{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8779a89-888d-4aed-913f-89922c19ed83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "377d03fb-0a18-4150-a7fa-ad4c3de85f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ll13Gww8/zOTJk/njjz/cjmNMnnPu3Dni4+OpW7cu999/f65/vz+FcqeItAKiRORyEXkRz8XneUqfPn04fvw4kye7fpelMXnOrFmz+P7774mPj0cko8uvg8ufQvkMUBM4A8wHjgHdg5gpJNWtW5c777yT8ePHc+rUKbfjGJNnnDlzhqFDh3Lddddxzz33uJLBZ6FU1ZOqOkA9k4xd4zw+nRvhQk3fvn05ePAgM2fOdDuKMXnGK6+8wo8//uhabxL8O+t9hYhME5HVIvJ+2pIb4ULNzTffzHXXXceYMWNITk52O44xEe/06dMMHz6c66+/nttvd2/SV3+mKlsITMEzAlBKcOOENhGhX79+NGvWjIULFwZ8SkxjzF+9+uqrHDhwgNmzZ7vWmwT/z3pPVtXPVXVz2hL0ZCHq3nvv5aqrrmLUqFE2BJsxQXT27FkSEhJo2LAht956q6tZMi2U6ebGWSEiT+eFOXP8kS9fPvr06cP27dt599133Y5jTMSaM2cOP/74IwMHDnS1Nwnee5Rpc+O0BXrz1/EoI3bOHH+0bNmSypUrM2rUKLejGBORkpOTGTlyJPXq1aNJkyZux8n8GKWqVgMQkULnn+UWkULBDhbKChQoQM+ePenevTsbN26kYcOGbkcyJqIsWLCA//znPyxevNj13iT4d4wyo4vL89wF5+d7/PHHKV26tPUqjQmwlJQUhg0bRq1atVy5CycjmfYoReRiPNMyXCAidfnfaOTFgcK5kC2kFSlShGeeeYbBgwfz9ddfU6NGDbcjGRMRFixYwDfffMPChQtz/Z7uzHhLcScwFqgEvAAkOkss0D/40UJf165dKVy4MKNHj3Y7ijERITk5mfj4eFdGCPLG2zHKWcAsEXlIVd/KxUxho3Tp0nTq1IlJkyYRHx/vdhxjwt6CBQv49ttveeutt0KmNwn+HaNcKyIviMgmZ0kUkRJBTxYmYmNjAXjhhRdcTmJMeEvfm2zWrJnbcf7Cn0L5CnAcaOEsx4DXghkqnFSuXJnWrVszffp0jh49fzQ6Y4y/Xn/9db777jsGDx4cUr1J8K9QXqqqg1T1e2cZAlQPdrBwEhcXx8mTJ1m8eLHbUYwJS2m9yZiYmJDrTYJ/hfKUiNyQ9kRErgdsnLF0rrrqKpo1a8aSJUtISkpyO44xYWfOnDn85z//YciQISFx3eT5/CmUTwEvicheEdkLTAI6e/9I3pM2sO/06dPdjmJMWDl37hxDhw6lfv363HvvvW7HyZA/41FuVdWrgTpAHVWtq6rbgh8tvDRo0ICYmBgSExM5e/as23GMCRuzZ8/mhx9+YPDgwSHZmwT/xqMsJyKvAG+o6jERqSEiHXMhW9hp2bIlP//8M/PmzXM7ijFhITU1lYSEBK655hrXRi/3hz+73jOB94AKzvNvyYNTQfjjn//8JzExMSQkJJCamup2HGNC3qpVq/juu+/o2bNnyPYmwb9CWUZV3wRSAVQ1GT8G8BWRQiLyuYhsE5GvRGRIBuvcIiJHRWSrszyf5S0IISJCnz59+Oabb1i2bJnbcYwJeRMmTKBixYo89NBDbkfxyp9CeUJESuNMUSsiDQB/Lhg8A9zqHN+MAe5yPnu+D1U1xlnC/vaW5s2bU716dRvY1xgffvjhB9asWcPTTz9NdHS023G88qdQxgLLgUtF5GNgNp6ZGb1Sj7RrZaKdJeIrR/78+enduzeff/4569evdzuOMSFr8eLFFCpUiE6dOrkdxSd/znpvAW4GGgFPAjVVdbs/jYtIlIhsBQ4Ca1T1swxWa+jsnq8SkZr+Rw9d7dq1o1y5cjYEmzGZ+O2331izZg2PPvooZcqUcTuOT96GWSsOlFPV71Q1WURqABcA9UTkPVX91VfjqpoCxIhISWCJiNRS1Z3pVtkCVFHVJBG5G1gKXJ5Blk5AJ4By5cqFbE8tKSnpz2z33Xcf06dPZ9q0aVxxxRXuBsuG9NsS7mxbQs/cuXM5c+YMjRo1Co/tUdUMF2Aa0C7d8z3ARDyzMU7J7HNe2hsE9PKxzl48J48yXad+/foaqtatW/fn4z/++EOLFy+uLVq0cC9QDqTflnBn2xJaTpw4oWXLltXrrrvO7Sh/AWzSTOqOt13vfwKz0j0/rqrdVPVxoJavAiwiZZ2eJCJyAfAvYPd561wszjUBInItnkMBR3y1HQ5KlCjB008/zaJFi/juu+/cjmNMyHjttdc4dOhQWE337K1Q5neqbJo26R6X9KPt8sA6EdkOfIHnGOXbItJZRNJugWwO7BSRbXh6q4+c951h7dlnnyU6OpqxY8e6HcWYkJCcnMzYsWNp2LAhderUcTuO3zI9RgmkisjFqvpfAHWOLYpIRZxrKr1Rzwmfuhm8PiXd40l47h2PSBdffDHt27fn1VdfZfDgwZQvX97tSMa46s0332Tv3r1MmDAhpC8wP5+3HuUYPHN63yQixZzlZjwnXMbkSroI0KtXL5KTkxk/frzbUYxxlaqSkJBAjRo1aNq0qdtxsiTTQqmqc4HngGF4TrL8AMQDz6vqnFxJFwEuvfRSWrRoweTJk/njjz/cjmOMa1atWsX27duJi4sLuYF5ffGaVlXfVdWbVLW0qpZR1ZtVdVVuhYsUaUOwTZ482e0oxrhm5MiRVK5cmVatWrkdJcvCq6yHqZiYGO666y7Gjx/PqVM25rHJez766CM++ugjevXqFfK3K2bECmUu6du3LwcPHmTmzJluRzEm1yUkJFC6dGk6dgzPERqtUOaSm266iQYNGjBmzBiSk5PdjmNMrtmxYwdvv/02zz77LEWKFHE7TrZkqVCKyNvBChLpRIS+ffvyww8/8Oabb7odx5hck5CQQJEiRejSpYvbUbItqz3KikFJkUfce++9XHXVVSQkJNgQbCZP+OGHH1iwYAFPPvkkpUqVcjtOtmW1UH4ZlBR5RL58+ejTpw/bt2/n3XffdTuOMUE3duxY8uXLR2xsrNtRciRLhVJVOwQrSF7RsmVLKleubEOwmYj366+/8uqrr9K2bVsqVgzvnVE7mZPLChQoQM+ePfnggw/45JNP3I5jTNCMHz+eM2fOEBcX53aUHLNC6YLHH3+cUqVKWa/SRKyjR4/y8ssv07x5cy6//G9DzIYdf6ar9TmkmsmaIkWK0K1bN1asWMHOnTt9f8CYMPPyyy9z7Ngx+vXr53aUgPCnRznFmU3x6bTxJU3Ode3alcKFCzN69Gi3oxgTUCdPnmTcuHHcdddd1K37twHEwpI/c+bcADwKVAY2icjrInJ70JNFuNKlS9OpUyfmz5/Pvn373I5jTMC8+uqrHDp0KGJ6k+DnMUpV/Q4YCPTBM9HYRBHZLSIPBjNcpIuNjUVEeOGFF9yOYkxAnDt3jjFjxtCoUSNuvPFGt+MEjD/HKOuIyDhgF3ArcK+qXuU8HhfkfBGtcuXKtG7dmunTp3P48GG34xiTY6+//jo//vgj/fv3D6uBeX3xp0c5Cc9siVerahf1TF+Lqh7A08s0OdC7d29Onz7NxIkT3Y5iTI6kpqYyatQo6tSpw9133+12nIDyWihFJAr4SVXnqOrfxgezAXxz7qqrrqJZs2ZMmjSJpKQkt+MYk21Lly5l9+7dEdebBN8D96YApUWkQC7lyZP69OnD77//zrRp09yOYky2qCojRozgsssuo3nz5m7HCThvk4ul2Qd8LCLLgRNpL6qqnYEIkOuuu47GjRuTmJhIly5dKFiwoNuRjMmSNWvWsHnzZqZPn05UVJTbcQLOn2OUB4C3nXWLOUvRYIbKi/r27cuBAweYN2+e21GMybIRI0ZQsWJF2rRp43vlMORPj/JrVV2Y/gUReThIefKs22+/nbp16zJ69Gjatm0bkf9XNpHp448/ZsOGDYwbNy5i94b86VFmdNVo5FxJGiLSBvb95ptvWLp0qdtxjPHbiBEjKFOmDE888YTbUYIm00IpIk1E5EWgoohMTLfMBHzOZSAihZxbH7eJyFciMiSDdcRpc4+IbBeRejnamjD30EMPcdlll9nAviZsfPnll6xcuZLu3buH7TQP/vDWozwAbAJOA5vTLcuBO/1o+wxwq6peDcQAd4lIg/PWaQJc7iydgDw9n2tUVBRxcXF88cUXvP/++27HMcankSNHUrx48bCe5sEfmRZKVd2mqrOAy4A3gU9VdZaqLlbV3301rB5pFwZGO8v53aT7gdnOup8CJUWkfLa2JEI89thjlC9f3oZgMyFv9+7dLFq0iK5du1KyZEm34wSVP8co7wK2Au8CiEiMc6mQTyISJSJbgYPAGlX97LxVKgI/pXu+nzw+L0/BggXp0aMH//73v9m0aZPbcYzJ1KhRoyhUqBDdu3d3O0rQ+XPWezBwLbAeQFW3ikhVfxp3LliPcYZnWyIitVQ1/QCMGV2+/7eDcyLSCc+uOeXKlWP9+vX+fH2uS0pKCki2GjVqULRoUXr27MmQIX87tJsrArUtocC2JfD++9//MmfOHB544AG++uqrbLURKtviF1X1ugCfOT+/TPfadl+fy6CdQUCv816bCrRM9/wboLy3durXr6+hat26dQFra8CAASoiunv37oC1mRWB3Ba32bYE3lNPPaXR0dH6008/ZbuNUNmWNMAmzaTu+LPrvVNEWgFRInK5cybc52QvIlI2baBfEbkA+Bew+7zVlgOPOWe/GwBHVfUXPzJFvG7dulGwYEHGjBnjdhRj/uLAgQO88sortG/fnkqVKrkdJ1f4UyifAWriOYs9HzgGdPfjc+WBdSKyHfgCzzHKt0Wks4h0dtZZCXwP7AGmA09nLX7kuuiii+jYsSOzZ8/m559/djuOMX9KTEwkJSWFPn36uB0l1/gzwvlJVR2gqv9U1Wucx6f9+Nx2Va2rqnVUtZaqxjuvT1HVKc5jVc/QbZeqam1VtbMX6fTs2ZPU1FTGjbNhP01oOHz4MFOmTKFly5ZUr17d7Ti5xp+Be68RkcUissW5KHy700s0QVatWjUeeeQRpkyZwm+//eZ2HGN46aWXOHnyJH379nU7Sq7yZ9d7HjATeAi4N91ickGfPn04ceIEL730kttRTB536tQpJk2axD333EPNmjXdjpOr/CmUh1R1uar+oKr70pagJzMA1K5dm3vuuYcJEyZw4sQJ3x8wJkhmz57N4cOH6d27t9tRcp0/hXKQiMwQkZYi8mDaEvRk5k/9+vXjyJEjvPLKK25HMXlUSkoKiYmJXHPNNdx0001ux8l1/hTK9jj3avO/3e6mQcxkznP99ddzww03MHbsWM6ePet2HJMHrVixgu+++45evXpF3DQP/vCnUF7tnO1uq6rtnaVD0JOZv+jXrx8//fQT8+fPdzuKyYPGjBlD1apVeeihh9yO4gp/CuWnIlIj6EmMV02aNKF27dqMGjWK1NRUt+OYPOTjjz/mk08+oUePHuTP789dz5HHn0J5A7BVRL5xLg3aYZcH5b60gX13797N8uV+jUliTECMGTOGUqVK0bFjR7ejuMbf0YMuB+7gf8cn7fIgF7Ro0YJq1aoxcuRIG9jX5Irdu3ezbNkyunbtGtED8/riz505+4CS/O9ETkm7PMgd+fPnp3fv3nz++efhM+qKCWtjx46lUKFCdO3a1e0orvLnzpxn8Vx0fpGzzBWRZ4IdzGSsffv2lCtXzgb2NUH3yy+/MGfOHDp06EDZsmXdjuMqf3a9OwLXqerzqvo80ACI3FmEQlyhQoXo0aMHq1evZvPmzW7HMRFswoQJJCcnExsb63YU1/lTKAVISfc8hYwH3DW5pHPnzpQoUYKEhAS3o5gIdezYMSZPnkzz5s259NJL3Y7jOn8K5WvAZyIyWEQGA58CdouIi0qUKMHTTz/NokWL+Pbbb92OYyLQtGnTOHbsGHFxcW5HCQn+nMx5Ac/dOb8BvwPtVXV8kHMZH5599lkKFizI6NGj3Y5iIsyZM2cYN24ct912G/Xr13c7Tkjwp0cJ8AOeOXM+xDMdd56efzsUlCtXjg4dOtjAvibgXn/9dQ4cOGC9yXT8Oes9FNgOTAQSnWVskHMZP/Tq1YvU1FReeOEFt6OYCJGamsqYMWOIiYnh9ttvdztOyPCnR9kCuFRVb1HVxs5ya7CDGd/SBvadOnWqDexrAmLFihXs2rWLuLi4PDn4RWb8mlwMzwXnJgT17duXEydOMGnSJLejmDCnqiQkJFCtWjUefvhht+OEFH8K5UjgSxF5T0SWpy3BDmb8U6tWLe69914mTpxoA/uaHPnoo4/YuHEjPXv2zLODX2TGn0I5C0gARvG/Y5SJwQxlsiZtYN/p06e7HcWEsYSEBMqUKUP79u3djhJy/CmUh1V1oqquU9UNaUvQkxm/NWzYkJtuuonExEQb2Ndky44dO3jnnXfo1q0bhQsXdjtOyPGnUG4WkZEi0lBE6qUtQU9msqRfv37s37+fuXPnuh3FhKHRo0dTpEgRunTp4naUkOTPgYi6zs8G6V5TwM58h5A777yTunXrkpCQQNu2bYmKinI7kgkT+/btY/78+XTr1o1SpUq5HSckee1RikgUsDzdZUF+Xx4kIpVFZJ2I7BKRr5xRiM5f5xYROSoiW53l+RxsS56WNrDvt99+y5IlS9yOY8JIYmIiIkKPHj3cjhKyvBZKVU0B7stm28lAT1W9Ck9vtEsmU0p8qKoxzhKfze8ywEMPPcTll19uA/savx0+fJgZM2bQunVrKleu7HackOXPMcpPRGSSiNyYlWOUqvqLqm5xHh8HdgEVc5jXeBEVFUVcXBxbtmxhzZo1bscxYeDFF1/k1KlTdruiD/4UykZATSCebN7CKCJV8Rzr/CyDtxuKyDYRWSUiNbPSrvm7Nm3aUKFCBUaOHOl2FBPi0m5UuP/++7nqqqvcjhPSJNi7aCJSFNgADFfVxee9VxxIVdUkEbkbmKCql2fQRiegE0C5cuXqL1iwIKiZsyspKYmiRYu6HYM333yTyZMn89JLL1GjRvYm0AyVbQkE25aMLVmyhIkTJ/Liiy9Sq1atgLSZFaH2d2ncuPFmVb0mwzdV1esClMMz/uQq53kNoKOvzznrRgPvAbF+rr8XKONtnfr162uoWrdundsRVFX1+PHjWqpUKb3vvvuy3UaobEsg2Lb8XXJyslavXl0bNGgQkPayI9T+LsAmzaTu+LPrPdMpdhWc598C3X19SDx31L8C7FLPmJYZrXOxsx4ici2eQwFH/MhkvChatCjdunVj+fLl7Ny50+04JgQtXbqU77//np49e7odJSz4UyjLqOqbQCqAqibz16khMnM90Aa4Nd3lP3eLSGcR6eys0xzYKSLb8Azj9ohT2U0OPfPMMxQpUsQmITMZSkxMpFq1ajzwwANuRwkL/lxwfkJESuO5yBwRaQAc9fUhVf0IH3PrqOokwIa9CYJSpUrRuXNnxo8fT3x8PNWrV3c7kgkRGzduZOPGjUycONFuTPCTPz3KWGA5cKmIfAzMBmy62jAQGxtLVFQUY8aMcTuKCSGJiYmULFnSBr/IAn/mzNkC3IznMqEngZqquj3YwUzOVahQgbZt2/Laa6/xyy+/uB3HhID//Oc/LFmyhM6dO4fUGedQ5++cOdcCVwP1gJYi8ljwIplAiouL49y5c4wbN87tKCYEjB8/nqioKJ55xnYKs8KfOXPm4LnA/Abgn86S8bVGJuRcdtll/N///R+TJ0/m999/dzuOcdFvv/3Gq6++SqtWrahQoYLvD5g/+dOjvAa4XlWfVtVnnKVbsIOZwOnbty9JSUk2XUQeN3XqVE6ePElsbKzbUcKOv3PmXBzsICZ46tSpQ9OmTZkwYYJNF5FHnT17lhdffJHbb7+dOnXquB0n7GRaKEVkhTM3Thnga5szJ7zZdBF52/z58/nll1/sAvNs8nYdpc3dHUEaNWrEzTffzNixY3nqqacoWLCg25FMLlFVEhMTqVWrFnfccYfbccKSt0I5QFXttxpB+vfvz5133smcOXN4/PHH3Y5jcsmaNWvYsWMHM2fOtLm6s8nbMcoyuZbC5Irbb7+d+vXrk5CQQEqKP3ehmkgwduxYKlSoQMuWLd2OEra8FcqSIvJgZkuuJTQBIyL079+fPXv2sGjRIrfjmFywbds21qxZwzPPPEOBAgXcjhO2vO16lwCakvH92goszuB1E+KaNWvGVVddxYgRI2jRooXtikW4xMREihQpwpNPPul2lLDmrVDuU9UOuZbE5Ip8+fLRt29f2rZty8qVK7nnnnvcjmSCZP/+/cyfP5+nn36aCy+80O04Yc3brrd1NSJUy5YtqVq1KsOHD7dJyCLYhAkTUFWbXTEAvBVKn/dzi+23haXo6Gji4uLYuHEjGzZscDuOCYKjR48ydepUHn74YapWrep2nLDnrVC+KCLPiMgl6V8UkQIicquIzALaBjeeCZb27dtTrlw5RowY4XYUEwTTp0/n+PHjdoF5gHgrlHfhGcl8vogcEJGvReR74DugJTBOVWfmQkYTBIUKFaJnz56sWbOGzz//3O04JoDOnj3L+PHjueWWW7jmGhu/JhAyLZSqelpVX1bV64EqwG1APVWtoqpPqOrW3AppgqNz585ceOGF1quMMG+88QY///wzvXr1cjtKxPBrPEpVPaeqv6jqH0HOY3JRsWLF6NatG8uWLWPHjh1uxzEBoKqMGTOGGjVq0KRJE7fjRAx/B+41Eapbt24ULVrUJiGLEKtXr2bHjh307t2bfPnsP+9Asd9kHleqVCmeeuopFixYwJ49e9yOY3JozJgxVKhQgVatWrkdJaJ4G2ZtdW4GMe7p0aMH0dHRJCQkuB3F5MCWLVtYu3Ytzz77rN2uGGDeepRlcy2FcVX58uXp2LEjs2bN4qeffnI7jsmmsWPHUqxYMbtdMQi8FcoSNihG3hEXF4eqMnasDUMajvbu3cubb75Jp06dKFGihNtxIo7XQolnUIx7M1ia+mpYRCqLyDoR2SUiX4nIsxmsIyIyUUT2iMh2EamXvc0wOVWlShXatGnDtGnT+PXXX92OY7Jo3Lhx5MuXj+7du7sdJSIFc1CMZKCnqm4RkWLAZhFZo6pfp1unCXC5s1wHTHZ+Ghf07duXWbNmMW7cOO666y634xg/HTlyhBkzZvDoo49SqVIlt+NEpKANiuFcd7nFeXwc2AVUPG+1+4HZ6vEpnjEwy+fke032XXHFFTz88MO89NJLHDt2zO04xk8vv/wyJ0+etAvMg8hboWwTqC8RkapAXeCz896qCKQ/e7CfvxdTk4v69+9PUlISS5YscTuK8cOpU6eYOHEiTZs2pWbNmm7HiViZ7nqr6s5AfIGIFAXeArqr6vndlMwGBT6/jU5AJ4By5cqxfv36QEQLuKSkpJDNlhWNGjVi0aJFPPzwwxQuXNjtODkWKX8X+Pu2LF26lMOHD/Ovf/0r7LYxrP4uqhq0BYgG3gNiM3l/KtAy3fNvgPLe2qxfv76GqnXr1rkdISA+++wzBTQhIcHtKAERKX8X1b9uy7lz57RatWraoEEDTU1NdS9UNoXa3wXYpJnUHW8XnK91fmbrKmRnrMpXgF2q+kImqy0HHnPOfjcAjqrqL9n5PhM41157Lddccw2JiYmcOnXK7TgmE4sWLeKHH36gT58+NqVHkHk7RlleRG4G7hORuiJSL/3iR9vX4znOeauIbHWWu0Wks4h0dtZZCXwP7AGmA0/nZGNM4LRp04aDBw8yY8YMt6OYDKgqCQkJXHnlldx3331ux4l43i4Peh7oC1QCzu8RKnCrt4ZV9SN8nDl3urtdfMc0ua1OnTrcdNNNJCQk0KlTJwoWLOh2JJPOv//9b7Zu3cqMGTNs8Itc4G08ykWq2gQYraqNz1u8FkkTGQYOHMjPP//MrFmz3I5izpOQkECFChVo3bq121HyBJ//K1LVoSJyn4iMdRafd+WYyPCvf/2L6667jpEjR3Lu3Dm34xjHpk2bWLt2Ld27d7eefi7xWShFZCTwLPC1szzrvGYinIgwcOBA9u7dy7x589yOYxwJCQmUKFHCBr/IRf4c3LgHuF1VX1XVV/HMpWOTQecR99xzDzExMYwYMYKUlBS34+R5P/30E2+99RZdunShePHibsfJM/w9Clwy3WMbmiQPSetVfvfdd7z55ptux8nz3njjDQoWLEi3bt3cjpKn+FMoRwJfishMZ4razYDNRpWHPPDAA9SsWZNhw4aRmprqdpw868CBA6xevfrPqYZN7vHnZM58oAGw2FkaquqCYAczoSNfvnwMGDCAr7/+msWLF7sdJ88aN24cKSkpNviFC/ydhfEXVV2uqstU9b/BDmVCT4sWLbjiiisYOnSo9Spd8PvvvzNlyhRuueUWqlev7nacPMeuVDV+iYqKYsCAAWzfvp0VK1a4HSfPmTRpEklJSTZpmEusUBq/tWrViurVqzN06NC0QUxMLjhx4gQTJkzgnnvu4dJLL3U7Tp7kz3WUc/x5zUS+/PnzM2DAADZv3szKlSvdjpNnzJgxgyNHjtCvXz+3o+RZ/vQo/zIaqIhEAfWDE8eEujZt2lClShXi4+OtV5kLzp49y9ixY7nxxhu5/vrr3Y6TZ3kbZq2fiBwH6ojIMWc5DhwEluVaQhNSoqOj6d+/P59//jmrV9vU78E2d+5c9u/fb71Jl3kbFGOkqhYDxqhqcWcppqqlVdX+anlYu3btqFy5MkOGDLFeZRClpKQwatQo6tata5O9ucxbj/IfzsOF549FadPK5m0FChSgX79+bNy4kbVr17odJ2K99dZbfPfdd/Tv398G5nWZt2OUsc7PxAyWsUHOZUJchw4dqFixovUqg0RVGTFiBP/4xz948MEH3Y6T53kbuHeN87Ojqn6fG2FM+ChYsCD9+vWja9eurFu3jltvtSFKA2nlypVs27aNmTNn2sC8IcDbXyDtOOSi3Ahiwk/Hjh2pWLEigwcPtl5lAKkqw4cP55JLLrELzEOEt0J5RETWAdVEZPn5S24FNKGrUKFC9OvXjw8//JB169a5HSdirF+/no0bN9K3b1+io6PdjmPwXijvwdOrPEzGxymNsV5lEAwdOpTy5cvTvn17t6MYh7fLg86q6qdAI1XdoKobgA+BL53HxlivMsA+/vhj1q1bR+/evSlUqJDbcYzDn6PEE0SkuIgUwTMVxDci0jvIuUwYSetVDho0yHqVOTR8+HDKlClDp06d3I5i0vGnUNZQ1WNAMzzzcF+CZ75uYwBPr7J///589NFHdl1lDmzevJlVq1YRGxtLkSJF3I5j0vGnUEaLSDSeQrlMVc/hmdfbmD917NiRypUr8/zzz1uvMpuGDh1KyZIl6dLFproPNf4UyqnAXqAI8IGIVAGO+fqQiLwqIgdFZGcm798iIkdFZKuzPJ+V4Ca0FCxYkP79+7Nx40a7Bzwbtm3bxrJly+jRo4dNGhaC/JkKYqKqVlTVu9VjH9DYj7Zn4pmx0ZsPVTXGWeL9aNOEsA4dOnDJJZfYscpsGDZsGMWLF7dJw0KUt3u9Wzs/Y89fAJ9/TVX9APgtcFFNqCtQoAADBw7ks88+s/Eqs+Crr75i0aJFdOvWjZIlS7odx2TAW48y7WhysUyWQGgoIttEZJWI1PS9ugl17dq1o3r16nasMguGDRtG0aJF6d69u9tRTCYkmP+YRaQq8Laq1srgveJAqqomicjdwARVvTyTdjoBnQDKlStXf8GC0JwEMikpiaJFi7odIyBysi3vvvsuCQkJxMfHc+ONNwY4WdaF8t9l3759tG/fnkceecSvS4JCeVuyKtS2pXHjxptV9ZoM31RVrwtQDXgBz1S1y9MWX59zPlsV2OnnunuBMr7Wq1+/voaqdevWuR0hYHKyLefOndMrrrhCa9WqpSkpKYELlU2h/Hdp2bKlFilSRA8dOuTX+qG8LVkVatsCbNJM6o4/Z72XOkXsRQJ4C6OIXCzOIHsici2ewwBHctqucV/+/PkZPHgwO3fuZOHChW7HCVm7du1iwYIFdO3alTJlyrgdx3jhT6E8rZ4z3+vUuZVR/biFUUTmAxuBK0Vkv4h0FJHOItLZWaU5sFNEtgETgUecqm4iQIsWLahZsybPP/88ycnJbscJSUOHDqVw4cL07NnT7SjGB2/jUaaZICKDgNXAmbQXVXWLtw+paksf708CJvkT0oSfqKgohg4dyoMPPsicOXNsgIfzpPUm4+LiKFu2rNtxjA/+FMraeG5ZvBVIdV5T57kxmWrWrBnXXHMNQ4YMoVWrVhQsWNDtSCFjyJAh1psMI/7sej8AVFfVm1W1sbNYkTQ+iQjDhw9n3759zJgxw+04IWPHjh28+eabPPvss9abDBP+FMptQMkg5zAR6vbbb+emm25i2LBhnDhxwu04IWHw4MEUK1bMepNhxJ9CWQ7YLSLv2QjnJqtEhBEjRvDf//6XF1980e04rvvyyy9ZvHgxPXr0oFSpUm7HMX7y5xjloKCnMBHt+uuvp2nTpiQkJPDkk09y4YUXuh3JNYMGDaJkyZJ2F06Y8WdQjA0ZLbkRzkSO4cOHc/ToURISEtyO4ppPP/2UFStW0KtXL7unO8zYPJgmV9SpU4dWrVoxYcIEfv75Z7fjuGLAgAGULVuWZ5991u0oJousUJpcEx8fT3JyMvHxeW9EvbVr1/L+++8zYMCAkLq/2fjH70IpItEiUldELgpmIBO5qlevTufOnXnllVfYvXu323FyjaoyYMAAKleuzJNPPul2HJMN3sajnJI29JmIlMBzmdBs4EsR8XrXjTGZee6557jgggvo16+f21FyzYoVK/jss88YNGiQzawYprz1KG9U1a+cx+2Bb1W1NlAfiAt6MhORLrroIuLi4li6dCkff/yx23GCLiUlhX79+nHFFVfQtm1bt+OYbPJWKM+me3w7nlGEUNX/BjOQiXyxsbFcfPHFxMXFRfzgvrNnz+brr79mxIgR5M/vz9V4JhR5K5R/iEhTEakLXA+8CyAi+YELciOciUxFihRhyJAhfPLJJyxZssTtOEFz6tQpnn/+ea699loefPBBt+OYHPBWKJ8EugKvAd3T9SRvA94JdjAT2Tp06ECNGjXo06cPZ8+e9f2BMDRp0iT279/P6NGjcYZeNWEq00Kpqt+q6l3qmSFxZrrX31NVu0nV5Ej+/PlJTExkz549vPzyy27HCbjffvuNESNG0KRJE26++Wa345gcyvSgiYhM9PZBVbV5NU2O3HXXXdxxxx3Ex8fz2GOPRdS9z8OGDePYsWN5+k6kSOJt17szcANwANgEbD5vMSbHxo4dy9GjRxk6dKjbUQJmz549TJo0iQ4dOlC7dm2345gA8FYoywPTgDvxDNwbjWdSsVmqOis3wpnIV7t2bR5//HEmTZrErl273I4TEH379qVAgQJ58g6kSOXtGOURVZ2iqo2BdnjGpPxKRNrkUjaTRwwbNowiRYrQo0ePsL9c6OOPP+att94iLi6O8uXLux3HBIjPWxhFpB7QHWgNrMJ2u02AlS1blsGDB/Pee+/xzjvhe0FFamoq3bt3p0KFCjYob4TxdgvjEBHZDMQCG4BrVLWjqn6da+lMntGlSxf+8Y9/0KNHD86cOeP7AyFo5syZbNq0idGjR1OkSBG345gA8tajfA4oAVwNjAS2iMh2EdkhIttzJZ3JM6Kjoxk/fjx79uzhhRdecDtOlh09epR+/frRqFEjWrVq5XYcE2De7qmqlmspjAHuvPNOHnzwQYYOHUqrVq2oUqWK25H8Fh8fz6FDh1i1apVdXB6BvJ3M2ZfRAuzHc9mQMQE3fvx4RCSsBrfdtWsXEydOpGPHjtSrV8/tOCYIvB2jLC4i/URkkojcIR7PAN8DLXw1LCKvishBEdmZyfsiIhNFZI+zS2//wgyVK1dm0KBBLFu2jLffftvtOD6pKp07d6ZYsWKMGDHC7TgmSLwdo5wDXAnsAB4HVgPNgftV9X4/2p4J3OXl/SbA5c7SCZjsR5smD+jevTs1atSga9euJCUluR3Hq9mzZ/PBBx8wevRom6M7gnkrlNVVtZ2qTgVaAtcATVV1qz8Nq+oHwG9eVrkfmK0enwIlRcQuPDMUKFCAqVOnsm/fPgYOHOh2nEwdOXKEXr160bBhQzp06OB2HBNE3grlubQHqpoC/KCqxwP43RWBn9I93++8Zgw33HADTz/9NBMnTuTTTz91O06G+vTpw++//86UKVPIl8+mn4pkktmdECKSApxIe4pnDMqTzmNV1eI+GxepCrytqrUyeO8dYKSqfuQ8XwvEqerfLmgXkU54ds8pV65c/QULFvjeMhckJSVFzMRRobAtJ06coH379hQpUoRp06YRHR2drXaCsS1ffPEFcXFxPPLII7k6D04o/F0CJdS2pXHjxptV9ZoM31TVoC1AVWBnJu9NBVqme/4NUN5Xm/Xr19dQtW7dOrcjBEyobMuKFSsU0P79+2e7jUBvy7Fjx/SSSy7RK6+8Uk+dOhXQtn0Jlb9LIITatgCbNJO64+b+wnLgMefsdwPgqKr+4mIeE4KaNm1Ku3btGDVqFJ988onbcQCIi4vjp59+4rXXXrPJwvKIoBVKEZkPbASuFJH9ItJRRDqLSGdnlZV4LjXaA0wHng5WFhPeJkyYQOXKlXnsscdcPwu+evVqpkyZQo8ePWjYsKGrWUzuCdpsR6rqdUpbp6vbJVjfbyJH8eLFmT17NrfccguxsbFMmzbNlRwHDx7kscceo0aNGhE1fqbxzU7VmbBw0003ERcXx/Tp05k/f36uf39qairt2rXjjz/+YMGCBRQuXDjXMxj3WKE0YWPo0KFcf/31PPHEE7k+yO/EiRNZtWoViYmJNmp5HmSF0oSN6Oho3njjDYoUKcJDDz2Ua8crN2zYQO/evbn//vt5+mk7lJ4XWaE0YaVixYq8/vrrfPPNN7Rp04aUlJSgft/evXtp3rw5l112GbNmzbKRgfIoK5Qm7Nx2222MGzeOpUuXBnUk8aSkJO6//36Sk5NZtmwZJUqUCNp3mdAWtLPexgRTt27d2Lt3L+PGjaNKlSr06NEjoO2fOnWKZs2asXPnTlatWsUVV1wR0PZNeLFCacLW2LFj+fHHH4mNjaVgwYIBO3549uxZmjdvztq1a5k1axZ33HFHQNo14csKpQlb+fLlY968eSQnJ9OlSxdOnDhB7969c9TmqVOnaNWqFStXrmTKlCk89thjAUprwpkdozRhrWDBgixcuJBHHnmEuLg4YmNjOXv2bLba+vnnn7nxxhtZtmwZEydOzNXBLkxosx6lCXvR0dHMnTuXMmXKMG7cOD7++GPmz59P9erV/W7j/fffp3Xr1hw/fpylS5dy3333BTGxCTfWozQRISoqihdffJGFCxfyzTffEBMTw+DBg/n9998BmLdjHlXHVyXfkHxUHV+VeTvmAbBv3z4efvhhbrvtNgoXLszGjRutSJq/sUJpIkrz5s358ssvue222xgyZIjnjPhrPeiwpAP7ju5DUfYd3UeHxR2o/WhtLrvsMt555x3i4+PZsWMHtWr9behUY6xQmshTrVo1lixZwrZt27j77rvZVnobZ/Wvxy3PcpbdFXYTGxvL7t27ee6557jgggtcSmxCnRVKE7Hq1KnDggULIJPrxFOKppCQkMAll1ySu8FM2LFCaSLeRQUvyvD1S0pYgTT+sUJpIt7j1R6ncPRfh0UrHF2Y4bcNdymRCTdWKE3E+1e5fzHt3mlUKVEFQahSogrT7p3Go7UfdTuaCRN2HaXJEx6t/agVRpNt1qM0xhgfrFAaY4wPViiNMcYHK5TGGOODFUpjjPHBCqUxxvhghdIYY3wQVXU7Q5aIyCFgn9s5MlEGOOx2iACxbQlNti3BU0VVy2b0RtgVylAmIptU9Rq3cwSCbUtosm1xh+16G2OMD1YojTHGByuUgTXN7QABZNsSmmxbXGDHKI0xxgfrURpjjA9WKLNBRAqJyOcisk1EvhKRIenee0ZEvnFeH+1mTn9kti0iEiMin4rIVhHZJCLXup3VXyISJSJfisjbzvNSIrJGRL5zfl7odkZ/ZbAtY0Rkt4hsF5ElIlLS5YhZcv72pHu9l4ioiJRxK5s3Viiz5wxwq6peDcQAd4lIAxFpDNwP1FHVmsBYFzP6K8NtAUYDQ1Q1BnjeeR4ungV2pXveF1irqpcDa53n4eL8bVkD1FLVOsC3QD9XUmXf+duDiFQGbgd+dCWRH6xQZoN6JDlPo51FgaeAUap6xlnvoEsR/eZlWxQo7rxeAjjgQrwsE5FKwD3AjHQv3w/Mch7PAprlcqxsyWhbVHW1qiY7Tz8FKrmRLTsy+dsAjAPi8PybC0lWKLPJ2YXYChwE1qjqZ8AVwI0i8pmIbBCRf7oa0k+ZbEt3YIyI/ISnZxwuPZfxeP6jS033WjlV/QXA+ZnxbGOhZzx/35b0OgCrci1Nzo3nvO0RkfuAn1V1m1uh/GGFMptUNcXZLa0EXCsitfBMrXEh0ADoDbwpIuJeSv9ksi1PAT1UtTLQA3jFxYh+EZGmwEFV3ex2lpzytS0iMgBIBublarBsymh7RKQwMADPoZ2QZnPm5JCq/iEi64G7gP3AYvVcc/W5iKTiuZ/1kIsR/XbetrTFczwJYCF/310KRdcD94nI3UAhoLiIzAV+FZHyqvqLiJTH03MOdRlui6q2FpG2QFPgNg2f6/v+tj3AHKAasM3pT1QCtojItar6X9eSZkRVbcniApQFSjqPLwA+xPMPtzMQ77x+BfATzrWqobp42ZZdwC3O67cBm93OmsXtugV423k8BujrPO4LjHY7Xw625S7ga6Cs27kCsT3nvb4XKON2vowW61FmT3lglohE4Tl88aaqvi0iBYBXRWQncBZoq86/gBCW2bb8AUwQkfzAaaCTixlzahSewyAd8ZxZfdjlPDkxCSgIrHF6YZ+qamd3I0U+uzPHGGN8sJM5xhjjgxVKY4zxwQqlMcb4YIXSGGN8sEJpjDE+WKE0xhgfrFAaY4wPViiNiTAi0kxEpovIMhG5w+08kcAKZQQRkQHO4LvbnQF3r8vi57uJyC4RCdpACyKSdN7zdiIyKZttlRSRp728n+L8HtKWqud/fyCIyAXOaFFRfq6fz7nzKe15jDNo7RXO86Ii8rOIFHWeTxWR6/3No6pLVfUJoB3wf04bBUTkA+dOK5NF9kuLECLSEM892vVU9YwzUnQBPz8rgABPA01U9YfgJQ2okngyv5zJ+6fUMyrSn4I0mFMHPIOhpPizsqqmisO5xfVJPON9po3/2QpYrv8bJ/Q6PNuZVQOBl5zvPCsia/EUzrAYcSiUWI8ycpQHDuv/Bg0+rKoHnF7UzrSVnCH3Bzuv7xKRl4EteIZRqw4sF5EezrpLRWSz00vtlK6Nx5xe6zYRmeO81lo8U0psdXpAfvWu0susjcxy4LmH+1Jn/THZ+L5YEdnpLN2d1+JEpJvzeJyIvO88vs0ZiSgjjwLL0rW7UEQmichHIrJPRG4Qkdki8q2IpA1XdwIo7PQabwSWAMWc954ApjhtXQV8m1aE/WnbqcEJwCpV3ZIu51Inq8kqt0flsCUwC1AU2IpneoCXgZud16sCO9Ot1wsY7LyeCjRI995e0o3eApRyfl4A7ARKAzWBb9LWA0oBVwErgGjntZeBxzLJmeLkTFt+xDPQQ6ZtZJQjo23z8V1LnNeSnJ/1gR1AEed39xVQF89YoguddT4EPscz6vsg4MkMvqMA8N/zXtsNxDqP453fV3ln3d/xDGqxG7gYz2AjzwFD8YzEXhf4JF1bsUCHLLbdDdiMp9h2TvfZKOCQ2/9Ww3GxXe8IoapJIlIfT++kMfCGiPQF1nv52D5V/dTL+91E5AHncWXgcuCfwCJVPex8728i0gpP4fnC2bW9gMzHfPzL7rCItAOuwTOUW2ZtZJTjiJfcGX7XeW7AUzxPODkW4/ndTQbqi0gxPPMJbXHy3YinAJ2vDPBHuu0phOeQwPi0DMAr6oywLiIn8YwsdQxPD7ID8ADQxnn+JDA1Xft3Au2z0raqTgQmnh9UVVNE5KyIFFPV45n8XkwGrFBGEPXsnq0H1ovIDjyD7/6bvx5iKZTu8YnM2hKRW4B/AQ1V9aR4BvQthOdY5vlDTgkwS1VzMl1Ehm14yZFTGR6sVNVzIrIXT3H6BNiO5388l3LepFiOU+flqQlsUdW06Q6uxlN80+aMOaCqKiLHgFuB/eoZUPg4np7hXXhGlE8bAbykqh7ISts+trsgnmHzTBbYMcoIISJXisjl6V6KAfYBvwIXiUhpESmI54SPP0oAvzvF6R94dknBM4thCxEp7XxvKee15iJyUdprIlIli5uQWRuZ5QA4zv+O62XVB0AzESksIkXw9Oo+TPdeL+fnh3gGZN6aURFS1