python/examples/3rd-harm-1d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Third Harmonic Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we consider wave propagation through a simple 1d nonlinear medium with a non-zero Kerr susceptibility $\\chi^{(3)}$. See also Materials and Units and Nonlinearity. We send in a narrow-band pulse at a frequency $\\omega$, and because of the nonlinearity we also get a signal at a frequency $3\\omega$.\n",
    "\n",
    "Since this is a 1d calculation, we could implement it via a 2d cell of `Vector3(S,0,0)`, specifying periodic boundary conditions in the `y` direction. However, this is slightly inefficient since the `y` periodic boundaries are implemented internally via extra \"ghost pixels\" in the `y` direction. Instead, Meep has special support for 1d simulations in the `z` direction. To use this, we must explicitly set dimensions to 1, and in that case we can only use $E_x$ (and $D_x$) and $H_y$ field components. This involves no loss of generality because of the symmetry of the problem.\n",
    "\n",
    "First, we'll load the necessary modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MPI version 3.1, 1 processes\n"
     ]
    }
   ],
   "source": [
    "import meep as mp\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll define some parameters of our simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sz = 100              # size of cell in z direction\n",
    "fcen = 1 / 3.0        # center frequency of source\n",
    "df = fcen / 20.0      # frequency width of source\n",
    "amp = 1               # amplitude of source\n",
    "k = 10**-5            # Kerr susceptibility\n",
    "dpml = 1.0            # PML thickness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to define our cell, we'll do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dimensions = 1\n",
    "cell = mp.Vector3(0, 0, sz)\n",
    "pml_layers = mp.PML(dpml)\n",
    "resolution = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this will only put PMLs at the $\\pm z$ boundaries.\n",
    "\n",
    "In this case, we're going to fill the entire computational cell with the nonlinear medium, so we don't need to use any objects. We can just use the special `default_material` which is ordinarily vacuum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_material = mp.Medium(index=1, chi3=k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, our source will be a Gaussian pulse of $J_x$ just next to the $−z$ PML layer. Since this is a nonlinear calculation, we may want to play with the amplitude of the current/field, so we set the amplitude property explicitly to our parameter `amp`, above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n",
    "                    center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want the frequency spectrum at the $+z$ end of the computational cell. In a linear problem, we normally look at the spectrum over the same frequency range as our source, because other frequencies are zero. In this case, however, we will look from `fcen/2` to `4*fcen`, to be sure that we can see the third-harmonic frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nfreq = 400\n",
    "fmin = fcen / 2.0\n",
    "fmax = fcen * 4\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell,\n",
    "                    geometry=[],\n",
    "                    sources=[sources],\n",
    "                    boundary_layers=[pml_layers],\n",
    "                    default_material=default_material,\n",
    "                    resolution=resolution,\n",
    "                    dimensions=dimensions)\n",
    "\n",
    "trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n",
    "                     mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll run the sources, plus additional time for the field to decay at the flux plane, and output the flux spectrum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357827005e-12 / 4.1691008357827005e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168812386e-08 / 1.0165420168812386e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785262405232e-06 / 4.650785262405232e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454866366283557 / 0.0005454866366283557 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017815669509667658 / 0.017815669509667658 = 1.0\n",
      "field decay(t = 350.175): 0.13192124155368243 / 0.13192124155368243 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2505510561658855 / 0.2505510561658855 = 1.0\n",
      "field decay(t = 450.225): 0.2499503102652878 / 0.2505510561658855 = 0.9976023014638582\n",
      "field decay(t = 500.25): 0.11167289094888443 / 0.2505510561658855 = 0.4457091207587955\n",
      "field decay(t = 550.275): 0.012841517437730344 / 0.2505510561658855 = 0.05125309641172774\n",
      "field decay(t = 600.3000000000001): 0.0003786349795018242 / 0.2505510561658855 = 0.0015112088741351644\n",
      "field decay(t = 650.325): 2.4161065872412755e-06 / 0.2505510561658855 = 9.643170634417973e-06\n",
      "field decay(t = 700.35): 4.490921803004586e-09 / 0.2505510561658855 = 1.7924178296144254e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n"
     ]
    }
   ],
   "source": [
    "sim.run(until_after_sources=mp.stop_when_fields_decayed(\n",
    "        50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a linear calculation, we normalize the transmission against some reference spectrum, but in this case there is no obvious normalization so we will just plot the raw data. To do so, we'll pull the frequency points using `get_flux_freqs()` and the corrensponding spectra using `get_flux_freqs()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs = mp.get_flux_freqs(trans)\n",
