{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "VSC-6f688dcb",
   "metadata": {
    "language": "markdown"
   },
   "source": [
    "# Emission Spectrum Basics\n",
    "\n",
    "Transmission tells us about the atmosphere at the limb; emission tells us about the temperature structure at the dayside. This notebook builds an `EmissionModel` using the same component-oriented workflow as [Transmission spectrum basics](https://escience-taurex.github.io/taurex3/examples/03_transmission_basics.html), but the physics is fundamentally different: the spectrum is now the planet's own thermal emission, and its shape is governed by the vertical temperature gradient of the atmosphere. [Inspecting profiles](https://escience-taurex.github.io/taurex3/examples/06_inspecting_profiles.html) takes that dependence further by visualising non-isothermal profiles directly.\n",
    "\n",
    "Emission spectroscopy measures the planet-to-star flux ratio $(F_p/F_s)$ as a function of wavelength. Unlike transmission, emission is sensitive to the vertical temperature structure: a temperature inversion, for instance, turns an absorption feature into an emission peak.\n",
    "\n",
    "This example starts with a simple isothermal atmosphere and molecular absorption only — no CIA or Rayleigh scattering — to keep the baseline as clean as possible. More information about forward models is [here](../user/taurex/models.rst), temperature profiles are [here](../user/taurex/temperature.rst), planet parameters are [here](../user/taurex/planet.rst), and star parameters are [here](../user/taurex/star.rst)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4931527f",
   "metadata": {},
   "source": [
    "## Data Note\n",
    "\n",
    "This notebook uses the opacity files set up in [Setup and opacity data](https://escience-taurex.github.io/taurex3/examples/01_setup_and_data.html). TauREx provides the software to work with these datasets; the files themselves are third-party products from [ExoMol](https://www.exomol.com)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "VSC-bc7b9353",
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emission model contributions: ['Absorption']\n"
     ]
    }
   ],
   "source": [
    "from _shared import build_emission_model\n",
    "\n",
    "context = build_emission_model(include_cia=False, include_rayleigh=False, download=False)\n",
    "em = context['em']\n",
    "\n",
    "print('Emission model contributions:', [c.name for c in em.contribution_list])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VSC-ccd8bc3d",
   "metadata": {
    "language": "markdown"
   },
   "source": [
    "## Running the Emission Model\n",
    "\n",
    "Calling `model()` on an `EmissionModel` returns the same tuple as in the transmission case: wavenumber grid, planet-to-star flux ratio $F_p/F_s$, optical depth, and extras. With a single gas and an isothermal profile the result is close to a diluted blackbody with shallow absorption features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "VSC-98bfbb76",
