtslstm_noregularization_ensemble = pickle.load(open(BASE_DIR / 'ensemble_smtslstm_noregularization.p', 'rb'))\n",
    "\n",
    "# predicting more than daily and hourly\n",
    "multifreq_mtslstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm_136H1D.p', 'rb'))\n",
    "\n",
    "BASINS = list(mtslstm_ensemble.keys())\n",
    "metrics = ['NSE', 'MSE', 'RMSE', 'KGE', 'Alpha-NSE', 'Pearson-r', 'Beta-NSE', 'FHV', 'FMS', 'FLV', 'Peak-Timing']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate or load metrics for NWM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not PRECALCULATED_DATA:\n",
    "    nwm = pickle.load(open(BASE_DIR / 'nwm_chrt_v2_1h.p', 'rb'))\n",
    "    nwm_results = defaultdict(lambda: defaultdict(dict))\n",
    "    for basin in tqdm(BASINS):\n",
    "        nwm_pred = nwm[basin][['streamflow']].rename({'streamflow': 'qobs_mm_per_hour_sim'}, axis=1)\n",
    "        nwm_pred.index = nwm_pred.index.rename('datetime')\n",
    "        # convert to mm/h\n",
    "        _, area = load_camels_us_forcings(DATA_DIR, basin, 'daymet')\n",
    "        nwm_pred['qobs_mm_per_hour_sim'] = 10**9 * nwm_pred['qobs_mm_per_hour_sim'] * 3600 / (area * 10**6)\n",
    "        nwm_pred = nwm_pred.resample('1H').first()  # fill missing hours with NaN\n",
    "        \n",
    "        # in the experiment with >2 frequencies, we only predict the last 18 hours of each day, so to have an equal\n",
    "        # comparison, we need to calculate NWM results over the last 18 hours only, too.\n",
    "        # For all other experiments though, we use the full day.\n",
    "        multifreq_nwm_pred = nwm_pred[nwm_pred.index.hour >= 6]\n",
    "        \n",
    "        # subset to test period, use observations from one of the other models' dfs\n",
    "        for freq_key in ['1H', '3H', '6H', '1D', '1H_mf']:  # 1H_mf stands for the experiment with >2 frequencies.\n",
    "            freq = freq_key.replace('_mf', '')\n",
    "            if freq == '1H':\n",
    "                nwm_freq = nwm_pred\n",
    "            elif freq_key == '1H_mf':\n",
    "                nwm_freq = multifreq_nwm_pred\n",
    "            else:\n",
    "                nwm_freq = nwm_pred.resample(freq).mean()\n",
    "            nwm_results[basin][freq_key]['xr'] = xr.Dataset.from_dataframe(nwm_freq.loc['2008-10-01':'2018-09-30 23:59:59'])\n",
    "            \n",
    "            # for the experiment with >2 frequencies, use the mf_multifreq_ensemble results, else the normal results.\n",
    "            if freq_key in ['1H_mf', '3H', '6H']:\n",
    "                nwm_results[basin][freq_key]['xr']['qobs_mm_per_hour_obs'] = \\\n",
    "                    multifreq_multifreqlstm_ensemble[basin][freq]['xr']['qobs_mm_per_hour_obs']\n",
    "            else:\n",
    "                nwm_results[basin][freq_key]['xr']['qobs_mm_per_hour_obs'] = \\\n",
    "                    multifreqlstm_ensemble[basin][freq]['xr']['qobs_mm_per_hour_obs']\n",
    "\n",
    "            # add metrics\n",
    "            freq_metrics = calculate_metrics(nwm_results[basin][freq_key]['xr']['qobs_mm_per_hour_obs'], \n",
    "                                             nwm_results[basin][freq_key]['xr']['qobs_mm_per_hour_sim'],\n",
    "                                             metrics=metrics, resolution=freq)\n",
    "\n",
    "            for metric in metrics:\n",
    "                nwm_results[basin][freq_key][f'{metric}_{freq}'] = freq_metrics[metric]\n",
    "\n",
    "        nwm_results[basin] = dict(nwm_results[basin])\n",
    "    pickle.dump(dict(nwm_results), (BASE_DIR / 'nwm_results.p').open('wb'))\n",
    "    \n",
    "nwm_results = pickle.load((BASE_DIR / 'nwm_results.p').open('rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate or load signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if not PRECALCULATED_DATA:\n",
    "    all_signatures = get_available_signatures()\n",
    "    all_signatures.remove('baseflow_index')  # we'll calculate that separately\n",
    "    precip = load_hourly_us_netcdf(DATA_DIR, 'nldas_hourly')['total_precipitation'] \\\n",
    "        .sel(date=slice('2008-10-01', '2018-09-30 23:59:59')).rename({'date': 'datetime'})\n",
    "    signatures = defaultdict(lambda: defaultdict(dict))\n",
    "    for basin in tqdm(BASINS):\n",
    "        for freq in ['1H', '1D']:\n",
    "            # resample precipitation\n",
    "            if freq == '1D':\n",
    "                f_precip = xr.DataArray(precip.sel(basin=basin).to_series().resample('1D').sum())\n",
    "            else:\n",
    "                f_precip = precip.sel(basin=basin)\n",
    "\n",
    "            # get obs signatures for one of the models (doesn't matter which one)\n",
    "            qobs = mtslstm_ensemble[basin][freq]['xr']['qobs_mm_per_hour_obs'] * (24 if freq == '1D' else 1)\n",
    "            signatures['obs'][basin][freq] = calculate_signatures(qobs, all_signatures + ['baseflow_index'],\n",
    "                                                                  datetime_coord='datetime', prcp=f_precip)\n",
    "\n",
    "            # get sim signatures\n",
    "            for model_name, model_results in zip(MODELS + ['sMTS-LSTM-no-reg'], \n",
    "                                                 [dailylstm_ensemble, hourlylstm_ensemble, mtslstm_ensemble, \n",
    "                                                  smtslstm_ensemble, nwm_results, \n",
    "                                                  smtslstm_noregularization_ensemble]):\n",
    "                if ('daily' in model_name and freq == '1H') or ('hourly' in model_name and freq == '1D'):\n",
    "                    continue\n",
    "                qsim = model_results[basin][freq]['xr']['qobs_mm_per_hour_sim'] * (24 if freq == '1D' else 1)\n",
    "                qsim = xr.where(qsim < 0, 0, qsim)  # clip predictions to 0\n",
    "                signatures[model_name][basin][freq] = \\\n",
    "                    calculate_signatures(qsim, all_signatures, datetime_coord='datetime', prcp=f_precip)\n",
    "                \n",
    "                # for baseflow index calculation, mask the array in the same places where obs has NaNs.\n",
    "                masked_sim = qsim.copy()\n",
    "                masked_sim[qobs.isnull()] = np.nan\n",
    "                signatures[model_name][basin][freq]['baseflow_index'] = \\\n",
    "                    calculate_signatures(masked_sim, ['baseflow_index'], datetime_coord='datetime')['baseflow_index']\n",
    "\n",
    "    pickle.dump({k: dict(v) for k, v in signatures.items()}, (BASE_DIR / 'signatures.p').open('wb'))\n",
    "\n",
    "signatures = pickle.load((BASE_DIR / 'signatures.p').open('rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ecdf(x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Calculates empirical cumulative density function. \"\"\"\n",
    "    xs = np.sort(x)\n",
    "    ys = np.arange(1, len(xs) + 1) / float(len(xs))\n",
    "    return xs, ys\n",
    "\n",
    "def get_inconsistency(results: dict, daily_results: dict = None) -> Dict[str, float]:\n",
    "    \"\"\"\n",
    "    If daily_results is provided, calculates inconsistency between day-aggregated\n",
    "    hourly results from `results` and daily results.\n",
    "    If daily_results is None, calculates inconsistency within `results`.\n",
    "    \"\"\"\n",
    "    inconsistency = {}\n",
    "    for basin, basin_results in results.items():\n",
    "        hourly_results = basin_results['1H']['xr']['qobs_mm_per_hour_sim']\n",
    "        hourly_results = hourly_results.to_series()\n",
    "        daily_aggregated = hourly_results.resample('1D').mean()\n",