    "spectra = mp.get_fluxes(trans)\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plt.semilogy(freqs,spectra)\n",
    "plt.grid(True)\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Transmitted Power (a.u.)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next want to see what happens as we slowly increase our nonlinearity term ($\\chi^{(3)}$). We'll wrap our routine in a function and parameterize it so that we can quickly loop over the various nonlinearities.\n",
    "\n",
    "It is also interesting to have a more detailed look at the dependence of the power at $\\omega$ and $3\\omega$ as a function of $\\chi^{(3)}$ and the current amplitude. We could, of course, interpolate the flux spectrum above to get the desired frequencies, but it is easier just to add two more flux regions to Meep and request exactly the desired frequency components. We'll add the additional fluxes to our function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_chi3(k_pow,amp=1):\n",
    "    k = 10**k_pow \n",
    "    default_material = mp.Medium(index=1, chi3=k)\n",
    "    \n",
    "    sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex,\n",
    "                    center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp)\n",
    "    \n",
    "    sim = mp.Simulation(cell_size=cell,\n",
    "                    geometry=[],\n",
    "                    sources=[sources],\n",
    "                    boundary_layers=[pml_layers],\n",
    "                    default_material=default_material,\n",
    "                    resolution=resolution,\n",
    "                    dimensions=dimensions)\n",
    "\n",
    "    trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq,\n",
    "                     mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n",
    "    \n",
    "    # Single frequency point at omega\n",
    "    trans1 = sim.add_flux(fcen, 0, 1,\n",
    "                      mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n",
    "\n",
    "    # Singel frequency point at 3omega\n",
    "    trans3 = sim.add_flux(3 * fcen, 0, 1,\n",
    "                      mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5)))\n",
    "    \n",
    "    sim.run(until_after_sources=mp.stop_when_fields_decayed(\n",
    "        50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6))\n",
    "    \n",
    "    omega_flux = mp.get_fluxes(trans1)\n",
    "    omega3_flux = mp.get_fluxes(trans3)\n",
    "    freqs = mp.get_flux_freqs(trans)\n",
    "    spectra = mp.get_fluxes(trans)\n",
    "    \n",
    "    return freqs, spectra, omega_flux, omega3_flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now loop over various nonlinearities to see what effect this has on our frequency response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n",
      "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n",
      "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n",
      "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n",
      "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n",
      "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n",
      "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n",
      "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835772216e-12 / 4.169100835772216e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420065576282e-08 / 1.0165420065576282e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650764664560235e-06 / 4.650764664560235e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005452160206480376 / 0.0005452160206480376 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017507545378757882 / 0.017507545378757882 = 1.0\n",
      "field decay(t = 350.175): 0.11744036969268859 / 0.11744036969268859 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.20769602060422127 / 0.20769602060422127 = 1.0\n",
      "field decay(t = 450.225): 0.2072148912173212 / 0.20769602060422127 = 0.9976834925122764\n",
      "field decay(t = 500.25): 0.10101160503184105 / 0.20769602060422127 = 0.48634347802130234\n",
      "field decay(t = 550.275): 0.012670991144119082 / 0.20769602060422127 = 0.06100738525108532\n",
      "field decay(t = 600.3000000000001): 0.00037847776427704205 / 0.20769602060422127 = 0.001822267769868624\n",
      "field decay(t = 650.325): 2.416057119618937e-06 / 0.20769602060422127 = 1.1632659656117805e-05\n",
      "field decay(t = 700.35): 4.491338910111841e-09 / 0.20769602060422127 = 2.162457854053155e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835677209e-12 / 4.169100835677209e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165419135520952e-08 / 1.0165419135520952e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650579070094068e-06 / 4.650579070094068e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005427300142038431 / 0.0005427300142038431 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.015051531229532927 / 0.015051531229532927 = 1.0\n",
      "field decay(t = 350.175): 0.10911319432715134 / 0.10911319432715134 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.22726682483704244 / 0.22726682483704244 = 1.0\n",
      "field decay(t = 450.225): 0.22744269278874502 / 0.22744269278874502 = 1.0\n",
      "field decay(t = 500.25): 0.10405323422921754 / 0.22744269278874502 = 0.45749209593585416\n",
      "field decay(t = 550.275): 0.011388920462070745 / 0.22744269278874502 = 0.05007380242657031\n",
      "field decay(t = 600.3000000000001): 0.00037704599306494387 / 0.22744269278874502 = 0.001657762614581575\n",
