{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 5.\n",
    "\n",
    "Atmospheric circulation regimes of (crosses) Trappist-1e and (circles) Proxima b simulations with (blue) *MassFlux*, (orange) *Adjust*, and\n",
    "(green) *NoCnvPm* set-up, defined by the estimates of the non-dimensional Rossby deformation radius ($L_d/R_p$, x-axis) and the non-dimensional Rhines length ($L_{R}/R_p$, y-axis)."
   ]
  },
  {
   "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": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aeolus.calc import last_year_mean\n",
    "from aeolus.core import Run\n",
    "from aeolus.subset import l_range_constr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from commons import (\n",
    "    GLM_MODEL_TIMESTEP,\n",
    "    PLANET_ALIASES,\n",
    "    RUN_ALIASES,\n",
    "    OUTPUT_NAME_PREFIX,\n",
    ")\n",
    "from gl_diag import (\n",
    "    calc_derived_cubes,\n",
    "    nondim_rhines_number,\n",
    "    nondim_rossby_deformation_radius,\n",
    ")\n",
    "import mypaths\n",
    "from plot_func import MARKER_KW, add_custom_legend, use_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global stylesheet for figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dictionary of `Run` objects with preprocessed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = {}\n",
    "for planet in PLANET_ALIASES.keys():\n",
    "    for run_key in RUN_ALIASES.keys():\n",
    "        label = f\"{planet}_{run_key}\"\n",
    "\n",
    "        fname = mypaths.sadir / label / \"_processed\" / f\"{label}.nc\"\n",
    "\n",
    "        runs[label] = Run(\n",
    "            files=fname,\n",
    "            name=label,\n",
    "            planet=planet,\n",
    "            timestep=GLM_MODEL_TIMESTEP,\n",
    "            processed=True,\n",
    "        )\n",
    "\n",
    "        # Calculate additional diagnostics\n",
    "        runs[label].add_data(calc_derived_cubes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the non-dimensional numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a level height constraint to select data between 0 and 15 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "HGT_0 = 0\n",
    "HGT_1 = 15\n",
    "hgt_cnstr = l_range_constr(HGT_0, HGT_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loop over planets and runs again to populate dictionaries of Rhines and Rossby number estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rhines = {}\n",
    "rossby = {}\n",
    "for planet in PLANET_ALIASES.keys():\n",
    "    for run_key in RUN_ALIASES.keys():\n",
    "        label = f\"{planet}_{run_key}\"\n",
    "        rhines[label] = last_year_mean(\n",
    "            nondim_rhines_number(runs[label].proc.extract(hgt_cnstr))\n",
    "        )\n",
    "        rossby[label] = last_year_mean(\n",
    "            nondim_rossby_deformation_radius(runs[label].proc.extract(hgt_cnstr))\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_plt_kw = dict(linestyle=\"\", ms=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Axes limits\n",
    "xlim = [0.5, 1.5]\n",
    "ylim = [0.5, 1.5]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for run_key in RUN_ALIASES.keys():\n",
    "    for planet in PLANET_ALIASES.keys():\n",
    "        label = f\"{planet}_{run_key}\"\n",
    "\n",
    "        ax.plot(\n",
    "            rossby[label].data,\n",
    "            rhines[label].data,\n",
    "            **MARKER_KW[run_key],\n",
    "            **MARKER_KW[planet],\n",
    "            **common_plt_kw,\n",
    "        )\n",
    "\n",
    "# Create two legends, for planets and runs, respectively\n",
    "add_custom_legend(\n",
    "    ax,\n",
    "    {\n",
    "        v: dict(color=\"k\", **common_plt_kw, **MARKER_KW[k])\n",
    "        for k, v in PLANET_ALIASES.items()\n",
    "    },\n",
    "    loc=3,\n",
    "    title=\"Planets\",\n",
    ")\n",
    "\n",
    "add_custom_legend(\n",
    "    ax,\n",
    "    {\n",
    "        v: dict(marker=\".\", **common_plt_kw, **MARKER_KW[k])\n",
    "        for k, v in RUN_ALIASES.items()\n",
    "    },\n",
    "    loc=2,\n",
    "    title=\"Simulations\",\n",
    ")\n",
    "\n",
    "# Vertical and horizontal guides\n",
    "ax.hlines(1.0, *xlim, linestyle=\"--\", linewidth=0.75)\n",
    "ax.vlines(1.0, *ylim, linestyle=\"--\", linewidth=0.75)\n",
    "ax.set_xlim(xlim)\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "# Axes labels\n",
    "ax.set_xlabel(\"Non-dimensional Rossby deformation radius\")\n",
    "ax.set_ylabel(\"Non-dimensional Rhines length\")\n",
    "\n",
    "# Annotations\n",
    "ax.text(1.05, 0.55, \"Rhines rotators\", color=\"grey\", fontsize=18)\n",
    "ax.text(1.05, 1.45, \"Slow rotators\", color=\"grey\", fontsize=18)\n",
    "ax.text(0.75, 0.55, \"Rapid rotators\", color=\"grey\", fontsize=18)\n",
    "\n",
    "plt.close()  # Show the figure in a separate cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x675 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And save it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgname = mypaths.plotdir / f\"{OUTPUT_NAME_PREFIX}__nondim_rossby_rhines__hgt{HGT_0}-{HGT_1}km.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to ../plots/trap1e_proxb__grcs_llcs_all_rain_acoff__nondim_rossby_rhines__hgt0-15km.png\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
}