{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 15.\n",
    "\n",
    "Day-night surface temperature difference ($\\overline{\\Delta T_{dn}}$, $K$) as a function of the average wind divergence in the free troposphere ($5-20~km$) of the substellar region ($\\overline{\\nabla\\cdot\\vec{u}}_{ss}$, $s^{-1}$) for the ensemble of simulations with different empirical parameters of the mass-flux scheme (gray markers) in (a) Trappist-1e and (b) Proxima b set-up. The blue marker shows the value for the control simulation, i.e. *MassFlux*. The dashed blue line shows linear fit, and the dark blue vertical lines show the divergence estimate from the *HighRes* simulation, along with extrapolated $\\overline{\\Delta T_{dn}}$ (horizontal dashed lines). Dark blue shaded area represents the uncertainty in high-resolution estimates due to the temporal variability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Skip code and jump to the figure](#Show-the-figure)\n",
    "\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Progress bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress import progress_bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scientific stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import linear_model\n",
    "\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aeolus.util import subplot_label_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Local modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from commons import (\n",
    "    ENS_LABELS,\n",
    "    GLM_RUNID,\n",
    "    NS_COLORS,\n",
    "    NS_MODEL_TYPES,\n",
    "    NS_OUTPUT_NAME_PREFIX,\n",
    "    NS_RUN_ALIASES,\n",
    "    PLANET_ALIASES,\n",
    "    SS_REGION,\n",
    ")\n",
    "from gl_diag import ONLY_LAM\n",
    "import mypaths\n",
    "from plot_func import use_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global stylesheet for figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a simple wrapper for the linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _simple_lin_reg(x, y):\n",
    "    \"\"\"Linear regression of y against x using scikit-learn.\"\"\"\n",
    "    xx = x.values.reshape((-1, 1))\n",
    "    yy = y.values.reshape((-1, 1))\n",
    "    wider_xx = np.linspace(\n",
    "        xx.min() * (1 - 0.05 * np.sign(xx.min())),\n",
    "        xx.max() * (1 + 0.05 * np.sign(xx.max())),\n",
    "        2,\n",
    "    ).reshape(-1, 1)\n",
    "    lin_reg = linear_model.LinearRegression().fit(xx, yy)\n",
    "    y_pred = lin_reg.predict(wider_xx)\n",
    "    score = lin_reg.score(xx, yy)\n",
    "\n",
    "    return lin_reg, score, wider_xx, y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_key = \"grcs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, load preprocessed metrics of the _HighRes_ simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_flist = sorted((mypaths.nsdir / \"_processed\").glob(\"*\"))\n",
    "# nc_flist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lam_results = {}\n",
    "for planet in PLANET_ALIASES.keys():\n",
    "    label = f\"{planet}_{run_key}\"\n",
    "    try:\n",
    "        lam_results[label] = xr.open_dataset(\n",
    "            mypaths.nsdir / \"_processed\" / f\"aggr_diags_ns_{label}_lam.nc\"\n",
    "        )\n",
    "    except OSError as e:\n",
    "        print(e)\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lam_ave = {}\n",
    "for label, ds in lam_results.items():\n",
    "    df = ds.to_dataframe()[[i for i in ds.data_vars]].mean()\n",
    "    df.index = [i + \"_ss\" for i in df.index]\n",
    "    df = df.to_frame()\n",
    "    df.columns = [label + \"_LAM\"]\n",
    "    lam_ave[label] = df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now load the aggregated metrics of \"ensemble\" global simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ens_dict = {}\n",
    "for planet in PLANET_ALIASES.keys():\n",
    "    df_list = []\n",
    "    for label in ENS_LABELS:\n",
    "        fpath = (\n",