      "field decay(t = 650.325): 2.4160426967236526e-06 / 0.22744269278874502 = 1.062264373983533e-05\n",
      "field decay(t = 700.35): 4.491856927586553e-09 / 0.22744269278874502 = 1.9749400926055307e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n",
      "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n",
      "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n",
      "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n",
      "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n",
      "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n",
      "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n",
      "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n"
     ]
    }
   ],
   "source": [
    "k_pow = [-3,-2,-1,0]\n",
    "freqs = []\n",
    "spectra = []\n",
    "for k_iter in k_pow:\n",
    "    freqs_iter, spectra_iter, omega_flux, omega3_flux = run_chi3(k_iter)\n",
    "    spectra.append(spectra_iter)\n",
    "    freqs = freqs_iter # Each iteration will simulate over the same frequencies, so just remember the last set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "plt.semilogy(freqs,np.array(spectra).T)\n",
    "plt.grid(True)\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Transmitted Power (a.u.)')\n",
    "plt.legend([\"$\\chi^{{(3)}} = 10^{{{}}}$\".format(i) for i in k_pow])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For small values of $\\chi^{(3)}$, we see a peak from our source at $\\omega=1/3$ and another peak precisely at the third-harmonic frequency $3\\oemega=1$. As the $\\chi^{(3)}$ gets larger, frequency-mixing within the peaks causes them to broaden, and finally for $\\chi^{(3)}=1$ we start to see a noisy, broad-spectrum transmission due to the phenomenon of _modulation instability_. Notice also that at around $10^{−13}$ the data looks weird; this is probably due to our finite simulation time, imperfect absorbing boundaries, etcetera. We haven't attempted to analyze it in detail for this case.\n",
    "\n",
    "Now, we can look specifically at our frequencies of interest. We'll run a quick simulation for a linear medium. We'll measure the PSD at $\\omega$ and use this as a calibration factor for much larger nonlinearities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016542016891584e-08 / 1.016542016891584e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785283023354e-06 / 4.650785283023354e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454869069704206 / 0.0005454869069704206 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017816014066636514 / 0.017816014066636514 = 1.0\n",
      "field decay(t = 350.175): 0.13193941695549694 / 0.13193941695549694 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2506140638300714 / 0.2506140638300714 = 1.0\n",
      "field decay(t = 450.225): 0.2500128531691806 / 0.2506140638300714 = 0.9976010497906516\n",
      "field decay(t = 500.25): 0.11168480654371428 / 0.2506140638300714 = 0.4456446092324733\n",
      "field decay(t = 550.275): 0.012841704978003306 / 0.2506140638300714 = 0.05124095903376999\n",
      "field decay(t = 600.3000000000001): 0.0003786351315011641 / 0.2506140638300714 = 0.001510829542901859\n",
      "field decay(t = 650.325): 2.4161060812423408e-06 / 0.2506140638300714 = 9.640744195747047e-06\n",
      "field decay(t = 700.35): 4.490904760943549e-09 / 0.2506140638300714 = 1.7919603921305078e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "Omega: 225.25726603587026, 3Omega: 5.026979907074879e-16\n"
     ]
    }
   ],
   "source": [
    "_, _, omega_flux_cal, omega3_flux_cal = run_chi3(-16)\n",
    "print(\"Omega: {}, 3Omega: {}\".format(omega_flux_cal[0],omega3_flux_cal[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, the linear transmission is 225.25726603587026 at $\\omega$, so we'll loop through several nonlinearities, divide by this value, and plot the fractional transmission at $\\omega$ and $3\\omega$ as a function of $\\chi1^{(3)}$ on a log-log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168905492e-08 / 1.0165420168905492e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785280961551e-06 / 4.650785280961551e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454868799362667 / 0.0005454868799362667 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017815979612345514 / 0.017815979612345514 = 1.0\n",
      "field decay(t = 350.175): 0.13193760003745314 / 0.13193760003745314 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2506077676780644 / 0.2506077676780644 = 1.0\n",
      "field decay(t = 450.225): 0.250006603482365 / 0.2506077676780644 = 0.9976011749305723\n",
      "field decay(t = 500.25): 0.1116836154292776 / 0.2506077676780644 = 0.4456510524955018\n",
      "field decay(t = 550.275): 0.012841686224530456 / 0.2506077676780644 = 0.051242171555620476\n",
      "field decay(t = 600.3000000000001): 0.00037863511629851986 / 0.2506077676780644 = 0.0015108674396115361\n",
      "field decay(t = 650.325): 2.416106131823971e-06 / 0.2506077676780644 = 9.640986607118051e-06\n",
      "field decay(t = 700.35): 4.4909064662334595e-09 / 0.2506077676780644 = 1.7920060929645904e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168894454e-08 / 1.0165420168894454e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785278757183e-06 / 4.650785278757183e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454868510330065 / 0.0005454868510330065 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.0178159427756053 / 0.0178159427756053 = 1.0\n",
      "field decay(t = 350.175): 0.13193565734819723 / 0.13193565734819723 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2506010350871025 / 0.2506010350871025 = 1.0\n",
