diff --git a/.ipynb_checkpoints/0-introduction-checkpoint.ipynb b/.ipynb_checkpoints/0-introduction-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Simulations in nanophotonics\n",
+    "\n",
+    "Solve Maxwell's equations for *e.g.* effective index, mode profile, transmission, etc. Need to fully simulate, since ray optics approximations break down when dimensions are order wavelength."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# What are your options?\n",
+    "\n",
+    "## Commercial\n",
+    "\n",
+    "Some I've used, but there are a lot more :\n",
+    "[Lumerical](https://www.lumerical.com/), [Synopsys](https://www.synopsys.com/photonic-solutions.html), [PhotonDesign](https://www.photond.com/products.htm). These may be easier to use in a \"production\" environment, and have more support.\n",
+    "\n",
+    "## Why MEEP/MPB (open-source)?\n",
+    "\n",
+    "* Free!\n",
+    "* Flexible. Good for research.\n",
+    "* Transparent. Good for teaching.\n",
+    "* Widely-used\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    " # What is PyMEEP?\n",
+    "\n",
+    "It is the Python wrapper to MEEP and MPB. These tools can also be used from a Scheme interface. \n",
+    "\n",
+    "* **MPB : MIT Periodic Bands**\n",
+    "    * \"MPB computes definite-frequency eigenstates, or harmonic modes, of Maxwell's equations in periodic dielectric structures for arbitrary wavevectors, using fully-vectorial and three-dimensional methods. It is applicable to many problems in optics, such as waveguides and resonator systems, and photonic crystals. [1]\n",
+    "    \n",
+    "\n",
+    "* **MEEP : MIT Electromagnetic Equation Propagation**\n",
+    "    * \"A time-domain electromagnetic simulation simply evolves Maxwell's equations over time within some finite computational volume, essentially performing a kind of numerical experiment. This can be used to calculate a wide variety of useful quantities. Major applications include:\n",
+    "        * Transmittance and Reflectance Spectra — by Fourier-transforming the response to a short pulse, a single simulation can yield the scattering amplitudes over a broadband spectrum.\n",
+    "        * Resonant Modes and Frequencies — by analyzing the response of the system to a short pulse, one can extract the frequencies, decay rates, and field patterns of the harmonic modes of lossy and lossless systems including waveguide and cavity modes.\n",
+    "        * Field Patterns (e.g. Green's functions) — in response to an arbitrary source via a continuous-wave (CW) input (fixed-ω).\"\n",
+    "    \n",
+    "    Meep's scriptable interface makes it possible to combine many sorts of computations along with multi-parameter optimization in sequence or in parallel.\" [2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to the Python interface\n",
+    "\n",
+    "Mostly follow Python syntax for objects, functions, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# References\n",
+    "\n",
+    "1. https://mpb.readthedocs.io/en/latest/\n",
+    "2. https://meep.readthedocs.io/en/latest/\n",
+    "3. http://www.simpetus.com/projects.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/1-simple_geometries-checkpoint.ipynb b/.ipynb_checkpoints/1-simple_geometries-checkpoint.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining geometries for PyMEEP\n",
+    "\n",
+    "Simple shapes can be directly defined in PyMEEP. Later, we will see how to import arbitrary shapes made with more specialized tools.\n",
+    "For visualization, we will call the Python packages [Matplotlib](https://matplotlib.org/) and [Ipyvolume](https://ipyvolume.readthedocs.io/en/latest/).\n",
+    "\n",
+    "References :\n",
+    "\n",
+    "1. https://mpb.readthedocs.io/en/latest/Python_Tutorial/\n",
+    "2. https://mpb.readthedocs.io/en/latest/Python_User_Interface/\n",
+    "2. http://www.simpetuscloud.com/projects.html#mpb_waveguide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import meep and mpb (from meep)\n",
+    "import meep as mp\n",
+    "from meep import mpb\n",
+    "\n",
+    "# arrays\n",
+    "import numpy as np\n",
+    "\n",
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipyvolume as ipv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cross-section of a strip waveguide\n",
+    "\n",
+    "As a (useful) example, let's characterize a strip waveguide (Figure from [3])\n",
+    "\n",
+    "![alt text](http://www.simpetus.com/files/SOI_waveguide_bands.png)\n",
+    "\n",
+    "The left image is the geometry we want to simulate. Since we have translational invariance along $x$, the propagation direction, we will only consider the $yz$ cross-section. The right image contains the information we want : dispersion relation (allowed modes), and field profiles.\n",
+    "\n",
+    "## 1. Computational domain\n",
+    "\n",
+    "Since the computer has finite memory, our geometry will live inside a \"super\" geometry. For MPB, this \"supercell\" always has periodic boundary conditions. But if it is big enough, we don't expect it to have an effect on localized modes, which is what we are trying to solve for a waveguide.\n",
+    "\n",
+    "### 1.1 A note on geometry\n",
+    "\n",
+    "By default, everything is 3D. For a waveguide, we can use the translational invariance along the propagation direction (say, $x$) to do a 2D simulation. Then, the only thing that matters is the geometries' intersection with the yz plane (x=0). We can set this when we define the size of the computational cell, controlled by the geometry_lattice argument, an object of the meep.Lattice class: we can set some of the dimensions to have a size 0, which reduces the dimensionality of the system. [1]\n",
+    "\n",
+    "### 1.2 A note on units\n",
+    "\n",
+    "Since Maxwell's equations with linear permittivity are [scale-invariant](https://mpb.readthedocs.io/en/latest/Python_Tutorial/#a-few-words-on-units), a simulation in MPB with units of $x$ outputs frequencies equivalent to $f=x/\\lambda$. By default, dimensions are in microns. For instance, to look for the wavelength response at $1.5\\mu$m, we look at frequency $1/1.5 = 0.6667$ (the physical frequency outside of MPB would be $f=c/\\lambda$).\n",
+    "\n",
+    "Let's define the supercell :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc_y = 2  # supercell width (um)\n",
+    "sc_z = 2  # supercell height (um)\n",
+    "resolution = 64  # pixels/um\n",
+    "geometry_lattice = mp.Lattice(size=mp.Vector3(0, sc_y, sc_z)) # Computational domain object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we used variables to parametrize the supercell. The resolution should be a multiple of 2 for better results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for the waveguide\n",
+    "w = 0.3  # Si width (um)\n",
+    "h = 0.25  # Si height (um)\n",
+    "\n",
+    "# Materials\n",
+    "Si = mp.Medium(index=3.45)\n",
+    "SiO2 = mp.Medium(index=1.45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Geometry\n",
+    "\n",
+    "The geometry that will be passed to the solver is a list of meep objects. Objects typically have a shape and index. Later objects in the list will be overlaid \"on top\" of earlier objects, effectively replacing them (useful to draw holes). See https://meep.readthedocs.io/en/latest/Python_User_Interface/#geometricobject for common geometries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the 2d blocks for the strip and substrate\n",
+    "geometry = [mp.Block(size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)),\n",
+    "                     center=mp.Vector3(z=0.25 * (sc_z + h)), material=SiO2),\n",
+    "            mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 - Visualizing the geometry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While we could directly convert the geometry object above into a visualization, MPB and MEEP offer an easy way to display the dielectric distribution once the simulation has been initialized. The function we are interested in is `ModeSolver.init_params(p, reset_fields)` [2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working in 3 dimensions.\n",
+      "Grid size is 1 x 64 x 64.\n",
+      "Solving for 1 bands at a time.\n",
+      "Creating Maxwell data...\n",
+      "Mesh size is 3.\n",
+      "Lattice vectors:\n",
+      "     (1, 0, 0)\n",
+      "     (0, 2, 0)\n",
+      "     (0, 0, 2)\n",
+      "Cell volume = 4\n",
+      "Reciprocal lattice vectors (/ 2 pi):\n",
+      "     (1, -0, 0)\n",
+      "     (-0, 0.5, -0)\n",
+      "     (0, -0, 0.5)\n",
+      "Geometric objects:\n",
+      "     block, center = (0,0,0.5625)\n",
+      "          size (1e+20,1e+20,0.875)\n",
+      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
+      "     block, center = (0,0,0)\n",
+      "          size (1e+20,0.3,0.25)\n",
+      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
+      "Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)\n",
+      "Initializing epsilon function...\n",
+      "Allocating fields...\n",
+      "Solving for band polarization: .\n",
+      "Initializing fields to random numbers...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ms = mpb.ModeSolver(\n",
+    "    geometry_lattice=geometry_lattice,\n",
+    "    geometry=geometry,\n",
+    "    resolution=resolution)\n",
+    "ms.init_params(mp.NO_PARITY, True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's ask the modesolver for the permittivity distribution it will simulate and plot it :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon: 1-11.9025, mean 1.68727, harm. mean 1.33783, 47.4609% > 1, 6.30378% \"fill\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "\n",
+    "n = np.sqrt(ms.get_epsilon())\n",
+    "\n",
+    "pos = ax.imshow(n.T, cmap='gray_r', interpolation='spline36', extent=[-sc_y/2,sc_y/2,-sc_z/2,sc_z/2] )\n",
+    "cbar = fig.colorbar(pos, ax=ax)\n",
+    "cbar.set_label('n')\n",
+    "ax.set_xlabel('y')\n",
+    "ax.set_ylabel('z')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2.5D photonic crystal\n",
+    "\n",
+    "It is just as easy to define repeated elements and to extend to 3D :\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working in 3 dimensions.\n",
+      "Grid size is 16 x 16 x 16.\n",
+      "Solving for 1 bands at a time.\n",
+      "Creating Maxwell data...\n",
+      "Mesh size is 3.\n",
+      "Lattice vectors:\n",
+      "     (0.5, 0, 0)\n",
+      "     (0, 0.5, 0)\n",
+      "     (0, 0, 0.5)\n",
+      "Cell volume = 0.125\n",
+      "Reciprocal lattice vectors (/ 2 pi):\n",
+      "     (2, -0, 0)\n",
+      "     (-0, 2, -0)\n",
+      "     (0, -0, 2)\n",
+      "Geometric objects:\n",
+      "     cylinder, center = (0,0,0)\n",
+      "          radius 0.2, height 1e+20, axis (0, 0, 1)\n",
+      "Geometric object tree has depth 1 and 3 object nodes (vs. 1 actual objects)\n",
+      "Initializing epsilon function...\n",
+      "Allocating fields...\n",
+      "Solving for band polarization: .\n",
+      "Initializing fields to random numbers...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Supercell\n",
+    "sc_x = 0.5 # um\n",
+    "sc_y = 0.5 # um\n",
+    "sc_z = 0.5 # um\n",
+    "resolution = 32  # pixels/um\n",
+    "geometry_lattice = mp.Lattice(size=mp.Vector3(sc_x, sc_y, sc_z)) # Computational domain object\n",
+    "\n",
+    "# Objects\n",
+    "geometry = [mp.Cylinder(0.2, material=Si)] # 200nm radius cylinder, infinite extent in x\n",
+    "\n",
+    "# ModeSolver\n",
+    "ms = mpb.ModeSolver(\n",
+    "    geometry_lattice=geometry_lattice,\n",
+    "    geometry=geometry,\n",
+    "    resolution=resolution)\n",
+    "ms.default_material = SiO2\n",
+    "ms.init_params(mp.NO_PARITY, True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon: 2.1025-11.9025, mean 7.02807, harm. mean 3.79119, 100% > 1, 50.2609% \"fill\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "\n",
+    "n = np.sqrt(ms.get_epsilon())\n",
+    "nxy = n[:,:,0]\n",
+    "\n",
+    "pos = ax.imshow(nxy, cmap='gray_r', interpolation='spline36', extent=[-sc_y/2,sc_y/2,-sc_z/2,sc_z/2] )\n",
+    "cbar = fig.colorbar(pos, ax=ax)\n",
+    "cbar.set_label('n')\n",
+    "ax.set_title('xy cut')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d984474050784edda3ccaf982a245fb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(VBox(children=(HBox(children=(Label(value='levels:'), FloatSlider(value=0.0, max=1.0, step=0.00…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ipv.quickvolshow(n, level=[0,1.45,3.45])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/2-MPB_simulation-checkpoint.ipynb b/.ipynb_checkpoints/2-MPB_simulation-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/2-MPB_simulation-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/3-MEEP_simulation-checkpoint.ipynb b/.ipynb_checkpoints/3-MEEP_simulation-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/3-MEEP_simulation-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/4-MEEP_S_parameters-checkpoint.ipynb b/.ipynb_checkpoints/4-MEEP_S_parameters-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/4-MEEP_S_parameters-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/5-complex_geometries-checkpoint.ipynb b/.ipynb_checkpoints/5-complex_geometries-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/5-complex_geometries-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/6-shell_simulations-checkpoint.ipynb b/.ipynb_checkpoints/6-shell_simulations-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/6-shell_simulations-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/0-introduction.ipynb b/0-introduction.ipynb
index 14b51e0..a49c8d3 100644
--- a/0-introduction.ipynb
+++ b/0-introduction.ipynb
@@ -67,7 +67,16 @@
    "source": [
     "# Introduction to the Python interface\n",
     "\n",
-    "Mostly follow Python syntax for objects, functions, etc."
