{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Parallelized Workflow\n",
    "This notebook demonstrates how GDTchron uses a parallelized workflow to process large numbers of time-temperature paths quickly. It creates a suite of pseudo-random t-T paths and predicts AHe ages from them using multiple processors, with the number of paths and processors set by the processors available on the system used. The saved output is from a test on a 64 core node on the Tufts HPC Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import os\n",
    "import time\n",
    "\n",
    "import cmcrameri.cm as cmc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import gdtchron as gdt\n",
    "\n",
    "plt.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below uses the CPU count of the current system to design a test with the number of cores available on the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum cores:  64\n",
      "Cores to run for scaling test:  [1, 2, 4, 8, 16, 32, 64]\n"
     ]
    }
   ],
   "source": [
    "# Set up number of processesors\n",
    "max_cores = int(os.cpu_count() / 2)\n",
    "print('Maximum cores: ', max_cores)\n",
    "core_list = [int(2**x) for x in range(max_cores) if 2**x <= max_cores]\n",
    "print('Cores to run for scaling test: ', core_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below determines the number of paths to run based on the maximum avaialable cores. The number of paths is variable so that the scaling test is as short as possible while still ensuring that performance improvements will still occur at the highest number of processors. The cell then generates the requisite number of paths by pseudo-randomly picking temperatures between 0°C and 75°C for each 100,000 years from 40 Ma to the present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of t-T paths:  96000\n"
     ]
    }
   ],
   "source": [
    "# Set number of paths based on max cores\n",
    "n_paths = max_cores * 1500\n",
    "print('Number of t-T paths: ', n_paths)\n",
    "\n",
    "# Generate list of pseudo-random t-T paths between 0 and 75 C over 40 Myr\n",
    "total_time = 40  # Myr\n",
    "interval = 0.1  # Myr\n",
    "n_steps = int(total_time / interval) + 1\n",
    "tsteps = np.arange(total_time, 0 - interval, -interval)  # Myr\n",
    "\n",
    "rng = np.random.default_rng(17)\n",