      "field decay(t = 450.225): 0.24999992057177803 / 0.2506010350871025 = 0.9976013087291696\n",
      "field decay(t = 500.25): 0.11168234185404839 / 0.2506010350871025 = 0.4456579431734209\n",
      "field decay(t = 550.275): 0.012841666174342072 / 0.2506010350871025 = 0.05124346820785731\n",
      "field decay(t = 600.3000000000001): 0.00037863510004548367 / 0.2506010350871025 = 0.0015109079653796314\n",
      "field decay(t = 650.325): 2.416106185906883e-06 / 0.2506010350871025 = 9.641245835505452e-06\n",
      "field decay(t = 700.35): 4.490908289112879e-09 / 0.2506010350871025 = 1.7920549639996314e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357827595e-12 / 4.1691008357827595e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016542016887149e-08 / 1.016542016887149e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785274196042e-06 / 4.650785274196042e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454867912281307 / 0.0005454867912281307 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017815866554169706 / 0.017815866554169706 = 1.0\n",
      "field decay(t = 350.175): 0.1319316371539862 / 0.1319316371539862 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.250587100703692 / 0.250587100703692 = 1.0\n",
      "field decay(t = 450.225): 0.24998608899268374 / 0.250587100703692 = 0.997601585599097\n",
      "field decay(t = 500.25): 0.11167970629203668 / 0.250587100703692 = 0.4456722073020547\n",
      "field decay(t = 550.275): 0.012841624687286904 / 0.250587100703692 = 0.05124615214121316\n",
      "field decay(t = 600.3000000000001): 0.00037863506641780544 / 0.250587100703692 = 0.0015109918481619068\n",
      "field decay(t = 650.325): 2.416106297824614e-06 / 0.250587100703692 = 9.64178240236536e-06\n",
      "field decay(t = 700.35): 4.49091206005567e-09 / 0.250587100703692 = 1.7921561195466212e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835782728e-12 / 4.169100835782728e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016542016882427e-08 / 1.016542016882427e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785264758402e-06 / 4.650785264758402e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454866674834187 / 0.0005454866674834187 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017815708836639688 / 0.017815708836639688 = 1.0\n",
      "field decay(t = 350.175): 0.13192331666924295 / 0.13192331666924295 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2505582526127686 / 0.2505582526127686 = 1.0\n",
      "field decay(t = 450.225): 0.24995745365544636 / 0.2505582526127686 = 0.9976021585756716\n",
      "field decay(t = 500.25): 0.11167425141378004 / 0.2505582526127686 = 0.4457017489915599\n",
      "field decay(t = 550.275): 0.012841538842926424 / 0.2505582526127686 = 0.05125170976815797\n",
      "field decay(t = 600.3000000000001): 0.000378634996846925 / 0.2505582526127686 = 0.0015111655389459305\n",
      "field decay(t = 650.325): 2.4161065294750964e-06 / 0.2505582526127686 = 9.642893436079026e-06\n",
      "field decay(t = 700.35): 4.49091985908172e-09 / 0.2505582526127686 = 1.7923655725769776e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357828475e-12 / 4.1691008357828475e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168726412e-08 / 1.0165420168726412e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785245230697e-06 / 4.650785245230697e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454864114377906 / 0.0005454864114377906 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017815382476511977 / 0.017815382476511977 = 1.0\n",
      "field decay(t = 350.175): 0.13190609125009525 / 0.13190609125009525 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2504984938450849 / 0.2504984938450849 = 1.0\n",
      "field decay(t = 450.225): 0.24989813526348434 / 0.2504984938450849 = 0.9976033445455691\n",
      "field decay(t = 500.25): 0.11166295794325522 / 0.2504984938450849 = 0.4457629913428168\n",
      "field decay(t = 550.275): 0.012841361210895873 / 0.2504984938450849 = 0.05126322723056899\n",
      "field decay(t = 600.3000000000001): 0.00037863485293490557 / 0.2504984938450849 = 0.0015115254671713265\n",
      "field decay(t = 650.325): 2.4161070090188778e-06 / 0.2504984938450849 = 9.645195753205065e-06\n",
      "field decay(t = 700.35): 4.490935979334019e-09 / 0.2504984938450849 = 1.792799593482321e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835782678e-12 / 4.169100835782678e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168523849e-08 / 1.0165420168523849e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785204825125e-06 / 4.650785204825125e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454858816409209 / 0.0005454858816409209 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017814707103397807 / 0.017814707103397807 = 1.0\n",
      "field decay(t = 350.175): 0.13187041021795567 / 0.13187041021795567 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.250374554159776 / 0.250374554159776 = 1.0\n",
      "field decay(t = 450.225): 0.24977510744804346 / 0.250374554159776 = 0.9976058001830729\n",
      "field decay(t = 500.25): 0.11163956208436386 / 0.250374554159776 = 0.4458902082083042\n",
      "field decay(t = 550.275): 0.012840993630635223 / 0.250374554159776 = 0.051287135283087794\n",
      "field decay(t = 600.3000000000001): 0.00037863455532979455 / 0.250374554159776 = 0.0015122725094826118\n",
      "field decay(t = 650.325): 2.4161080020963407e-06 / 0.250374554159776 = 9.649974256387518e-06\n",