+    "Mostly follow Python syntax for objects, functions, etc.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Image rom https://www.w3schools.com/python/python_classes.asp\n",
+    "\n",
+    "### Neat Notebook tricks\n",
+    "\n",
+    "* Autocomplete/suggest : press tab\n",
+    "* Cell magics : %%capture to suppress or capture output, etc."
    ]
   },
   {
@@ -76,9 +85,15 @@
    "source": [
     "# References\n",
     "\n",
+    "Most of the following is extracted from the Python User Interface and Tutorial pages of :\n",
+    "\n",
     "1. https://mpb.readthedocs.io/en/latest/\n",
     "2. https://meep.readthedocs.io/en/latest/\n",
-    "3. http://www.simpetus.com/projects.html"
+    "\n",
+    "The source code (thanks open-source!) itself and examples are also a good source of information :\n",
+    "\n",
+    "3. https://github.com/NanoComp/meep\n",
+    "4. http://www.simpetus.com/projects.html"
    ]
   },
   {
diff --git a/1-simple_geometries.ipynb b/1-simple_geometries.ipynb
index 8f416d6..9479562 100644
--- a/1-simple_geometries.ipynb
+++ b/1-simple_geometries.ipynb
@@ -77,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, we used variables to parametrize the supercell. The resolution"
+    "Here, we used variables to parametrize the supercell. The resolution should be a multiple of 2 for better results."
    ]
   },
   {
@@ -92,15 +92,7 @@
     "\n",
     "# Materials\n",
     "Si = mp.Medium(index=3.45)\n",
-    "SiO2 = mp.Medium(index=1.45)\n",
-    "\n",
-    "# Define the computational cell.  We'll make x the propagation direction.\n",
-    "# the other cell sizes should be big enough so that the boundaries are\n",
-    "# far away from the mode field.\n",
-    "sc_y = 2  # supercell width (um)\n",
-    "sc_z = 2  # supercell height (um)\n",
-    "resolution = 32  # pixels/um\n",
-    "geometry_lattice = mp.Lattice(size=mp.Vector3(0, sc_y, sc_z))\n"
+    "SiO2 = mp.Medium(index=1.45)"
    ]
   },
   {
@@ -235,12 +227,12 @@
    "source": [
     "# 2.5D photonic crystal\n",
     "\n",
-    "It is just as easy to define repeated elements and to extend to 3D :\n"
+    "It is just as easy to define different elements and to extend to 3D. The periodic boundary conditions of MPB make a cell containing a single element automatically a photonic crystal. To simulate defect modes, one can proceed as above i.e. have a larger computational domain with the bounded mode decaying before reaching the boundary.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -248,23 +240,23 @@
      "output_type": "stream",
      "text": [
       "Working in 3 dimensions.\n",
-      "Grid size is 16 x 16 x 16.\n",
+      "Grid size is 16 x 16 x 64.\n",
       "Solving for 1 bands at a time.\n",
       "Creating Maxwell data...\n",
       "Mesh size is 3.\n",
       "Lattice vectors:\n",
       "     (0.5, 0, 0)\n",
       "     (0, 0.5, 0)\n",
-      "     (0, 0, 0.5)\n",
-      "Cell volume = 0.125\n",
+      "     (0, 0, 2)\n",
+      "Cell volume = 0.5\n",
       "Reciprocal lattice vectors (/ 2 pi):\n",
       "     (2, -0, 0)\n",
       "     (-0, 2, -0)\n",
-      "     (0, -0, 2)\n",
+      "     (0, -0, 0.5)\n",
       "Geometric objects:\n",
       "     cylinder, center = (0,0,0)\n",
-      "          radius 0.2, height 1e+20, axis (0, 0, 1)\n",
-      "Geometric object tree has depth 1 and 3 object nodes (vs. 1 actual objects)\n",
+      "          radius 0.2, height 0.875, axis (0, 0, 1)\n",
+      "Geometric object tree has depth 1 and 1 object nodes (vs. 1 actual objects)\n",
       "Initializing epsilon function...\n",
       "Allocating fields...\n",
       "Solving for band polarization: .\n",
@@ -276,37 +268,80 @@
     "# Supercell\n",
     "sc_x = 0.5 # um\n",
     "sc_y = 0.5 # um\n",
-    "sc_z = 0.5 # um\n",
+    "sc_z = 2 # um\n",
     "resolution = 32  # pixels/um\n",
     "geometry_lattice = mp.Lattice(size=mp.Vector3(sc_x, sc_y, sc_z)) # Computational domain object\n",
     "\n",
     "# Objects\n",
-    "geometry = [mp.Cylinder(0.2, material=Si)] # 200nm radius cylinder, infinite extent in x\n",
+    "geometry = [mp.Cylinder(radius=0.2, height=0.5 * (sc_z - h), material=Si)] # 200nm radius cylinder\n",
     "\n",
     "# ModeSolver\n",
     "ms = mpb.ModeSolver(\n",
     "    geometry_lattice=geometry_lattice,\n",
     "    geometry=geometry,\n",
     "    resolution=resolution)\n",
-    "ms.default_material = SiO2\n",
+    "ms.default_material = SiO2 # Default fill for background\n",
     "ms.init_params(mp.NO_PARITY, True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epsilon: 2.1025-11.9025, mean 4.25806, harm. mean 2.62689, 100% > 1, 21.9955% \"fill\"\n",
+      "(16, 16, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2,figsize=(6, 4))\n",
+    "\n",
+    "n = np.sqrt(ms.get_epsilon())\n",
+    "print(np.shape(n))\n",
+    "nxy = n[:,:,int(sc_z*resolution/2)]\n",
+    "\n",
+    "pos = ax.imshow(nxy, cmap='gray_r', interpolation='spline36', extent=[-sc_x/2,sc_x/2,-sc_y/2,sc_y/2] )\n",
+    "cbar = fig.colorbar(pos, ax=ax)\n",
+    "cbar.set_label('n')\n",
+    "ax.set_title('xy cut for z={0}um'.format(sc_z/2))\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epsilon: 2.1025-11.9025, mean 7.02807, harm. mean 3.79119, 100% > 1, 50.2609% \"fill\"\n"
+      "epsilon: 2.1025-11.9025, mean 4.25806, harm. mean 2.62689, 100% > 1, 21.9955% \"fill\"\n",
+      "(16, 16, 64)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -321,12 +356,13 @@
     "fig, ax = plt.subplots(figsize=(6, 4))\n",
     "\n",
     "n = np.sqrt(ms.get_epsilon())\n",
+    "print(np.shape(n))\n",
     "nxy = n[:,:,0]\n",
     "\n",
-    "pos = ax.imshow(nxy, cmap='gray_r', interpolation='spline36', extent=[-sc_y/2,sc_y/2,-sc_z/2,sc_z/2] )\n",
+    "pos = ax.imshow(nxy, cmap='gray_r', interpolation='spline36', extent=[-sc_x/2,sc_x/2,-sc_y/2,sc_y/2] )\n",
     "cbar = fig.colorbar(pos, ax=ax)\n",
     "cbar.set_label('n')\n",
-    "ax.set_title('xy cut')\n",
+    "ax.set_title('xy cut for z={0}um'.format(0))\n",
     "ax.set_xlabel('x')\n",
     "ax.set_ylabel('y')\n",
     "plt.show()"
@@ -334,13 +370,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d984474050784edda3ccaf982a245fb5",
+       "model_id": "63217db69aee42e5b19f938a991931dd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -355,13 +391,6 @@
    "source": [
     "ipv.quickvolshow(n, level=[0,1.45,3.45])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/2-MPB_simulation.ipynb b/2-MPB_simulation.ipynb
new file mode 100644
index 0000000..6932342
--- /dev/null
+++ b/2-MPB_simulation.ipynb
@@ -0,0 +1,1632 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MPB ModeSolver\n",
+    "\n",
+    "Now let's obtain the plot on the right (Figure from [3]) :\n",
+    "\n",
+    "![alt text](http://www.simpetus.com/files/SOI_waveguide_bands.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import meep and mpb (from meep)\n",
+    "import meep as mp\n",
+    "from meep import mpb\n",
+    "\n",
+    "# arrays\n",
+    "import numpy as np\n",
+    "\n",
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for parsing text files\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start by defining the geometry from last time :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for the waveguide\n",
+    "w = 0.3  # Si width (um)\n",
+    "h = 0.25  # Si height (um)\n",
+    "\n",
+    "# Materials\n",
+    "Si = mp.Medium(index=3.45)\n",
+    "SiO2 = mp.Medium(index=1.45)\n",
+    "\n",
+    "sc_y = 2  # supercell width (um)\n",
