{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heat\n",
    "\n",
    "In this example the laser-excitation of a sample `Structure` is shown.\n",
    "It includes the actual absorption of the laser light as well as the transient temperature profile calculation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Do all necessary imports and settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import udkm1Dsim as ud\n",
    "u = ud.u #  import the pint unit registry from udkm1Dsim\n",
    "import scipy.constants as constants\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "u.setup_matplotlib() #  use matplotlib with pint units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure\n",
    "\n",
    "Refer to the [structure-example](structure.ipynb) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "O = ud.Atom('O')\n",
    "Ti = ud.Atom('Ti')\n",
    "Sr = ud.Atom('Sr')\n",
    "Ru = ud.Atom('Ru')\n",
    "Pb = ud.Atom('Pb')\n",
    "Zr = ud.Atom('Zr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c-axis lattice constants of the two layers\n",
    "c_STO_sub = 3.905*u.angstrom\n",
    "c_SRO = 3.94897*u.angstrom\n",
    "# sound velocities [nm/ps] of the two layers\n",
    "sv_SRO = 6.312*u.nm/u.ps\n",
    "sv_STO = 7.800*u.nm/u.ps\n",
    "\n",
    "# SRO layer\n",
    "prop_SRO = {}\n",
    "prop_SRO['a_axis'] = c_STO_sub # aAxis\n",
    "prop_SRO['b_axis'] = c_STO_sub # bAxis\n",
    "prop_SRO['deb_Wal_Fac'] = 0 # Debye-Waller factor\n",
    "prop_SRO['sound_vel'] = sv_SRO # sound velocity\n",
    "prop_SRO['opt_ref_index'] = 2.44+4.32j\n",
    "prop_SRO['therm_cond'] = 5.72*u.W/(u.m *u.K) # heat conductivity\n",
    "prop_SRO['lin_therm_exp'] = 1.03e-5 # linear thermal expansion\n",
    "prop_SRO['heat_capacity'] = 'lambda T: 455.2 + 0.112*T - 2.1935e6/T**2' # heat capacity [J/kg K]\n",
    "\n",
    "SRO = ud.UnitCell('SRO', 'Strontium Ruthenate', c_SRO, **prop_SRO)\n",
    "SRO.add_atom(O, 0)\n",
    "SRO.add_atom(Sr, 0)\n",
    "SRO.add_atom(O, 0.5)\n",
    "SRO.add_atom(O, 0.5)\n",
    "SRO.add_atom(Ru, 0.5)\n",
    "\n",
    "# STO substrate\n",
    "prop_STO_sub = {}\n",
    "prop_STO_sub['a_axis'] = c_STO_sub # aAxis\n",
    "prop_STO_sub['b_axis'] = c_STO_sub # bAxis\n",
    "prop_STO_sub['deb_Wal_Fac'] = 0 # Debye-Waller factor\n",
    "prop_STO_sub['sound_vel'] = sv_STO # sound velocity\n",
    "prop_STO_sub['opt_ref_index'] = 2.1+0j\n",
    "prop_STO_sub['therm_cond'] = 12*u.W/(u.m *u.K) # heat conductivity\n",
    "prop_STO_sub['lin_therm_exp'] = 1e-5 # linear thermal expansion\n",
    "prop_STO_sub['heat_capacity'] = 'lambda T: 733.73 + 0.0248*T - 6.531e6/T**2' # heat capacity [J/kg K]\n",
    "    \n",
    "STO_sub = ud.UnitCell('STOsub', 'Strontium Titanate Substrate', c_STO_sub, **prop_STO_sub)\n",
    "STO_sub.add_atom(O, 0)\n",
    "STO_sub.add_atom(Sr, 0)\n",
    "STO_sub.add_atom(O, 0.5)\n",
    "STO_sub.add_atom(O, 0.5)\n",
    "STO_sub.add_atom(Ti, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "S = ud.Structure('Single Layer')\n",
    "S.add_sub_structure(SRO, 100) # add 100 layers of SRO to sample\n",
    "S.add_sub_structure(STO_sub, 200) # add 200 layers of STO substrate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Heat\n",
    "\n",
    "The `Heat` class requires a `Structure` object and a boolean `force_recalc` in order overwrite previous simulation results.\n",
    "\n",
    "These results are saved in the `cache_dir` when `save_data` is enabled.\n",
    "Printing simulation messages can be en-/disabled using `disp_messages` and progress bars can using the boolean switch `progress_bar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heat simulation properties:\n",
      "\n",
      "This is the current structure for the simulations:\n",
      "\n",
      "Structure properties:\n",
      "\n",
      "Name   : Single Layer\n",
      "Thickness : 117.59 nanometer\n",
      "Roughness : 0.00 nanometer\n",
      "----\n",
      "100 times Strontium Ruthenate: 39.49 nanometer\n",
      "200 times Strontium Titanate Substrate: 78.10 nanometer\n",
      "----\n",
      "no substrate\n",
      "\n",
      "\n",
      "Display properties:\n",
      "\n",
      "================================  =======================================================\n",
      "                       parameter  value\n",
      "================================  =======================================================\n",
      "                    force recalc  True\n",
      "                 cache directory  ./\n",
      "                display messages  True\n",
      "                       save data  False\n",
      "                    progress bar  True\n",
      "              excitation fluence  [] mJ/cm²\n",
      "                excitation delay  [0.0] ps\n",
      "         excitation pulse length  [0.0] ps\n",
      "           excitation wavelength  799.9999999999999 nm\n",
      "                excitation theta  90.0 deg\n",