    "        if daily_results is None:\n",
    "            basin_daily_results = basin_results['1D']['xr']['qobs_mm_per_hour_sim'].to_series()\n",
    "        else:\n",
    "            basin_daily_results = daily_results[basin]['1D']['xr']['qobs_mm_per_hour_sim'].to_series()\n",
    "        # convert to mm/d and calculate inconsistency\n",
    "        daily_aggregated = daily_aggregated * 24\n",
    "        basin_daily_results = basin_daily_results * 24\n",
    "        inconsistency[basin] = np.sqrt(np.mean(np.square(daily_aggregated - basin_daily_results)))\n",
    "    \n",
    "    return inconsistency\n",
    "    \n",
    "def cohens_d(list_one: List[float], list_two: List[float]) -> float:\n",
    "    \"\"\"Calculate Cohen's d (effect size). \"\"\"\n",
    "    s = np.sqrt(((len(list_one) - 1) * np.var(list_one) + (len(list_two) - 1) * np.var(list_two)) /\n",
    "                (len(list_one) + len(list_two) - 2))\n",
    "    d = (np.abs(np.mean(list_one) - np.mean(list_two))) / s\n",
    "    return d\n",
    "\n",
    "def wilcoxon_cohen(list_one: List[float], list_two: List[float]) -> Tuple[float, float]:\n",
    "    \"\"\"Calculate Wilcoxon p-value and Cohen's d effect size. \"\"\"\n",
    "    _, p = wilcoxon(list_one, list_two)\n",
    "    d = cohens_d(list_one, list_two)\n",
    "    return p, d\n",
    "\n",
    "def dict_to_df(model: str, results: dict, freqs: List[str]) -> pd.DataFrame:\n",
    "    \"\"\"Convert a nested dict into a MultiIndex DataFrame. \"\"\"\n",
    "    results = results.copy()\n",
    "    for basin in results.keys():\n",
    "        for freq in freqs:\n",
    "            keys = list(results[basin][freq].keys())\n",
    "            for key in keys:\n",
    "                results[basin][freq][key.replace(f'_{freq}', '')] = results[basin][freq].pop(key)\n",
    "    df = pd.DataFrame.from_dict({(model, basin, freq): results[basin][freq]\n",
    "                                 for basin in results.keys() \n",
    "                                 for freq in freqs},\n",
    "                                orient='index').drop(columns='xr')\n",
    "    df = df.swaplevel(1, 2, axis=0)\n",
    "    df.index.rename(['model', 'freq', 'basin'], inplace=True)\n",
    "    return df\n",
    "\n",
    "def significance(results_df: pd.DataFrame, reference_model: str, freq: str, metric: str) -> Tuple[dict, dict]:\n",
    "    \"\"\"Calculate Wilcoxon p-value + Cohen's d for each model in comparison to the reference model w.r.t. the metric. \"\"\"\n",
    "    reference_metrics = results_df.loc[(reference_model, freq, slice(None)), metric]\n",
    "    grouped = results_df.loc(axis=0)[:, freq, :].groupby('model')\n",
    "    p, d = {}, {}\n",
    "    for model in grouped.groups:\n",
    "        if model == reference_model:\n",
    "            p[model], d[model] = np.nan, np.nan\n",
    "            continue\n",
    "        \n",
    "        # make sure reference and group have same order\n",
    "        group_metrics = grouped.get_group(model)[metric]\n",
    "        ref_and_group = pd.merge(reference_metrics, group_metrics, on='basin')\n",
    "\n",
    "        # perform significance test and get effect size\n",
    "        p[model], d[model] = wilcoxon_cohen(ref_and_group[f'{metric}_x'], ref_and_group[f'{metric}_y'])\n",
    "        \n",
    "    return p, d\n",
    "\n",
    "def highlight_nonsig(s: pd.Series, freq: str, direction: str, \n",
    "                     res_df: pd.DataFrame, metric: str, p_sig: float = 0.001) -> List[str]:\n",
    "    \"\"\"Highlight entries that are not significantly worse than the best entry. \"\"\"\n",
    "    if direction == 'larger':\n",
    "        best_model = s.idxmax()\n",
    "    elif direction == 'diverging':\n",
    "        best_model = s.abs().idxmin()\n",
    "    elif direction == 'lower':\n",
    "        best_model = s.idxmin()\n",
    "    else:\n",
    "        raise ValueError(f'Cannot find best value based on unknown direction {direction}.')\n",
    "\n",
    "    p, d = significance(res_df, best_model, freq, metric)\n",
    "    not_sig = [(p[r] >= p_sig) or (r == best_model) for r in s.index.get_level_values('model')]\n",
    "    return ['font-weight: bold' if v else '' for v in not_sig]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarking: compare metrics and signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a DataFrame with metrics for each basin and frequency\n",
    "results_df = pd.concat([dict_to_df(model_name, model, ['1D', '1H']) \n",
    "                        for model_name, model in zip(MODELS,\n",
    "                                                     [dailylstm_ensemble, hourlylstm_ensemble, mtslstm_ensemble, \n",
    "                                                      smtslstm_ensemble, nwm_results])]).sort_index()\n",
    "\n",
    "# do the same for the signatures\n",
    "sig_df = pd.DataFrame.from_dict({(model, freq, basin): signatures[model][basin][freq]\n",
    "                                 for model in signatures.keys()\n",
    "                                 for basin in signatures[model].keys() \n",
    "                                 for freq in signatures[model][basin].keys()},\n",
    "                                orient='index').sort_index()\n",
    "sig_df.index.rename(['model', 'freq', 'basin'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics 1D\n"
     ]
    },
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       "        }</style><table id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.762</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.002</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.048</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.727</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.819</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.891</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >-0.055</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-17.656</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-9.025</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >9.617</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >0.306</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.662</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >10.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >naive-daily</th>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.755</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.002</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.048</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.760</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.873</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.885</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >-0.042</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-13.336</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-10.273</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >12.195</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >0.310</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.631</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >18.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.750</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.002</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.049</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.714</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.813</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.882</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >-0.043</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >-17.834</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >-13.421</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >9.730</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >0.333</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.602</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >12.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >NWM</th>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.636</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.004</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.059</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.666</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.847</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.821</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >-0.038</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >-15.053</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >-5.099</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >0.775</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >0.474</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.471</td>\n",