+    "sc_z = 2  # supercell height (um)\n",
+    "resolution = 32  # pixels/um\n",
+    "geometry_lattice = mp.Lattice(size=mp.Vector3(0, sc_y, sc_z))\n",
+    "\n",
+    "# define the 2d blocks for the strip and substrate\n",
+    "geometry = [mp.Block(size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)),\n",
+    "                     center=mp.Vector3(z=0.25 * (sc_z + h)), material=SiO2),\n",
+    "            mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the simulation\n",
+    "\n",
+    "Now, instead of only initializing the geometry, we will load simulation parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The k (i.e. beta, i.e. propagation constant) points to look at, in\n",
+    "# units of 2*pi/um.  We'll look at num_k points from k_min to k_max.\n",
+    "num_k = 30\n",
+    "k_min = 0.1\n",
+    "k_max = 3.0\n",
+    "k_points = mp.interpolate(num_k, [mp.Vector3(k_min), mp.Vector3(k_max)])\n",
+    "\n",
+    "# Increase this to see more modes.  (The guided ones are the ones below the\n",
+    "# light line, i.e. those with frequencies < kmag / 1.45, where kmag\n",
+    "# is the corresponding column in the output if you grep for \"freqs:\".)\n",
+    "num_bands = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ModeSolver object\n",
+    "ms = mpb.ModeSolver(\n",
+    "    geometry_lattice=geometry_lattice,\n",
+    "    geometry=geometry,\n",
+    "    # Add new things pertaining to simulation\n",
+    "    k_points=k_points,\n",
+    "    resolution=resolution,\n",
+    "    num_bands=num_bands,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the simulation\n",
+    "\n",
+    "The simulation will be initialized as before when run() is called."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing eigensolver data\n",
+      "Computing 4 bands with 1e-07 tolerance\n",
+      "Working in 3 dimensions.\n",
+      "Grid size is 1 x 64 x 64.\n",
+      "Solving for 4 bands at a time.\n",
+      "Creating Maxwell data...\n",
+      "Mesh size is 3.\n",
+      "Lattice vectors:\n",
+      "     (1, 0, 0)\n",
+      "     (0, 2, 0)\n",
+      "     (0, 0, 2)\n",
+      "Cell volume = 4\n",
+      "Reciprocal lattice vectors (/ 2 pi):\n",
+      "     (1, -0, 0)\n",
+      "     (-0, 0.5, -0)\n",
+      "     (0, -0, 0.5)\n",
+      "Geometric objects:\n",
+      "     block, center = (0,0,0.5625)\n",
+      "          size (1e+20,1e+20,0.875)\n",
+      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
+      "     block, center = (0,0,0)\n",
+      "          size (1e+20,0.3,0.25)\n",
+      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
+      "Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)\n",
+      "Initializing epsilon function...\n",
+      "Allocating fields...\n",
+      "Solving for band polarization: .\n",
+      "Initializing fields to random numbers...\n",
+      "32 k-points\n",
+      "  Vector3<0.1, 0.0, 0.0>\n",
+      "  Vector3<0.1935483870967742, 0.0, 0.0>\n",
+      "  Vector3<0.2870967741935484, 0.0, 0.0>\n",
+      "  Vector3<0.38064516129032255, 0.0, 0.0>\n",
+      "  Vector3<0.4741935483870968, 0.0, 0.0>\n",
+      "  Vector3<0.567741935483871, 0.0, 0.0>\n",
+      "  Vector3<0.6612903225806451, 0.0, 0.0>\n",
+      "  Vector3<0.7548387096774194, 0.0, 0.0>\n",
+      "  Vector3<0.8483870967741935, 0.0, 0.0>\n",
+      "  Vector3<0.9419354838709677, 0.0, 0.0>\n",
+      "  Vector3<1.035483870967742, 0.0, 0.0>\n",
+      "  Vector3<1.1290322580645162, 0.0, 0.0>\n",
+      "  Vector3<1.2225806451612904, 0.0, 0.0>\n",
+      "  Vector3<1.3161290322580645, 0.0, 0.0>\n",
+      "  Vector3<1.409677419354839, 0.0, 0.0>\n",
+      "  Vector3<1.503225806451613, 0.0, 0.0>\n",
+      "  Vector3<1.5967741935483872, 0.0, 0.0>\n",
+      "  Vector3<1.6903225806451614, 0.0, 0.0>\n",
+      "  Vector3<1.7838709677419355, 0.0, 0.0>\n",
+      "  Vector3<1.87741935483871, 0.0, 0.0>\n",
+      "  Vector3<1.970967741935484, 0.0, 0.0>\n",
+      "  Vector3<2.064516129032258, 0.0, 0.0>\n",
+      "  Vector3<2.1580645161290324, 0.0, 0.0>\n",
+      "  Vector3<2.2516129032258068, 0.0, 0.0>\n",
+      "  Vector3<2.3451612903225807, 0.0, 0.0>\n",
+      "  Vector3<2.438709677419355, 0.0, 0.0>\n",
+      "  Vector3<2.532258064516129, 0.0, 0.0>\n",
+      "  Vector3<2.6258064516129034, 0.0, 0.0>\n",
+      "  Vector3<2.7193548387096778, 0.0, 0.0>\n",
+      "  Vector3<2.8129032258064517, 0.0, 0.0>\n",
+      "  Vector3<2.906451612903226, 0.0, 0.0>\n",
+      "  Vector3<3.0, 0.0, 0.0>\n",
+      "elapsed time for initialization: 0.03134512901306152\n",
+      "solve_kpoint (0.1,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 29 iterations.\n",
+      "freqs:, 1, 0.1, 0, 0, 0.1, 0.0807582, 0.0855765, 0.3147, 0.394675\n",
+      "elapsed time for k point: 0.1492457389831543\n",
+      "solve_kpoint (0.193548,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 15 iterations.\n",
+      "freqs:, 2, 0.193548, 0, 0, 0.193548, 0.155365, 0.16422, 0.341458, 0.410987\n",
+      "elapsed time for k point: 0.08380365371704102\n",
+      "solve_kpoint (0.287097,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 3, 0.287097, 0, 0, 0.287097, 0.228276, 0.240091, 0.381337, 0.436147\n",
+      "elapsed time for k point: 0.07639384269714355\n",
+      "solve_kpoint (0.380645,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 4, 0.380645, 0, 0, 0.380645, 0.298937, 0.311941, 0.430273, 0.467754\n",
+      "elapsed time for k point: 0.07965588569641113\n",
+      "solve_kpoint (0.474194,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 5, 0.474194, 0, 0, 0.474194, 0.367072, 0.379151, 0.483705, 0.502991\n",
+      "elapsed time for k point: 0.07943391799926758\n",
+      "solve_kpoint (0.567742,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 16 iterations.\n",
+      "freqs:, 6, 0.567742, 0, 0, 0.567742, 0.432542, 0.441894, 0.536768, 0.538894\n",
+      "elapsed time for k point: 0.08882355690002441\n",
+      "solve_kpoint (0.66129,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 16 iterations.\n",
+      "freqs:, 7, 0.66129, 0, 0, 0.66129, 0.494782, 0.500315, 0.573355, 0.58507\n",
+      "elapsed time for k point: 0.08732295036315918\n",
+      "solve_kpoint (0.754839,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 15 iterations.\n",
+      "freqs:, 8, 0.754839, 0, 0, 0.754839, 0.551001, 0.553305, 0.608206, 0.629108\n",
+      "elapsed time for k point: 0.08421730995178223\n",
+      "solve_kpoint (0.848387,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 15 iterations.\n",
+      "freqs:, 9, 0.848387, 0, 0, 0.848387, 0.593884, 0.598211, 0.651795, 0.674567\n",
+      "elapsed time for k point: 0.08363509178161621\n",
+      "solve_kpoint (0.941935,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 10, 0.941935, 0, 0, 0.941935, 0.622664, 0.633694, 0.706501, 0.725007\n",
+      "elapsed time for k point: 0.07930493354797363\n",
+      "solve_kpoint (1.03548,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 11, 1.03548, 0, 0, 1.03548, 0.645081, 0.662012, 0.765683, 0.779634\n",
+      "elapsed time for k point: 0.07721424102783203\n",
+      "solve_kpoint (1.12903,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 17 iterations.\n",
+      "freqs:, 12, 1.12903, 0, 0, 1.12903, 0.665138, 0.686152, 0.824909, 0.836268\n",
+      "elapsed time for k point: 0.09172987937927246\n",
+      "solve_kpoint (1.22258,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 24 iterations.\n",
