{
 "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'] = '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'] = '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": [],
   "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_: 0.009594 s\n",
      "Elapsed time for _temp_map_: 0.043767 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, shading='auto')\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_: 0.008001 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcb2997aac2d4348b9d55dc4a447522c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 3.790561 s\n",
      "Elapsed time for _temp_map_: 3.846340 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, shading='auto')\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_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "449e94d51f4544498f88848fd22807c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 0.370573 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_: 0.003000 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88d27fa139224a1a9d66cca4a685c0e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 0.412945 s\n",
      "Elapsed time for _temp_map_: 0.805569 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, shading='auto')\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, 5]*u.mJ/u.cm**2,\n",
    "                'delay_pump':  [0, 10, 20, 20.5]*u.ps,\n",
    "                'pulse_width':  [0.1, 0.1, 0.1, 0.5]*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 1:4 ...\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": "fbf71410779e4114a0a476414f593199",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 0.363856 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44d5c01d03b04a6e8bb53752dbf3a1fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 0.389768 s\n",
      "Calculating _heat_diffusion_ for excitation 2:4 ...\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": "434c58a45f1441be8e87f91444ff8a52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 0.267384 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "541664b2864042109fc014e3b8cca643",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 0.317999 s\n",
      "Calculating _heat_diffusion_ for excitation 3-4:4...\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": "a300490e16be4fe2a2e62b6887dcee1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_ with 2 excitation(s): 0.361693 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8415b3d2d87d45109870b142733f1f5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 0.411706 s\n",
      "Elapsed time for _temp_map_: 2.231762 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, shading='auto')\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 corresponding to different sub-systems.\n",
    "The actual external laser-excitation is always set to happen within the __first__ sub-system, which is usually the electron-system.\n",
    "\n",
    "In addition the `sub_system_coupling` must be provided in order to allow for energy-flow between the sub-systems.\n",
    "`sub_system_coupling` is often set to a constant prefactor multiplied with the difference between the electronic and phononic temperatures, as in the example below. \n",
    "For sufficiently high temperatures, this prefactor also depdends on temperature. See [here](https://faculty.virginia.edu/CompMat/electron-phonon-coupling/) for an overview. \n",
    "\n",
    "In case the thermo-elastic parameters are provided as functions of the temperature $T$, the `sub_system_coupling` requires the temperature `T` to be a vector of all sub-system-temperatures which can be accessed in the function string via the underscore-notation. The `heat_capacity` and `lin_therm_exp` instead require the temperature `T` to be a scalar of only the current sub-system-temperature. For the `therm_cond` both options are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subsystems changed from 1 to 2.\n",
      "Number of subsystems changed from 1 to 2.\n"
     ]
    }
   ],
   "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 = ['0.112*T',\n",
    "                     '455.2 - 2.1935e6/T**2']\n",
    "SRO.sub_system_coupling = ['5e17*(T_1-T_0)',\n",
    "                           '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 = ['0.0248*T',\n",
    "                         '733.73 - 6.531e6/T**2']\n",
    "STO_sub.sub_system_coupling = ['5e17*(T_1-T_0)',\n",
    "                               '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": "d27ba282c00b4ec3be47ef60c949d65c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_ with 1 excitation(s): 1.204569 s\n",
      "Calculating _heat_diffusion_ without excitation...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b7482cdf50d45b7a27c72d01f707d21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for _heat_diffusion_: 1.451723 s\n",
      "Elapsed time for _temp_map_: 2.718771 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], shading='auto')\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], shading='auto')\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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\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": "3.8.9"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