d+BKKe3B1AbSD//Sx08xRY8hS3t8TE8u9VT0m3LE3h6uaec1xoD69K15W/bGXL+ZodU9Zy39czfWaGMHEXxDMD7tYhsB2oAg53/KOKBz4C38Rzj8se7QH6nraF4ZvxDVb8ChgMbRGQb8IKqfo3nDOtqZ/01eHpHfvPSRoY5nM8cAT52TsZk6WSOek5yzMRzDPIzYIaqfum8/aHz3RtV9Vc8PbAPM2rHsRrPrjx4itlW+HNX+QKnmMJfC9tRPP/9rXWeH8dzSCH9bncTPNufxt+2M9MYWOljHZMBG7jXmBwSkbp4TrC0CXC7W4DrAtUDdI7D9lPVbwLRXl5iPUpjcsjpia7LziVRPtqtF8AiWQBYakUye6xHaYwxPliP0hhjfLBCaYwxPlihNMYYH6xQGmOMD1YojTHGByuUxhjjgxVKY4zxwQqlMcb48P9gfYIkP1n5UgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'mantle_geotherm_plot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m T, depth, p, depth_intercepts \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mcalculate_hasterok2011_geotherm(SHF \u001b[38;5;241m=\u001b[39m shf_solution, BDL_T \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m800\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m273\u001b[39m, T_0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, max_depth \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m, moho \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m38\u001b[39m, kinked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, adiabat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m#Making a plot of the solution.\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m \u001b[43mmantle_geotherm_plot\u001b[49m(T \u001b[38;5;241m=\u001b[39m T, P \u001b[38;5;241m=\u001b[39m p, Depth \u001b[38;5;241m=\u001b[39m depth, plot_style \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDepth\u001b[39m\u001b[38;5;124m'\u001b[39m, Temp_unit \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCelsius\u001b[39m\u001b[38;5;124m'\u001b[39m, T_Sample \u001b[38;5;241m=\u001b[39m T_ext, P_Sample \u001b[38;5;241m=\u001b[39m P_ext, T_std \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m, P_std \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.3\u001b[39m, plot_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshow\u001b[39m\u001b[38;5;124m'\u001b[39m, max_depth \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m, moho \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m38\u001b[39m, lab \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m230\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'mantle_geotherm_plot' is not defined"
     ]
    }
   ],
   "source": [
    "file = \"branchetti_2021.xlsx\" #Wherever that /Examples/Garnet is\n",
    "data = pt.import_excel(file, sheet_name = \"Sheet_1\")\n",
    "tb_data = data['my_input']\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8298af5e-2f4e-47a9-86cd-80b2b562dc35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RSINAN PLEASE REPLACE THIS WITH THERMOBAR FUNCTIONS SHOWING HOW TO CALCULATE THESE AGAIN\n",
    "T_ext = np.array(tb_data['Temp']) + 273.15\n",
    "P_ext = np.array(tb_data['P'])\n",
    "\n",
    "#Inverting for SHF for the function\n",
    "shf_solution, T_solution, depth_solution, p_solution, misfit_solution = pt.invert_generalised_mantle_geotherm(P_sample = P_ext, T_sample = T_ext, std_P = 0.3, std_T = 50,\n",