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed 76744 spectral points.\n",
      "Temperature profile spans 2000.0 K to 2000.0 K\n"
     ]
    }
   ],
   "source": [
    "wngrid, fpfs, tau, _ = em.model()\n",
    "wlgrid = 10000 / wngrid[::-1]\n",
    "fpfs = fpfs[::-1]\n",
    "\n",
    "print(f'Computed {len(wngrid)} spectral points.')\n",
    "print(f'Temperature profile spans {em.temperatureProfile.min():.1f} K to {em.temperatureProfile.max():.1f} K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "VSC-7cde8de3",
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet/star flux-ratio range: 1.462632e-09 to 4.652038e-03\n",
      "Wavelength range: 0.300 to 50.002 um\n"
     ]
    }
   ],
   "source": [
    "print(f'Planet/star flux-ratio range: {fpfs.min():.6e} to {fpfs.max():.6e}')\n",
    "print(f'Wavelength range: {wlgrid.min():.3f} to {wlgrid.max():.3f} um')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "VSC-3363b8c4",
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.plot(wlgrid, fpfs, lw=2)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Wavelength (um)')\n",
    "plt.ylabel('$(F_p/F_s)^2$')\n",
    "plt.title('Baseline emission spectrum')\n",
    "plt.grid(alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VSC-773a6ea3",
   "metadata": {
    "language": "markdown"
   },
   "source": [
    "## Temperature Profile\n",
    "\n",
    "Emission spectra are directly sensitive to atmospheric temperature structure, and plotting the T–P profile alongside the spectrum makes that connection tangible. An isothermal profile has no vertical contrast, so molecular features emerge only faintly against a near-blackbody continuum.\n",
    "\n",
    "[Inspecting profiles](https://escience-taurex.github.io/taurex3/examples/06_inspecting_profiles.html) uses a `Guillot2010` profile to show what structured temperature looks like and how it changes the spectrum. More information about temperature profile types is [here](../user/taurex/temperature.rst)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6693f957",
   "metadata": {},
   "source": [
    "### Composite emission regions\n",
    "\n",
    "The same multimodel interface is available for emission and direct-imaging calculations through `model_type = multi_eclipse` and `model_type = multi_directimage`. These composite models combine several 1D regions using the same `parfiles` and `fractions` pattern used for transmission, which is useful for day-night or patchy-emission setups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "VSC-b1d3d8fa",
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mo3k3btxAcXFxpeu4ePEi1q1bp0zn5eVh6dKlCA4ONjgNXJbIyEjY2tris88+MxiLzz//HLm5uXjyySer/FjK4+HhgYiICMybNw+XLl0yml/69pbypKenG7xHumfPHvz666/KC7xa21FLaGgo3N3dkZaWhqKiIqV9yZIlVX7elLfvVdXgwYNhbW2NxMREo6MpEcGff/5Z7rIlJSVG1xp4eHjAy8vL6PaY4uJi5fYx4K9/zObNmwd3d3eEhIQA+Gtfv3DhAhYsWGC0rdu3b6OgoADAX++lW1lZ4f333zc6c3TvYzBlbPz9/ZGbm4v//ve/StulS5cMnj8PAh6x1nHh4eF4/fXXkZSUhEOHDqFPnz6wsbFBZmYmVq9ejZkzZ+K5555TbXt2dnbYtGkToqOj0blzZ2