+      "freqs:, 13, 1.22258, 0, 0, 1.22258, 0.684281, 0.708024, 0.869466, 0.892966\n",
+      "elapsed time for k point: 0.12187790870666504\n",
+      "solve_kpoint (1.31613,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 17 iterations.\n",
+      "freqs:, 14, 1.31613, 0, 0, 1.31613, 0.703123, 0.728677, 0.894377, 0.947171\n",
+      "elapsed time for k point: 0.09132170677185059\n",
+      "solve_kpoint (1.40968,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 15 iterations.\n",
+      "freqs:, 15, 1.40968, 0, 0, 1.40968, 0.721964, 0.748699, 0.91638, 0.994817\n",
+      "elapsed time for k point: 0.08275747299194336\n",
+      "solve_kpoint (1.50323,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 14 iterations.\n",
+      "freqs:, 16, 1.50323, 0, 0, 1.50323, 0.740959, 0.768431, 0.937664, 1.03361\n",
+      "elapsed time for k point: 0.0791776180267334\n",
+      "solve_kpoint (1.59677,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 13 iterations.\n",
+      "freqs:, 17, 1.59677, 0, 0, 1.59677, 0.760193, 0.788079, 0.958655, 1.06537\n",
+      "elapsed time for k point: 0.07450485229492188\n",
+      "solve_kpoint (1.69032,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 11 iterations.\n",
+      "freqs:, 18, 1.69032, 0, 0, 1.69032, 0.779709, 0.807772, 0.979518, 1.09248\n",
+      "elapsed time for k point: 0.06581711769104004\n",
+      "solve_kpoint (1.78387,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 11 iterations.\n",
+      "freqs:, 19, 1.78387, 0, 0, 1.78387, 0.799526, 0.827589, 1.00034, 1.11654\n",
+      "elapsed time for k point: 0.06593608856201172\n",
+      "solve_kpoint (1.87742,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 10 iterations.\n",
+      "freqs:, 20, 1.87742, 0, 0, 1.87742, 0.81965, 0.847578, 1.02116, 1.13857\n",
+      "elapsed time for k point: 0.06191587448120117\n",
+      "solve_kpoint (1.97097,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 10 iterations.\n",
+      "freqs:, 21, 1.97097, 0, 0, 1.97097, 0.840077, 0.867771, 1.04203, 1.15923\n",
+      "elapsed time for k point: 0.06505203247070312\n",
+      "solve_kpoint (2.06452,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 10 iterations.\n",
+      "freqs:, 22, 2.06452, 0, 0, 2.06452, 0.8608, 0.888182, 1.06295, 1.17897\n",
+      "elapsed time for k point: 0.061644792556762695\n",
+      "solve_kpoint (2.15806,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 23, 2.15806, 0, 0, 2.15806, 0.881807, 0.908821, 1.08394, 1.19812\n",
+      "elapsed time for k point: 0.05744528770446777\n",
+      "solve_kpoint (2.25161,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 24, 2.25161, 0, 0, 2.25161, 0.903085, 0.929689, 1.10502, 1.21689\n",
+      "elapsed time for k point: 0.05728340148925781\n",
+      "solve_kpoint (2.34516,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 25, 2.34516, 0, 0, 2.34516, 0.924621, 0.950785, 1.12618, 1.23544\n",
+      "elapsed time for k point: 0.060393333435058594\n",
+      "solve_kpoint (2.43871,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 26, 2.43871, 0, 0, 2.43871, 0.946402, 0.972103, 1.14743, 1.25389\n",
+      "elapsed time for k point: 0.057176828384399414\n",
+      "solve_kpoint (2.53226,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 27, 2.53226, 0, 0, 2.53226, 0.968413, 0.993637, 1.16878, 1.27231\n",
+      "elapsed time for k point: 0.05736899375915527\n",
+      "solve_kpoint (2.62581,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 28, 2.62581, 0, 0, 2.62581, 0.990643, 1.01538, 1.19021, 1.29076\n",
+      "elapsed time for k point: 0.05733036994934082\n",
+      "solve_kpoint (2.71935,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 9 iterations.\n",
+      "freqs:, 29, 2.71935, 0, 0, 2.71935, 1.01308, 1.03733, 1.21175, 1.3093\n",
+      "elapsed time for k point: 0.0572965145111084\n",
+      "solve_kpoint (2.8129,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 89 iterations.\n",
+      "freqs:, 30, 2.8129, 0, 0, 2.8129, 1.03571, 1.05946, 1.23338, 1.31269\n",
+      "elapsed time for k point: 0.40296435356140137\n",
+      "solve_kpoint (2.90645,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 6 iterations.\n",
+      "freqs:, 31, 2.90645, 0, 0, 2.90645, 1.05852, 1.08179, 1.25511, 1.33173\n",
+      "elapsed time for k point: 0.04461216926574707\n",
+      "solve_kpoint (3,0,0):\n",
+      "Solving for bands 1 to 4...\n",
+      "Finished solving for bands 1 to 4 after 6 iterations.\n",
+      "freqs:, 32, 3, 0, 0, 3, 1.0815, 1.10429, 1.27693, 1.35105\n",
+      "elapsed time for k point: 0.04412245750427246\n",
+      "Band 1 range: 0.08075820828162611 at Vector3<0.1, 0.0, 0.0> to 1.0814996959234677 at Vector3<3.0, 0.0, 0.0>\n",
+      "Band 2 range: 0.08557653594456045 at Vector3<0.1, 0.0, 0.0> to 1.1042853219436242 at Vector3<3.0, 0.0, 0.0>\n",
+      "Band 3 range: 0.31469968133185044 at Vector3<0.1, 0.0, 0.0> to 1.2769288155070349 at Vector3<3.0, 0.0, 0.0>\n",
+      "Band 4 range: 0.3946748121624943 at Vector3<0.1, 0.0, 0.0> to 1.351049789287694 at Vector3<3.0, 0.0, 0.0>\n",
+      "total elapsed time for run: 2.7607743740081787\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "ms.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It solved, great! We can see from the text output lines like :\n",
+    "\n",
+    "`solve_kpoint (0.1,0,0):\n",
+    "Solving for bands 1 to 4...\n",
+    "Finished solving for bands 1 to 4 after 28 iterations.\n",
+    "freqs:, 1, 0.1, 0, 0, 0.1, 0.0807582, 0.0855765, 0.3147, 0.394675\n",
+    "elapsed time for k point: 0.14001059532165527`\n",
+    "\n",
+    "This contains our band information. Unfortunately, MPB only keeps in memory one at a time. So we only have access to the last one :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector3<3.0, 0.0, 0.0>\n",
+      "[1.0814996959234677, 1.1042853219436242, 1.2769288155070349, 1.351049789287694]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ms.current_k)\n",
+    "print(ms.freqs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So let's do it again, this time capturing the output :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr output\n",
+    "ms.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Initializing eigensolver data',\n",
+       " 'Computing 4 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 4 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " 'Initializing fields to random numbers...',\n",
+       " '32 k-points',\n",
+       " '  Vector3<0.1, 0.0, 0.0>',\n",
+       " '  Vector3<0.1935483870967742, 0.0, 0.0>',\n",
+       " '  Vector3<0.2870967741935484, 0.0, 0.0>',\n",
+       " '  Vector3<0.38064516129032255, 0.0, 0.0>',\n",
+       " '  Vector3<0.4741935483870968, 0.0, 0.0>',\n",
+       " '  Vector3<0.567741935483871, 0.0, 0.0>',\n",
+       " '  Vector3<0.6612903225806451, 0.0, 0.0>',\n",
+       " '  Vector3<0.7548387096774194, 0.0, 0.0>',\n",
+       " '  Vector3<0.8483870967741935, 0.0, 0.0>',\n",
+       " '  Vector3<0.9419354838709677, 0.0, 0.0>',\n",
+       " '  Vector3<1.035483870967742, 0.0, 0.0>',\n",
+       " '  Vector3<1.1290322580645162, 0.0, 0.0>',\n",
+       " '  Vector3<1.2225806451612904, 0.0, 0.0>',\n",
+       " '  Vector3<1.3161290322580645, 0.0, 0.0>',\n",
+       " '  Vector3<1.409677419354839, 0.0, 0.0>',\n",
+       " '  Vector3<1.503225806451613, 0.0, 0.0>',\n",
+       " '  Vector3<1.5967741935483872, 0.0, 0.0>',\n",
+       " '  Vector3<1.6903225806451614, 0.0, 0.0>',\n",
+       " '  Vector3<1.7838709677419355, 0.0, 0.0>',\n",