    " SHF_start = 35, SHF_end=45, SHF_increment=0.1, max_depth=300, kinked=False, BDL_T = 170, adiabat = True,\n",
    " plot_solution = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e82ff46-a6df-41c8-bcf9-35d22263c99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Recalculating with the solution\n",
    "T, depth, p, depth_intercepts = pt.calculate_hasterok2011_geotherm(\n",
    "    SHF = shf_solution, BDL_T = 800+273, T_0 = 0, max_depth = 300,\n",
    "    moho = 38, kinked = False, adiabat = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6425ea82-0e33-4dac-9a7c-a20ad3dd2dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a plot of the solution.\n",
    "pt.mantle_geotherm_plot(T = T, P = p, Depth = depth, \n",
    "                        plot_style = 'Depth', Temp_unit = 'Celsius', \n",
    "                        T_Sample = T_ext, P_Sample = P_ext, T_std = 50, \n",
    "                        P_std = 0.3, plot_type = 'show', max_depth = 300, \n",
    "                        moho = 38, lab = 230)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d91a07d-0b05-4c25-900f-a248cbb837b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function mantle_geotherm_plot in module Thermobar.plotting:\n",
      "\n",
      "mantle_geotherm_plot(T, P, Depth, plot_style, Temp_unit, T_Sample, P_Sample, T_std, P_std, max_depth, plot_type, **kwargs)\n",
      "    A function to plot calculate geotherm alongside the thermobarometric\n",
      "    calculations.\n",
      "    \n",
      "    ###Parameters###\n",
      "    T: Temperature array of the geotherm.\n",
      "    \n",
      "    P: Pressure array of the geotherm.\n",
      "    \n",
      "    Depth: Depth array of the geotherm in meters.\n",
      "    \n",
      "    plot_style: String parameter for the y-axis of the geotherm plot 'Pressure' or 'Depth'.\n",
      "    \n",
      "    Temp_unit: String parameter for the temperature unit, 'Celsius' or 'Kelvin'.\n",
      "    \n",
      "    T_Sample: Array of temperature of the thermobarometric solutions.\n",
      "    \n",
      "    P_Sample: Array of pressure of the thermobarometric solutions in GPa.\n",
      "    \n",
      "    T_std: Standart deviation of thermobarometric temperature estimation. Could be array or a single value.\n",
      "    \n",
      "    P_std: Standart deviation of thermobarometric pressure estimation. Could be array or a single value.\n",
      "    \n",
      "    max_depth: Maximum depth to show the plot.\n",
      "    \n",
      "    plot_type: 'show' or 'save' the figure.\n",
      "    \n",
      "    moho: moho depth in km.\n",
      "    \n",
      "    lab: lab depth in km.\n",
      "    \n",
      "    Depth_Sample: Array of depths of the thermobarometric solutions.\n",
      "    \n",
      "    filename_save: string parameter for filename to save the figures.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pt.mantle_geotherm_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d360b70-c577-432e-b3b8-51232d6bf8de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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