    "            mypaths.sadir\n",
    "            / f\"{planet}_grcs_ensemble\"\n",
    "            / label\n",
    "            / \"_processed\"\n",
    "            / f\"{planet}_grcs_ens_{label}_aggr_diag.csv\"\n",
    "        )\n",
    "        df_list.append(pd.read_csv(fpath, index_col=0).T)\n",
    "    df_ens_dict[planet] = pd.concat(df_list, axis=0, sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to plot regression with all the bells and whistles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot_w_lin_reg(ax, x_vrbl, y_vrbl, ds_x, add_legend=True):\n",
    "    x = x_vrbl\n",
    "    y = y_vrbl\n",
    "\n",
    "    # Calculate regression\n",
    "    lin_reg, score, x_lin, y_lin = _simple_lin_reg(x, y)\n",
    "\n",
    "    try:\n",
    "        ax.plot(\n",
    "            x.loc[\"base\"],\n",
    "            y.loc[\"base\"],\n",
    "            linestyle=\"\",\n",
    "            marker=\".\",\n",
    "            color=NS_COLORS[run_key][\"lam\"],\n",
    "            ms=12,\n",
    "            label=NS_RUN_ALIASES[run_key],\n",
    "            zorder=100,\n",
    "        )\n",
    "    except KeyError:\n",
    "        pass\n",
    "    ax.plot(\n",
    "        x.squeeze(),\n",
    "        y.squeeze(),\n",
    "        linestyle=\"\",\n",
    "        marker=\".\",\n",
    "        color=\"grey\",\n",
    "        alpha=0.75,\n",
    "        ms=10,\n",
    "        label=f\"{NS_RUN_ALIASES[run_key]} with\\nperturbed parameters\",\n",
    "    )\n",
    "\n",
    "    ns_predict = None\n",
    "    # NS data\n",
    "    ns_estimate_mean = float(ds_x.mean())\n",
    "    ns_predict = float(lin_reg.predict(np.array(ns_estimate_mean).reshape((-1, 1))))\n",
    "    ns_estimate_std = float(ds_x.std())\n",
    "    xx = np.concatenate(\n",
    "        [x, [ds_x.mean(), ds_x.mean() + ds_x.std(), ds_x.mean() - ds_x.std()]]\n",
    "    )\n",
    "    wider_xx = np.linspace(\n",
    "        xx.min() * (1 - 0.05 * np.sign(xx.min())),\n",
    "        xx.max() * (1 + 0.05 * np.sign(xx.max())),\n",
    "        2,\n",
    "    ).reshape(-1, 1)\n",
    "\n",
    "    ax.plot(\n",
    "        wider_xx.squeeze(),\n",
    "        lin_reg.predict(wider_xx).squeeze(),\n",
    "        linestyle=\"--\",\n",
    "        color=NS_COLORS[run_key][\"global\"],\n",
    "        label=\"Linear regression\",\n",
    "    )\n",
    "\n",
    "    ylim = ax.get_ylim()\n",
    "\n",
    "    ns_predict_upper = float(\n",
    "        lin_reg.predict(np.array(ns_estimate_mean + ns_estimate_std).reshape((-1, 1)))\n",
    "    )\n",
    "    ns_predict_lower = float(\n",
    "        lin_reg.predict(np.array(ns_estimate_mean - ns_estimate_std).reshape((-1, 1)))\n",
    "    )\n",
    "    ax.vlines(\n",
    "        ns_estimate_mean,\n",
    "        *ylim,\n",
    "        color=NS_COLORS[run_key][\"lam\"],\n",
    "        linewidth=3,\n",
    "        label=f\"{NS_MODEL_TYPES['lam']['title']} estimate\",\n",
    "    )\n",
    "    ax.fill_betweenx(\n",
    "        ylim,\n",
    "        ns_estimate_mean - ns_estimate_std,\n",
    "        ns_estimate_mean + ns_estimate_std,\n",
    "        alpha=0.5,\n",
    "        facecolor=NS_COLORS[run_key][\"lam\"],\n",
    "        edgecolor=\"none\",\n",
    "        label=NS_MODEL_TYPES[\"lam\"][\"title\"] + r\" uncertainty ($\\pm\\sigma$)\",\n",
    "    )\n",
    "\n",
    "    xlim = ax.get_xlim()\n",
    "\n",
    "    if ns_predict is not None:\n",
    "        # print(ns_predict_lower)\n",
    "        # print(ns_predict)\n",
    "        # print(ns_predict_upper)\n",
    "        ax.plot(\n",
    "            [xlim[0], ns_estimate_mean],\n",
    "            [ns_predict] * 2,\n",
    "            marker=\"o\",\n",
    "            markevery=[-1],\n",
    "            linestyle=\"--\",\n",
    "            linewidth=3,\n",
    "            color=NS_COLORS[run_key][\"lam\"],\n",
    "        )\n",
    "        ax.plot(\n",
    "            [xlim[0], ns_estimate_mean - ns_estimate_std],\n",