zcuBEbNmzAO++8o5ziVSNTnz59YGtriwEDBuD1119Hfn4+FixYAA8PD6MX5ZCQEMydOxcffPABAgIC4OHhgZ49e6JPnz7w8fHBiBEjMH78eFhbW2PRokVwd3fH2bNnq/R4x48fj/Xr1+Opp55CTEwMQkJCUFBQgCNHjmDNmjU4c+YM3NzcKlxHq1atMGLECOzduxeenp5YtGgRcnJyyvwnoTR3d3fEx8cjMTERffv2xdNPP40TJ04gNTUVjz76qGofFDJnzhx069YNgYGBGDlyJFq0aIGcnBzs3r0b58+fx+HDhytdR0BAALp164a///3vKCwsREpKCho1amRwGl2N7ajFxsYGH3zwAV5//XX07NkTQ4YMQVZWFhYvXlzl91hDQkKwdetWTJ8+HV5eXmjevLlyoUxV+Pv744MPPkB8fDzOnDmDgQMHwsnJCVlZWVi3bh1ee+01jBs3rsxlb968iWbNmuG5555DUFAQHB0dsXXrVuzduxeffvqpQV8vLy8kJyfjzJkzaNWqFVauXIlDhw5h/vz5yvu4L730ElatWoVRo0Zh27ZtePzxx1FSUoLjx49j1apVyj3eAQEBePfddzF16lR0794dgwcPhlarxd69e+Hl5YWkpCRlbMp6Xlbkb3/7G95++20MGjQIb775Jm7duoW5c+eiVatWBvfmWryavxCZKlLWfawif90iEBISIvb29uLk5CSBgYEyYcIEuXjxotLH19dXnnzySaNlARhdsq+/heLjjz9W2qKjo8XBwUFOnz6t3O/m6ekpCQkJRpfd328mEZH169dLx44dxc7OTrk/ctGiRUaXzWdnZ8uTTz4pTk5ORrdJ7N+/Xzp37iy2trbi4+Mj06dPL/d2m/Jy3Lx5U+Lj4yUgIEBsbW3Fzc1NunbtKp988onBPbll0a938+bN0rFjR9FqtdKmTRtZvXq1QT99pr1795a5ntmzZ0ubNm3ExsZGPD095e9//7vRPY73c7uNiMjp06fl5ZdflsaNG4uNjY00bdpUnnrqKVmzZk2Fj/HefeXTTz8Vb29v5f5O/b201d1OeeOhv5Xm3tvAyrvdpvQYl/e4U1NTpXnz5qLVaiU0NFR27NhhtM7yHD9+XJ544gmxt7cXAMqtN+X9Lcra90REvvrqK+nWrZs4ODiIg4ODtGnTRkaPHi0nTpwod9uFhYUyfvx4CQoKEicnJ3FwcJCgoCBJTU016BceHi7t27eXffv2SZcuXcTOzk58fX0N7gfXKyoqkuTkZGnfvr1otVpp2LChhISESGJiouTm5hr0XbRokXTq1EnpFx4eLt9//70yv7znZWX7+pYtW6RDhw5ia2srrVu3li+++KLc222q8rolUv4+UVs0InXk3V6qdTExMVizZg3y8/NrO4rF8PPzQ4cOHfDtt9/WdhR6SEVERODq1atlfgoW1Q6+x0pERKQiFlYiIiIVsbASERGpiO+xEhERqYhHrERERCpiYSUiIlIRPyCiEjqdDhcvXoSTk9N9f7wZERFZJhHBzZs34eXlZfStP6WxsFbi4sWLqn84NRERWaZz586hWbNmFfZhYa2E/jv/zp07B2dnZ5PWISLK16BZwlEv85qXpeUFLC8z85rXw5g3Ly8P3t7eBt8DWx4W1kro/wjOzs73VVhFBM7OzhazEzKv+VhaXsDyMjOveT3MeauyPC9eIiIiUhELKxERkYpYWImIiFTEwkpERKQiFlYiIiIVsbASERGpiIWViIhIRSysREREKmJhJSIiUhELKxFVSERwq6gYt4tKwK9vJqocCysRVej23RK0T9iCLtN/we27JbUdh6jOY2ElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhU9NIX11q1b8PX1xbhx42o7ChERPcAemsI6bdo0PPbYY7Udg4iIHnAPRWHNzMzE8ePH0a9fv9qOQkRED7haL6w7duzAgAED4OXlBY1Gg/T0dKM+c+bMgZ+fH+zs7NC5c2fs2bOnWtsYN24ckpKSVEpMRERUvlovrAUFBQgKCsKcOXPKnL9y5UrExcUhISEBBw4cQFBQEKKionD58mWlT3BwMDp06GD0c/HiRXz99ddo1aoVWrVqVVMPiYiIHmL1ajtAv379KjxFO336dIwcORLDhw8HAKSlpWHDhg1YtGgRJk6cCAA4dOhQucv/8ssvWLFiBVavXo38/HzcvXsXzs7OmDx5cpn9CwsLUVhYqEzn5eUBAEQEIlLdh2ewrKnL1zTmNS9LzHvv75aQ2xLHmHnNR4281Vm21gtrRYqKirB//37Ex8crbVZWVoiMjMTu3burtI6kpCTlNPCSJUtw9OjRcouqvn9iYqJRe25u7n0V1vz8fACARqMxaR01iXnNy9Ly3i4qUX7Py81DsbZOv2wAsLwxZl7zUiOv/iCrKur0M+Tq1asoKSmBp6enQbunpyeOHz9ulm3Gx8cjLi5Omc7Ly4O3tzdcXFzg7Oxs0jr1BdnFxcVidkKAec3F0vLaFBUrvzu7OMNBa1OLaarG0saYec1LjbzVWa5OF1a1xcTEVNpHq9VCq9UatWs0mvvagfTLW8JOCDCvuVlS3nszWkpmwLLGGGBec7vfvNVZrtYvXqqIm5sbrK2tkZOTY9Cek5ODxo0b11IqIiKi8tXpwmpra4uQkBBkZGQobTqdDhkZGejSpUstJiMiIipbrZ8Kzs/Px6lTp5TprKwsHDp0CK6urvDx8UFcXByio6MRGhqKsLAwpKSkoKCgQLlKmIiIqC6p9cK6b98+9OjRQ5nWXzgUHR2NJUuWYMiQIbhy5QomT56M7OxsBAcHY9OmTUYXNBEREdUFtV5YIyIiKr2NJTY2FrGxsTWUiIiIyHR1+j1WIiIiS8PCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhU98IX13LlziIiIQLt27dCxY0esXr26tiMREdEDrF5tBzC3evXqISUlBcHBwcjOzkZISAj69+8PBweH2o5GREQPoAe+sDZp0gRNmjQBADRu3Bhubm64du0aCysREZlFrZ8K3rFjBwYMGAAvLy9oNBqkp6cb9ZkzZw78/PxgZ2eHzp07Y8+ePSZta//+/SgpKYG3t/d9piYiIipbrR+xFhQUICgoCK+88goGDx5sNH/lypWIi4tDWloaOnfujJSUFERFReHEiRPw8PAAAAQHB6O4uNho2S1btsDLywsAcO3aNbz88stYsGBBhXkKCwtRWFioTOfl5QEARAQiYtJj1C9r6vI1jXnNyxLz3vu7JeS2xDFmXvNRI291lq31wtqvXz/069ev3PnTp0/HyJEjMXz4cABAWloaNmzYgEWLFmHixIkAgEOHDlW4jcLCQgwcOBATJ05E165dK+yblJSExMREo/bc3Nz7Kqz5+fkAAI1GY9I6ahLzmpel5b1dVKL8npebh2Jtrb9sVMrSxph5zUuNvPqDrKqo08+QoqIi7N+/H/Hx8UqblZUVIiMjsXv37iqtQ0QQExODnj174qWXXqq0f3x8POLi4pTpvLw8eHt7w8XFBc7OztV/EPi//3RcXFwsZicEmNdcLC2vTdH/nQ1ydnGGg9amFtNUjaWNMfOalxp5q7NcnS6sV69eRUlJCTw9PQ3aPT09cfz48SqtY9euXVi5ciU6duyovH+7bNkyBAYGltlfq9VCq9UatWs0mvvagfTLW8JOCDCvuVlS3nszWkpmwLLGGGBec7vfvA9MYVVDt27doNPpajsGERE9JGr9quCKuLm5wdraGjk5OQbtOTk5aNy4cS2lIiIiKl+dLqy2trYICQlBRkaG0qbT6ZCRkYEuXbrUYjIiIqKy1fqp4Pz8fJw6dUqZzsrKwqFDh+Dq6gofHx/ExcUhOjoaoaGhCAsLQ0pKCgoKCpSrhImIiOqSWi+s+/btQ48ePZRp/RW50dHRWLJkCYYMGYIrV65g8uTJyM7ORnBwMDZt2mR0QRMREVFdUOuFNSIiotL7Q2NjYxEbG1tDiYiIiExXp99jJSIisjQsrERERCpiYSUiIlIRCysREZGKWFiJiIhUZNJVwVlZWdi5cyf++OMP3Lp1C+7u7ujUqRO6dOkCOzs7tTMSERFZjGoV1uXLl2PmzJnYt28fPD094eXlBXt7e1y7dg2nT5+GnZ0dhg0bhrfffhu+vr7mykxERFRnVbmwdurUCba2toiJicFXX30Fb29vg/mFhYXYvXs3VqxYgdDQUKSmpuL//b//p3pgIiKiuqzKhfXDDz9EVFRUufO1Wi0iIiIQERGBadOm4cyZM2rkIyIisihVLqwVFdXSGjVqhEaNGpkUiIiIyJLd90ca3rlzB0VFRQZtzs7O97taIiIii2TS7Ta3bt1CbGwsPDw84ODggIYNGxr8EBERPaxMKqzjx4/HDz/8gLlz50Kr1WLhwoVITEyEl5cXli5dqnZGIiIii2HSqeBvvvkGS5cuRUREBIYPH47u3bsjICAAvr6+WL58OYYNG6Z2TiIiIotg0hHrtWvX0KJFCwB/vZ967do1AEC3bt2wY8cO9dIRERFZGJMKa4sWLZCVlQUAaNOmDVatWgXgryPZBg0aqBaOiIjI0phUWIcPH47Dhw8DACZOnIg5c+bAzs4OY8eOxfjx41UNSEREZElMeo917Nixyu+RkZE4fvw49u/fj4CAAHTs2FG1cERERJamWoVVp9Ph448/xvr161FUVIRevXohISEBvr6+/GxgIiIiVPNU8LRp0/DOO+/A0dERTZs2xcyZMzF69GhzZSMiIrI41SqsS5cuRWpqKjZv3oz09HR88803WL58OXQ6nbnyERERWZRqFdazZ8+if//+ynRkZCQ0Gg0uXryoejAiIiJLVK3CWlxcbPRF5jY2Nrh7966qoYiIiCxVtS5eEhHExMRAq9UqbXfu3MGoUaPg4OCgtK1du1a9hERERBakWoU1OjraqO3FF19ULQwREZGlq1ZhXbx4sblyEBERPRBM+uQlIiIiKluVC+uoUaNw/vz5KvVduXIlli9fbnIoIiIiS1XlU8Hu7u5o3749Hn/8cQwYMAChoaHw8vKCnZ0drl+/jmPHjuGnn37CihUr4OXlhfnz55szNxERUZ1U5cI6depUxMbGYuHChUhNTcWxY8cM5js5OSEyMhLz589H3759VQ9KRERkCap18ZKnpyfeffddvPvuu7h+/TrOnj2L27dvw83NDf7+/tBoNObKSUREZBFM+nYbAGjYsCEaNmyoZhYiIiKLx6uCiYiIVMTCSkREpCIWViIiIhWxsBIREanI5MJaXFyMrVu3Yt68ebh58yYA4OLFi8jPz1ctHBERkaUx6argP/74A3379sXZs2dRWFiI3r17w8nJCcnJySgsLERaWpraOYmIiCyCSUesb731FkJDQ3H9+nXY29sr7YMGDUJGRoZq4YiIiCyNSUesO3fuxM8//wxbW1uDdj8/P1y4cEGVYERERJbIpCNWnU6HkpISo/bz58/DycnpvkMRERFZKpMKa58+fZCSkqJMazQa5OfnIyEhAf3791crGxERkcUxqbB+8skn2LVrF9q1a4c7d+5g6NChymng5ORktTPet2+//RatW7dGy5YtsXDhwtqOQ0REDzCT3mP19vbG4cOHsXLlShw+fBj5+fkYMWIEhg0bZnAxU11QXFyMuLg4bNu2DS4uLggJCcGgQYPQqFGj2o5GREQPoGoX1rt376JNmzb49ttvMWzYMAwbNswcuVSzZ88etG/fHk2bNgUA9OvXD1u2bMELL7xQy8mIiOhBVO1TwTY2Nrhz5445spRpx44dGDBgALy8vKDRaJCenm7UZ86cOfDz84OdnR06d+6MPXv2KPMuXryoFFUAaNq0Ka9cJiIiszHpPdbRo0cjOTkZxcXFaucxUlBQgKCgIMyZM6fM+StXrkRcXBwSEhJw4MABBAUFISoqCpcvXzZ7NiIiotJMeo917969yMjIwJYtWxAYGAgHBweD+WvXrlUlHPDXqdt+/fqVO3/69OkYOXIkhg8fDgBIS0vDhg0bsGjRIkycOBFeXl4GR6gXLlxAWFhYuesrLCxEYWGhMp2XlwcAEBGIiEmPQb+sqcvXNOY1L0vMe+/vlpDbEseYec1HjbzVWdakwtqgQQM8++yzpiyqqqKiIuzfvx/x8fFKm5WVFSIjI7F7924AQFhYGI4ePYoLFy7AxcUFGzduxKRJk8pdZ1JSEhITE43ac3Nz76uw6j9DWaPRmLSOmsS85mVpeW8X/d8963m5eSjWmvSyUaMsbYyZ17zUyKs/yKoKk54hixcvNmUx1V29ehUlJSXw9PQ0aPf09MTx48cBAPXq1cOnn36KHj16QKfTYcKECRVeERwfH4+4uDhlOi8vD97e3nBxcYGzs7NJOfUF2cXFxWJ2QoB5zcXS8toU/d9bPs4uznDQ2tRimqqxtDFmXvNSI291lqv7/3qq4Omnn8bTTz9dpb5arRZardaoXaPR3NcOpF/eEnZCgHnNzZLy3pvRUjIDljXGAPOa2/3mNXthbd68eYUb+f33301ZbbW5ubnB2toaOTk5Bu05OTlo3LhxjWQgIiK6l0mFdcyYMQbTd+/excGDB7Fp0yaMHz9ejVxVYmtri5CQEGRkZGDgwIEA/voc44yMDMTGxtZYDiIiIj2TCutbb71VZvucOXOwb9+++wpUWn5+Pk6dOqVMZ2Vl4dChQ3B1dYWPjw/i4uIQHR2N0NBQhIWFISUlBQUFBcpVwkRERDVJ1fdY+/Xrh/j4eFUvbtq3bx969OihTOsvLIqOjsaSJUswZMgQXLlyBZMnT0Z2djaCg4OxadMmowuaiIiIaoKqhXXNmjVwdXVVc5WIiIio9DaX2NhYnvolIqI6waTC2qlTJ4OLl0QE2dnZuHLlClJTU1ULR0REZGlMKqz6C4X0rKys4O7ujoiICLRp00aNXERERBbJpMKakJCgdg4iIqIHgkkfwn/gwAEcOXJEmf76668xcOBAvPPOOygqKlItHBERkaUxqbC+/vrrOHnyJIC/PgxiyJAhqF+/PlavXo0JEyaoGpCIiMiSmFRYT548ieDgYADA6tWrER4ejv/85z9YsmQJvvrqKzXzERERWRSTCquIQKfTAQC2bt2K/v37AwC8vb1x9epV9dIRERFZGJMKa2hoKD744AMsW7YMP/74I5588kkAf30qEj+YgYiIHmYmFdaUlBQcOHAAsbGxePfddxEQEADgrw+I6Nq1q6oBiYiILIlJt9t07NjR4KpgvY8//hjW1tb3HYqIiMhSmXTEeu7cOZw/f16Z3rNnD8aMGYOlS5fCxqbufwkyERGRuZhUWIcOHYpt27YBALKzs9G7d2/s2bMH7777Lt5//31VAxIREVkSkwrr0aNHERYWBgBYtWoVOnTogJ9//hnLly/HkiVL1MxHRERkUUwqrHfv3oVWqwXw1+02Tz/9NACgTZs2uHTpknrpiIiILIxJhbV9+/ZIS0vDzp078f3336Nv374AgIsXL6JRo0aqBiQiIrIkJhXW5ORkzJs3DxEREXjhhRcQFBQEAFi/fr1yipiIiOhhZNLtNhEREbh69Sry8vLQsGFDpf21115D/fr1VQtHRERkaUw6YgX++ljD/fv3Y968ebh58yYAwNbWloWViIgeaiYdsf7xxx/o27cvzp49i8LCQvTu3RtOTk5ITk5GYWEh0tLS1M5JRERkEUw6Yn3rrbcQGhqK69evw97eXmkfNGgQMjIyVAtHRERkaUw6Yt25cyd+/vln2NraGrT7+fnhwoULqgQjIiKyRCYdsep0OpSUlBi1nz9/Hk5OTvcdioiIyFKZVFj79OmDlJQUZVqj0SA/Px8JCQnKd7MSERE9jEw6FfzJJ5+gb9++aNeuHe7cuYOhQ4ciMzMTbm5u+PLLL9XOSEREZDFMKqze3t44fPgwVq5cicOHDyM/Px8jRozAsGHDDC5mIiIiethUu7DevXsXbdq0wbfffothw4Zh2LBh5shFRERkkar9HquNjQ3u3LljjixEREQWz6SLl0aPHo3k5GQUFxernYeIiMiimfQe6969e5GRkYEtW7YgMDAQDg4OBvPXrl2rSjgiIiJLY1JhbdCgAZ599lm1sxAREVk8kwrr4sWL1c5BRET0QKjWe6w6nQ7Jycl4/PHH8eijj2LixIm4ffu2ubIRERFZnGoV1mnTpuGdd96Bo6MjmjZtipkzZ2L06NHmykZERGRxqlVYly5ditTUVGzevBnp6en45ptvsHz5cuh0OnPlIyIisijVKqxnz541+CzgyMhIaDQaXLx4UfVgRERElqhahbW4uBh2dnYGbTY2Nrh7966qoYiIiCxVta4KFhHExMRAq9UqbXfu3MGoUaMM7mXlfaxERPSwqlZhjY6ONmp78cUXVQtDRERk6apVWHn/KhERUcVM+qxgIiIiKhsLKxERkYpYWImIiFTEwkpERKSiB76wnjt3DhEREWjXrh06duyI1atX13YkIiJ6gJn07TaWpF69ekhJSUFwcDCys7MREhKC/v37G32HLBERkRoe+MLapEkTNGnSBADQuHFjuLm54dq1ayysRERkFnX+VPCOHTswYMAAeHl5QaPRID093ajPnDlz4OfnBzs7O3Tu3Bl79uwpc1379+9HSUkJvL29zZyaiIgeVnX+iLWgoABBQUF45ZVXMHjwYKP5K1euRFxcHNLS0tC5c2ekpKQgKioKJ06cgIeHh9Lv2rVrePnll7FgwYIKt1dYWIjCwkJlOi8vD8BfH+coIiY9Bv2ypi5f05jXvCwx772/W0JuSxxj5jUfNfJWZ9k6X1j79euHfv36lTt/+vTpGDlyJIYPHw4ASEtLw4YNG7Bo0SJMnDgRwF/FcuDAgZg4cSK6du1a4faSkpKQmJho1J6bm3tfhTU/Px8AoNFoTFpHTWJe87K0vLeLSpTf83LzUKyt8y8bFjfGzGteauTVH2RVRd1/hlSgqKgI+/fvR3x8vNJmZWWFyMhI7N69G8D/fXFAz5498dJLL1W6zvj4eMTFxSnTeXl58Pb2houLC5ydnU3KqS/ILi4uFrMTAsxrLpaW16aoWPnd2cUZDlqbWkxTNZY2xsxrXmrkrc5yFl1Yr