+       " '  Vector3<1.87741935483871, 0.0, 0.0>',\n",
+       " '  Vector3<1.970967741935484, 0.0, 0.0>',\n",
+       " '  Vector3<2.064516129032258, 0.0, 0.0>',\n",
+       " '  Vector3<2.1580645161290324, 0.0, 0.0>',\n",
+       " '  Vector3<2.2516129032258068, 0.0, 0.0>',\n",
+       " '  Vector3<2.3451612903225807, 0.0, 0.0>',\n",
+       " '  Vector3<2.438709677419355, 0.0, 0.0>',\n",
+       " '  Vector3<2.532258064516129, 0.0, 0.0>',\n",
+       " '  Vector3<2.6258064516129034, 0.0, 0.0>',\n",
+       " '  Vector3<2.7193548387096778, 0.0, 0.0>',\n",
+       " '  Vector3<2.8129032258064517, 0.0, 0.0>',\n",
+       " '  Vector3<2.906451612903226, 0.0, 0.0>',\n",
+       " '  Vector3<3.0, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.010184764862060547',\n",
+       " 'solve_kpoint (0.1,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 27 iterations.',\n",
+       " 'freqs:, 1, 0.1, 0, 0, 0.1, 0.0807582, 0.0855765, 0.3147, 0.394675',\n",
+       " 'elapsed time for k point: 0.12482213973999023',\n",
+       " 'solve_kpoint (0.193548,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 15 iterations.',\n",
+       " 'freqs:, 2, 0.193548, 0, 0, 0.193548, 0.155365, 0.16422, 0.341458, 0.410987',\n",
+       " 'elapsed time for k point: 0.07110595703125',\n",
+       " 'solve_kpoint (0.287097,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 3, 0.287097, 0, 0, 0.287097, 0.228276, 0.240091, 0.381337, 0.436147',\n",
+       " 'elapsed time for k point: 0.06656813621520996',\n",
+       " 'solve_kpoint (0.380645,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 4, 0.380645, 0, 0, 0.380645, 0.298937, 0.311941, 0.430273, 0.467754',\n",
+       " 'elapsed time for k point: 0.06655502319335938',\n",
+       " 'solve_kpoint (0.474194,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 5, 0.474194, 0, 0, 0.474194, 0.367072, 0.379151, 0.483705, 0.502991',\n",
+       " 'elapsed time for k point: 0.06666064262390137',\n",
+       " 'solve_kpoint (0.567742,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 16 iterations.',\n",
+       " 'freqs:, 6, 0.567742, 0, 0, 0.567742, 0.432542, 0.441894, 0.536768, 0.538894',\n",
+       " 'elapsed time for k point: 0.07534289360046387',\n",
+       " 'solve_kpoint (0.66129,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 16 iterations.',\n",
+       " 'freqs:, 7, 0.66129, 0, 0, 0.66129, 0.494782, 0.500315, 0.573355, 0.58507',\n",
+       " 'elapsed time for k point: 0.07526564598083496',\n",
+       " 'solve_kpoint (0.754839,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 15 iterations.',\n",
+       " 'freqs:, 8, 0.754839, 0, 0, 0.754839, 0.551001, 0.553305, 0.608206, 0.629108',\n",
+       " 'elapsed time for k point: 0.07287859916687012',\n",
+       " 'solve_kpoint (0.848387,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 15 iterations.',\n",
+       " 'freqs:, 9, 0.848387, 0, 0, 0.848387, 0.593884, 0.598211, 0.651795, 0.674567',\n",
+       " 'elapsed time for k point: 0.06881570816040039',\n",
+       " 'solve_kpoint (0.941935,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 10, 0.941935, 0, 0, 0.941935, 0.622664, 0.633694, 0.706501, 0.725007',\n",
+       " 'elapsed time for k point: 0.06473851203918457',\n",
+       " 'solve_kpoint (1.03548,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 11, 1.03548, 0, 0, 1.03548, 0.645081, 0.662012, 0.765683, 0.779634',\n",
+       " 'elapsed time for k point: 0.0645139217376709',\n",
+       " 'solve_kpoint (1.12903,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 17 iterations.',\n",
+       " 'freqs:, 12, 1.12903, 0, 0, 1.12903, 0.665138, 0.686152, 0.824909, 0.836268',\n",
+       " 'elapsed time for k point: 0.07735013961791992',\n",
+       " 'solve_kpoint (1.22258,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 24 iterations.',\n",
+       " 'freqs:, 13, 1.22258, 0, 0, 1.22258, 0.684281, 0.708024, 0.869466, 0.892966',\n",
+       " 'elapsed time for k point: 0.10673928260803223',\n",
+       " 'solve_kpoint (1.31613,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 17 iterations.',\n",
+       " 'freqs:, 14, 1.31613, 0, 0, 1.31613, 0.703123, 0.728677, 0.894377, 0.947171',\n",
+       " 'elapsed time for k point: 0.07732057571411133',\n",
+       " 'solve_kpoint (1.40968,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 15 iterations.',\n",
+       " 'freqs:, 15, 1.40968, 0, 0, 1.40968, 0.721964, 0.748699, 0.91638, 0.994817',\n",
+       " 'elapsed time for k point: 0.06882405281066895',\n",
+       " 'solve_kpoint (1.50323,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 14 iterations.',\n",
+       " 'freqs:, 16, 1.50323, 0, 0, 1.50323, 0.740959, 0.768431, 0.937664, 1.03361',\n",
+       " 'elapsed time for k point: 0.0644369125366211',\n",
+       " 'solve_kpoint (1.59677,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 13 iterations.',\n",
+       " 'freqs:, 17, 1.59677, 0, 0, 1.59677, 0.760193, 0.788079, 0.958655, 1.06537',\n",
+       " 'elapsed time for k point: 0.06036710739135742',\n",
+       " 'solve_kpoint (1.69032,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 11 iterations.',\n",
+       " 'freqs:, 18, 1.69032, 0, 0, 1.69032, 0.779709, 0.807772, 0.979518, 1.09248',\n",
+       " 'elapsed time for k point: 0.05170416831970215',\n",
+       " 'solve_kpoint (1.78387,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 11 iterations.',\n",
+       " 'freqs:, 19, 1.78387, 0, 0, 1.78387, 0.799526, 0.827589, 1.00034, 1.11654',\n",
+       " 'elapsed time for k point: 0.051616668701171875',\n",
+       " 'solve_kpoint (1.87742,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 10 iterations.',\n",
+       " 'freqs:, 20, 1.87742, 0, 0, 1.87742, 0.81965, 0.847578, 1.02116, 1.13857',\n",
+       " 'elapsed time for k point: 0.04729151725769043',\n",
+       " 'solve_kpoint (1.97097,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 10 iterations.',\n",
+       " 'freqs:, 21, 1.97097, 0, 0, 1.97097, 0.840077, 0.867771, 1.04203, 1.15923',\n",
+       " 'elapsed time for k point: 0.047316789627075195',\n",
+       " 'solve_kpoint (2.06452,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 10 iterations.',\n",
+       " 'freqs:, 22, 2.06452, 0, 0, 2.06452, 0.8608, 0.888182, 1.06295, 1.17897',\n",
+       " 'elapsed time for k point: 0.04728507995605469',\n",
+       " 'solve_kpoint (2.15806,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 23, 2.15806, 0, 0, 2.15806, 0.881807, 0.908821, 1.08394, 1.19812',\n",
+       " 'elapsed time for k point: 0.04306530952453613',\n",
+       " 'solve_kpoint (2.25161,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 24, 2.25161, 0, 0, 2.25161, 0.903085, 0.929689, 1.10502, 1.21689',\n",
+       " 'elapsed time for k point: 0.04363417625427246',\n",
+       " 'solve_kpoint (2.34516,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 25, 2.34516, 0, 0, 2.34516, 0.924621, 0.950785, 1.12618, 1.23544',\n",
+       " 'elapsed time for k point: 0.04438018798828125',\n",
+       " 'solve_kpoint (2.43871,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 26, 2.43871, 0, 0, 2.43871, 0.946402, 0.972103, 1.14743, 1.25389',\n",
+       " 'elapsed time for k point: 0.04436993598937988',\n",
+       " 'solve_kpoint (2.53226,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 27, 2.53226, 0, 0, 2.53226, 0.968413, 0.993637, 1.16878, 1.27231',\n",
+       " 'elapsed time for k point: 0.04305839538574219',\n",
+       " 'solve_kpoint (2.62581,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 28, 2.62581, 0, 0, 2.62581, 0.990643, 1.01538, 1.19021, 1.29076',\n",