      "excitation multilayer absorption  True\n",
      "                  heat diffusion  False\n",
      "       interpolate at interfaces  11\n",
      "                         backend  scipy\n",
      "                       distances  no distance mesh is set for heat diffusion calculations\n",
      "               top boundary type  isolator\n",
      "            bottom boundary type  isolator\n",
      "================================  =======================================================\n"
     ]
    }
   ],
   "source": [
    "h = ud.Heat(S, True)\n",
    "\n",
    "h.save_data = False\n",
    "h.disp_messages = True\n",
    "\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Excitation\n",
    "\n",
    "In order to calculate the temperature of the sample after quasi-instantaneous (delta) photoexcitation the `excitation` must be set with the following parameters:\n",
    "* `fluence`\n",
    "* `delay_pump`\n",
    "* `pulse_width`\n",
    "* `multilayer_absorption`\n",
    "* `wavelength`\n",
    "* `theta`\n",
    "\n",
    "The angle of incidence `theta` does change the footprint of the excitation on the sample for any type excitation.\n",
    "The `wavelength` and `theta` angle of the excitation are also relevant if `multilayer_absorption = True`.\n",
    "Otherwise the _Lambert_Beer_-law is used and its absorption profile is independent of `wavelength` and `theta`.  \n",
    "__Note:__ the `fluence`, `delay_pump`, and `pulse_width` must be given as `array` or `list`.\n",
    "\n",
    "The simulation requires also a `delay` array as temporal grid as well as an initial temperature `init_temp`.\n",
    "The later can be either a scalar which is then the constant temperature of the whole sample structure, or the initial temperature can be an array of temperatures for each single layer in the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\schick\\general\\python\\wpy64-3770\\python-3.7.7.amd64\\lib\\site-packages\\pint\\quantity.py:1138: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  magnitude = magnitude_op(self._magnitude, other_magnitude)\n"
     ]
    }
   ],
   "source": [
    "h.excitation = {'fluence': [5]*u.mJ/u.cm**2,\n",
    "                'delay_pump':  [0]*u.ps,\n",
    "                'pulse_width':  [0]*u.ps,\n",
    "                'multilayer_absorption': True,\n",
    "                'wavelength': 800*u.nm,\n",
    "                'theta': 45*u.deg}\n",
    "\n",
    "# when calculating the laser absorption profile using Lamber-Beer-law\n",
    "# the opt_pen_depth must be set manually or calculated from the refractive index\n",
    "SRO.set_opt_pen_depth_from_ref_index(800*u.nm)\n",
    "STO_sub.set_opt_pen_depth_from_ref_index(800*u.nm)\n",
    "\n",
    "# temporal and spatial grid\n",
    "delays = np.r_[-10:200:0.1]*u.ps\n",
    "_, _, distances = S.get_distances_of_layers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Laser Absorption Profile\n",
    "\n",
    "Here the difference in the spatial laser absorption profile is shown between the multilayer absorption algorithm and the Lambert-Beer law.  \n",
    "Note that Lambert-Beer does not include reflection of the incident light from the surface of the sample structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n",
      "Absorption profile is calculated by Lambert-Beer's law.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "dAdz, _, _, _ = h.get_multilayers_absorption_profile()\n",
    "plt.plot(distances.to('nm'), dAdz, label='multilayer')\n",
    "dAdz = h.get_Lambert_Beer_absorption_profile()\n",
    "plt.plot(distances.to('nm'), dAdz, label='Lamber-Beer')\n",
    "plt.legend()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Differnetial Absorption')\n",
    "plt.title('Laser Absorption Profile')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface incidence fluence scaled by factor 0.7071 due to incidence angle theta=45.00 deg\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n",
      "Elapsed time for _temperature_after_delta_excitation_: 0.016403 s\n",
      "Elapsed time for _temp_map_: 0.088288 s\n"
     ]
    }
   ],
   "source": [
    "temp_map, delta_temp = h.get_temp_map(delays, 300*u.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 8])\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[101, :])\n",
    "plt.xlim([0, distances.to('nm').magnitude[-1]])\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Temperature [K]')\n",
    "plt.title('Temperature Profile')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heat Diffusion\n",
    "\n",
    "In order to enable heat diffusion the boolean switch `heat_diffusion` must be `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface incidence fluence scaled by factor 0.7071 due to incidence angle theta=45.00 deg\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n",