       "                        <td id=\"T_fa0d72e0_07c9_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >37.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eff5f0ce310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics 1H\n"
     ]
    },
    {
     "data": {
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       "        }</style><table id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.752</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.003</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.054</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.731</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.828</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.885</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >-0.045</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-16.296</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-9.274</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >-35.214</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >3.540</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.652</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >13.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >naive-hourly</th>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.751</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.003</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.054</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.739</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.837</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.882</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >-0.039</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-14.467</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-8.896</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >-35.097</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >3.754</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.644</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >13.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.748</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.003</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.055</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.726</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.825</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.882</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >-0.039</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >-16.115</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >-12.772</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >-35.354</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >3.757</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.620</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >17.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >NWM</th>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.559</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.005</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.071</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.638</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.846</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.779</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >-0.034</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >-14.174</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >-5.264</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >6.315</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >5.957</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.364</td>\n",
       "                        <td id=\"T_fa42a96a_07c9_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >46.000</td>\n",
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      "Signatures 1D\n"
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     "text": [
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2975: RuntimeWarning: invalid value encountered in greater\n",
      "  r_plus = np.sum((d > 0) * r)\n",
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2976: RuntimeWarning: invalid value encountered in less\n",
      "  r_minus = np.sum((d < 0) * r)\n"
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       "                <tr>\n",
       "                        <th id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.486</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.463</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.657</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.285</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.286</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.978</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >0.945</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >0.984</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >0.943</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >0.430</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >0.560</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.957</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >0.818</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >naive-daily</th>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.598</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.491</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.774</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.280</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.409</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.980</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >0.979</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >0.986</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >0.945</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >0.679</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >0.615</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.962</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >0.904</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >NWM</th>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.730</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.457</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.796</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.303</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.502</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.956</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >0.928</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >0.972</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >0.908</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >0.663</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >0.537</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.924</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >0.865</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.599</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.512</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.774</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.316</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.392</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.979</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >0.970</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >0.985</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >0.930</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >0.556</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >0.601</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.960</td>\n",