      "field decay(t = 700.35): 4.490969255239367e-09 / 0.250374554159776 = 1.7937003503852332e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835782697e-12 / 4.169100835782697e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420168104837e-08 / 1.0165420168104837e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650785121220347e-06 / 4.650785121220347e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454847854047326 / 0.0005454847854047326 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01781330928257498 / 0.01781330928257498 = 1.0\n",
      "field decay(t = 350.175): 0.1317964137861031 / 0.1317964137861031 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.25011687030360397 / 0.25011687030360397 = 1.0\n",
      "field decay(t = 450.225): 0.24951931317382292 / 0.25011687030360397 = 0.9976108883456933\n",
      "field decay(t = 500.25): 0.11159103280876947 / 0.25011687030360397 = 0.4461555618911866\n",
      "field decay(t = 550.275): 0.012840232906414287 / 0.25011687030360397 = 0.051336932574073034\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "field decay(t = 600.3000000000001): 0.0003786339402465762 / 0.25011687030360397 = 0.0015138280747994807\n",
      "field decay(t = 650.325): 2.416110058754147e-06 / 0.25011687030360397 = 9.659924401826058e-06\n",
      "field decay(t = 700.35): 4.491037714235971e-09 / 0.25011687030360397 = 1.795575687791284e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835782566e-12 / 4.169100835782566e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016542016723775e-08 / 1.016542016723775e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650784948230582e-06 / 4.650784948230582e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454825170836912 / 0.0005454825170836912 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01781041537070784 / 0.01781041537070784 = 1.0\n",
      "field decay(t = 350.175): 0.1316425937154995 / 0.1316425937154995 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.24957847104378408 / 0.24957847104378408 = 1.0\n",
      "field decay(t = 450.225): 0.2489848350078133 / 0.24957847104378408 = 0.99762144533746\n",
      "field decay(t = 500.25): 0.11149010814744109 / 0.24957847104378408 = 0.4467136435333084\n",
      "field decay(t = 550.275): 0.012838658222915229 / 0.24957847104378408 = 0.05144136899798106\n",
      "field decay(t = 600.3000000000001): 0.0003786326703923277 / 0.24957847104378408 = 0.0015170886687814647\n",
      "field decay(t = 650.325): 2.4161143069600103e-06 / 0.24957847104378408 = 9.680780144438605e-06\n",
      "field decay(t = 700.35): 4.4911771950517e-09 / 0.24957847104378408 = 1.7995050519657217e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357823855e-12 / 4.1691008357823855e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420165443837e-08 / 1.0165420165443837e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650784590290659e-06 / 4.650784590290659e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454778233702294 / 0.0005454778233702294 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017804420515894783 / 0.017804420515894783 = 1.0\n",
      "field decay(t = 350.175): 0.13132132371111363 / 0.13132132371111363 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.24844276804284895 / 0.24844276804284895 = 1.0\n",
      "field decay(t = 450.225): 0.24785728763909465 / 0.24844276804284895 = 0.9976433992892346\n",
      "field decay(t = 500.25): 0.11127911965592859 / 0.24844276804284895 = 0.4479064556096729\n",
      "field decay(t = 550.275): 0.01283539726004091 / 0.24844276804284895 = 0.05166339660902179\n",
      "field decay(t = 600.3000000000001): 0.0003786300533999004 / 0.24844276804284895 = 0.0015240131817183668\n",
      "field decay(t = 650.325): 2.416122931610749e-06 / 0.24844276804284895 = 9.725068476108912e-06\n",
      "field decay(t = 700.35): 4.491452301556477e-09 / 0.24844276804284895 = 1.8078418369504868e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357820155e-12 / 4.1691008357820155e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420161731733e-08 / 1.0165420161731733e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650783849662967e-06 / 4.650783849662967e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454681103843687 / 0.0005454681103843687 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.0177919867184528 / 0.0177919867184528 = 1.0\n",
      "field decay(t = 350.175): 0.13064418737686334 / 0.13064418737686334 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.24608078239905923 / 0.24608078239905923 = 1.0\n",
      "field decay(t = 450.225): 0.2454910539147474 / 0.24608078239905923 = 0.9976035167047076\n",
      "field decay(t = 500.25): 0.11083352926803994 / 0.24608078239905923 = 0.45039489954280826\n",
      "field decay(t = 550.275): 0.012828638329285625 / 0.24608078239905923 = 0.052131817057058695\n",
      "field decay(t = 600.3000000000001): 0.00037862466733590196 / 0.24608078239905923 = 0.0015386194063781123\n",
      "field decay(t = 650.325): 2.4161389249309716e-06 / 0.24608078239905923 = 9.818478718150437e-06\n",
      "field decay(t = 700.35): 4.491929211746334e-09 / 0.24608078239905923 = 1.8253880566999964e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835781244e-12 / 4.169100835781244e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420154050911e-08 / 1.0165420154050911e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650782317199479e-06 / 4.650782317199479e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454480084043649 / 0.0005454480084043649 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017766133980041062 / 0.017766133980041062 = 1.0\n",