    "temp_paths = [rng.uniform(low=273, high=348, size=n_steps) for x in range(n_paths)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below iterates through the core list and runs the t-T paths through GDTchron to predict AHe ages while logging the time it takes to process all paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 96000/96000 [25:34<00:00, 62.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 :  1534.25910282135  seconds\n",
      "\n",
      "2  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 96000/96000 [12:43<00:00, 125.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 :  763.2714729309082  seconds\n",
      "\n",
      "4  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 96000/96000 [06:27<00:00, 247.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 :  388.79385447502136  seconds\n",
      "\n",
      "8  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 96000/96000 [03:11<00:00, 501.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 :  192.71459794044495  seconds\n",
      "\n",
      "16  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 96000/96000 [01:35<00:00, 1007.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 :  97.4874198436737  seconds\n",
      "\n",
      "32  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 96000/96000 [00:52<00:00, 1839.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 :  54.79542279243469  seconds\n",
      "\n",
      "64  cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 96000/96000 [00:33<00:00, 2896.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 :  36.645010232925415  seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Run through AHe\n",
    "times = []\n",
    "for cores in core_list:\n",
    "    print(cores, ' cores')\n",
    "    start = time.time()\n",
    "    ages = gdt.run_tt_paths(temp_paths=temp_paths, tsteps=tsteps, system='AHe',\n",
    "                            batch_size=100, processes=cores)\n",
    "    end = time.time()\n",
    "    elapsed = end - start\n",
    "    times.append(elapsed)\n",
    "    print(cores, ': ', elapsed, ' seconds\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below saves the core list and ages as arrays for re-use so that plotting can take place without needing to re-run the scaling test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array and write to disk for reuse\n",
    "output = np.array([core_list, times])\n",
    "np.save('scaling.npy', output)\n",
    "\n",
    "age_array = np.array(ages)\n",