       "                        <td id=\"T_facb26be_07c9_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >0.897</td>\n",
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       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col0 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col1 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col2 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col4 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col5 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col7 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col9 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col11 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col3 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col4 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col5 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col6 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col7 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col8 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col10 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col11 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col12 {\n",
       "            font-weight:  bold;\n",
       "        }</style><table id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >high_q_freq</th>        <th class=\"col_heading level0 col1\" >high_q_dur</th>        <th class=\"col_heading level0 col2\" >low_q_freq</th>        <th class=\"col_heading level0 col3\" >low_q_dur</th>        <th class=\"col_heading level0 col4\" >zero_q_freq</th>        <th class=\"col_heading level0 col5\" >q95</th>        <th class=\"col_heading level0 col6\" >q5</th>        <th class=\"col_heading level0 col7\" >q_mean</th>        <th class=\"col_heading level0 col8\" >hfd_mean</th>        <th class=\"col_heading level0 col9\" >slope_fdc</th>        <th class=\"col_heading level0 col10\" >stream_elas</th>        <th class=\"col_heading level0 col11\" >runoff_ratio</th>        <th class=\"col_heading level0 col12\" >baseflow_index</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.537</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.416</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.697</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.274</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.401</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.979</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >0.955</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >0.983</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >0.948</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >0.633</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >0.563</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.954</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >0.908</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >naive-hourly</th>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.619</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.471</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.782</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.307</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.493</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.979</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >0.964</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >0.983</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >0.941</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >0.647</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >0.626</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.952</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >0.932</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >NWM</th>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.719</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.316</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.789</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.160</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.505</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.956</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >0.927</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >0.970</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >0.907</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >0.712</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >0.588</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.918</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >0.869</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.588</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.433</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.764</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.309</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.363</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.980</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >0.968</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >0.984</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >0.944</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >0.635</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >0.601</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.955</td>\n",
       "                        <td id=\"T_fb55299a_07c9_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >0.935</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eff5dbf7e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare metrics\n",
    "medians = results_df.groupby(['model', 'freq']).median()\n",
    "medians['NSE_mean'] = results_df['NSE'].groupby(['model', 'freq']).mean()\n",
    "medians['NSE<0'] = results_df['NSE'].groupby(['model', 'freq']).apply(lambda c: (c<0).sum())\n",
    "for freq in ['1D', '1H']:\n",
    "    print('Metrics', freq)\n",
    "    f_medians = medians.loc(axis=0)[:,freq].sort_values('NSE', ascending=False)\n",
    "    f_medians.index = f_medians.index.droplevel(1)\n",
    "\n",
    "    display(f_medians.style.format('{:.3f}') \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'larger', results_df, s.name),\n",
    "               subset=['NSE', 'KGE', 'Alpha-NSE', 'Pearson-r']) \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'diverging', results_df, s.name),\n",
    "               subset=['Beta-NSE', 'FHV', 'FMS', 'FLV']) \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'lower', results_df, s.name),\n",
    "               subset=['MSE', 'RMSE', 'Peak-Timing']))\n",
    "    \n",
    "# compare signatures\n",
    "for freq in ['1D', '1H']:\n",
    "    print('Signatures', freq)\n",
    "    sig_corr = pd.DataFrame(index=MODELS, columns=sig_df.columns, dtype=float)\n",
    "    sig_corr.index.rename('model', inplace=True)\n",
    "    for model in sig_corr.index:\n",
    "        if ('daily' in model and freq == '1H') or ('hourly' in model and freq == '1D'):\n",