      "field decay(t = 350.175): 0.12943453172485406 / 0.12943453172485406 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.24162814253773343 / 0.24162814253773343 = 1.0\n",
      "field decay(t = 450.225): 0.2410719556831018 / 0.24162814253773343 = 0.9976981702181286\n",
      "field decay(t = 500.25): 0.10987489072588702 / 0.24162814253773343 = 0.45472720839514214\n",
      "field decay(t = 550.275): 0.012814604477285866 / 0.24162814253773343 = 0.05303440378549736\n",
      "field decay(t = 600.3000000000001): 0.00037861349073161515 / 0.24162814253773343 = 0.0015669262973889296\n",
      "field decay(t = 650.325): 2.4161558408009105e-06 / 0.24162814253773343 = 9.999480256831406e-06\n",
      "field decay(t = 700.35): 4.492861541918341e-09 / 0.24162814253773343 = 1.8594115299366343e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835779622e-12 / 4.169100835779622e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420138158315e-08 / 1.0165420138158315e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650779146309852e-06 / 4.650779146309852e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454063955002492 / 0.0005454063955002492 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01771662173687749 / 0.01771662173687749 = 1.0\n",
      "field decay(t = 350.175): 0.12689706943678725 / 0.12689706943678725 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.23320693569541642 / 0.23320693569541642 = 1.0\n",
      "field decay(t = 450.225): 0.23267453504759023 / 0.23320693569541642 = 0.9977170462523398\n",
      "field decay(t = 500.25): 0.10799807195922452 / 0.23320693569541642 = 0.46309974288362116\n",
      "field decay(t = 550.275): 0.012785358594898285 / 0.23320693569541642 = 0.05482409241720325\n",
      "field decay(t = 600.3000000000001): 0.000378589249816587 / 0.23320693569541642 = 0.0016234047614735157\n",
      "field decay(t = 650.325): 2.416107138602139e-06 / 0.23320693569541642 = 1.0360357128304853e-05\n",
      "field decay(t = 700.35): 4.493125804229867e-09 / 0.23320693569541642 = 1.9266690293028782e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835776254e-12 / 4.169100835776254e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420105274407e-08 / 1.0165420105274407e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.6507725852541425e-06 / 4.6507725852541425e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005453202108380395 / 0.0005453202108380395 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017624098227291815 / 0.017624098227291815 = 1.0\n",
      "field decay(t = 350.175): 0.12218039718656669 / 0.12218039718656669 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.21924792050420605 / 0.21924792050420605 = 1.0\n",
      "field decay(t = 450.225): 0.21875381922070578 / 0.21924792050420605 = 0.9977463809811103\n",
      "field decay(t = 500.25): 0.10436138938509092 / 0.21924792050420605 = 0.47599716861665214\n",
      "field decay(t = 550.275): 0.012733297749038119 / 0.21924792050420605 = 0.05807716542877698\n",
      "field decay(t = 600.3000000000001): 0.00037853854019597556 / 0.21924792050420605 = 0.0017265319521637774\n",
      "field decay(t = 650.325): 2.4161208970651143e-06 / 0.21924792050420605 = 1.102004019700047e-05\n",
      "field decay(t = 700.35): 4.491390315511021e-09 / 0.21924792050420605 = 2.0485440888935858e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835769312e-12 / 4.169100835769312e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420037232922e-08 / 1.0165420037232922e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650759009321587e-06 / 4.650759009321587e-06 = 1.0\n",
      "field decay(t = 250.125): 0.000545141533396581 / 0.000545141533396581 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017421291008439022 / 0.017421291008439022 = 1.0\n",
      "field decay(t = 350.175): 0.11463570073897522 / 0.11463570073897522 = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "field decay(t = 400.20000000000005): 0.20163815113463796 / 0.20163815113463796 = 1.0\n",
      "field decay(t = 450.225): 0.20120253259282825 / 0.20163815113463796 = 0.9978396025783889\n",
      "field decay(t = 500.25): 0.10109030457587416 / 0.20163815113463796 = 0.5013451274326259\n",
      "field decay(t = 550.275): 0.01262521101057978 / 0.20163815113463796 = 0.06261320558404479\n",
      "field decay(t = 600.3000000000001): 0.0003784354270167391 / 0.20163815113463796 = 0.0018768046864506805\n",
      "field decay(t = 650.325): 2.4161436765026146e-06 / 0.20163815113463796 = 1.1982572062413455e-05\n",
      "field decay(t = 700.35): 4.492170330890932e-09 / 0.20163815113463796 = 2.2278374928618628e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835754938e-12 / 4.169100835754938e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.01654198964457e-08 / 1.01654198964457e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650730917976917e-06 / 4.650730917976917e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005447703360772811 / 0.0005447703360772811 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01702522296927303 / 0.01702522296927303 = 1.0\n",
      "field decay(t = 350.175): 0.10518227880576582 / 0.10518227880576582 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.1772727477817735 / 0.1772727477817735 = 1.0\n",
      "field decay(t = 450.225): 0.17689224598144007 / 0.1772727477817735 = 0.9978535798361865\n",
      "field decay(t = 500.25): 0.0941896635654982 / 0.1772727477817735 = 0.5313262458223283\n",
      "field decay(t = 550.275): 0.01239882951385902 / 0.1772727477817735 = 0.06994210711463807\n",
      "field decay(t = 600.3000000000001): 0.00037822000681681363 / 0.1772727477817735 = 0.0021335485095679255\n",