+       " 'elapsed time for k point: 0.04314303398132324',\n",
+       " 'solve_kpoint (2.71935,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 9 iterations.',\n",
+       " 'freqs:, 29, 2.71935, 0, 0, 2.71935, 1.01308, 1.03733, 1.21175, 1.3093',\n",
+       " 'elapsed time for k point: 0.043036460876464844',\n",
+       " 'solve_kpoint (2.8129,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 89 iterations.',\n",
+       " 'freqs:, 30, 2.8129, 0, 0, 2.8129, 1.03571, 1.05946, 1.23338, 1.31269',\n",
+       " 'elapsed time for k point: 0.3859236240386963',\n",
+       " 'solve_kpoint (2.90645,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 6 iterations.',\n",
+       " 'freqs:, 31, 2.90645, 0, 0, 2.90645, 1.05852, 1.08179, 1.25511, 1.33173',\n",
+       " 'elapsed time for k point: 0.03020763397216797',\n",
+       " 'solve_kpoint (3,0,0):',\n",
+       " 'Solving for bands 1 to 4...',\n",
+       " 'Finished solving for bands 1 to 4 after 6 iterations.',\n",
+       " 'freqs:, 32, 3, 0, 0, 3, 1.0815, 1.10429, 1.27693, 1.35105',\n",
+       " 'elapsed time for k point: 0.030241727828979492',\n",
+       " 'Band 1 range: 0.08075820708714998 at Vector3<0.1, 0.0, 0.0> to 1.0814996959234677 at Vector3<3.0, 0.0, 0.0>',\n",
+       " 'Band 2 range: 0.08557653659868339 at Vector3<0.1, 0.0, 0.0> to 1.1042853219436248 at Vector3<3.0, 0.0, 0.0>',\n",
+       " 'Band 3 range: 0.3146996824185554 at Vector3<0.1, 0.0, 0.0> to 1.2769288155070335 at Vector3<3.0, 0.0, 0.0>',\n",
+       " 'Band 4 range: 0.3946747992334725 at Vector3<0.1, 0.0, 0.0> to 1.3510497892876934 at Vector3<3.0, 0.0, 0.0>',\n",
+       " 'total elapsed time for run: 2.2840616703033447',\n",
+       " 'done',\n",
+       " '']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_text = output.stdout\n",
+    "output_text.split('\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analyzing the results\n",
+    "\n",
+    "Let's parse every line that begins with 'freqs :' in the output.\n",
+    "\n",
+    "Notice the lines all have the same syntax `'freqs:, 22, 2, 0, 0, 2, 0.846477, 0.874081, 1.04852, 1.16543',`\n",
+    "\n",
+    "i.e. for all the lines that start by 'freqs:' contains all the information we need in the form `freqs:, k index, kx, ky, kz, kmag/2pi, band 1, band 2, band 3, band 4, band 5, band 6, band 7, band 8 ...`. Python's `split` function and treating strings as arrays (slicing and indexing) is handy :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "freqs:, 1, 0.1, 0, 0, 0.1, 0.0807582, 0.0855765, 0.3147, 0.394675\n",
+      "['freqs:', '1', '0.1', '0', '0', '0.1', '0.0807582', '0.0855765', '0.3147', '0.394675']\n",
+      "freqs:, 2, 0.193548, 0, 0, 0.193548, 0.155365, 0.16422, 0.341458, 0.410987\n",
+      "['freqs:', '2', '0.193548', '0', '0', '0.193548', '0.155365', '0.16422', '0.341458', '0.410987']\n",
+      "freqs:, 3, 0.287097, 0, 0, 0.287097, 0.228276, 0.240091, 0.381337, 0.436147\n",
+      "['freqs:', '3', '0.287097', '0', '0', '0.287097', '0.228276', '0.240091', '0.381337', '0.436147']\n",
+      "freqs:, 4, 0.380645, 0, 0, 0.380645, 0.298937, 0.311941, 0.430273, 0.467754\n",
+      "['freqs:', '4', '0.380645', '0', '0', '0.380645', '0.298937', '0.311941', '0.430273', '0.467754']\n",
+      "freqs:, 5, 0.474194, 0, 0, 0.474194, 0.367072, 0.379151, 0.483705, 0.502991\n",
+      "['freqs:', '5', '0.474194', '0', '0', '0.474194', '0.367072', '0.379151', '0.483705', '0.502991']\n",
+      "freqs:, 6, 0.567742, 0, 0, 0.567742, 0.432542, 0.441894, 0.536768, 0.538894\n",
+      "['freqs:', '6', '0.567742', '0', '0', '0.567742', '0.432542', '0.441894', '0.536768', '0.538894']\n",
+      "freqs:, 7, 0.66129, 0, 0, 0.66129, 0.494782, 0.500315, 0.573355, 0.58507\n",
+      "['freqs:', '7', '0.66129', '0', '0', '0.66129', '0.494782', '0.500315', '0.573355', '0.58507']\n",
+      "freqs:, 8, 0.754839, 0, 0, 0.754839, 0.551001, 0.553305, 0.608206, 0.629108\n",
+      "['freqs:', '8', '0.754839', '0', '0', '0.754839', '0.551001', '0.553305', '0.608206', '0.629108']\n",
+      "freqs:, 9, 0.848387, 0, 0, 0.848387, 0.593884, 0.598211, 0.651795, 0.674567\n",
+      "['freqs:', '9', '0.848387', '0', '0', '0.848387', '0.593884', '0.598211', '0.651795', '0.674567']\n",
+      "freqs:, 10, 0.941935, 0, 0, 0.941935, 0.622664, 0.633694, 0.706501, 0.725007\n",
+      "['freqs:', '10', '0.941935', '0', '0', '0.941935', '0.622664', '0.633694', '0.706501', '0.725007']\n",
+      "freqs:, 11, 1.03548, 0, 0, 1.03548, 0.645081, 0.662012, 0.765683, 0.779634\n",
+      "['freqs:', '11', '1.03548', '0', '0', '1.03548', '0.645081', '0.662012', '0.765683', '0.779634']\n",
+      "freqs:, 12, 1.12903, 0, 0, 1.12903, 0.665138, 0.686152, 0.824909, 0.836268\n",
+      "['freqs:', '12', '1.12903', '0', '0', '1.12903', '0.665138', '0.686152', '0.824909', '0.836268']\n",
+      "freqs:, 13, 1.22258, 0, 0, 1.22258, 0.684281, 0.708024, 0.869466, 0.892966\n",
+      "['freqs:', '13', '1.22258', '0', '0', '1.22258', '0.684281', '0.708024', '0.869466', '0.892966']\n",
+      "freqs:, 14, 1.31613, 0, 0, 1.31613, 0.703123, 0.728677, 0.894377, 0.947171\n",
+      "['freqs:', '14', '1.31613', '0', '0', '1.31613', '0.703123', '0.728677', '0.894377', '0.947171']\n",
+      "freqs:, 15, 1.40968, 0, 0, 1.40968, 0.721964, 0.748699, 0.91638, 0.994817\n",
+      "['freqs:', '15', '1.40968', '0', '0', '1.40968', '0.721964', '0.748699', '0.91638', '0.994817']\n",
+      "freqs:, 16, 1.50323, 0, 0, 1.50323, 0.740959, 0.768431, 0.937664, 1.03361\n",
+      "['freqs:', '16', '1.50323', '0', '0', '1.50323', '0.740959', '0.768431', '0.937664', '1.03361']\n",
+      "freqs:, 17, 1.59677, 0, 0, 1.59677, 0.760193, 0.788079, 0.958655, 1.06537\n",
+      "['freqs:', '17', '1.59677', '0', '0', '1.59677', '0.760193', '0.788079', '0.958655', '1.06537']\n",
+      "freqs:, 18, 1.69032, 0, 0, 1.69032, 0.779709, 0.807772, 0.979518, 1.09248\n",
+      "['freqs:', '18', '1.69032', '0', '0', '1.69032', '0.779709', '0.807772', '0.979518', '1.09248']\n",
+      "freqs:, 19, 1.78387, 0, 0, 1.78387, 0.799526, 0.827589, 1.00034, 1.11654\n",
+      "['freqs:', '19', '1.78387', '0', '0', '1.78387', '0.799526', '0.827589', '1.00034', '1.11654']\n",
+      "freqs:, 20, 1.87742, 0, 0, 1.87742, 0.81965, 0.847578, 1.02116, 1.13857\n",
+      "['freqs:', '20', '1.87742', '0', '0', '1.87742', '0.81965', '0.847578', '1.02116', '1.13857']\n",
+      "freqs:, 21, 1.97097, 0, 0, 1.97097, 0.840077, 0.867771, 1.04203, 1.15923\n",
+      "['freqs:', '21', '1.97097', '0', '0', '1.97097', '0.840077', '0.867771', '1.04203', '1.15923']\n",
+      "freqs:, 22, 2.06452, 0, 0, 2.06452, 0.8608, 0.888182, 1.06295, 1.17897\n",
+      "['freqs:', '22', '2.06452', '0', '0', '2.06452', '0.8608', '0.888182', '1.06295', '1.17897']\n",
+      "freqs:, 23, 2.15806, 0, 0, 2.15806, 0.881807, 0.908821, 1.08394, 1.19812\n",
+      "['freqs:', '23', '2.15806', '0', '0', '2.15806', '0.881807', '0.908821', '1.08394', '1.19812']\n",
+      "freqs:, 24, 2.25161, 0, 0, 2.25161, 0.903085, 0.929689, 1.10502, 1.21689\n",
+      "['freqs:', '24', '2.25161', '0', '0', '2.25161', '0.903085', '0.929689', '1.10502', '1.21689']\n",
+      "freqs:, 25, 2.34516, 0, 0, 2.34516, 0.924621, 0.950785, 1.12618, 1.23544\n",
+      "['freqs:', '25', '2.34516', '0', '0', '2.34516', '0.924621', '0.950785', '1.12618', '1.23544']\n",
+      "freqs:, 26, 2.43871, 0, 0, 2.43871, 0.946402, 0.972103, 1.14743, 1.25389\n",