    "                sig_corr = sig_corr.drop(model, axis=0)\n",
    "                continue\n",
    "        for sig in sig_corr.columns:\n",
    "            obs_sig = sig_df.loc(axis=0)['obs', freq][sig].sort_index()\n",
    "            sim_sig = sig_df.loc(axis=0)[model, freq][sig].reindex(obs_sig.index)\n",
    "            mask = ~(pd.isna(obs_sig) | pd.isna(sim_sig))\n",
    "            if mask.sum() == 0:\n",
    "                continue\n",
    "            sig_corr.loc[model, sig] = pearsonr(obs_sig[mask].values, sim_sig[mask].values)[0]\n",
    "\n",
    "    display(sig_corr.reindex(sorted(sig_corr.index, key=lambda x: x.lower())) \\\n",
    "            .style.format('{:.3f}') \\\n",
    "            .apply(lambda s: highlight_nonsig(s, freq, 'larger', sig_df, s.name)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D: Significance sMTS-LSTM vs. naive-daily: 8.669e-02\n",
      "1D: Significance sMTS-LSTM vs. MTS-LSTM: 3.248e-29\n",
      "1H: Significance sMTS-LSTM vs. naive-hourly: 2.688e-03\n",
      "1H: Significance sMTS-LSTM vs. MTS-LSTM: 3.726e-19\n"
     ]
    }
   ],
   "source": [
    "p, d = significance(results_df, 'sMTS-LSTM', '1D', 'NSE')\n",
    "print(f'1D: Significance sMTS-LSTM vs. naive-daily: {p[\"naive-daily\"]:.3e}')\n",
    "print(f'1D: Significance sMTS-LSTM vs. MTS-LSTM: {p[\"MTS-LSTM\"]:.3e}')\n",
    "p, d = significance(results_df, 'sMTS-LSTM', '1H', 'NSE')\n",
    "print(f'1H: Significance sMTS-LSTM vs. naive-hourly: {p[\"naive-hourly\"]:.3e}')\n",
    "print(f'1H: Significance sMTS-LSTM vs. MTS-LSTM: {p[\"MTS-LSTM\"]:.3e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-frequency consistency\n",
    "Mean squared difference between daily and day-aggregated hourly predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >median</th>        <th class=\"col_heading level0 col1\" >max</th>        <th class=\"col_heading level0 col2\" >p</th>        <th class=\"col_heading level0 col3\" >d</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.37591</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >1.66999</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >sMTS-LSTM (no regularization)</th>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.39832</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >2.17620</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >1.49e-25</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.091</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >naive</th>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.48984</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >2.22558</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >7.09e-72</td>\n",
       "                        <td id=\"T_03fc2684_07ca_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.389</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eff5d9e2c10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate inconsistency\n",
    "mtslstm_inconsistency = get_inconsistency(mtslstm_ensemble)\n",
    "smtslstm_inconsistency = get_inconsistency(smtslstm_ensemble)\n",
    "smtslstm_noreg_inconsistency = get_inconsistency(smtslstm_noregularization_ensemble)\n",
    "naivelstm_inconsistency = get_inconsistency(hourlylstm_ensemble, dailylstm_ensemble)\n",
    "\n",
    "inconsistency = pd.DataFrame({'MTS-LSTM': mtslstm_inconsistency,\n",
    "                              'sMTS-LSTM': smtslstm_inconsistency,\n",
    "                              'sMTS-LSTM (no regularization)': smtslstm_noreg_inconsistency,\n",
    "                              'naive': naivelstm_inconsistency}).T.agg(['median', 'max'], axis=1).sort_values('median')\n",
    "inconsistency['p'] = np.nan\n",
    "inconsistency['d'] = np.nan\n",
    "\n",
    "# calculate significance and effect size\n",
    "inconsistency.loc['MTS-LSTM', ['p', 'd']] = wilcoxon_cohen(list(smtslstm_inconsistency.values()), \n",
    "                                                           list(mtslstm_inconsistency.values()))\n",
    "inconsistency.loc['naive', ['p', 'd']] = wilcoxon_cohen(list(smtslstm_inconsistency.values()), \n",
    "                                                        list(naivelstm_inconsistency.values()))\n",
    "inconsistency.loc['sMTS-LSTM (no regularization)', ['p', 'd']] = wilcoxon_cohen(list(smtslstm_inconsistency.values()), \n",
    "                                                                                list(smtslstm_noreg_inconsistency.values()))\n",
    "inconsistency.loc[['sMTS-LSTM', 'sMTS-LSTM (no regularization)','naive']].style.format('{:.2e}', subset=['p']) \\\n",
    "    .format('{:.5f}', subset=['median', 'max']) \\\n",
    "    .format('{:.3f}', subset=['d'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metric CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = 'NSE'\n",
    "model_name = {'MTS-LSTM': 'MTS-LSTM', 'sMTS-LSTM': 'sMTS-LSTM', 'NWM': 'NWM',\n",
    "              'naive-daily': 'naive', 'naive-hourly': 'naive'}\n",
    "freq_name = {'1D': 'daily', '1H': 'hourly'}\n",
    "colors = dict(zip(['naive', 'MTS-LSTM', 'sMTS-LSTM', 'NWM'], COLORS))\n",
    "\n",
    "fig = plt.figure(constrained_layout=True, figsize=(11,8))\n",
    "gs = GridSpec(3, 4, figure=fig, height_ratios=[0.495, 0.01, 0.495])\n",
    "ax_all = fig.add_subplot(gs[2, 1:3])\n",
    "ax_d = fig.add_subplot(gs[0, :2])\n",
    "ax_h = fig.add_subplot(gs[0, 2:])\n",
    "    \n",
    "def plot(ax, models, freqs):\n",
    "    for model in models:\n",
    "        for freq in freqs:\n",
    "            ls = {'1D': '-' , '1H': '--'}[freq]\n",
    "            if ('daily' in model and freq == '1H') or ('hourly' in model and freq == '1D'):\n",
    "                continue\n",
    "            vals = results_df.loc[model, freq][metric]\n",
    "            bins, cdf = ecdf(vals)\n",
    "            ax.plot(bins, cdf, label=f'{model_name[model]} {freq_name[freq]}',\n",
    "                    color=colors[model_name[model]], ls=ls)\n",
    "            if freqs == ['1D']:\n",
    "                title = 'Daily results' \n",
    "            elif freqs == ['1H']:\n",
    "                title = 'Hourly results'\n",
    "            else:\n",
    "                title = 'Daily and hourly results'\n",
    "            ax.set_title(title)\n",
    "\n",
    "    ax.set_xlim(0, 1)\n",
    "    ax.grid()\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.set_xlabel(metric)\n",
    "    ax.set_ylabel('Cumulative distribution')\n",
    "\n",
    "for ax, freqs, models in zip([ax_all, ax_d, ax_h], [['1D', '1H'], ['1D'], ['1H']],\n",
    "                      [MODELS, ['naive-daily', 'MTS-LSTM', 'sMTS-LSTM', 'NWM'],\n",
    "                       ['naive-hourly', 'MTS-LSTM', 'sMTS-LSTM', 'NWM']]):\n",
    "    plot(ax, models, freqs)\n",
    "\n",
    "handles, labels = ax_all.get_legend_handles_labels()\n",
    "_ = fig.legend(handles, labels, frameon=False, loc='lower center', ncol=1,\n",
    "               bbox_to_anchor=(0.4,0.1,1,1), bbox_transform=plt.gcf().transFigure)\n",
    "plt.savefig(BASE_DIR / f'{metric}-cdf.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Per-timescale input variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2975: RuntimeWarning: invalid value encountered in greater\n",
      "  r_plus = np.sum((d > 0) * r)\n",
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2976: RuntimeWarning: invalid value encountered in less\n",
      "  r_minus = np.sum((d < 0) * r)\n"
     ]
    },
    {
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       "                <tr>\n",