      "field decay(t = 650.325): 2.4160847007280536e-06 / 0.1772727477817735 = 1.3629194170907224e-05\n",
      "field decay(t = 700.35): 4.493256393452313e-09 / 0.1772727477817735 = 2.5346571594769923e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835725167e-12 / 4.169100835725167e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016541960513757e-08 / 1.016541960513757e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650672789399802e-06 / 4.650672789399802e-06 = 1.0\n",
      "field decay(t = 250.125): 0.000543995966140328 / 0.000543995966140328 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.016307143716002143 / 0.016307143716002143 = 1.0\n",
      "field decay(t = 350.175): 0.09153211905255858 / 0.09153211905255858 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.22855260392168325 / 0.22855260392168325 = 1.0\n",
      "field decay(t = 450.225): 0.22862376766772263 / 0.22862376766772263 = 1.0\n",
      "field decay(t = 500.25): 0.0829714083179759 / 0.22862376766772263 = 0.36291680941311816\n",
      "field decay(t = 550.275): 0.011966639931831521 / 0.22862376766772263 = 0.0523420642302755\n",
      "field decay(t = 600.3000000000001): 0.0003777734295152949 / 0.22862376766772263 = 0.0016523803862087667\n",
      "field decay(t = 650.325): 2.416110201923171e-06 / 0.22862376766772263 = 1.0568062221049122e-05\n",
      "field decay(t = 700.35): 4.492316812744696e-09 / 0.22862376766772263 = 1.9649386669516103e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835663596e-12 / 4.169100835663596e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165419002380455e-08 / 1.0165419002380455e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650552497483136e-06 / 4.650552497483136e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005423672260522596 / 0.0005423672260522596 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01462324952255367 / 0.01462324952255367 = 1.0\n",
      "field decay(t = 350.175): 0.11286948557899079 / 0.11286948557899079 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.22340193165372965 / 0.22340193165372965 = 1.0\n",
      "field decay(t = 450.225): 0.22423295751360775 / 0.22423295751360775 = 1.0\n",
      "field decay(t = 500.25): 0.10875776760662008 / 0.22423295751360775 = 0.4850213314428591\n",
      "field decay(t = 550.275): 0.011253173596244885 / 0.22423295751360775 = 0.050185190085458235\n",
      "field decay(t = 600.3000000000001): 0.0003768391018113586 / 0.22423295751360775 = 0.0016805696450241478\n",
      "field decay(t = 650.325): 2.416028682322177e-06 / 0.22423295751360775 = 1.0774636829091274e-05\n",
      "field decay(t = 700.35): 4.491221029097323e-09 / 0.22423295751360775 = 2.002926366800817e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835536189e-12 / 4.169100835536189e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.016541775519228e-08 / 1.016541775519228e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650303529004109e-06 / 4.650303529004109e-06 = 1.0\n",
      "field decay(t = 250.125): 0.000538888737006022 / 0.000538888737006022 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.012940856099568963 / 0.012940856099568963 = 1.0\n",
      "field decay(t = 350.175): 0.10610514124453076 / 0.10610514124453076 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.21497310015816543 / 0.21497310015816543 = 1.0\n",
      "field decay(t = 450.225): 0.22343131384615714 / 0.22343131384615714 = 1.0\n",
      "field decay(t = 500.25): 0.12060305444368463 / 0.22343131384615714 = 0.5397768664007654\n",
      "field decay(t = 550.275): 0.010356579521407651 / 0.22343131384615714 = 0.046352408456670664\n",
      "field decay(t = 600.3000000000001): 0.0003748549630386882 / 0.22343131384615714 = 0.0016777190116547107\n",
      "field decay(t = 650.325): 2.4161257892828187e-06 / 0.22343131384615714 = 1.0813729497855586e-05\n",
      "field decay(t = 700.35): 4.502116718465644e-09 / 0.22343131384615714 = 2.0149891440756423e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835272573e-12 / 4.169100835272573e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165415174584912e-08 / 1.0165415174584912e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.649788088402476e-06 / 4.649788088402476e-06 = 1.0\n",
      "field decay(t = 250.125): 0.00053174759709517 / 0.00053174759709517 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.01247454839135486 / 0.01247454839135486 = 1.0\n",
      "field decay(t = 350.175): 0.08976065034162566 / 0.08976065034162566 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.18870963593996914 / 0.18870963593996914 = 1.0\n",
      "field decay(t = 450.225): 0.22826836798863853 / 0.22826836798863853 = 1.0\n",
      "field decay(t = 500.25): 0.12497876557323505 / 0.22826836798863853 = 0.5475080348384299\n",
      "field decay(t = 550.275): 0.009665581444509555 / 0.22826836798863853 = 0.042343061063066935\n",
      "field decay(t = 600.3000000000001): 0.0003923542204648252 / 0.22826836798863853 = 0.0017188286923940053\n",
      "field decay(t = 650.325): 2.7457665901575746e-06 / 0.22826836798863853 = 1.2028677535795227e-05\n",
      "field decay(t = 700.35): 5.739133736098904e-09 / 0.22826836798863853 = 2.5142045683633898e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.16910083472712e-12 / 4.16910083472712e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165409834939769e-08 / 1.0165409834939769e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.648720329217878e-06 / 4.648720329217878e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005182735319569587 / 0.0005182735319569587 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.013974697322011669 / 0.013974697322011669 = 1.0\n",