    "np.save('scaling_ages.npy', age_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below reloads the saved arrays if the scaling test above was run in a previous session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload array\n",
    "output = np.load('scaling.npy')\n",
    "core_list = output[0]\n",
    "times = output[1]\n",
    "\n",
    "ages = np.load('scaling_ages.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below just sets the color used for plotting the results and the name of the device used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Color and Device\n",
    "ahe_color = cmc.batlowS.colors[6]\n",
    "device = 'Tufts HPC Node (Dual Intel Xeon Gold 6448Y)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below plots the scaling results along with a histogram showing the ages predicted from the pseudo-random t-T paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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AAAAAQPkILKDcJk2aFE8//bSaBACoR1599dUsM0GvXr2KXa59m1Zxw7AT4/r7noxz/31LzJ2/IOeyj73yTvQ5dlhcc/axse3GPSqh1AAAAABAWQgsoNy6d+8effr0KTJjwWuvvaaGAQDqmA8++CAefPDBbHratGmxww47RIMGDXIun+YN2W+n2Hz9NWPIH/8dH3/1bc5lJ/84NQ4449I4bcB+cfrh+0WjRkZvAwAAAIDqJrCAchs8eHD2WNLEiROjZ8+eahgAoA758ssvY9y4cQXPn3nmmSy4YJ999lnmUAo911glnvj3sDjj76Pj1kdfyLncokV5cenoe+L5tz6Ia845Nlbq1KFC/wcAAAAAoHR0/wEAAEpkypQpccstt8SCBYsPZ/Dmm2/GrbfeGvPmzVvmNlq3aB5XnXl0XHXGUdGqebNil33+7f9Gn2PPi0dffksLAQAAAEA1ElgAAAAsUwoaGDNmTMyePbvI+R9//HHccMMN2XBYJdF3t22y7AXrr96t2OV+/GVGHHbuX+MP19wa8+YvHtAAAAAAAFQNgQUAAMAyNW3aNLbeeutil/nmm29ixIgR8dNPP5WoRtfqtmI8+s8/xND9dlrmsv+6/eHY65SL4vPJP2gtAAAAAKhiAgsAAIAS2WyzzeLQQw+Nxo0b51zm559/zoILvvrqqxJts3nTJnHZb46IUeedGG1btSh22QkffBp9jhsW9zzzqhYDAAAAgCoksAAAACixddddNwYOHBgtWuQOApg1a1Y2LMKHH35Y4u3ut33veOaaC6L3umsUu9y0mbPjyAuuilP/emPMnjtPywEAAABAFRBYAAAAlEq3bt1iyJAh0a5du5zLLFiwIG699dZ4/fXXS7zdVbp0igeuPCt+c9iey1z2+vufjF1//cf48IvJJd4+AAAAAFA2AgsAAIBS69SpUwwdOjS6dOmSc5m8vLy4//7748knn8ymS6JJ48Yx/OhDY+yFv4tO7dsUu+x7n30VO50wPG5++LkSbx8AAAAAKD2BBQAAQJm0adMmBg8eHKuvvnqxyz3zzDNx7733xsKFC0u87V023yCevvr82G7jHsUuN2vOvPj1ZSPi+Euui+mzZpd4+wAAAABAyQksAAAAyqxZs2bRv3//2HDDDYtd7s0338yGRpg3b16Jt71ipw5x5yWnx9mDD4yGDRsUu+zYx16MnY4/P55/64MSbx8AAAAAKBmBBQAAQLk0atQoDjjggNh2222LXe7jjz+OUaNGxYwZM0qx7YZx+uH7xb2Xn5kFGhTnk6+/i31PvSR+debl8dr7n5T4PQAAAACA4gksAAAAyq1Bgwax8847x1577VXscpMnT44RI0bEjz/+WKrtb73hOvHMNefH7ltutMxln3p9Yux20p+i/x/+Fu98/EWp3gcAAAAAWJrAAgAAoMJsttlmceihh0bjxo1zLjN16tQsuOCrr74q1baXa9cmbv7jyfGn4/pGk8aNlrn8Qy++GX2OGxZHXnBV/Pfzb0r1XgAAAADA/yewAAAAqFDrrrtuDBw4MFq0aJFzmdmzZ8fNN98cc+fOLXVmhBMO3j3G//Wc6L5i5xKtc88zr8Y2R/8+jr/4uvjsm+9L9X4AAAAAgMACAACgEnTr1i2GDBkS7dq1y7nMvvvuG82aNSvT9jftsXo89e/hcdCOW5Ro+UWL8uK2x16ILY48J357xaj46rvSDcUAAAAAAPWZjAUAAECl6NSpUwwdOjS6dOmy1Lw999wzy2xQHm1bt4zrzj0u7vvLmbFlz7VKtM6ChQvjxgefjt6Dz4qz/jkmvvvpl3KVAQAAAADqA4EFAABApWnTpk0MHjw41lhjjYLXttlmm9h8880r7D222ahHPHDl2XH7Rb+LTdbuXqJ15s1fENfe/VhsesQZMezasfHjL9MrrDwAAAAAUNcILAAAACpVGu6gX79+sdFGG8WGG24YO++8c4W/R4MGDWLnzTaIx/51Xtx0/kmx3morl2i92XPnxT/Gjo9NDj8jLhx1V/wyY1aFlw0AAAAAajuBBQAAQKVr1KhR7L///rHffvtlQQCVJW17r202jWeuOT/+c+5xsVa3pYdhKMqM2XPi8pvujU2OOCOuvPn+7DkAAAAA8H8EFgAAAFUi3fRPAQZVoWHDhvGrHbeI5//zp/jXGUNj1S6dS7Te1Okz448jx2VDJFx1x8NZRgMAAAAAqO8EFgAAADVOXl5ePPjgg/HVV1+VazuNGzWKfrttGy9ff2Fc8duBsWKnDiVab8rU6fH7q2+N3gPPjJH3PhHz5i8oVzkAAAAAoDYTWAAAANQ4jz/+eLz66qtxww03xH//+99yb69pk8YxeJ8d4/UbL4kLT+gXndu3LdF6k3+cGqf9fXRsNvisGPPQs7Fg4cJylwUAAAAAahuBBQAAQI3yyiuvxPPPP59NL1iwIG677bZ47bXXKmTbzZs2ieN+tVtMGH1pDDvq4GjfplWJ1vvyux/jpMtHxlZDzo1xT7wUixYtqpDyAAAAAEBtILAAAACoMd5///0YP378UsMiPPDAA/HEE09k0xWhVYtmcXLfvePN0ZfGmQP3j9Ytm5dovU++/i6OvvCa2O6Y8+L+516vsPIAAAAAQE0msAAAAKgRvv322xg3blzO+c8++2zcc889sbAChyNo27plnDnwgHhz9GXx2757RcvmTUu03vuTvo6Bw/8ZO594QTz6ytsCDAAAAACo0wQWAAAANULnzp2jZ8+exS7z1ltvxS233BJz586t0Pfu2K51nHfUIdkQCWmohGZNGpdovTc/nBSHnXNl7PnbC+PZN9+v0DIBAAAAQE1RsqtlUIxRo0ZljyXNnDlTvQEAUGKNGjWK/fffP9q2bZtlJ8jlk08+iRtuuCH69+8frVu3rtAaXr5Du7jwhH5x4iG7xxVj7o/R45+JBSXIkPDKxI9j/9Muje03WTfOHnxgbLH+WhVaLgAAAACoTgILKLdJkybF008/rSYBACi3Bg0axE477ZQFFzz44IM5hxiYPHlyjBgxIgYMGBCdOnWq8Jrv2rlj/OW3A+M3h+0Zl950b9z26POxaFHRZSnsmTfezx67br5hnHPkgbHRWt0rvGwAAAAAUNUEFlBu3bt3jz59+hSZseC1115TwwAAlFrv3r2zbATjxo2LBQsWFLnM1KlTY+TIkdGvX7/o1q1bpdTyqit2jn+dPjR+23evuOTGe+LOJ18u0XqPvvJ29thn215x1qADYr3VVq6U8gEAAABAVWhYJe9CnTZ48OB46qmnlnoUNTwCAACUVI8ePWLgwIHRokWLnMvMnj07brzxxvjggw8qtWLX6rZi/Ofc4+LZay+IvbfZtMTr3f/c67HdMefFMRdeE5989W2llhEAAAAAKovAAgAAoMZKmQiGDh0a7du3z7lMymgwduzYKsmWtf7q3WL0+SfFY//8Q+y82QYlWicN53DHEy/FlkPOjZMuHxlffDul0ssJAAAAABVJYAEAAFCjLbfccllwwYorrljszfsHHnggHn/88Wy6sm3aY/W4/aLfxYNXnh3bbLhOidZZuGhRjHno2dhs8Flx+t9Hx+QpP1d6OQEAAACgIggsAAAAarzWrVvHoEGDYo011ih2ueeeey7uueeeWLhwYZWUa8sN1o57/3Jm3HXp6dGrx+olWmf+goUx4t4notfAM+P3V98aP/w8rdLLCQAAAADlIbAAAACoFZo1axb9+vWLjTbaqNjl3nrrrbjlllti7ty5VVKuBg0aRJ9N14tH/vH7uPVPv40N1lilROvNmTc/rrrj4dj0iDPiTyPHxdTpMyu9rAAAAABQFgILAACAWqNRo0ax//77x3bbbVfscp988knccMMNMWPGjCorWwow2G3LjeLJfw+L6887IdZZdaUSrTdzzty44ub7Y+PDT4/Lbro3ps2cXellBQAAAIDSEFgAAADUKukG/k477RR77713Np3L5MmT484774yq1rBhw9h/+83iuWv/GNecfUysttLyJVovBRRcNOquLIPB328bH7PmVE3GBQAAAABYFoEFAABArdS7d+847LDDonHjxkXOb9GiRey1115RXRo1ahiH7LxVvDTyz/G3U4+MlZdfrkTr/