       "                        <th id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >MTS-LSTM Multiforcing 2</th>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.811</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.002</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.040</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.782</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.879</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.912</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >0.014</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-10.993</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-14.179</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >14.034</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >0.250</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.661</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >18.000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >MTS-LSTM Multiforcing 1</th>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.805</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.002</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.039</td>\n",
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       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.874</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.911</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >0.018</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-11.870</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-14.388</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >12.850</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >0.273</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.663</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >16.000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.785</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.002</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.043</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.779</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.865</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.902</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >-0.014</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >-13.562</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >-9.336</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >9.931</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >0.286</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.669</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >13.000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.766</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.002</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.042</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.760</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.853</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.895</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >-0.007</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >-14.042</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >-13.085</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >14.486</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >0.308</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.603</td>\n",
       "                        <td id=\"T_09898826_07ca_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >15.000</td>\n",
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       "        }</style><table id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >MTS-LSTM Multiforcing 2</th>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.812</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.002</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.045</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.801</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.905</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.911</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >0.014</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-8.086</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-13.114</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >-26.969</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >3.846</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.679</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >17.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >MTS-LSTM Multiforcing 1</th>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.781</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.002</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.049</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.788</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.888</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.898</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >0.007</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-9.854</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-13.437</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >-27.567</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >3.711</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.630</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >21.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >sMTS-LSTM</th>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.783</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.002</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.048</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.779</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.886</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.901</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >-0.006</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >-10.557</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >-10.606</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >-34.003</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >3.571</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.657</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >20.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row3\" class=\"row_heading level0 row3\" >MTS-LSTM</th>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col0\" class=\"data row3 col0\" >0.776</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col1\" class=\"data row3 col1\" >0.002</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col2\" class=\"data row3 col2\" >0.049</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col3\" class=\"data row3 col3\" >0.768</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col4\" class=\"data row3 col4\" >0.874</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col5\" class=\"data row3 col5\" >0.895</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col6\" class=\"data row3 col6\" >-0.002</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col7\" class=\"data row3 col7\" >-11.232</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col8\" class=\"data row3 col8\" >-12.996</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col9\" class=\"data row3 col9\" >-62.439</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col10\" class=\"data row3 col10\" >3.800</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col11\" class=\"data row3 col11\" >0.615</td>\n",
       "                        <td id=\"T_09be312a_07ca_11eb_b6d3_47bffd3bfd0erow3_col12\" class=\"data row3 col12\" >20.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eff59154e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# validation results for single-forcings models\n",
    "val_mtslstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm_validation.p', 'rb'))\n",
    "val_smtslstm_ensemble = pickle.load(open(BASE_DIR / 'ensemble_smtslstm_validation.p', 'rb'))\n",