      "field decay(t = 350.175): 0.07995289002032732 / 0.07995289002032732 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.17867229467477655 / 0.17867229467477655 = 1.0\n",
      "field decay(t = 450.225): 0.23241654769547887 / 0.23241654769547887 = 1.0\n",
      "field decay(t = 500.25): 0.18817882350661239 / 0.23241654769547887 = 0.8096618996043756\n",
      "field decay(t = 550.275): 0.014319414613175686 / 0.23241654769547887 = 0.06161099437694745\n",
      "field decay(t = 600.3000000000001): 0.0003754598154438489 / 0.23241654769547887 = 0.0016154607714756648\n",
      "field decay(t = 650.325): 2.9312708100424914e-06 / 0.23241654769547887 = 1.261214332244172e-05\n",
      "field decay(t = 700.35): 1.8599873539004834e-07 / 0.23241654769547887 = 8.002818096831515e-07\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n"
     ]
    }
   ],
   "source": [
    "pts = np.linspace(-6,0,20)\n",
    "_, _, omega_psd, omega3_psd = zip(*[run_chi3(k_iter) for k_iter in pts])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quad = (10 ** pts) ** 2\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plt.loglog(10 ** pts,np.array(omega_psd)/omega_flux_cal[0],'o-',label='$\\omega$')\n",
    "plt.loglog(10 ** pts,np.array(omega3_psd)/omega_flux_cal[0],'o-',label='$3\\omega$')\n",
    "plt.loglog(10**pts,quad,'k',label='quadratic line')\n",
    "plt.grid(True)\n",
    "plt.xlabel('$\\chi^{(3)}$')\n",
    "plt.ylabel('Transmission/ Incident Power')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be shown from coupled-mode theory or, equivalently, follows from Fermi's golden rule, the third-harmonic power must go as the square of $\\chi^{(3)}$ as long as the nonlinearity is weak (i.e. in the first Born approximation limit, where the $\\omega$ source is not depleted significantly). This is precisely what we see on the above graph, where the slope of the black line indicates an exact quadratic dependence, for comparison. Once the nonlinearity gets strong enough, however, this approximation is no longer valid and the dependence is complicated.\n",
    "\n",
    "Finally, we note that increasing the current amplitude by a factor of $F$ or the Kerr susceptibility $\\chi^{(3)}$ by a factor $F^3$ should generate the same third-harmonic power in the weak nonlinearity approximation. And indeed, we see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.1691008357817126e-12 / 4.1691008357817126e-12 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420158581846e-08 / 1.0165420158581846e-08 = 1.0\n",
      "field decay(t = 200.10000000000002): 4.650783221205379e-06 / 4.650783221205379e-06 = 1.0\n",
      "field decay(t = 250.125): 0.0005454598673598967 / 0.0005454598673598967 = 1.0\n",
      "field decay(t = 300.15000000000003): 0.017781404956060017 / 0.017781404956060017 = 1.0\n",
      "field decay(t = 350.175): 0.13014055244440104 / 0.13014055244440104 = 1.0\n",
      "field decay(t = 400.20000000000005): 0.2443206506136912 / 0.2443206506136912 = 1.0\n",
      "field decay(t = 450.225): 0.2437445632391615 / 0.2443206506136912 = 0.9976420848050188\n",
      "field decay(t = 500.25): 0.11044620451678894 / 0.2443206506136912 = 0.4520543156682302\n",
      "field decay(t = 550.275): 0.012822890989720101 / 0.2443206506136912 = 0.05248386068681144\n",
      "field decay(t = 600.3000000000001): 0.00037862010232358545 / 0.2443206506136912 = 0.0015496852246118257\n",
      "field decay(t = 650.325): 2.41614906850782e-06 / 0.2443206506136912 = 9.8892543976077e-06\n",
      "field decay(t = 700.35): 4.492194624953141e-09 / 0.2443206506136912 = 1.838647127727241e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 100.05000000000001): 4.169100835782652e-10 / 4.169100835782652e-10 = 1.0\n",
      "field decay(t = 150.07500000000002): 1.0165420167882405e-06 / 1.0165420167882405e-06 = 1.0\n",
      "field decay(t = 200.10000000000002): 0.0004650785076841832 / 0.0004650785076841832 = 1.0\n",
      "field decay(t = 250.125): 0.05454842035005856 / 0.05454842035005856 = 1.0\n",
      "field decay(t = 300.15000000000003): 1.7812567092193932 / 1.7812567092193932 = 1.0\n",
      "field decay(t = 350.175): 13.175704416847648 / 13.175704416847648 = 1.0\n",
      "field decay(t = 400.20000000000005): 24.99794167845129 / 24.99794167845129 = 1.0\n",
      "field decay(t = 450.225): 24.9382864152439 / 24.99794167845129 = 0.9976135929919857\n",
      "field decay(t = 500.25): 11.156520728608672 / 24.99794167845129 = 0.44629757410090326\n",
      "field decay(t = 550.275): 1.2839829021813205 / 24.99794167845129 = 0.051363544994912066\n",
      "field decay(t = 600.3000000000001): 0.03786336141250103 / 24.99794167845129 = 0.0015146591627237845\n",
      "field decay(t = 650.325): 0.0002416111150361613 / 24.99794167845129 = 9.665240368347396e-06\n",
      "field decay(t = 700.35): 4.491073802270905e-07 / 24.99794167845129 = 1.7965774382705666e-08\n",
      "run 0 finished at t = 700.35 (28014 timesteps)\n",
      "-------------------------------\n",
      "Difference between powers: 1.3663690082658835%\n"
     ]
    }
   ],
   "source": [
    "_, _, omega_flux_1, omega3_flux_1 = run_chi3(-3,1)\n",
    "_, _, omega_flux_2, omega3_flux_2 = run_chi3(-6,10)\n",
    "\n",
    "print('-------------------------------')\n",
    "print(\"Difference between powers: {}%\".format(abs(omega3_flux_1[0]-omega3_flux_2[0])/omega3_flux_1[0] * 100))"
   ]
  },
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   "source": [
    "which have third-harmonic powers differing by about 1%."
   ]
  }
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