169ipKSEnh6ehq0e3p64vjx4wCAXbt2YeXKlejYsaPy/uyyZcsQGBhY5jq1Wq3Bt/foaTSa+9qB9Mtbwk4IMK+5WVLeezNaSmbAssYYYF5zu9+8D01hrYpu3bpBp9PVdgwiInpI1Pmrgivi5uYGa2tr5OTkGLTn5OSgcePGtZSKiIgeZhZdWG1tbRESEoKMjAylTafTISMjA126dKnFZERE9LCq86eC8/PzcerUKWU6KysLhw4dgqurK3x8fBAXF4fo6GiEhoYiLCwMKSkpKCgoUK4SJiIiqkl1vrDu27cPPXr0UKb1V+xGR0djyZIlGDJkCK5cuYLJkycjOzsbwcHB2LRpk9EFTURERDWhzhfWiIiISu8fjY2NRWxsbA0lIiIiKp9Fv8dKRERU17CwEhERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFbGwEhERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFbGwEhERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFbGwEhERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFbGwEhERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFbGwEhERqeihKay3bt2Cr68vxo0bV9tRiIjoAfbQFNZp06bhscceq+0YRET0gHsoCmtmZiaOHz+Ofv361XYUIiJ6wNX5wrpjxw4MGDAAXl5e0Gg0SE9PN+ozZ84c+Pn5wc7ODp07d8aePXsM5o8bNw5JSUk1lJiIiB5mdb6wFhQUICgoCHPmzClz/sqVKxEXF4eEhAQcOHAAQUFBiIqKwuXLlwEAX3/9NVq1aoVWrVrVZGwiInpI1avtAJXp169fhadwp0+fjpEjR2L48OEAgLS0NGzYsAGLFi3CxIkT8csvv2DFihVYvXo18vPzcffuXTg7O2Py5Mllrq+wsBCFhYXKdF5eHgBARCAiJj0G/bKmLl/TmNe8LDHvvb9bQm5LHGPmNR818lZn2TpfWCtSVFSE/fv3Iz4+XmmzsrJCZGQkdu/eDQBISkpSTgMvWbIER48eLbeo6vsnJiYatefm5t5XYc3PzwcAaDQak9ZRk5jXvCwt7+2iEuX3vNw8FGvr/suGpY0x85qXGnn1B1lVUfefIRW4evUqSkpK4OnpadDu6emJ48ePm7TO+Ph4xMXFKdN5eXnw9vaGi4sLnJ2dTVqnviC7uLhYzE4IMK+5WFpem6Ji5XdnF2c4aG1qMU3VWNoYM695qZG3OstZdGGtrpiYmEr7aLVaaLVao3aNRnNfO5B+eUvYCQHmNTdLyntvRkvJDFjWGAPMa273m7c6y9X5i5cq4ubmBmtra+Tk5Bi05+TkoHHjxrWUioiIHmYWXVhtbW0REhKCjIwMpU2n0yEjIwNdunSpxWRERPSwqvOngvPz83Hq1CllOisrC4cOHYKrqyt8fHwQFxeH6OhohIaGIiwsDCkpKSgoKFCuEiYiIqpJdb6w7tu3Dz169FCm9RcWRUdHY8mSJRgyZAiuXLmCyZMnIzs7G8HBwdi0aZPRBU1EREQ1oc4X1oiIiEpvc4mNjUVsbGwNJSIiIiqfRb/HSkREVNewsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqqlfbAYiobrO3scb/EvsgLzcP9jbWtR2HqM5jYSWiCmk0GtS3rYe7ttbQaDS1HYeozuOpYCIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIH2lYCREBAOTl5d3XOvLy8qDRaCziI+GY17wsLS9geZmZ17wexrz6GqCvCRVhYa3EzZs3AQDe3t61nISIiGrbzZs34eLiUmEfjVSl/D7EdDodLl68CCcnp/v6T8fb2xvnzp2Ds7OzygnVx7zmZWl5AcvLzLzm9TDmFRHcvHkTXl5esLKq+F1UHrFWwsrKCs2aNVNlXc7OzhaxE+oxr3lZWl7A8jIzr3k9bHkrO1LV48VLREREKmJhJSIiUhELaw3QarVISEiAVqut7ShVwrzmZWl5AcvLzLzmxbwV48VLREREKuIRKxERkYpYWImIiFTEwkpERKQiFlYiIiIVsbBW0Y4dOzBgwAB4eXlBo9EgPT3dYH5OTg5iYmLg5eWF+vXro2/fvsjMzDToc+fOHYwePRqNGjWCo6Mjnn32WeTk5Bj0OXv2LJ588knUr18fHh4eGD9+PIqLi2s877Vr1/CPf/wDrVu3hr29PXx8fPDmm28iNzfXYD36z96892fFihU1nhcAIiIijLKMGjXKoE9dGd8zZ86UOXYajQarV69W+qk1vklJSXj00Ufh5OQEDw8PDBw4ECdOnDDoo9b+uX37djzyyCPQarUICAjAkiVLaiXv4cOH8cILL8Db2xv29vZo27YtZs6caZS1rDHOzs6u8bxA1f7edWV8lyxZUu4+fPnyZSVrTY3v/PnzERERAWdnZ2g0Gty4ccNoPdeuXcOwYcPg7OyMBg0aYMSIEcjPzzfo89///hfdu3eHnZ0dvL298dFHH1UrKwBAqEq+++47effdd2Xt2rUCQNatW6fM0+l08thjj0n37t1lz549cvz4cXnttdfEx8dH8vPzlX6jRo0Sb29vycjIkH379sljjz0mXbt2VeYXFxdLhw4dJDIyUg4ePCjfffeduLm5SXx8fI3nPXLkiAwePFjWr18vp06dkoyMDGnZsqU8++yzBtsBIIsXL5ZLly4pP7dv367xvCIi4eHhMnLkSIMsubm5yvy6NL7FxcUGOS9duiSJiYni6OgoN2/eVNal1vhGRUXJ4sWL5ejRo3Lo0CHp37+/WfbP33//XerXry9xcXFy7NgxmTVrllhbW8umTZtqPO/nn38ub775pmzfvl1Onz4ty5YtE3t7e5k1a5bSZ9u2bQJATpw4YTDGJSUlNZ5XpPK/d10a31u3bhntw1FRURIeHq70qcnxnTFjhiQlJUlSUpIAkOvXrxutp2/fvhIUFCS//PKL7Ny5UwICAuSFF15Q5ufm5oqnp6cMGzZMjh49Kl9++aXY29vLvHnzqpWXhdUEpV9IT5w4IQDk6NGjSltJSYm4u7vLggULRETkxo0bYmNjI6tXr1b6/PbbbwJAdu/eLSJ/vVhbWVlJdna20mfu3Lni7OwshYWFNZq3LKtWrRJbW1u5e/duuetWg6l5w8PD5a233ip3vXV9fIODg+WVV16pcN1quXz5sgCQH3/8UUTU2z8nTJgg7du3N9jWkCFDJCoqqsbzluWNN96QHj16KNP6F/6yXoRrI29lf++6PL6XL18WGxsbWbp0qdJWU+N7r/K2eezYMQEge/fuVdo2btwoGo1GLly4ICIiqamp0rBhQ4PXg7fffltat25drXw8FayCwsJCAICdnZ3SZmVlBa1Wi59++gkAsH//fty9exeRkZFKnzZt2sDHxwe7d+8GAOzevRuBgYHw9PRU+kRFRSEvLw//+9//ajRvWXJzc+Hs7Ix69Qw/Ynr06NFwc3NDWFgYFi1aVKWvVTJX3uXLl8PNzQ0dOnRAfHw8bt26pcyry+O7f/9+HDp0CCNGjDCaZ47x1Z/Sd3V1Vbavxv65e/dug3Xo++jXUZN5y1uPfh33Cg4ORpMmTdC7d2/s2rXrvrLeb96K/t51eXyXLl2K+vXr47nnnjOaZ+7xrYrdu3ejQYMGCA0NVdoiIyNhZWWFX3/9VenzxBNPwNbWVukTFRWFEydO4Pr161XeFgurCvQ7XHx8PK5fv46ioiIkJyfj/PnzuHTpEgAgOzsbtra2aNCggcGynp6eyvsN2dnZBi9a+vn6eTWZt7SrV69i6tSpeO211wza33//faxatQrff/89nn32WbzxxhuYNWuWalmrk3fo0KH44osvsG3bNsTHx2PZsmV48cUXlfl1eXw///xztG3bFl27djVoN8f46nQ6jBkzBo8//jg6dOgAQL39s7w+eXl5uH37do3mLe3nn3/GypUrDfbhJk2aIC0tDV999RW++uoreHt7IyIiAgcOHDAp6/3mrezvXZfH9/PPP8fQoUNhb2+vtNXU+FZFdnY2PDw8DNrq1asHV1dX1V+D+e02KrCxscHatWsxYsQIuLq6wtraGpGRkejXr5/qR29qqG7evLw8PPnkk2jXrh2mTJliMG/SpEnK7506dUJBQQE+/vhjvPnmmzWe994XzMDAQDRp0gS9evXC6dOn4e/vr1oetfLq3b59G//5z38MxlLPHOM7evRoHD16tMKzE3WJGnmPHj2KZ555BgkJCejTp4/S3rp1a7Ru3VqZ7tq1K06fPo0ZM2Zg2bJlNZ63Jp5Ppakxvrt378Zvv/1mNGZ1bXxrCo9YVRISEoJDhw7hxo0buHTpEjZt2oQ///wTLVq0AAA0btwYRUVFRleq5eTkoHHjxkqf0lcJ6qf1fWoqr97NmzfRt29fODk5Yd26dbCxsalwvZ07d8b58+eV06E1nbd0FgA4deoUgLo5vgCwZs0a3Lp1Cy+//HKl673f8Y2NjcW3336Lbdu2GXwdolr7Z3l9nJ2dDY5kaiKv3rFjx9CrVy+89tpreO+99yrdZlhYmLLP1Ebee5X+e9fF8QWAhQsXIjg4GCEhIZVu0xzjWxWNGzdWrlbWKy4uxrVr19R/Da7WO7IkIlW7oOTkyZNiZWUlmzdvFpH/uxhgzZo1Sp/jx4+XeXFITk6O0mfevHni7Owsd+7cqdG8In9dIffYY49JeHi4FBQUVGlbH3zwgTRs2NDkrCKm5y3tp59+EgBy+PBhEal746sXHh5udLV1eUwdX51OJ6NHjxYvLy85efKk0Xy19s8JEyZIhw4dDNb9wgsvVPviGjXyiogcPXpUPDw8ZPz48VXedmRkpAwaNKhW8pZW+u9d18ZXROTmzZvi6OhocLV1Rcwxvveq7OKlffv2KW2bN28u8+KloqIipU98fHy1L15iYa2imzdvysGDB+XgwYMCQKZPny4HDx6UP/74Q0T+umJ227Ztcvr0aUlPTxdfX18ZPHiwwTpGjRolPj4+8sMPP8i+ffukS5cu0qVLF2W+/naGPn36yKFDh2TTpk3i7u5u0u0g95s3NzdXOnfuLIGBgXLq1CmDS+WLi4tFRGT9+vWyYMECOXLkiGRmZkpqaqrUr19fJk+eXON5T506Je+//77s27dPsrKy5Ouvv5YWLVrIE088USfHVy8zM1M0Go1s3LjRaJ6a4/v3v/9dXFxcZPv27QZ/y1u3bil91Ng/9beDjB8/Xn777TeZM2eOSbeDqJH3yJEj4u7uLi+++KLBOi5fvqz0mTFjhqSnp0tmZqYcOXJE3nrrLbGyspKtW7fWeN6q/L3r0vjqLVy4UOzs7Mq88rcmx/fSpUty8OBBWbBggQCQHTt2yMGDB+XPP/9U+vTt21c6deokv/76q/z000/SsmVLg9ttbty4IZ6envLSSy/J0aNHZcWKFVK/fn3ebmMu+v+CSv9ER0eLiMjMmTOlWbNmYmNjIz4+PvLee+8Z3cJx+/ZteeONN6Rhw4ZSv359GTRokFy6dMmgz5kzZ6Rfv35ib28vbm5u8s9//tPg9paaylve8gAkKytLRP66VD04OFgcHR3FwcFBgoKCJC0trdr3qKmR9+zZs/LEE0+Iq6uraLVaCQgIkPHjxxvcxypSd8ZXLz4+Xry9vcscMzXHt7y/5eLFi5U+au2f27Ztk+DgYLG1tZUWLVoYbKMm8yYkJJS5Dl9fX6VPcnKy+Pv7i52dnbi6ukpERIT88MMPtZK3qn/vujK+el26dJGhQ4eWuZ2aHN/y/t739vnzzz/lhRdeEEdHR3F2dpbhw4cb3DcuInL48GHp1q2baLVaadq0qXz44YfVzsuvjSMiIlIRL14iIiJSEQsrERGRilhYiYiIVMTCSkREpCIWViIiIhWxsBIREamIhZWIiEhFLKxEREQqYmElIos2adIko68zrMzf/vY3fPrpp2ZKRA87FlYiFWg0mgp/Sn/d3oPAz88PKSkptZohOzsbM2fOxLvvvqu0xcTEYODAgQb91qxZAzs7O6WYvvfee5g2bZryhdlEamJhJVLBpUuXlJ+UlBQ4OzsbtI0bN662I1aJiKC4uLhGt1lUVGTysgsXLkTXrl3h6+tbYZ9hw4Zh7ty5+Oc//wkA6NChA/z9/fHFF1+YvG2i8rCwEqmgcePGyo+Liws0Go1B24oVK9C2bVvY2dmhTZs2SE1NVZY9c+YMNBoNVq1ahe7du8Pe3h6PPvooTp48ib179yI0NBSOjo7o168frly5oiynPzJLTEyEu7s7nJ2dMWrUKINCpdPpkJSUhObNm8Pe3h5BQUFYs2aNMn/79u3QaDTYuHEjQkJCoNVq8dNPP+H06dN45pln4OnpCUdHRzz66KPYunWrslxERAT++OMPjB07VjkqB4ApU6YgODjYYGxSUlLg5+dnlHvatGnw8vJSvgj73LlzeP7559GgQQO4urrimWeewZkzZyoc9xUrVmDAgAHlzv/oo4/wj3/8AytWrMDw4cMN5g0YMAArVqyocP1EpmBhJTKz5cuXY/LkyZg2bRp+++03/Otf/8KkSZPw73//26BfQkIC3nvvPRw4cAD16tXD0KFDMWHCBMycORM7d+7EqVOnMHnyZINlMjIy8Ntvv2H79u348ssvsXbtWiQmJirzk5KSsHTpUqSlpeF///sfxo4dixdffBE//vijwXomTpyIDz/8EL/99hs6duyI/Px89O/fHxkZGTh48CD69u2LAQMG4OzZswCAtWvXolmzZnj//feVo/LqyMjIwIkTJ/D999/j22+/xd27dxEVFQUnJyfs3LkTu3btgqOjI/r27VvuEe21a9dw7NgxhIaGljn/7bffxtSpU/Htt99i0KBBRvPDwsKwZ88ek780nqhc1f4+HCKq0OLFi8XFxUWZ9vf3l//85z8GfaZOnap8t2VWVpYAkIULFyrzv/zySwEgGRkZSltSUpLBFy5HR0eLq6urwZfQz507VxwdHaWkpETu3Lkj9evXl59//tlg2yNGjFC+g1L/9Xfp6emVPq727dsbfJm1r6+vzJgxw6BPQkKCBAUFGbTNmDHD4KvaoqOjxdPT0+Br9JYtWyatW7cWnU6ntBUWFoq9vX25X2av/y7cs2fPGrRHR0eLra2t0fiVdvjwYQEgZ86cKbcPkSnq1WZRJ3rQFRQU4PTp0xgxYgRGjhyptBcXF8PFxcWgb8eOHZXfPT09AQCBgYEGbZcvXzZYJigoCPXr11emu3Tpgvz8fJw7dw75+fm4desWevfubbBMUVEROnXqZNBW+qgvPz8fU6ZMwYYNG3Dp0iUUFxfj9u3byhHr/QoMDIStra0yffjwYZw6dQpOTk4G/e7cuYPTp0+XuY7bt28DAOzs7IzmdezYEVevXkVCQgLCwsLg6Oho1Mfe3h4AcOvWLZMfB1FZWFiJzCg/Px8AsGDBAnTu3NlgnrW1tcG0jY2N8rv+PcvSbTqdrtrb3rBhA5o2bWowT6vVGkw7ODgYTI8bNw7ff/89PvnkEwQEBMDe3h7PPfdcpRcaWVlZQUp9xfPdu3eN+pXeXn5+PkJCQrB8+XKjvu7u7mVuy83NDQBw/fp1oz5NmzbFmjVr0KNHD/Tt2xcbN240KtrXrl2rcP1EpmJhJTIjT09PeHl54ffff8ewYcNUX//hw4dx+/Zt5ejrl19+gaOjI7y9veHq6gqtVouzZ88iPDy8WuvdtWsXYmJilPcm8/PzjS4ksrW1RUlJiUGbu7s7srOzISLKPweHDh2qdHuPPPIIVq5cCQ8PDzg7O1cpo7+/P5ydnXHs2DG0atXKaL6vry9+/PFHpbhu2rTJoLgePXoUzZo1Uwo0kVp48RKRmSUmJiIpKQmfffYZTp48iSNHjmDx4sWYPn36fa+7qKgII0aMwLFjx/Ddd98hISEBsbGxsLKygpOTE8aNG4exY8fi3//+N06fPo0DBw5g1qxZRhdOldayZUusXbsWhw4dwuHDhzF06FCjo2U/Pz/s2LEDFy5cwNWrVwH8dbXwlStX8NFHH+H06dOYM2cONm7cWOnjGDZsGNzc3PDMM89g586dyMrKwvbt2/Hmm2/i/PnzZS5jZWWFyMhI/PTTT+Wu19vbG9u3b8fly5cRFRWFvLw8Zd7OnTvRp0+fSrMRVRcLK5GZvfrqq1i4cCEWL16MwMBAhIeHY8mSJWjevPl9r7tXr15o2bIlnnjiCQwZMgRPP/20wYdRTJ06FZMmTUJSUhLatm2Lvn37YsOGDZVue/r06WjYsCG6du2KAQMGICoqCo888ohBn/fffx9nzpyBv7+/cjq1bdu2SE1NxZw5cxAUFIQ9e/ZU6R7e+vXrY8eOHfDx8cHgwYPRtm1bjBgxAnfu3KnwCPbVV1/FihUrKjxF3qxZM2zfvh1Xr15ViuudO3eQnp5u8L43kVo0UvoNESKyCDExMbhx4wbS09NrO0qtERF07twZY8eOxQsvvFDl5ebOnYt169Zhy5YtZkxHDysesRKRxdJoNJg/f361Py3KxsYGs2bNMlMqetjxiJXIQvGIlahuYmElIiJSEU8FExERqYiFlYiISEUsrERERCpiYSUiIlIRCysREZGKWFiJiIhUxMJKRESkIhZWIiIiFf1/JEEpcgEhYecAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "plt.plot(em.temperatureProfile, em.pressureProfile)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Temperature (K)')\n",
    "plt.ylabel('Pressure (Pa)')\n",
    "plt.title('Temperature profile behind the spectrum')\n",
    "plt.grid(alpha=0.2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}