    "\n",
    "# validation results for multi-forcing MTS-LSTM with Daymet+Maurer as daily and NLDAS as hourly inputs\n",
    "val_mtslstm_multiforcing_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm_multiforcing_validation.p', 'rb'))\n",
    "\n",
    "# validation results for multi-forcing multifrequency LSTM with Daymet+Maurer as daily and NLDAS+Daymet+Maurer as hourly inputs\n",
    "val_mtslstm_multiforcing_dailyhourly_ensemble = pickle.load(open(BASE_DIR / 'ensemble_mtslstm_multiforcing_dailyhourly_validation.p', 'rb'))\n",
    "\n",
    "# create a dataframe with val metrics for each basin and frequency\n",
    "val_results_df = pd.concat([dict_to_df(model_name, model, ['1D', '1H'])\n",
    "                        for model_name, model in zip(['MTS-LSTM', 'sMTS-LSTM', 'MTS-LSTM Multiforcing 1', 'MTS-LSTM Multiforcing 2'],\n",
    "                                                     [val_mtslstm_ensemble, val_smtslstm_ensemble,\n",
    "                                                      val_mtslstm_multiforcing_ensemble,\n",
    "                                                      val_mtslstm_multiforcing_dailyhourly_ensemble])]).sort_index()\n",
    "\n",
    "# compare values\n",
    "val_medians = val_results_df.groupby(['model', 'freq']).median()\n",
    "val_medians['NSE_mean'] = val_results_df['NSE'].groupby(['model', 'freq']).mean()\n",
    "val_medians['NSE<0'] = val_results_df['NSE'].groupby(['model', 'freq']).apply(lambda c: (c<0).sum())\n",
    "for freq in ['1D', '1H']:\n",
    "    print(freq)\n",
    "    f_medians = val_medians.loc(axis=0)[:,freq].sort_values('NSE', ascending=False)\n",
    "    f_medians.index = f_medians.index.droplevel(1)\n",
    "    display(f_medians.loc[['MTS-LSTM Multiforcing 2','MTS-LSTM Multiforcing 1', 'sMTS-LSTM', 'MTS-LSTM']].style.format('{:.3f}') \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'larger', val_results_df, s.name),\n",
    "               subset=['NSE', 'KGE', 'Alpha-NSE', 'Pearson-r']) \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'diverging', val_results_df, s.name),\n",
    "               subset=['Beta-NSE', 'FHV', 'FMS', 'FLV']) \\\n",
    "        .apply(lambda s: highlight_nonsig(s, freq, 'lower', val_results_df, s.name),\n",
    "               subset=['MSE', 'RMSE', 'Peak-Timing']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial distribution of metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/ipykernel_launcher.py:24: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/ipykernel_launcher.py:26: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/ipykernel_launcher.py:27: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = 'sMTS-LSTM'\n",
    "freqs = ['1D', '1H']\n",
    "metrics = ['NSE', 'Peak-Timing']\n",
    "v = {'NSE': {'1D': (0, 1), '1H': (0, 1)}, 'Peak-Timing': {'1D': (0, 3), '1H': (0, 12)}}\n",
    "extend = ['min', 'neither']\n",
    "cm = ['plasma', 'plasma_r']\n",
    "\n",
    "# load dataframe with lat/lon information\n",
    "basin_attrs = load_camels_us_attributes(DATA_DIR, BASINS)\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "j = 1\n",
    "for freq in freqs:\n",
    "    plot_data = basin_attrs.join(results_df.loc[(model, freq)])\n",
    "\n",
    "    for i, metric in enumerate(metrics):\n",
    "        ax = fig.add_subplot(len(freqs), len(metrics), j)\n",
    "        if i == 0:\n",
    "            plt.text(-0.17, 0.5, 'hourly' if freq == '1H' else 'daily',\n",
    "                     transform=ax.transAxes, fontsize=17, fontweight='bold')\n",
    "        if j <= len(metrics):\n",
    "            ax.set_title(metric, pad=15)\n",
    "        m = Basemap(llcrnrlon=-118,llcrnrlat=22,urcrnrlon=-62,urcrnrlat=49,\n",
    "                    projection='lcc',lat_1=33,lat_2=45,lon_0=-95)\n",
    "        m.drawcoastlines(linewidth=0.7)\n",
    "        m.drawcountries(linewidth=0.7)\n",
    "        m.drawstates(linewidth=0.2)\n",
    "\n",
    "        x, y = m(plot_data['gauge_lon'].values, plot_data['gauge_lat'].values)  # transform coordinates\n",
    "        scatter = m.scatter(x, y, c=plot_data[metric], vmin=v[metric][freq][0], vmax=v[metric][freq][1], cmap=cm[i])\n",
    "        ax.axis(False)\n",
    "        m.colorbar(scatter, ax=ax, pad=0, shrink=.8, location='bottom', extend=extend[i])\n",
    "        j += 1\n",
    "plt.tight_layout()\n",
    "plt.savefig(BASE_DIR / f'{\"-\".join(metrics)}-map.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting more timescales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1H\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2975: RuntimeWarning: invalid value encountered in greater\n",
      "  r_plus = np.sum((d > 0) * r)\n",
      "/home/mgauch/miniconda3/envs/pytorch/lib/python3.7/site-packages/scipy/stats/morestats.py:2976: RuntimeWarning: invalid value encountered in less\n",
      "  r_minus = np.sum((d < 0) * r)\n"
     ]
    },
    {
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       "        }</style><table id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >daily/hourly</th>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.748</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.003</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.055</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.726</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.825</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.882</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >-0.039</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-16.115</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-12.772</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >-35.354</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >3.757</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.620</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >17.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >1-, 3-, 6-hourly</th>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.747</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.003</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.055</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.734</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.837</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.882</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >-0.037</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-15.517</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-13.478</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >-40.877</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >nan</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.613</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >17.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0elevel0_row2\" class=\"row_heading level0 row2\" >NWM</th>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col0\" class=\"data row2 col0\" >0.562</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col1\" class=\"data row2 col1\" >0.005</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col2\" class=\"data row2 col2\" >0.071</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col3\" class=\"data row2 col3\" >0.631</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col4\" class=\"data row2 col4\" >0.844</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col5\" class=\"data row2 col5\" >0.780</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col6\" class=\"data row2 col6\" >-0.034</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col7\" class=\"data row2 col7\" >-14.191</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col8\" class=\"data row2 col8\" >-5.493</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col9\" class=\"data row2 col9\" >6.173</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col10\" class=\"data row2 col10\" >nan</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col11\" class=\"data row2 col11\" >0.344</td>\n",