      "Elapsed time for _temperature_after_delta_excitation_: 0.018351 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26d47e3db9604a0bbce8800e60f9bb73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 6.034580 s\n",
      "Elapsed time for _temp_map_: 6.145684 s\n"
     ]
    }
   ],
   "source": [
    "# enable heat diffusion\n",
    "h.heat_diffusion = True\n",
    "# set the boundary conditions\n",
    "h.boundary_conditions = {'top_type': 'isolator', 'bottom_type': 'isolator'}\n",
    "# The resulting temperature profile is calculated in one line:\n",
    "temp_map, delta_temp = h.get_temp_map(delays, 300*u.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 8])\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[101, :], label=np.round(delays[101]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[501, :], label=np.round(delays[501]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[-1, :], label=np.round(delays[-1]))\n",
    "plt.xlim([0, distances.to('nm').magnitude[-1]])\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Temperature [K]')\n",
    "plt.legend()\n",
    "plt.title('Temperature Profile with Heat Diffusion')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map with Heat Diffusion')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heat Diffusion Parameters\n",
    "\n",
    "For heat diffusion simulations various parameters for the underlying pdepe solver can be altered.\n",
    "\n",
    "By default, the `backend` is set to `scipy` but can be switched to `matlab`.\n",
    "Currently, the is no obvious reason to choose _MATLAB_ above _SciPy_.\n",
    "\n",
    "Depending on the `backend` either the `ode_options` or `ode_options_matlab` can be configured and are directly handed to the actual solver.\n",
    "Please refer to the documentation of the actual backend and solver and the __API documentation__ for more details.\n",
    "\n",
    "The speed but also the result of the heat diffusion simulation strongly depends on the spatial grid handed to the solver.\n",
    "By default, one spatial grid point is used for every `Layer` (`AmorphousLayer` or `UnitCell`) in the `Structure`.\n",
    "The resulting `temp_map` will also always be interpolated in this spatial grid which is equivalent to the distance vector returned by `S.get_distances_of_layers()`.\n",
    "\n",
    "As the solver for the heat diffusion usually suffers from large gradients, e.g. of thermal properties or initial temperatures, additional spatial grid points are added by default only for internal calculations.\n",
    "The number of additional points (should be an odd number, default is 11) is set by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "h.intp_at_interface = 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The internally used spatial grid can be returned by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_interp, original_indicies = S.interp_distance_at_interfaces(h.intp_at_interface)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The internal spatial grid can also be given by hand, e.g. to realize logarithmic steps for rather large `Structure`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "h.distances = np.linspace(0, distances.magnitude[-1], 100)*u.m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As already shown above, the heat diffusion simulation supports also an top and bottom  boundary condition. The can have the types:\n",
    "* `isolator`\n",
    "* `temperature`\n",
    "* `flux`\n",
    "\n",
    "For the later types also a value must be provides:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heat simulation properties:\n",
      "\n",
      "This is the current structure for the simulations:\n",
      "\n",
      "Structure properties:\n",
      "\n",
      "Name   : Single Layer\n",
      "Thickness : 117.59 nanometer\n",
      "Roughness : 0.00 nanometer\n",
      "----\n",
      "100 times Strontium Ruthenate: 39.49 nanometer\n",
      "200 times Strontium Titanate Substrate: 78.10 nanometer\n",
      "----\n",
      "no substrate\n",
      "\n",
      "\n",
      "Display properties:\n",
      "\n",
      "================================  =======================================================\n",
      "                       parameter  value\n",
      "================================  =======================================================\n",
      "                    force recalc  True\n",
      "                 cache directory  ./\n",
      "                display messages  True\n",
      "                       save data  False\n",
      "                    progress bar  True\n",
      "              excitation fluence  [5.0] mJ/cm²\n",
      "                excitation delay  [0.0] ps\n",
      "         excitation pulse length  [0.0] ps\n",
      "           excitation wavelength  800.0 nm\n",
      "                excitation theta  45.0 deg\n",
      "excitation multilayer absorption  True\n",
      "                  heat diffusion  True\n",