TRtRgy/bmz0GnhmXHvXYzF33vxKLysAAAAAFEdgAQAAUGuts846MXDgwCyIoLAUbNCvX7/o1KlTVLcmjRvHEXtuH6+OuiguPenwWKFjuxKt991Pv8RZ/xoTvQedFTc88HTMX7Cg0ssKAAAAAEURWAAAANRq3bp1i6FDh0b79u2z52l4hIMOOih7vSZp1rRJHLX/zvH6jZfE+cccGh3bti7Rel//8FOccuWo2OLIc+K2R1+IhQsXVXpZAQAAAKAwgQUAAECtt9xyy2XBBSuuuGLsueee0aNHj6ipWjZvFicdume8cdOlcc7gA6Ntq8WzLeQyafIPcfwl18U2R/8h7n761Vi0SIABAAAAAFVDYAEAAFAntG7dOgsu2GyzzaI2aNOyRZx2+H7x5k2Xxan994lWzZuVaL0Pv/gmhvzxqtjx+PPj4ZfejLy8vEovKwAAAAD1m8ACAACgzmjUqFHUNu3btIpzhxyUZTA48ZDdo3nTJiVa751Pvoh+v/9b7HbSn+LJ1ycKMAAAAACg0ggsAAAA6qU5c+bEnXfeGdOnT4+aoFP7tvHHY/vG6zdeEkftv1M0aVyyIInXP/g0Djrz8tj31EvixXc+rPRyAgAAAFD/CCwAAADqnQULFsRtt90W77zzTowYMSKmTJkSNcWKnTrEpScdEa/dcHEcvud20ahhyU7bXnj7v7H3KRfFwWf9JSZ88GmllxMAAACA+kNgAQAAUK/k5eXF3XffHZMmTcqe//LLLzFy5Mj44osvoibptkKn+PupQ+KlkX+OQ3beMho0aFCi9Z547d3Y5dd/jAF/+FtM/PTLSi8nAAAAAHWfwAIAAKBeefTRR2PixImLvTZ79uwYPXp0vP/++1HTrLFyl7jm7GPjuev+GPtu17vE641/8c3Y7pjzYuif/h0ffjG5UssIAAAAQN0msAAAAKg3JkyYEC+++GLO4RHGjh0br7zyStRE63bvGjcMOzGe/Pew2G2LjUq83l1PvRJbH3VunHjpf2LSN99XahkBAAAAqJsEFgAAAPXG2muvHSuuuGKxy4wfPz4ee+yxbMiEmmijtbrHrX/+bTz093OjzybrlWidRYvy4pZHno/NjzwnfvfXG+LrH36q9HICAAAAUHcILAAAAOqN1q1bx+DBg2PNNdcsdrnnn38+7r777li4cGHUVJuvt2bcddnpcc/lZ8Tm6xf//+RbsHBhjLr/qeg98Mw4+6qb47uffqn0cgIAAABQ+wksAAAA6pWmTZtG3759Y+ONNy52ubfffjtuvvnmmDt3btRk2228boz/6zkx9sLfxcZrdy/ROnPnL4hr7nw0eg08I86/7vb46ZcZlV5OAAAAAGovgQUAAEC906hRo9hvv/1i++23L3a5Tz/9NEaNGhXTp0+PmqxBgwaxy+YbxOP/Oi9Gn39SrNu9a4nWmzVnXvzttgdj4yNOj4tvuDumzZhV6WUFAAAAoPYRWAAAANRL6Wb8jjvuGPvss082ncu3334bI0aMiClTpkRNl/6PvbfZNJ699oK47tzjYs2Vu5RovRmz5sSlo++JjY84I/56ywMxc3bNztIAAAAAQNUSWAAAANRrvXr1isMOOywaN26cc5lffvklRo4cGV988UXUBg0bNoyDdtwiXhjxp/jn6UNjlS6dSrTe1Okz44IRd8SmR5wR/x73SMyZN7/SywoAAABAzSewAAAAqPfWWWedGDRoULRs2TJnXcyePTtGjx4d77//fq2pr8aNGkX/3beNV66/KP5y8sBYcbn2JVrvh6nT4tx/3xK9Bp4Z19/3ZMybv6DSywoAAABAzSWwAAAAICJWXnnlGDJkSHTo0CFnfSxYsCDGjh0br7zySq2qs6ZNGseR++4Yr914Sfz5+H7RqX2bEq03ecrPcerfbozNjzw7bn74uViwcGGllxUAAACAmkdgAQAAwP8st9xyWXDBSiutVGydjB8/Ph577LHIy8urVXXXolnTOP6g3WLC6EvjvKEHR/s2rUq03hffTolfXzYith76+xj35MuxaNGiSi8rAAAAADWHwAIAAIBCWrdunQ2LsOaaaxZbL88//3zcfffdsbAW9uJv3aJ5/Lbf3vHm6EvjjCP2j9Ytm5dovY+/+jaO/vPVsf2xw+KB5yfUusAKAAAAAMpGYAEAAMASmjZtGn379o2NN9642Lp5++2345lnnqm19de2dcs4a9AB8eboy+Lkw/bKMhqUxHuffRVHDPtH7HLiBfHYK+8IMAAAAACo4wQWAAAAFKFRo0ax3377xfbbb5+zflZcccXYZpttan39dWzXOoYdfUg2RMIxB+4STZs0LtF6b3w4KQ4954rY67cXxXNvflDp5QQAAACgeggsAAAAyKFBgwax4447xj777JNNF9ahQ4fo379/lt2grlihY7u4+MQB8doNF8egvftE40aNSrTeyxM/iv1OuyQOPP2yePW9Tyq9nAAAAABULYEFAAAAy9CrV69saIQmTZpkz1u2bBkDBgyI1q1b18m6W3n55eLKUwbHK9dfGH133ToaNlw8qCKXp994L3b/zZ+i77l/jbc/+rzSywkAAABA1RBYAAAAUAJrr712DBo0KNq1axf9+vWL5ZZbrs7XW/eVlo+rzjw6nr/uT3FAn81KvN4jL78VOxw/PAad/694f9LXlVpGqCyjRo3KMpUs6/HYY4/l3MZ3330Xp556aqyzzjrRokWL6NixY2y33Xbxn//8J/Ly8pZZhk8++SSOPfbYWG211aJ58+ax/PLLx+677x7jxo0r0f8wYcKEOPzww2PllVeOZs2aZcO3HHjggfHEE0+Uqi4AAACgZANnwjIutqTHkmbOnKneAACoU7p27RonnXRSNCrhEAF1xTqrrhQj/3BC/K7/F3HRqLti/Itvlmi9+559Le5/7vU4eKct4syBB8TqXVeo9LJCRWvYsGF07tw55/x0w74or7/+ehYE8OOPP2bPU4aT6dOnx3PPPZc9br/99rj33ntzrv/ggw/GIYccErNmzcqet23bNtvWI488kj2OPPLIGDFixFLDtORLwQvHH398LFiwIHuegqJSoMPdd9+dPYYNGxbDhw8vdX0AAABQP8lYQLlNmjQpnn766aUer732mtoFAKDOqW9BBYX1XGOVGPPHk+PRf/4hduy1fonWSb2yb3/8pdjiyHPiN38ZGV9+N6XSywkVqVu3bvHtt9/mfKQMBEv65ZdfYp999skCAXr06BGvvvpqFlSQAvD/+c9/ZsOqpOCAU045pcj3/Oyzz+LQQw/Nggq22Wab+O9//5ttMz3OO++8bJnrr78+LrvssiLXf/HFF+O4447LggoOOOCA+PLLL2Pq1Knxww8/ZBkQkvPPPz/Gjh1boXUFAABA3SWwgHLr3r179OnTZ6lH79691S4AAPVa6h18//33x8KFC6Mu6dVj9Rh3yWlx/xVnxVYbrF2idRYuWhQ3jX82eg86K874x+iYPOXnSi8nVJfLL788CzpIwx+kzAP558dNmzaNE088Mbupn1x77bXx4YcfLrV+Ch5IQQhdunTJfkPSUCz5WQ/Susccc0z2/M9//nP8/PPS36Uzzjgj+93ZYIMNsuCBNBRCkoZwufrqq7NMCoWXAwAAgGURWEC5DR48OJ566qmlHkUNjwAAAPVF6lk8ZsyYLB16+jt37tyoa7becJ0suCAFGWzaY/USrTN/wcL4zz1PRK+BZ8Z519wWU6ZOq/RyQlW78cYbs799+/aN1VZbban5aUiVFCSQbuqn34fCUkDBuHHjsuk0lEH79u2XWv/ss8/O/k6bNi0b1qCwTz/9NBtqITnttNOy7Ai51v/888/jmWeeKcd/CgAAQH0hsAAAAKCCzZ49O7tZmFKf56c1T2nL85/XJWl89zQswqP/+H3c/MeTo+ca3Uq03px58+Oftz8Umx5xZvz5+jvjlxn/N4481HZp2IIvvvgim95zzz2LXCYFFeQPoZCGRCgsBQWk35Di1k+ZA9ddd90i13/00UcLpvfYY48i1992222jTZs2Ra4PAAAARWlc5KsAAACUSRrT/LbbbsvGMl9yWIT//Oc/cfjhh0fnzp3rZIDBHlttHLttsWHc++zrcdENd8VHX0xe5nozZs+Jv4y5L/5zz+Px60P2iGMO3CXatGxRJWWGkkjf5V69emUBAynDwIorrhhbb711HHXUUbHDDjsstfy7775bMN2zZ8+c203zxo8fH++9917O9ddff/1i13///fdj4sSJRa6//PLLZ4+iNGrUKHr06BGvvvrqUuuXxffff7/Ub96yfPzxx+V+XwAAAKqOwAIAAIAKlMZDT+nFi5LSlo8cOTJLj77qqqvWyXpv2LBhHNBns9h3215xxxMvxSU33h2TJi/7hmPKWJAyF1x95yNxct+9Y8i+O0bL5s2qpMxQnFmzZsWECROiQ4cO2TAFKQNJeqSsJEceeWRce+210bjx/7+88s033xRMd+3aNed28+el34UZM2ZkWQwKr5/er2XLlstcv/D7FX5e3Hvnz0+BBUuuXxZXXXVVnH/++eXeDgAAADWXoRAAAAAq0GabbVbszcA5c+bE6NGjl+qlXNc0atQwDtt163j5+gvjylMGx0qdO5RovR9/mRHnXXNb9B50VpbFYO68+ZVeVijKSiutFMOGDYu33nor+97+9NNPWZDB888/H7vssku2TBri5JRTTllsvcJDnhT3W1B4XuF18qeLW7fw/CWHWCnv+gAAAFAUgQUAAAAVKPUCHjp0aNbbOJeUTv3222+Pl19+uc7XfZPGjWPQ3n3itRsuiYtPHBDLd2hbovW+/XFqnPGPm2KzwWfH6PHPxPwFCyq9rFDYbrvtFsOHD48NN9wwmjVrVjCEQBoG4eGHH47999+/oLf+Rx99pPIAAACo0wQWAAAAVLCOHTtmwQWpx3NxHnrooXj00UcjLy+vzrdB86ZN4pgDd4kJoy+N4UcfGh3b/l/a92X56vsf4+S/XB9bDT03bn/8