    "            [ns_predict_lower] * 2,\n",
    "            marker=\"o\",\n",
    "            mfc=\"none\",\n",
    "            alpha=0.5,\n",
    "            markevery=[-1],\n",
    "            linestyle=\"--\",\n",
    "            linewidth=2,\n",
    "            color=NS_COLORS[run_key][\"lam\"],\n",
    "        )\n",
    "        ax.plot(\n",
    "            [xlim[0], ns_estimate_mean + ns_estimate_std],\n",
    "            [ns_predict_upper] * 2,\n",
    "            marker=\"o\",\n",
    "            mfc=\"none\",\n",
    "            alpha=0.5,\n",
    "            markevery=[-1],\n",
    "            linestyle=\"--\",\n",
    "            linewidth=2,\n",
    "            color=NS_COLORS[run_key][\"lam\"],\n",
    "        )\n",
    "        if (abs(ax.get_yticks() - ns_predict) >= 1.5).all():\n",
    "            ax.set_yticks(list(ax.get_yticks()) + [np.round(ns_predict, 1)])\n",
    "    ax.set_ylim(*ylim)\n",
    "    ax.set_xlim(*xlim)\n",
    "\n",
    "    if add_legend:\n",
    "        ax.legend(loc=1, fontsize=\"small\")  # , bbox_to_anchor=[1.0, 1.0]\n",
    "\n",
    "    return lin_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionary with x-diagnostics (here only the horizontal divergence is left)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_x_diags = {\n",
    "    \"hdiv_5_20km_ss\": {\n",
    "        \"factor\": 1e6,\n",
    "        \"xlabel\": \"Horizontal divergence in the upper troposphere\\n\"\n",
    "        + r\"of the substellar region $\\overline{\\nabla \\cdot \\vec{u}}_{ss}$ [$10^{-6}$ $s^{-1}$]\",\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose variables for the regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_name = \"hdiv_5_20km_ss\"\n",
    "y_name = \"t_sfc_diff_dn\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assemble the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncol = len(PLANET_ALIASES)\n",
    "nrow = 1\n",
    "\n",
    "fig, axs = plt.subplots(nrows=nrow, ncols=ncol, figsize=(8 * ncol, 4 * nrow))\n",
    "\n",
    "iletters = subplot_label_generator()\n",
    "for i, (planet, ax) in enumerate(zip(PLANET_ALIASES, axs.T)):\n",
    "    ax.set_title(f\"({next(iletters)})\", fontsize=\"small\", pad=5, loc=\"left\")\n",
    "    ax.set_title(PLANET_ALIASES[planet], fontsize=\"large\", pad=5, loc=\"center\")\n",
    "\n",
    "    y_vrbl = df_ens_dict[planet][y_name]\n",
    "    x_vrbl = df_ens_dict[planet][x_name] * chosen_x_diags[x_name][\"factor\"]\n",
    "    ds_x = (\n",
    "        lam_results[f\"{planet}_{run_key}\"][x_name.replace(\"_ss\", \"\")]\n",
    "        * chosen_x_diags[x_name][\"factor\"]\n",
    "    )\n",
    "\n",
    "    lr = scatter_plot_w_lin_reg(\n",
    "        ax, x_vrbl=x_vrbl, y_vrbl=y_vrbl, ds_x=ds_x, add_legend=(i == 0),\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel(\n",
    "        chosen_x_diags[x_name][\"xlabel\"], fontsize=\"medium\",\n",
    "    )\n",
    "\n",
    "    if ax.is_first_col():\n",
    "        ax.set_ylabel(\n",
    "            \"Surface day-night temperature\\n\"\n",
    "            + r\"difference $\\overline{\\Delta T_{dn}}$ [$K$]\",\n",
    "            fontsize=\"medium\",\n",
    "        )\n",
    "plt.subplots_adjust(wspace=0.15, hspace=0.3)\n",
    "\n",
    "plt.close()  # Show the figure in a separate cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And save it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgname = (\n",
    "    mypaths.plotdir / f\"{NS_OUTPUT_NAME_PREFIX}__scatter_w_linreg__{x_name}__{y_name}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to ../plots/trap1e_proxb__grcs__scatter_w_linreg__hdiv_5_20km_ss__t_sfc_diff_dn\n"
     ]
    }
   ],
   "source": [
    "fig.savefig(imgname, dpi=200)\n",
    "print(f\"Saved to ../{imgname.relative_to(mypaths.topdir)}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:exo]",
   "language": "python",
   "name": "conda-env-exo-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}