      "       interpolate at interfaces  11\n",
      "                         backend  scipy\n",
      "                       distances  a distance mesh is set for heat diffusion calculations.\n",
      "               top boundary type  temperature\n",
      "        top boundary temperature  500 K\n",
      "            bottom boundary type  flux\n",
      "            bottom boundary flux  500000000000.0 W/m²\n",
      "================================  =======================================================\n"
     ]
    }
   ],
   "source": [
    "h.boundary_conditions = {'top_type': 'temperature', 'top_value': 500*u.K,\n",
    "                         'bottom_type': 'flux', 'bottom_value': 5e11*u.W/u.m**2}\n",
    "\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface incidence fluence scaled by factor 0.7071 due to incidence angle theta=45.00 deg\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2e7e46161d449f2b6edc0594c9817fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 0.670097 s\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n",
      "Elapsed time for _temperature_after_delta_excitation_: 0.006478 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "077c3a609f5346d9a8679b3022da48cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 0.729121 s\n",
      "Elapsed time for _temp_map_: 1.451789 s\n"
     ]
    }
   ],
   "source": [
    "# The resulting temperature profile is calculated in one line:\n",
    "temp_map, delta_temp = h.get_temp_map(delays, 300*u.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 8])\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[101, :], label=np.round(delays[101]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[501, :], label=np.round(delays[501]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[-1, :], label=np.round(delays[-1]))\n",
    "plt.xlim([0, distances.to('nm').magnitude[-1]])\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Temperature [K]')\n",
    "plt.legend()\n",
    "plt.title('Temperature Profile with Heat Diffusion and BC')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map with Heat Diffusion and BC')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multipulse Excitation\n",
    "\n",
    "As already stated above, also multiple pulses of variable fluence, pulse width and, delay are possible.\n",
    "\n",
    "The heat diffusion simulation automatically splits the calculation in parts with and without excitation and adjusts the initial temporal step width according to the pulse width.\n",
    "Hence the solver does not miss any excitation pulses when adjusting its temporal step size.\n",
    "\n",
    "The temporal laser pulse profile is always assumed to be Gaussian and the pulse width must be given as FWHM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "h.excitation = {'fluence': [5, 5, 5]*u.mJ/u.cm**2,\n",
    "                'delay_pump':  [0, 10, 20]*u.ps,\n",
    "                'pulse_width':  [0.1, 0.1, 0.1]*u.ps,\n",
    "                'multilayer_absorption': True,\n",
    "                'wavelength': 800*u.nm,\n",
    "                'theta': 45*u.deg}\n",
    "\n",
    "h.boundary_conditions = {'top_type': 'isolator', 'bottom_type': 'isolator'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface incidence fluence scaled by factor 0.7071 due to incidence angle theta=45.00 deg\n",
      "Calculating _heat_diffusion_ for excitation 1:1 ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 0.651250 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93d0e885cf3a4200857fd180673d560c",
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       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 0.684780 s\n",
      "Calculating _heat_diffusion_ for excitation 2:2 ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2e12e196b2042639430fe964de3d733",
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      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 0.565439 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d5e8fee904c4745b89aaa8650e36729",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 0.561970 s\n",
      "Calculating _heat_diffusion_ for excitation 3:3 ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "138420f496a54ed495edde39c74c1b6b",
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       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 0.561475 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9cefde081894b37aac671bfedc2458a",
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       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 0.919587 s\n",
      "Elapsed time for _temp_map_: 4.096767 s\n"
     ]
    }
   ],
   "source": [
    "# The resulting temperature profile is calculated in one line:\n",
    "temp_map, delta_temp = h.get_temp_map(delays, 300*u.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 8])\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[101, :], label=np.round(delays[101]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[201, :], label=np.round(delays[201]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[301, :], label=np.round(delays[301]))\n",