xVi4cFGllxVKMuTH5Zdfnk0vWrQo7rvvvoJ5bdq0KZhOGQ5yKTyv8Dr508WtW3h+4XUrYv2yOOGEE+Ldd98t1ePuu+8u9/sCAABQdf7/IIAAAABUmFatWsWgQYPijjvuKLY38wsvvJClIk+9n1Nv6LquZfNm8ZvD9ozB++wQ19z1aPxz7EMxfdbsZa736dffx7EXXRtX3Hx/nD3owNhn202zm7tQXdZcc83o1KlTTJkyJT799NOC1wsHFH399dfRtm3RWTrSvCTNb9269VLr//zzz9nN/1xDGuSvv2QAU/7z/Pm55Fq/LJZffvnsAQAAQN3lKgwAAEAladq0afTt2zc22WSTYpd75513YsyYMdk47vVF21Yt4vTD94s3b7o0ftd/n2jV/P9SzS/Lfz//JgZf8K/Y6YTz45GX3qoX2R6oXXr27FkwnXrm55I/b7311su5/sSJE5e5/vrrr1/k+t9//3388MMPOYdj+eCDD4pcHwAAAIoisAAAAKASpV71++67b/Tp06fY5T777LMYNWpUlr2gPunQtnX8fshB2RAJJxy8ezRrUrLEem9//EX0/f1fY/ff/DmenvCeAAOq3CeffJJlK0hWW221gtfXWWedWGWVVQqGOynKzJkz49lnn82md9ttt8XmbbvtttGiRYti1//888/j/fffL3L9XXfdtWA61/rPP/98wW/NkusDAABAUQQWAAAAVLIGDRrEDjvskAUYpOlcvvvuu/jPf/6Ts5dxXda5Q9v403F94/XRl8bQ/XaKJo1LNizEa+9/EgeecVnsf9ql8dI7H1Z6OakflpUJI80//fTTC4KH9tlnn8XmDxw4MPt76623xqRJk5Za/1//+lfMmDEjG/5kwIABSw2jctBBB2XT//73v+OXX35Zav1LLrk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",
      "text/plain": [
       "<Figure size 2100x1050 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Scaling Results\n",
    "fig, axs = plt.subplots(1, 2, dpi=300, figsize=(7, 3.5))\n",
    "\n",
    "axs[0].plot(core_list, times, c=ahe_color, label=device)\n",
    "axs[0].loglog()\n",
    "axs[0].set_xlabel('Cores', fontsize=8)\n",
    "axs[0].set_ylabel('Time (s)', fontsize=8)\n",
    "axs[0].set_title('AHe Scaling Test', fontsize=10)\n",
    "\n",
    "ref_x = np.arange(1, max(core_list), 1).astype(float)\n",
    "ref_y = max(times) * ref_x**-1\n",
    "axs[0].plot(ref_x, ref_y, c='grey', linestyle='--', label='Idealized Scaling Behavior')\n",
    "axs[0].legend(fontsize=6)\n",
    "axs[0].set_xticks(core_list)\n",
    "axs[0].set_xticklabels(core_list)\n",
    "\n",
    "# Plot age results\n",
    "hist_range = (6, 24)\n",
    "axs[1].hist(ages, color=ahe_color, edgecolor='black', range=hist_range, bins=9)\n",
    "axs[1].set_title('Age Results', fontsize=10)\n",
    "axs[1].set_xlabel('AHe Age (Ma)', fontsize=8)\n",
    "axs[1].set_ylabel('Frequency', fontsize=8)\n",
    "axs[1].set_xlim(hist_range)\n",
    "\n",
    "for k, ax in enumerate(axs):\n",
    "    ax.tick_params(axis='both', labelsize=6)\n",
    "    ax.tick_params(which='minor', labelbottom=False, labelleft=False)\n",
    "    ax.annotate(chr(97 + k) + ')', (-0.1, 1.1), xycoords='axes fraction', fontsize=12) \n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('scaling_test.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}