       "                        <td id=\"T_13c5432a_07ca_11eb_b6d3_47bffd3bfd0erow2_col12\" class=\"data row2 col12\" >47.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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      "3H\n"
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       "        }</style><table id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >1-, 3-, 6-hourly</th>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.746</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.003</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.054</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.726</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.820</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.880</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >-0.036</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-16.934</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-17.450</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >9.888</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >1.215</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.628</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >15.000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >NWM</th>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.570</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.005</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.070</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.644</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.842</td>\n",
       "                        <td id=\"T_13dddcbe_07ca_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.782</td>\n",
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       "        }    #T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col9 {\n",
       "            font-weight:  bold;\n",
       "        }</style><table id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >NSE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >KGE</th>        <th class=\"col_heading level0 col4\" >Alpha-NSE</th>        <th class=\"col_heading level0 col5\" >Pearson-r</th>        <th class=\"col_heading level0 col6\" >Beta-NSE</th>        <th class=\"col_heading level0 col7\" >FHV</th>        <th class=\"col_heading level0 col8\" >FMS</th>        <th class=\"col_heading level0 col9\" >FLV</th>        <th class=\"col_heading level0 col10\" >Peak-Timing</th>        <th class=\"col_heading level0 col11\" >NSE_mean</th>        <th class=\"col_heading level0 col12\" >NSE<0</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0elevel0_row0\" class=\"row_heading level0 row0\" >1-, 3-, 6-hourly</th>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col0\" class=\"data row0 col0\" >0.734</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col1\" class=\"data row0 col1\" >0.003</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col2\" class=\"data row0 col2\" >0.055</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col3\" class=\"data row0 col3\" >0.685</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col4\" class=\"data row0 col4\" >0.760</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col5\" class=\"data row0 col5\" >0.880</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col6\" class=\"data row0 col6\" >-0.032</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col7\" class=\"data row0 col7\" >-23.405</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col8\" class=\"data row0 col8\" >-20.267</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col9\" class=\"data row0 col9\" >20.055</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col10\" class=\"data row0 col10\" >0.685</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col11\" class=\"data row0 col11\" >0.619</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow0_col12\" class=\"data row0 col12\" >12.000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0elevel0_row1\" class=\"row_heading level0 row1\" >NWM</th>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col0\" class=\"data row1 col0\" >0.586</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col1\" class=\"data row1 col1\" >0.005</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col2\" class=\"data row1 col2\" >0.069</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col3\" class=\"data row1 col3\" >0.651</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col4\" class=\"data row1 col4\" >0.839</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col5\" class=\"data row1 col5\" >0.790</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col6\" class=\"data row1 col6\" >-0.035</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col7\" class=\"data row1 col7\" >-14.734</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col8\" class=\"data row1 col8\" >-5.098</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col9\" class=\"data row1 col9\" >6.578</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col10\" class=\"data row1 col10\" >1.226</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col11\" class=\"data row1 col11\" >0.411</td>\n",
       "                        <td id=\"T_13f5e548_07ca_11eb_b6d3_47bffd3bfd0erow1_col12\" class=\"data row1 col12\" >42.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eff5e358dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs = ['1H', '3H', '6H']\n",
    "# write the hourly results from the experiment with >2 timescales (where only the last 18 hours\n",
    "# of each day are predicted) over the 1H-results, so we'll be using the right hourly results for this experiment.\n",
    "multifreq_nwm_results = nwm_results.copy()\n",
    "for basin in nwm_results:\n",
    "    multifreq_nwm_results[basin]['1H'] = nwm_results[basin]['1H_mf']\n",
    "\n",
    "# create a dataframe with metrics for each basin and frequency    \n",
    "mf_results_df = pd.concat([dict_to_df(model_name, model, freqs) \n",
    "                          for model_name, model in zip(['1-, 3-, 6-hourly', 'daily/hourly', 'NWM'],\n",
    "                                                       [multifreq_mtslstm_ensemble, mtslstm_ensemble, \n",
    "                                                        multifreq_nwm_results])]).sort_index()\n",
    "\n",
    "# compare values\n",
    "mf_medians = mf_results_df.groupby(['model', 'freq']).median()\n",
    "mf_medians['NSE_mean'] = mf_results_df['NSE'].groupby(['model', 'freq']).mean()\n",
    "mf_medians['NSE<0'] = mf_results_df['NSE'].groupby(['model', 'freq']).apply(lambda c: (c<0).sum())\n",
    "for freq in freqs:\n",
    "    print(freq)\n",
    "    f_medians = mf_medians.loc(axis=0)[:,freq].sort_values('NSE', ascending=False)\n",
    "    f_medians.index = f_medians.index.droplevel(1)\n",
    "    display(f_medians.style.format('{:.3f}') \\\n",
    "    .apply(lambda s: highlight_nonsig(s, freq, 'larger', mf_results_df, s.name),\n",
    "           subset=['NSE', 'KGE', 'Alpha-NSE', 'Pearson-r']) \\\n",
    "    .apply(lambda s: highlight_nonsig(s, freq, 'diverging', mf_results_df, s.name),\n",
    "           subset=['Beta-NSE', 'FHV', 'FMS', 'FLV']) \\\n",
    "    .apply(lambda s: highlight_nonsig(s, freq, 'lower', mf_results_df, s.name),\n",
    "           subset=['MSE', 'RMSE', 'Peak-Timing']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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