    "plt.plot(distances.to('nm').magnitude, temp_map[-1, :], label=np.round(delays[-1]))\n",
    "plt.xlim([0, distances.to('nm').magnitude[-1]])\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Temperature [K]')\n",
    "plt.legend()\n",
    "plt.title('Temperature Profile Multiplulse')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map Multiplulse')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $N$-Temperature Model\n",
    "\n",
    "The heat diffusion is also capable of simulating an _N_-temperature model which is often applied to empirically simulate the energy flow between _electrons_, _phonons_, and _spins_.\n",
    "In order to run the _NTM_ all thermo-elastic properties must be given as a list of _N_ elements.\n",
    "In addition the `sub_system_coupling` must be provided in order to allow for energy-flow between the sub-systems.\n",
    "The actual external laser-excitation is always set to happen within the __first__ sub-system, which is usually the _electron_-system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the relevant thermo-elastic properties of the layers in the sample structure\n",
    "SRO.therm_cond = [0,\n",
    "                  5.72*u.W/(u.m *u.K)]\n",
    "SRO.lin_therm_exp = [1.03e-5,\n",
    "                     1.03e-5]\n",
    "SRO.heat_capacity = ['lambda T: 0.112*T',\n",
    "                     'lambda T: 455.2 - 2.1935e6/T**2']\n",
    "SRO.sub_system_coupling = ['lambda T: 5e17*(T[1]-T[0])',\n",
    "                           'lambda T: 5e17*(T[0]-T[1])']\n",
    "\n",
    "STO_sub.therm_cond = [0,\n",
    "                      12*u.W/(u.m *u.K)]\n",
    "STO_sub.lin_therm_exp = [1e-5,\n",
    "                         1e-5]\n",
    "STO_sub.heat_capacity = ['lambda T: 0.0248*T',\n",
    "                         'lambda T: 733.73 - 6.531e6/T**2']\n",
    "STO_sub.sub_system_coupling = ['lambda T: 5e17*(T[1]-T[0])',\n",
    "                               'lambda T: 5e17*(T[0]-T[1])']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As no new `Structure` is build, the `num_sub_systems` must be updated by hand.\n",
    "Otherwise this happens automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "S.num_sub_systems = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the excitation conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "h.excitation = {'fluence': [5]*u.mJ/u.cm**2,\n",
    "                'delay_pump':  [0]*u.ps,\n",
    "                'pulse_width':  [0.25]*u.ps,\n",
    "                'multilayer_absorption': True,\n",
    "                'wavelength': 800*u.nm,\n",
    "                'theta': 45*u.deg}\n",
    "\n",
    "h.boundary_conditions = {'top_type': 'isolator', 'bottom_type': 'isolator'}\n",
    "\n",
    "delays = np.r_[-5:15:0.01]*u.ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface incidence fluence scaled by factor 0.7071 due to incidence angle theta=45.00 deg\n",
      "Calculating _heat_diffusion_ for excitation 1:1 ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18705d63ff0749e49416ac80b1e9b0d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 2.048976 s\n",
      "Calculating _heat_diffusion_ ...\n",
      "Absorption profile is calculated by multilayer formalism.\n",
      "Total reflectivity of 56.1 % and transmission of 5.7 %.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eae6083fafa4ee19fc79ebf09d96c83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elapsed time for _heat_diffusion_: 2.306404 s\n",
      "Elapsed time for _temp_map_: 4.498229 s\n"
     ]
    }
   ],
   "source": [
    "# The resulting temperature profile is calculated in one line:\n",
    "temp_map, delta_temp = h.get_temp_map(delays, 300*u.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 8])\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map[:, :, 0])\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map Electrons')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map[:, :, 1])\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance [nm]')\n",
    "plt.ylabel('Delay [ps]')\n",
    "plt.title('Temperature Map Phonons')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+t2uLtm1y6dmxkMqaEDv21WQ4KmNMKllSMJ7tqw6SnxsgJyD06lgAwJc7KjMclTEmlSwpGM/2Vgdpl++cxdyzYyEAGy0pGNOiWFIwnu2rDtG2jXMlqCUFY1omSwrGs31VB5JCl3ZtaJMbYONOuxWjMS2JJQXj2b6aEG3bOM1HIkKvjoV2TMFkVHPrOrs5sKRgPNtXFYzWFAB6diyw5iOTMc2x6+zmwJKC8Wxv9YGaAkDPDoWWFEzG+N119tq1azn88MOZNGkSZWVlfOc732HfvgNdu9x3332MHj2aI444guXLlwNOdxbnnnsuZWVljBs3LtrtxG233call17K+PHjGTBgAPfee290PnfffTfDhw9n+PDh0R5R165dy5AhQ7jiiisYNmwYp512GpWVzrZ27733Rq/injhxYpPXY13WIZ7xrLK6bk2hkM27q6gOhmmTa/sXrdqrU+CrT1M7z0OOgDPvTDja766zwelf6ZFHHuHYY4/l0ksv5YEHHoh2e11SUsKCBQt44IEHuOuuu3j44Yf59a9/zahRo3jhhRf497//zSWXXBJtulq+fDlvv/02u3fv5rDDDuPqq69m8eLF/O1vf2POnDmoKkcffTQnnnginTp1YsWKFTz55JM89NBDfPe73+W5557j4osv5s4772TNmjXk5+ezY8eOJq/mumxLNp7trQ7RLv/AxtSrYyGqsGmXHWw26RfpOnvatGl07dqVCy+8kEcffTQ6/qKLLqK0tJTf//73XHfddUDDus4G6N27N8ceeywAF198Me+//350XLyurN9//31+8IMfAHDyySezdevW6PGKb33rW+Tn51NSUkK3bt3YtGkT77//Pueddx7t2rWjqKiI888/P9onUv/+/Rk5cuRByygrK+Oiiy7i8ccfJzc39fv1vtYURORG4HJAgU+BHwJtgaeBfsBa4Luqut0tfwtwGRACfqyqr/sZn2mYyuoQhXkxzUcxp6XafRVauSR79H7ys+ts4KBEEfs+XlfW8bp9iUwTKR87TbJuYuqWjzQfvfzyy7z33nvMnDmT3/72tyxZsiSlycG3moKI9AJ+DIxR1eFADjARmALMUtXBwCz3PSIy1B0/DDgDeEBEcuLN26SfqroXr9U+0Ax2VbPJDL+7zgZYv349H374IQBPPvkkxx13XNKYTjjhBGbMmAE492MuKSmhuLg4afkXXniBffv2sXfvXp5//nmOP/74hOXD4TAbNmzgpJNO4g9/+AM7duxgz549SWNqKL+bj3KBQhHJxakhbATOAaa746cD57qvzwGeUtUqVV0DrATG+hyf8agqGEYVCvJqH1MA+HK7JQWTfn53nQ0wZMgQpk+fTllZGdu2bePqq69OGtNtt93GvHnzKCsrY8qUKUyfPj1p+dGjRzN58mTGjh3L0UcfzeWXX86oUaMSlg+FQlx88cUcccQRjBo1ihtvvJGOHTsmXUZD+dp1tohcD0wFKoE3VPUiEdmhqh1jymxX1U4icj/wkao+7g5/BHhVVZ+tM88rgSsB+vTpc2Tdm14Yf+zaX0PZbW/wy28N4fLjB0SHj7njTU4Z0p07/6ssg9GZTGjpXWevXbuWs846i88++yzToTRJ1nSdLSKdcPb++wM9gXYicnGySeIMOyhjqeo0VR2jqmO6drVbQaZLVU0YgPw6ZxnZBWzGtCx+Nh+dAqxR1a9VtQb4J/ANYJOI9ABwnze75cuB3jHTl+I0N5ksUB2KJIXah3l6dbKkYFqmfv36NftaQmP4mRTWA+NEpK04h98nAMuAmcAkt8wk4EX39Uxgoojki0h/YDAw18f4TANU1Tj3Uqh7PUKvjs4FbHazndbJvvfs1pjvx7dTUlV1jog8CywAgsAnwDSgCHhGRC7DSRwXuOWXiMgzwFK3/LWqGvIrPtMwVcHEzUf7a8Js3VtNSVF+vElNC1VQUMDWrVvp0qVL3HP8TWapKlu3bqWgoKBB0/l6nYKq/hr4dZ3BVTi1hnjlp+IcmDZZptpNCgfVFDo51yd8ub3SkkIrU1paSnl5OV9//XWmQzEJFBQUUFpa2qBprJsL48mBmkLtYwqx1yqM6J3aU+NMdsvLy6N///6ZDsOkmHVzYTyJ1BTy82r/ZEo7HqgpGGOaP0sKxpOqoHugOaf2T6a4MJei/Fw7A8mYFsKSgvGkKkFNIXKznXKrKRjTIlhSMJ5EDzTnHPyT6dXJ7qtgTEthScF4Emk+ys87uI9Cu6rZmJbDkoLxpDrBdQrg1BR2VtawpyqY7rCMMSlmScF4UpXgOgWw3lKNaUksKRhPEl3RDE7zEcCXO/YdNM4Y07xYUjCeVCU50FzayWoKxrQUlhSMJ1XBEG1yA3H7uOlalE+bnADldrDZmGbPkoLxpDoYjtt0BBAICD06Fti1Csa0AJYUjCdVSZICQJ/ObSnfZscUjGnuLCkYT6qD4bjHEyL6dG7LeksKxjR7lhSMJzWhMHn11BS276th1/6aNEZljEk1SwrGk5pQmLx6agoA67dabcGY5sySgvGkJqTkBhLfXau3mxQ2WBOSMc2aJQXjSU0oHPdq5og+XZyksM6SgjHNmiUF40mwnppCcUEendrm2cFmY5o5SwrGk+p6jimAc1zBmo+Mad4sKRhPgh6SQm87LdWYZs+SgvGkJqTk5SRuPgLo26UtX26vJBgKpykqY0yqWVIwntSEwuR6aD4KhpWKnfvTFJUxJtUsKRhPgmFNekUzHDgt1ZqQjGm+LCkYT5yaQvLmo8gFbOvsAjZjmi1LCsaTYEjrPdDco0MheTliNQVjmjHfkoKIHCYiC2Meu0TkBhHpLCJvisgK97lTzDS3iMhKEflcRE73KzbTcM4pqclrCjkBobSTnZZqTHPmW1JQ1c9VdaSqjgSOBPYBzwNTgFmqOhiY5b5HRIYCE4FhwBnAAyKS41d8pmG8nJIKznGFddv2piEiY4wf0tV8NAFYparrgHOA6e7w6cC57utzgKdUtUpV1wArgbFpis/Uw+n7qP6fS5/Ohazbug9VTUNUxphUS1dSmAg86b7urqoVAO5zN3d4L2BDzDTl7rBaRORKEZknIvO+/vprH0M2sZyus5M3HwH0Lyli9/4g2/ZWpyEqY0yq+Z4URKQNcDbwj/qKxhl20O6mqk5T1TGqOqZr166pCNF4UBMKk+ehpjCgpB0Aa7ZYE5IxzVE6agpnAgtUdZP7fpOI9ABwnze7w8uB3jHTlQIb0xCfqUcorIQVT8cU+rtJYbUlBWOapdxEI0TkXg/T71LVX9ZT5nscaDoCmAlMAu50n1+MGf6EiNwN9AQGA3M9xGB8VuN2W1HfdQoApZ2c01KtpmBM85QwKeAc+L21numnAAmTgoi0BU4FrooZfCfwjIhcBqwHLgBQ1SUi8gywFAgC16pqqN5PYHwXDDutePVd0QyQmxOgT+e2rP56j99hGWN8kCwp3KOq05OMJ/Yag3hUdR/Qpc6wrThnI8UrPxWYmmyeJv1qgt5rCuAcbLaagjHNU7Jdv2cTjRCRbwOo6h9THpHJOjVhJyl4OaYAMKBrO9Zu3UcobKelGtPcJNvKZ4lIv7oDReRSwJJBK1ITcv7c67uiOaJ/STuqg2E27qj0MyxjjA+SJYUbgTdFZHBkgIjc4g4/0e/ATPaI3B/Ba02hv52WakyzlXArV9VXgB8Br4rIcBH5I3AWcIKqlqcrQJN5B84+8t58BJYUjGmOkm7lqjoLmAy8AwwAJqjqdv/DMtkk2nwU8NZ81LUon6L8XEsKxjRDya5T2I1zRbEA+ThnDG0WEQFUVYvTE6LJtJoGNh+JCP1L2tkFbMY0QwmTgqq2T2cgJntFagpeT0kF57jCJxusUmlMc2M32TH1itQUvFy8FtG/pB3l2yvZX2PXHxrTnCTcykVkQX0Teyljmr9gtKbgPSkM7FaEqh1sNqa5SXZF8xARWZxkvAAdUhyPyUIHjil4bz46tHsRACs272FIDzv8ZExzkSwpHO5hemsbaAUaeqAZnOajnICwYtNuv8Iyxvgg2YHmdekMxGSvA1c0e08K+bk59O3SlhWbrGM8Y5oTO9Bs6hUMN6xDvIjB3Yr4YrPVFIxpTiwpmHpVBxt+9hHAod3bs27rPqqC1spoTHPhaSsXkb4icor7ulBE7BqGViRyP4WG1hQGdSsiFFY7A8mYZqTepCAiV+B0o/0Xd1Ap8IKfQZns0tAO8SIO7e7sO9hxBWOaDy9b+bXAscAuAFVdAXTzMyiTXaqjfR81LCn0L2lHQLAzkIxpRrxs5VWqWh15IyK5OH0imVYiWlPIbVjzUUFeDv26tGPFZqspGNNceEkK74rI/wCFInIq8A/gX/6GZbJJtOvsBtYUwDmu8IXVFIxpNrxs5TcDXwOfAlcBrwC/9DMok10aeue1WId2b8/arfuiZzAZY7JbsiuaEZEAsFhVhwMPpSckk21qQmFyA4LTa3rDDO7unIG0esseDj/EurswJtvVd5OdMLBIRPqkKR6ThYJhbfCZRxGRRLC8wpqQjGkOktYUXD2AJSIyF4iecK6qZ/sWlckq1cFwg69RiBjQtR1tcgMsrdjFuaN6pTgyY0yqeUkKt/sehclqwXC4wVczR+TlBDi0exHLKnalOCpjjB/qTQqq+m46AjHZqyaoja4pAAztUcysZZtR1UYdlzDGpI+XK5p3i8gu97FfREIiYrt9rUhNONzoYwrgJIWte6vZvLsqhVEZY/xQ75auqu1Vtdh9FAD/BdzvZeYi0lFEnhWR5SKyTESOEZHOIvKmiKxwnzvFlL9FRFaKyOcicnrjP5ZJpZpQ4w80A9Gb7Cy1JiRjsl6Dt3RVfQE42WPxPwGvqerhwAhgGTAFmKWqg4FZ7ntEZCgwERgGnAE8ICI5DY3PpF4wFG7UNQoRQ3q6SWGjJQVjsl29xxRE5PyYtwFgDB66uRCRYuAEYDKA21VGtYicA4x3i00H3sG5QO4c4ClVrQLWiMhKYCzwobePYvziXKfQ+JpCcUEepZ0K7WCzMc2Al7OPvh3zOgisxfkDr88AnCuh/yYiI4D5wPVAd1WtAFDVChGJdK7XC/goZvpyd1gtInIlcCVAnz52+UQ61ISUvNym3XpjaI9iaz4yphnwkhQeVtUPYgeIyLHAZg/zHg1cp6pzRORPuE1FCcRrnzioRqKq04BpAGPGjLGO+dKgJhQmL9C0s4aG9CjmzWWb2FcdpG0bLz87Y0wmeNn9u8/jsLrKgXJVneO+fxYnSWwSkR4A7vPmmPK9Y6YvBTZ6WI7xWTDUtFNSAYb1LEYVa0IyJssl3GUTkWOAbwBdReSmmFHFQL0HgFX1KxHZICKHqernwARgqfuYBNzpPr/oTjITeEJE7gZ6AoOBuQ3/SCbVqkNh2uc1be9+RO+OACzasJMj+3ZORVjGGB8k29LbAEVumdjbb+4CvuNx/tcBM0SkDbAa+CFO7eQZEbkMWA9cAKCqS0TkGZykEQSuVVW7uW8WCDbxOgWA7sUFdC/OZ3H5jhRFZYzxQ8Kk4F7J/K6IPKqq6xozc1VdiHO2Ul0TEpSfCkxtzLKMf2qC2qRTUiNGlHZkUfnOFERkjPGLlzaBfSLyvzjXDxREBqqq12sVTDNXEw6T28SaAjhNSG8s3cTOfTV0aJuXgsiMManmZUufASwH+uN0jrcW+NjHmEyWqQk1vkO8WCNKneMKi7+0JiRjspWXLb2Lqj4C1Kjqu6p6KTDO57hMFgmGlNwmnpIKcERpBwAWWxOSMVnLS/NRjftcISLfwjlNtNS/kEy2qQmFm3zxGkCHwjwGlLRj4QarKRiTrbwkhTtEpAPwE5zrE4qBG32NymSVmpA2+eK1iBG9O/KfVVtSMi9jTOol3f1zO6QbrKo7VfUzVT1JVY9U1Zlpis9kgZpQ009JjSgr7cCmXVVU7KxMyfyMMalV3z2aQ4DddrOVc65oTk1SOLKv01P6vLXbUzI/Y0xqednS/yMi94vI8SIyOvLwPTKTFVTVvclOapqPhvYopm2bHOat3ZaS+RljUsvLMYVvuM+/iRmmeL+ngmnGQmFFlZQ1H+XmBBjVpyMfW03BmKzk5R7NJ6UjEJOdakJOR7SpSgoAR/XrzJ9mrWDX/hqKC+wiNmOyiZd7NOP7LQ4AABnjSURBVHcXkUdE5FX3/VC33yLTCtSEwwApaz4CJymowoJ1VlswJtt42f17FHgdp+dSgC+AG/wKyGSXmmAkKaSupjCqT0dyAsLHdlzBmKzjZUsvUdVngDCAqgYB6720lQiGU9981LZNLsN7FttxBWOykJctfa+IdMG9C5qIjAOsn4JWotqtKTT1Jjt1HdWvMws37GB/je1fGJNNvCSFm3BugDNQRD4A/o5znwTTCkRqCqnoEC/WsYNKqA6GmW/HFYzJKl7OPlogIicCh+HcR/lzVa2pZzLTQtSE/KkpjO3fmbwcYfaKLRw7qCSl8zbGNF69SUFECoBrgONwmpBmi8iDqrrf7+BM5kWSQiqPKQC0y89lVJ9OvL/ya+DwlM7bGNN4Xrb0v+PcYOc+4H5gKPCYn0GZ7HHgOoXU1hQAjhtUwpKNu9i2tzrl8zbGNI6XpHCYql6mqm+7jyuBQ/0OzGQHv2oKAMcNLkEV6zXVmCziZUv/xD3jCAARORr4wL+QTDaJHlMIpD4plPXqQPuCXN5fYUnBmGzhpe+jo4FLRGS9+74PsExEPgVUVct8i85kXKT5qE1u6puPcnMCfGNgF9794mtUFZHUL8MY0zBeksIZvkdhslbQx+YjgAlDuvP6kk0s2biL4b06+LIMY4x39W7pqroO2AV0ALpEHqq6zh1nWjA/m48AJhzejYDAG0s3+TJ/Y0zDeDkl9bfAZGAV7lXNWNfZrYafzUcAXYryObJvJ95auombTrXzF4zJNC/NR98FBqqqnTfYCvldUwA4dWh3/s8ryynfvo/STm19W44xpn5etvTPgI5+B2KyUzBynUKuf0nhlCHdAXjLmpCMyTgvW/rvcE5LfV1EZkYeXmYuImtF5FMRWSgi89xhnUXkTRFZ4T53iil/i4isFJHPReT0xn0kk0rVkQPNAf/ODBrQtYhB3Yp45bOvfFuGMcYbL81H04HfA5/idp/dQCepauyJ6FOAWap6p4hMcd/fLCJDgYk4V0/3BN4SkUNV1brRzCA/L16LdfaIntz95hds3FFJz46Fvi7LGJOYly19i6re617N/G7k0YRlnoOTaHCfz40Z/pSqVqnqGmAlMLYJyzEpEGk+SnWHeHWdPcK5h9O/Fm30dTnGmOS8JIX5IvI7ETlGREZHHh7nr8AbIjJfRK50h3VX1QoA97mbO7wXsCFm2nJ3WC0icqWIzBOReV9//bXHMExjVaepptCvpB0je3fkxYWWFIzJJC/NR6Pc53Exw7yeknqsqm4UkW7AmyKyPEnZeLuietAA1WnANIAxY8YcNN6kVvRAs89JAeCckT25/V9LWbFpN4O7t/d9ecaYg3m5eO2kOA9P1yio6kb3eTPwPE5z0CYR6QHgPm92i5cDvWMmLwVstzHDakJhAgI5Ph5ojvhWWQ9yAsJzC770fVnGmPjqTQoi0l1EHhGRV933Q0XkMg/TtROR9pHXwGk4p7fOBCa5xSYBL7qvZwITRSRfRPoDg4G5Df1AJrVqwuG01BIAurUvYMLh3fjHvA3R24AaY9LLy9b+KPA6zhlBAF8AN3iYrjvwvogswvlzf1lVXwPuBE4VkRXAqe57VHUJ8AywFHgNuNbOPMq8mqCmLSkAXDSuL1v3VvP6Ejs91ZhMSHhMQURyVTUIlKjqMyJyC4CqBkWk3j9rVV0NjIgzfCswIcE0U4GpXoM3/guGw77cYCeR4weV0LtzITPmrOPbI3rWP4ExJqWS7QJGmm72ikgX3IO+7r0VdvodmMkONaEwuWmsKQQCwvfH9uWj1dtYsWl32pZrjHEk29oju4c34bT3DxSRD3Buz3md34GZ7FAdVNqkMSkAfHdMKQV5AR6avTqtyzXGJE8KXUXkJmA8zplDfwBeBR4CTvE/NJMNguGw7xeu1dWlKJ8Lx/Tm+U++pGJnZVqXbUxrlywp5ABFQHugHc7xhxygrTvMtAI1ofSdfRTr8uMHEFZ4ePaatC/bmNYs2cVrFar6m7RFYrJSTSi9Zx9F9O7clnNG9OSJOeu5evxASory0x6DMa2Rl2MKphVzagqZ+Slce/IgqkNh7p21IiPLN6Y1SpYU4p42alqXYIZqCgADuxYx8ajePDFnPau/3pORGIxpbRJu7aq6LZ2BmOxUHQqTm4YuLhK54ZRDaZMb4PevJes2yxiTKpnZBTTNRjAUpo2Pd12rT9f2+Vx94kBeX7KJfy+3O7MZ4zdLCiapmpBmtKYAcOWJAzi0exG/eP4zdu+vyWgsxrR0lhRMUpk6JTVWfm4Od/5XGV/t2s+dr1ozkjF+sqRgksqGpAAwuk8nLj+uPzPmrOeVTysyHY4xLVbmt3aT1ZzrFLLj7OSfnX44I3p35OfPLmbtlr2ZDseYFsmSgkkqmOYO8ZJpkxvgz98fRU5AuGz6x+zYV53pkIxpcbJjazdZqzqLagoApZ3a8pcfHMmGbZVcPn0e+2vslhvGpJIlBZNUdTBEfm5OpsOoZdyALtx94QjmrdvOFX+fx77qYKZDMqbFsKRgkqrO8HUKiZxV1pO7LhjBByu3MOmvc9lZaaeqGpMK2be1m6yhqlQHw2m/n4JX3zmylPu+N5pP1u/gvD9/YDflMSYFsnNrN1khGFbCCvlZWFOI+FZZD564Yhy79tdw7p8/4Jl5G1DVTIdlTLOVvVu7ybjqYBggK5uPYo3t35mXrjueYT078PNnFzP5bx/bKavGNFJ2b+0mo5pLUgA4pEMBT105jtvPHsbHa7dxyt3vcuuLn/HVzv2ZDs2YZiXZTXZMK1cdcpJCtp19lEggIEz6Rj/OPOIQ7p21ghlz1vPEnPWceUQPLjmmL0f26UQgw/04GZPtLCmYhKpqmk9NIVa39gXcce4RXHXCQB79z1qe+XgD/1q0kR4dCjhzeA9OG9adUX06NptkZ0w6WVIwCVWHnAvDmltSiOjduS2/OmsoN556KG8u/YqXF1fw+Efr+OsHayjICzCmb2fG9u/MsJ7FDO1ZzCHFBYhYTcK0bpYUTEJVwUjzUfNMChFF+bmcN6qU80aVsnt/DR+u2sqHq7fyn5VbufvNL6LlOrbNo2+XdvTuVEjvzm0p7VRIl3b5dClqQ+d2bejctg0dCvOsCcq0aJYUTELN6UCzV+3zcznt8C6cNqgIJvRg7749rKrYxuqKbazZsptNO7exaUMl7y+pJBwOE0AJ4DwLSq6EaZsXoCAvQEFeDoV5OeS7z21yc8jNEXJzcsjLEXICAXJznEdeQKKvAwFBRAggSEAIiPteIBAIIDHvRQKIQI5EhjtlAiLglkMEQRAJgBCdXt35O+ME3OfINM573Oki45zlCThlIstFIBBAODD/yDxxP48QO4/Icqi1XIn5HCKBA/EFAs4ywB3mjItwZiUHYovEERlnNbyU8T0piEgOMA/4UlXPEpHOwNNAP2At8F1V3e6WvQW4DAgBP1bV1/2OzyQWrSlk6cVrVO6AneWwu8J57KqAPZtg/06o2uU879/lvK7eA8FqCFWBhqOzaAeUuY9a8upZdtB9VKbyA5l4wur84Su4qVmirx1y0Dii42uPi50PdeYT+6xyYD7Umq/ULldnvMRMX3f+td9HyhwYXnc50ZhEYuZx4LNsP+QbHHfF3Y1ZpUmlo6ZwPbAMKHbfTwFmqeqdIjLFfX+ziAwFJgLDgJ7AWyJyqKpaj2cZkjU1hWA1bPoUyufBpiWwZQVsXQF7vz64bGEn55FfDAUdoKSb85zXDnLznUdOfszrNgeeAzk4u9qBBA+p/RpAD/wFJXqvOL3NhsJKTSiMqhJWdZ7DzrMC4XA4OlwVVMOE3WdVCKsSDodBcefhzEtUUdRd9IHxaBhF3VDUiUX1wOtovLHTqDuNuuGHkei8D54H7vy11ns9aLnR51rLjR0Wu/5ipokzH4lZr5HYpdY01Jm+znxQJPbzcODvuvbyiS6r9rzqzifsJAN3nlInVmdwuFYaip3Pwcuok05iPpfELLdNm3z84GtSEJFS4FvAVOAmd/A5wHj39XTgHeBmd/hTqloFrBGRlcBY4EM/YzSJVQczdEqqKmxeCl+8DivfcpJBqMoZV9gZuh4Gh50JXQZDp77Qvge0PwSKujt/8FlGcCoeeUBBhmMxpj5+1xT+CPwcaB8zrLuqVgCoaoWIdHOH9wI+iilX7g6rRUSuBK4E6NOnjx8xG1fkOoW01RR2rIdFT8HCJ2D7GmfYIUfAUZdD76Og9Cgo7nVgL90Yk3K+JQUROQvYrKrzRWS8l0niDDuoExtVnQZMAxgzZox1cuOjqmCaTkktnwfv3wPLXwYU+p8Ax90Ag0+D4p7+LtsYU4ufNYVjgbNF5Js4teZiEXkc2CQiPdxaQg9gs1u+HOgdM30psNHH+Ew9qv0+JXXTEnj9F7D6bSjoCCf8FEb9wGkSMsZkhG+7gKp6i6qWqmo/nAPI/1bVi4GZwCS32CTgRff1TGCiiOSLSH9gMDDXr/hM/Xw70Lx/F7z8E3jwOKhYCKfdATcugZN/aQnBmAzLxHUKdwLPiMhlwHrgAgBVXSIizwBLcU72u9bOPMqsKj+Swrr/wPNXOaeSHnUFjJ8CbTunbv7GmCZJS1JQ1XdwzjJCVbcCExKUm4pzppLJAim9olkVPvgTvHUbdOoHl74Ovcc2fb7GmJSyK5pNQtHmo6ZevBasgn9dD4uehGHnwdn3Q35RCiI0xqSaJQWTUFXQuT9zk7oQqKmEpy92rjcY/z9w4s/tlFJjspglBZNQZXWQtm2acOFa9T546nuw+l04+z4YfUnqgjPG+MKSgkmosiZEYV4jk0I4BM9d7iSEcx+Akd9PbXDGGF9YUjAJ7asOUdiYmoIqvHYLfP4ynPkHSwjGNCNZ2v2lyQb7G1tTmPdXmPsXOOa/4eirUh+YMcY3lhRMQo1qPtq4EF6bAoNOgVN/609gxhjfWFIwCTW4+Wj/TvjHZGhbAudNg4D9vIxpbmyrNQlVVjewpvD6L2DHOvjOX6FdF/8CM8b4xpKCSWh/TQNqCqv+DZ88Bt/4MfQ9xt/AjDG+saRgEtpXHfJ2nULVHph5vXPTm/FT/A/MGOMbOyXVJFRZE6LAS/PRe/8LO9c7/RnlFfofmDHGN1ZTMHGpKpVeagrbVsNHD8CI70GfcekJzhjjG0sKJq7KmhDBsNK+IC95wTd+BYE8mPDr9ARmjPGVJQUT1+79QQCKkyWFtR/A8pfg+JuguEeaIjPG+MmSgolrV2UNAO0Lkhx2eud3UHQIHHNtmqIyxvjNkoKJa9d+JykUFyaoKayZDWtnw3E32sFlY1oQSwomrl1u81HCmsK7v3dqCUdOij/eGNMsWVIwcUWaj+IeU1j7vtUSjGmhLCmYuA4caI5TU5h9N7TrZrUEY1ogSwomri17qgDo2LZN7REVi2DVLBh3tdUSjGmBLCmYuDbvrqJLuza0ya3zE/ngT9CmPYy5NDOBGWN8ZUnBxLV51366FRfUHrhtDSx5HsZMhsKOGYnLGOMvSwomrk27quhenF974If3g+TAuGsyE5QxxneWFExcG3dUckhsTWHHeljwdxgxEYp7Zi4wY4yvLCmYg2zbW83WvdUM7Fp0YOC/p4IErGtsY1o435KCiBSIyFwRWSQiS0Tkdnd4ZxF5U0RWuM+dYqa5RURWisjnInK6X7GZ5BaX7wBgSI9iZ8CKN2HxU84ZRx1KMxiZMcZvftYUqoCTVXUEMBI4Q0TGAVOAWao6GJjlvkdEhgITgWHAGcADItLAu8abVHh5cQUFeQGO7F0My16Cf/wQug2FE62WYExL59tNdlRVgT3u2zz3ocA5wHh3+HTgHeBmd/hTqloFrBGRlcBY4MNUx7ZmyRwCz10WiTQ6XGJfq1KXJCjrrYy6wxKX1ejrmDKafJmJltfw+A4M+6Uqv2mTQ+FdQQhWOgnh4ucgr+Cg8saYlsXXO6+5e/rzgUHAn1V1joh0V9UKAFWtEJFubvFewEcxk5e7w+rO80rgSoA+ffo0Kq68/HZsbjsg+l5j/oZVav9t1+WUPXg4MdMpyedRezm1p5OY1/FmcWD4wTGLl2VLojIHhnVo28ZpOsrJhV6jYcjZkFvnIjZjTIvka1JQ1RAwUkQ6As+LyPAkxeP9ex60K6uq04BpAGPGjIm/O1yP0kHDKf3pzMZMaowxLVpazj5S1R04zURnAJtEpAeA+7zZLVYO9I6ZrBTYmI74jDHGOPw8+6irW0NARAqBU4DlwEwg0pPaJOBF9/VMYKKI5ItIf2AwMNev+IwxxhzMz+ajHsB097hCAHhGVV8SkQ+BZ0TkMmA9cAGAqi4RkWeApUAQuNZtfjLGGJMmonHOsmkuxowZo/Pmzct0GMYY06yIyHxVHRNvnF3RbIwxJsqSgjHGmChLCsYYY6IsKRhjjIlq1geaReRrYF0TZlECbElROKlkcTWMxdUwFlfDtMS4+qpq13gjmnVSaCoRmZfoCHwmWVwNY3E1jMXVMK0tLms+MsYYE2VJwRhjTFRrTwrTMh1AAhZXw1hcDWNxNUyriqtVH1MwxhhTW2uvKRhjjIlhScEYY0xUq0kKInKbiHwpIgvdxzcTlDtDRD4XkZUikpabEovI/4rIchFZLCLPR7ocj1NurYh86sbvS0+A9X1+cdzrjl8sIqP9iKPOMnuLyNsiskxElojI9XHKjBeRnTHf761+xxWz7KTfS4bW2WEx62KhiOwSkRvqlEnLOhORv4rIZhH5LGZYZxF5U0RWuM+dEkzr2/aYIK6Mb4sJ4krf/5eqtooHcBvw03rK5ACrgAFAG2ARMDQNsZ0G5Lqvfw/8PkG5tUCJj3HU+/mBbwKv4twpbxwwJw3rpwcw2n3dHvgiTlzjgZcy9NtK+r1kYp3F+V6/wrlgKe3rDDgBGA18FjPsD8AU9/WUeL95v7fHBHFlfFtMEFfa/r9aTU3Bo7HASlVdrarVwFPAOX4vVFXfUNWg+/YjnLvOZYKXz38O8Hd1fAR0jNxJzy+qWqGqC9zXu4FlxLl/dxZL+zqrYwKwSlWbcvV/o6nqe8C2OoPPAaa7r6cD58aZ1NftMV5c2bAtJlhfXqRkfbW2pPDfbrXwrwmqq72ADTHvy0n/n8+lOHuV8SjwhojMF5ErfVi2l8+f0XUkIv2AUcCcOKOPEZFFIvKqiAxLV0zU/71k+nc1EXgywbhMrbPuqloBTtIHusUpk+n1lsltMZ60/H/5eee1tBORt4BD4oz6BfD/gN/ifJm/Bf4vzpdeaxZxpk3JObvJYlPVF90yv8C569yMBLM5VlU3ikg34E0RWe7uVaSKl8/v2zqqj4gUAc8BN6jqrjqjF+A0j+xx21tfwLmlazrU971kcp21Ac4GbokzOpPrzItMrrdMb4t1pe3/q0UlBVU9xUs5EXkIeCnOqHKgd8z7UmBjCkKrNzYRmQScBUxQt4Ewzjw2us+bReR5nOpiKn+IXj6/b+soGRHJw0kIM1T1n3XHxyYJVX1FRB4QkRJV9b0jMw/fS0bWmetMYIGqbqo7IpPrDNgkIj1UtcJtStscp0ymfmvZsC3WXV70+/P7/6vVNB/VacM9D/gsTrGPgcEi0t/dw5oIzExDbGcANwNnq+q+BGXaiUj7yGucA2LxPkNTePn8M4FL3DNqxgE7I80AfhERAR4Blqnq3QnKHOKWQ0TG4vy2t/oZl7ssL99L2tdZjO+RoOkoU+vMNROY5L6eBLwYp0zat8cs2hbrLjN9/19+HD3PxgfwGPApsNhdUT3c4T2BV2LKfRPn7JZVOE076YhtJU5b4EL38WDd2HDOKFjkPpb4FVu8zw/8CPiR+1qAP7vjPwXGpGH9HIdTDV4cs46+WSeu/3bXyyKcA4TfSNN3F/d7yfQ6c5fbFudPvkPMsLSvM5ykVAHU4OzNXgZ0AWYBK9znznV/84l+jz7HlfFtMUFcafv/sm4ujDHGRLWa5iNjjDH1s6RgjDEmypKCMcaYKEsKxhhjoiwpGGOMibKkYFolEQm5vU0ucbt5uElEkm4PItIvtufKJiw70jvpK42c/n9F5CsR+WlTYzGmrhZ1RbMxDVCpqiMB3K4KngA6AL9O0/Jnq+pZjZlQVX8mIntTHZAxYDUFY1DVzcCVOB2OiYjkuHvjH7sdkF1Vdxq31jBbRBa4j2+4wx8TkXNiys0QkbOTLd+tObwnTv/9S0XkQREJuHE8KiKfidN3/42p/uzG1GU1BWMAVV3tNh91w+lueKeqHiUi+cAHIvIGtTsX2wycqqr7RWQwzlWoY4CHgRuBF0WkA/ANDnTnkMxYYCiwDngNOB9YA/RS1eEAkuCGL8akktUUjDkg0svkaTh9FS3E6aK7Cwf3HpoHPCQinwL/wPlDR1XfBQa5TVLfA57TA/3zJzNXnX7wQzgJ5jhgNTBARO5z++Sp2zOsMSlnNQVjABEZAIRwagACXKeqr9cp0y/m7Y3AJmAEzs7V/phxjwEX4XRIVrd740Tq9jejqrpdREYApwPXAt9twPyMaRSrKZhWT0S6Ag8C96vTGdjrwNVud92IyKFub5ixOgAVqhoGfoBzK8SIR4EbAFR1iccwxrq9WwaAC4H3RaQECKjqc8CvcG7RaIyvrKZgWqtCt3koD+dmKo8BkW65Hwb6AQvcrqW/5uDbRT4APCciFwBvA9GzgVR1k4gsw7lpjVcfAncCR+D0y/+8+/pvMafKxrtRjjEpZUnBtEqqmpNkXBj4H/cRaycw3C2zAiiLGRf9wxaRtjjHIBLdAjOefap6YZ1hi7DagUkzaz4yJoVE5BRgOXCfqu5MUKwaGN6Ui9eAi4mpnRiTKnY/BWOMMVFWUzDGGBNlScEYY0yUJQVjjDFRlhSMMcZEWVIwxhgT9f8BQx/vCx4WxH0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(delays.to('ps'), np.mean(temp_map[:, S.get_all_positions_per_unique_layer()['SRO'], 0], 1), label='SRO electrons')\n",
    "plt.plot(delays.to('ps'), np.mean(temp_map[:, S.get_all_positions_per_unique_layer()['SRO'], 1], 1), label='SRO phonons')\n",
    "plt.ylabel('Temperature [K]')\n",
    "plt.xlabel('Delay [ps]')\n",
    "plt.legend()\n",
    "plt.title('Temperature Electrons vs. Phonons')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