+      "['freqs:', '26', '2.43871', '0', '0', '2.43871', '0.946402', '0.972103', '1.14743', '1.25389']\n",
+      "freqs:, 27, 2.53226, 0, 0, 2.53226, 0.968413, 0.993637, 1.16878, 1.27231\n",
+      "['freqs:', '27', '2.53226', '0', '0', '2.53226', '0.968413', '0.993637', '1.16878', '1.27231']\n",
+      "freqs:, 28, 2.62581, 0, 0, 2.62581, 0.990643, 1.01538, 1.19021, 1.29076\n",
+      "['freqs:', '28', '2.62581', '0', '0', '2.62581', '0.990643', '1.01538', '1.19021', '1.29076']\n",
+      "freqs:, 29, 2.71935, 0, 0, 2.71935, 1.01308, 1.03733, 1.21175, 1.3093\n",
+      "['freqs:', '29', '2.71935', '0', '0', '2.71935', '1.01308', '1.03733', '1.21175', '1.3093']\n",
+      "freqs:, 30, 2.8129, 0, 0, 2.8129, 1.03571, 1.05946, 1.23338, 1.31269\n",
+      "['freqs:', '30', '2.8129', '0', '0', '2.8129', '1.03571', '1.05946', '1.23338', '1.31269']\n",
+      "freqs:, 31, 2.90645, 0, 0, 2.90645, 1.05852, 1.08179, 1.25511, 1.33173\n",
+      "['freqs:', '31', '2.90645', '0', '0', '2.90645', '1.05852', '1.08179', '1.25511', '1.33173']\n",
+      "freqs:, 32, 3, 0, 0, 3, 1.0815, 1.10429, 1.27693, 1.35105\n",
+      "['freqs:', '32', '3', '0', '0', '3', '1.0815', '1.10429', '1.27693', '1.35105']\n"
+     ]
+    }
+   ],
+   "source": [
+    "freqs = []\n",
+    "ks = []\n",
+    "line_header = \"freqs:\"\n",
+    "for ln in output_text.split('\\n'):\n",
+    "    if ln.startswith(line_header):\n",
+    "        print(ln)\n",
+    "        line = ln.split(', ')\n",
+    "        print(line)\n",
+    "        ks.append([line[2], line[3], line[4]])\n",
+    "        freqs.append([line[-4], line[-3], line[-2], line[-1]])\n",
+    "        \n",
+    "freqs = np.array(freqs, dtype=np.float32)\n",
+    "ks = np.array(ks, dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.0807582 0.0855765 0.3147    0.394675 ]\n",
+      " [0.155365  0.16422   0.341458  0.410987 ]\n",
+      " [0.228276  0.240091  0.381337  0.436147 ]\n",
+      " [0.298937  0.311941  0.430273  0.467754 ]\n",
+      " [0.367072  0.379151  0.483705  0.502991 ]\n",
+      " [0.432542  0.441894  0.536768  0.538894 ]\n",
+      " [0.494782  0.500315  0.573355  0.58507  ]\n",
+      " [0.551001  0.553305  0.608206  0.629108 ]\n",
+      " [0.593884  0.598211  0.651795  0.674567 ]\n",
+      " [0.622664  0.633694  0.706501  0.725007 ]\n",
+      " [0.645081  0.662012  0.765683  0.779634 ]\n",
+      " [0.665138  0.686152  0.824909  0.836268 ]\n",
+      " [0.684281  0.708024  0.869466  0.892966 ]\n",
+      " [0.703123  0.728677  0.894377  0.947171 ]\n",
+      " [0.721964  0.748699  0.91638   0.994817 ]\n",
+      " [0.740959  0.768431  0.937664  1.03361  ]\n",
+      " [0.760193  0.788079  0.958655  1.06537  ]\n",
+      " [0.779709  0.807772  0.979518  1.09248  ]\n",
+      " [0.799526  0.827589  1.00034   1.11654  ]\n",
+      " [0.81965   0.847578  1.02116   1.13857  ]\n",
+      " [0.840077  0.867771  1.04203   1.15923  ]\n",
+      " [0.8608    0.888182  1.06295   1.17897  ]\n",
+      " [0.881807  0.908821  1.08394   1.19812  ]\n",
+      " [0.903085  0.929689  1.10502   1.21689  ]\n",
+      " [0.924621  0.950785  1.12618   1.23544  ]\n",
+      " [0.946402  0.972103  1.14743   1.25389  ]\n",
+      " [0.968413  0.993637  1.16878   1.27231  ]\n",
+      " [0.990643  1.01538   1.19021   1.29076  ]\n",
+      " [1.01308   1.03733   1.21175   1.3093   ]\n",
+      " [1.03571   1.05946   1.23338   1.31269  ]\n",
+      " [1.05852   1.08179   1.25511   1.33173  ]\n",
+      " [1.0815    1.10429   1.27693   1.35105  ]]\n",
+      "[[0.1      0.       0.      ]\n",
+      " [0.193548 0.       0.      ]\n",
+      " [0.287097 0.       0.      ]\n",
+      " [0.380645 0.       0.      ]\n",
+      " [0.474194 0.       0.      ]\n",
+      " [0.567742 0.       0.      ]\n",
+      " [0.66129  0.       0.      ]\n",
+      " [0.754839 0.       0.      ]\n",
+      " [0.848387 0.       0.      ]\n",
+      " [0.941935 0.       0.      ]\n",
+      " [1.03548  0.       0.      ]\n",
+      " [1.12903  0.       0.      ]\n",
+      " [1.22258  0.       0.      ]\n",
+      " [1.31613  0.       0.      ]\n",
+      " [1.40968  0.       0.      ]\n",
+      " [1.50323  0.       0.      ]\n",
+      " [1.59677  0.       0.      ]\n",
+      " [1.69032  0.       0.      ]\n",
+      " [1.78387  0.       0.      ]\n",
+      " [1.87742  0.       0.      ]\n",
+      " [1.97097  0.       0.      ]\n",
+      " [2.06452  0.       0.      ]\n",
+      " [2.15806  0.       0.      ]\n",
+      " [2.25161  0.       0.      ]\n",
+      " [2.34516  0.       0.      ]\n",
+      " [2.43871  0.       0.      ]\n",
+      " [2.53226  0.       0.      ]\n",
+      " [2.62581  0.       0.      ]\n",
+      " [2.71935  0.       0.      ]\n",
+      " [2.8129   0.       0.      ]\n",
+      " [2.90645  0.       0.      ]\n",
+      " [3.       0.       0.      ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(freqs)\n",
+    "print(ks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now plot. Let's add the lightline too :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2af1c6a79090>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EStnlE2BdKZelaE5GRgbvv/++XhXk6upKXFwc7u7uZvasc1FfX8+LL77I008/reePAEaOHInBYGDgwIFm9K5z0uWl1s7OjsLCwi79BSulpLCwEDs7O3O7omhl0tLSeO+993TxcHd3Z+HChUo8fiMZGRlcffXVPP7447p42Nra8uKLL/LNN98o8WghXT4C8fPzIzs7m/z8fHO70qbY2dnh5+dnbjcUrUhycjL/+c9/9JufHj16EBsbi4uLi5k96zw0NDTw0ksv8eSTT1JTU6Pbhw0bhsFgUFWLV0iXFxBra+suX4an6HokJiby0Ucf6cdeXl7Exsbi5ORkRq86F0eOHGHBggXs2bNHt9nY2LB8+XL+8Ic/YGXV5b/+2hz1G1QoOhj79u1j+/bt+nHPnj2ZP3++andzmTQ0NPCvf/2Lxx9/XF+lDzBkyBAMBoNqbd+KKAFRKDoQe/bsYceOHfpx7969mTdvHg4ODmb0qvNw/PhxFi5cyO7du3WbtbU1Tz31FI8++ijW1tZm9K7roQREoeggfPvtt3z++ef6sa+vL/PmzVPFEZeB0Wjktdde49FHH9UbSwJERUVhMBjUboxthBIQhaIDsHPnTr7++mv92N/fn7lz52Jra2s+pzoJJ0+eZNGiRezcuVO3WVlZ8cQTT/D4449jY2NjRu+6NkpAFAozIqXkq6++4ptvvtFtgYGBzJkzR33x/QpGo5HXX3+dhx9+mMrKxobe4eHhGAwGoqOjzehd90AJiEJhJqSUfP7553z33Xe6rV+/fsyaNUvN1f8Kp06dIj4+ni+++EK3WVpa8thjj/Hkk0+qyK2dUAKiUJgBKSU7duzghx9+0G1BQUHMnDlTlZf+AlJK3nzzTR588EF9B0aAkJAQDAYDQ4cONaN33Y8uvxJdoehoSCnZvn17M/EYNGiQEo9fISsri0mTJrF48WJdPCwsLHj00Uc5cOCAEg8zoD6tCkU7YjQa+fjjjzl48KBuCwkJ4fbbb8fS0tKMnnVcpJQYDAZ+//vfU1ZWptsHDRqEwWBgxIgRZvSue6MiEIWinTAajfznP/9pJh5hYWFMmzZNicclyMnJ4ZZbbmHRokW6eAghePDBBzl48KASDzOjIhCFoh1oaGjgww8/bLZdamRkJLfeeqtqH34RpJRs3LiRZcuWUVJSotuDgoIwGAxcc801ZvROcYE2++QKIdYKIfKEEGmXGB8rhCgVQiSZHk81GZskhDgihDguhHjsYtcrFJ2FhoYG3n///WbiER0dzZQpU5R4XIRz585x2223ERsb20w87r//fpKTk5V4dCDaMgIxAK8CCb9wzjdSyluaGoQQlsBrwAQgG9gnhNgmpcxoK0cViraivr6ed999l6NHj+q2oUOHcuONN3b5PWp+K1JK3n77bZYuXUpRUZFu79evH2vXrmXMmDFm9E5xMdrs9kdKuQso+tUTf84w4LiU8qSUshZ4G5jSqs4pFO1AXV0db7/9djPxGDFihBKPi5CXl8f06dOZM2dOM/FYunQpKSkpSjw6KOaOn0cKIZKFEJ8IIUJNNl8gq8k52SbbRRFCLBZC7BdC7O/qe34oOg+1tbVs3ryZEydO6LZrrrmGG264QYnHT3j33XcJDQ3lgw8+0G19+vThyy+/5JVXXlFdiDsw5hSQRCBQShkJvAJ8aLJf7K/rktsJSilXSyljpJQxXl5ebeCmQvHbqKmpYdOmTfz444+6bcyYMVx33XVKPJpQUFDAzJkzmTFjBgUFBbr9nnvuISUlhXHjxpnRO8XlYLYqLCllWZN/bxdCrBRCeKJFHP5NTvUDzrS3fwpFS6iuruatt94iOztbt40fP57Ro0eb0auOxwcffMC9995LXl6ebvP392fNmjVMmDDBjJ4pfgtmi0CEEL2E6XZMCDHM5EshsA8YIIToK4SwAWYB28zlp0JxuVRVVbFhw4Zm4jFhwgQlHk0oLCxkzpw5TJs2rZl43HnnnaSmpirx6GS0WQQihNgMjAU8hRDZwNOANYCUchUwHbhXCFEPVAGzpLb5c70QYimwA7AE1kop09vKT4WiNTh//jwbNmzg3Llzum3SpEkMHz7cjF51LLZt28bixYvJzc3Vbb6+vrzxxhvceOONZvRM0VKE9p3dNYiJiZH79+83txuKbkZlZSUJCQnN7qhvvvlmYmJizOhVx6G4uJj777+fDRs2NLMvWLCAf/zjH7i5uZnJMwWAEOKAlLJFH1a1El2huALKy8tJSEholgS+9dZbGTx4sBm96jhs376du+66izNnGtOYvXr14o033uCWW275hSsVnQFzl/EqFJ2WsrIyDAaDLh5CCKZOnarEAygtLWXRokXcfPPNzcRj3rx5pKenK/HoIqgIRKFoASUlJaxfv15vtSGEYNq0aYSGhv7KlV2fHTt2cOeddzYrJvD29mbVqlVMnTrVjJ4pWhsVgSgUv5GioiIMBoMuHhYWFsyYMaPbi0dZWRmLFy9m0qRJzcRjxowZpKenK/HogqgIRKH4DRQUFJCQkEB5eTmgbaM6Y8YMBg4caGbPzMsXX3zBokWLOH36tG7z9PRk5cqV3HHHHWb0TNGWqAhEobhM8vPzMRgMunhYWVkxe/bsbi0eFRUVLFmyhOuvv76ZeNx+++2kp6cr8ejiqAhEobgMcnNzSUhI4Pz58wBYW1sze/Zs+vbta2bPzMfXX3/NokWLmrVs8fDw4LXXXmPmzJmqbUs3QEUgCsWvcPbsWdavX6+Lh42NDXPnzu224lFZWcmyZcsYN25cM/G49dZbSU9PZ9asWUo8ugkqAlEofoGcnBw2btxIdXU1ALa2tsybNw8/Pz8ze2Yedu/ezYIFC5p1GXZzc+OVV15h7ty5Sji6GUpAFIpLcPr0ad566y1qa2sBsLOzY/78+fj4+JjZs/anqqqKJ554gn/+85807V5x00038cYbb3TL30lnpr64mOq0dKrTr6xLlBIQheIiZGZmsmnTJurq6gBwcHBg/vz59OrVy8yetT/ff/89CxYsaLYxlouLC//85z9ZsGCBijo6OA0lJVSlp1OdnkF1WhrVaWnUnWmdBudKQBSKn3Dy5Ek2b95MfX09AI6OjsTGxuLt7W1mz9qX6upqnnrqKf7+979jNBp1+8SJE3nzzTe77TReR6a+uJjqjAztYRKMuiZrcqwDArCPisR97lzsQkOxCw0BF5cWv54SEIWiCceOHWPLli00NDQA4OTkRFxcHJ6enmb2rH3Zu3cvCxYs4NChQ7rN2dmZl156ifj4eBV1dADqi4o0kUjXpqKqMzKoy8nRx639/LALC8N91kxNLEJCsHR1bVUflIAoFCaOHDnCu+++q4uHi4sLcXFxeHh4mNmz9qOmpobly5fzwgsvNIs6rrvuOtasWUNgYKAZveuaGGtraSgowFhdg6ypxlhdjaypwVhVpf2srkaaxhrKyqk+cpjq9Azqz57Vn8M6MAC7iHDcZ89qM7G4GEpAFAogIyOD999/X//SdHNzIy4urlu1Gk9MTCQuLo60tDTd5ujoyN/+9jfuvvtuFXW0Ag0VldQcOUx1xiFtmunQIWqOHwfTdOnlYNOnDw7R0diFhJjEIhjLK5iGuhKUgCi6PampqWzdulWvLnJ3dycuLg7XdriD6wjU1tayYsUKnnvuOT36Ahg3bhxr1qzptutdrpQL+YiaQ4d0wag9dQpMnzPLHj2wCwnBacwYbPz9ELZ2CDtbLOzsELYXftphYWeLsLPDwtYW4eCAhY2Nmd9ZI0pAFN2apKQktm3bpotHjx49iIuLw9nZ2cyetQ/JycnExcWRnJys2xwcHPjrX//Kvffei4WFWmv8a0gpqT97luoLQnFIezSbYvLxwS40BJdbJ2uRQ3AIVt5enT6qUwKi6LYkJiby0Ucf6cdeXl7Exsbi5ORkRq/ah7q6Op5//nmeeeYZvdoMYPTo0axbt47+/fub0buOi2xooPbUKZNQNEYXDabOzAiBTd++pimmYJNYBGPZwaZCjdLIiZITJOUnXdHztOWe6GuBW4A8KWXYRcbnAo+aDiuAe6WUyaaxTKAcaADqW7rdokJxKfbt28f27dv14549ezJ//nwcHR3N6FX7kJaWRlxcHImJibrN3t6ev/zlL/zud79TUYcJY20tNceOmUQiQxONo0eRppY2wtoa2wEDcJ5wPbbBwdgFB2M3aBAWDg5m9vznVNZVkpKfQlJ+Esl5yaTkp1BeV37Fz9uWEYgBeBVIuMT4j8AYKWWxEOJGYDUwvMn4OCllwcUvVShazp49e9ixY4d+3Lt3b+bPn4+9vb0ZvWp76uvrefHFF3n66af1BZIAV199NevWrevWXYX15HZ6hj4F1TS5beHoiG3wVbhNm6YJRUgwtv37I6ytzez5z5FSklORQ1J+Ekl5SSTnJ3O0+ChGaUQgCHIPYlLfSUR5RxHlFUXggpZX1rWZgEgpdwkh+vzC+HdNDvcAalWSos3ZvXs3X3zxhX7s5+fH3LlzsbOzM6NXbU9GRgYLFixg3759us3W1pYVK1bwwAMPYGlpaUbv2pf6wsImuYoMajIOacltE5Y9emAXHIzT6NH6NJS1vz+ig0ZmtQ21ZBRmkJyfTFJeEkn5SRRUaffeDlYORHhFsDhiMVFeUUR4ReBs03r5vY6SA4kHPmlyLIFPhRASeF1KufpSFwohFgOLAQICAtrUSUXnZufOnXz99df6cUBAAHPmzMHW1tZ8TrUxDQ0NvPTSSzz55JPU1NTo9mHDhmEwGAgODjajd22LlJK6nDONuQpTdFGfl6efY+3ri11ICK63TTFNQ3X85HZBVYEmFKboIr0wnTqjFlH6OfkxovcIoryiiPSOZIDbACwt2u7mwOwCIoQYhyYgo5qYr5FSnhFCeAOfCSEOSyl3Xex6k7isBoiJiZEXO0fRvZFS8tVXX/HNN9/otj59+jB79mxsOlBJZGtz5MgRFixYwJ49e3SbjY0Ny5cv5w9/+ANWVmb/82819OT2hSko0xoLY2mpdoKFBbb9++EwYjh2wVpi2y74qnZZbHcl1BvrOVZ8TIsuTFNSORXaanMbCxtCPUOZGzxXFwxP+/btmGDWT5AQIgJ4E7hRSll4wS6lPGP6mSeE2AoMAy4qIArFLyGl5PPPP+e77xpnTPv168esWbOw7oDz161BQ0MDL7/8Mk888YTehh5gyJAhGAwGwsJ+VtPSqZC1tdQcP64JxQXBOHwYWVUFgLCxwXbgQFwmTtSmoIKDsR04EItOkOMqrSnVp6JS8lNIKUihql57X172XkR6RTL7qtlEeUcR7BGMjaV5b4DMJiBCiADgA2C+lPJoE7sjYCGlLDf9+wbgGTO5qejESCnZsWMHP/zwg24bMGAAM2bM6FJ33005duwYCxcu5Ntvv9Vt1tbWPPnkkzz22GOdTjSN1dXUHDnSvEHgsWNgKgKwcHDANiQYt+nTtZLZkGBs+/XrkMntn2KURn4s/bFZ7uLHUm2DLkthySCPQdwWdBtRXlFEeUfR27F3h5taa8sy3s3AWMBTCJENPA1YA0gpVwFPAT2AlaZfyoVy3Z7AVpPNCtgkpfxfW/mp6JpIKfnvf//LgQMHdNugQYOYPn16lxQPo9HIq6++ymOPPUaV6U4cICoqCoPBQGRkpBm9uzwaKiqpOXyoUSgyMqg5eRJMq+MtXV2xCw2lR1ysSSxCsA4I6LDJ7Z9SUVtBSkEKyfnJJOebSmlrtVJaV1tXIr0imdxvMlHeUYT2CMXBuuOVA/8U0XRzmM5OTEyM3L9/v7ndUJgZo9HIRx99RFJS4yKpkJAQbr/99i5ZbXTy5EkWLlzIrl2Ns7xWVlY88cQTPP744x0yz9NQUdG8k2x6erM2H1ZeXqZeTyG6WFj17nh34JdCSsmpslO6WCTlJ3G8+DgSiUDQ360/kV6RRHlHEekVSR+XPmZ7b0KIAy1da9f1bsUU3Rqj0ch//vMfUlJSdFt4eDi33XZbl1sgZzQaWbVqFY888giVlZW6PTw8HIPBQHR0tBm9a6ShvFwrm70gFmlpzcpmrXr1wi40FJdbJ2MfGoptcDDWnWzvlfN150krSNMFIzk/mZIabXW6k7UTEV4RTAiYQKRXJOFe4a1aSmtOlIAouoIec8IAACAASURBVAwNDQ1s3bqV9CbbdEZFRTF58uQuJx6ZmZnEx8fz5Zdf6jZLS0see+wxnnzySbOVJhvPn9eS2mlpVKVqu9/VZmbq41a9e2MXqpXNahsahWLVo4dZfG0pUkqyy7O1Vd0msbiwUA+gr2tfxvqP1SIMryj6ufXDQnStz98FlIAougQNDQ289957HD58WLdFR0dzyy23dJppj8tBSskbb7zBQw89REVFhW4PCQnBYDAwdOjQdvPFWFtLzZGjVKel6mJRc/w4mFriW/XsiV1YGK5Tbu20YgFadJFemN4sd1FUXQRoC/XCvcK5M/xOfaGeq23HLg1uTZSAKDo99fX1vPPOOxw7dky3DR06lBtvvLFLiUdWVhbx8fF89tlnus3CwoKHH36YP/3pT226ml5KSW1mJtUpKVQlp1CVkkLNkSNIUzWUpZsbduHhOF9/HXZh4diFhXa6aShojC6SC5JJzmuMLhqklsgPdAlklO8oIr0iifSKJMgtqE0X6nV0lIAoOjV1dXVs2bKFEydO6LaRI0cyYcKELiMeUkrWrVvHAw88QFlZmW4fNGgQBoOBESNGtPpr1hcXNxOLqtRUfVGehYMDdmFheMTFYhcWhl1YONa+Pp3y9/2r0YVnOIvCFhHpFUmEVwTudu5m9rhjoQRE0Wmpra1l8+bNZDaZYx81ahTjx4/vlF9mFyMnJ4e77rqLTz5p7PQjhODBBx/kz3/+c6s0gJT19dQcPcr5gwepSkqmKiWZulOntUELC2yDgnC5YQJ2ERHYR0ZqTQQ7YTWblJKs8qxmYqGiiytDCYiiU1JTU8OmTZs4ffq0bhszZgxjxozpEuIhpWTDhg0sW7aM0gvtOICgoCAMBgPXXHNNi5+7obxciywSEzl/MJHq5BSMphblVt7e2EdG4DZ9OvYRkdiHhWLRSVvcn687T2pBKin5KbpgFNcUA425i/jweC268IzAza5j7dnRGVACouh0VFdX89Zbb5Gdna3bxo8fz+jRo83oVetx9uxZ7r777mabXQkhuP/++3n22Wdx+A37TVxoKFiVeECLMBIPUnP0qLbewsIC20GDcL3tNuyjo3GIHtyp1lo0xSiN+rqLC4JxvOR4s8qoa/2uJdJbiy76u/ZX0UUroARE0amoqqpiw4YNnG2yXeiECRO4+uqrzehV6yClZPPmzSxdupTi4mLd3q9fP9atW8e11157Wc9Tm53D+b17Of/DD1Tu20v9Ge13ZeHoiH1kJM733YdD9GDsIiKxdOqc0UV5bTmp+akkF2iCkZKfQlmtlh9ytnYm3Cuc8QHjifCM6HaVUe2JEhBFp+H8+fMkJCSQm5ur22688UaGDRtmRq9ah9zcXO699162bt3azL506VKef/75X9wpURcM06PuzBkALN3dcRg6FIdF8TjEDMF2wIBOmbtoMDZwsvSk3lwwOS+Zk6Unm63qnhA4QU9093Xt22XXXbQKUkLBUTj9PZze8+vn/wJKQBSdgoqKChISEsjPz9dtt9xyC0OGDDGjV63DO++8w5IlSygs1BtS06dPH9auXcu4ceN+dn59URGV335L5Xffa4KRo7X3tnRzw2HYMDwWLcJh2FBsg4I6TZ+ophRXF5NakKonu9MK0qis01bau9q6EuEZwaS+k7RV3Z7hONl0/T3sr4j6Wjib1CgYp/dAlVZphsOVrctRAqLo8JSXl5OQkEBBQeMOx1OmTCEqKsqMXl05+fn53Hfffbz77rvN7HfffTcvvvgizs5auwtZX09VSiqVu7+h4pvdVKelgZSNgrFwYacVjDpjHUeLj2rTUab8xelyrTDCUlgy0H0gt/S7RY8uApwDOmWOpl2pKoHsfY2CkXMA6k1t/T36w6CbIGAEMmAEmbI3PNrytipKQBQdmtLSUhISEigq0u6YhBBMnTqV8PBwM3t2ZXzwwQfcc889zSIqf39/1qxZw4QJE6jLy6Pk08+o2P0Nld99r63BsLDAPjISr2W/w3HUaOxCQzqdYORW5pJSkKLnLdIL06lp0HZK7GHXg0ivSKYNnEaEZwQhPUI6RUdasyIllGY1Rhan90BeBiBBWELvSIiJh4ARNPgN53CFHXt/LGLfoSL2bj9FQcXRX32JX0IJiKLDUlxcTEJCAiUlWlM6CwsLpk2bRkhIiJk9azmFhYX87ne/Y/Pmzc3s8fHxrIi/E4vvv+fkayupMbVksfLywvm663C6djSOI0d2+B30mlJdX82hokPNymhzz2v5K2sLa0J6hDBj0Aw90d0R97vocBgbIDcNTv/QGGGUazkvbJzBfyiE3gb+w6npNZjUvHp++LGIfT8UcWBLEuU19QD4utkzKqgHQ/t6MO+Flruj2rkrOiRFRUWsX79eX3ltYWHBHXfcwVVXXWVmz1rOtm3bWLx4cbMiAB9vb/42eTJDT52mLisLLC1xGDIEx9GjcLr2WmwHDuwUX6pNF+ldSHYfLTpKvTR9YTn5EuEVoa+5GOQxyOy76XUKasohez9k/aCJRfZ+MO0hgosvBIyAgJHgP5xy14EkZpez78ci9mYWkZRVQm29VsYc5O3E0D4eDOvrztA+Hvi5N0Z2V9LOXQmIosNRUFBAQkIC5eXaH4qlpSUzZ85kwIABZvasZRQXF3P//fezYcOGZvbbe/XmEScnXGxtcRw5EpdJE3EaPx4r947fLqOstoy0gjR9Kiq1IFVvX+5g5UCYZxgRXhFEeEYQ7hXe7nt1d1pKszWhuCAYuWkgjYCAnqHgP1wTjIAR5Fl6sT+zWJuSyizi0NkyjBIsLQShPi4M7eNherjTw+nS3ZnVfiCKLkNeXh4JCQn6/hZWVlbMmjWL/v37m9mzlvHf//6XxYsXc8ZUWgvgaWnJM75+3DxpIi4TJ+E8fhyWbh13FXS9sZ4TJSdIzk/WV3afLD0JoJfRjg8YT7hnOBFeEWqR3uXSUK8JRNZeyNqjTUuVmRbHWjuCXwxc+zD4D0P6xnCq0pq9mUXsO1LEvh3HySzU9ryxs7ZgsL87S8cPYGgfd6ID3HG0bZ+vdiUgig7DuXPn2LBhA+dNbTWsra2ZM2cOffr0Ma9jLaC0tJT7lyxh/aZNzexTBwzg708+ScCtt3bYfEb++fyfJbqr6rVtct1t3YnwiuCmvjcR4RVBmGdYl9kcqc2pKmmcjsraA9kHwFSejLMPBAwH/99BwAjqvUI4lFvFvswi9u0pYl/mfgoqtGIDNwdrYgI9mDM8gJg+HoT5uGJjZZ5iirbcE30tcAuQJ6UMu8i4AF4GbgLOAwuklImmsUmmMUvgTSnl823lp6JjcObMGTZs2EB1tVZuaGNjw9y5cwkICDCzZ78NKSXbXnuNex9/nLOmKTgAL1dX/r1yJdPmzDGjdz+nur6aw0WHm0UXZyu1letWFlYEewQzNWiqNh3lFYGfk1+nyMmYHSmh6GRjdJG1F/IOoVVHWUCvcBg8V5uS8h/OeYfeJGWVsO/HYvZ/UkTiqS+prNWaPPq62TN6gCcxfbT8RZCXExYWHeP/4FcFRAjxMhAMSCAZ2CSlTPrlqwAwAK8CCZcYvxEYYHoMB/4NDBdCWAKvAROAbGCfEGKblDLjMl5T0QnJzs5m48aN1NRod1i2trbMmzcPPz8/M3t2+TSUlZH19hYee3YFW5o0eASYNWsWr7zyCp6e5s0DSCk5XX66Wd7iSNERPdHt4+hDhFcE80PmE+4ZTnCPYGwtzbOzYaejrgrOJJmii73az/OmdUu2LuA3FEKngv8w8I2hoM6a/ZnF7M8sYt+u06TnpFJvlAgBg3o6c3u0H0P7ehAT6I6P25V3XG4rLicCOQR8DFgDIcBGIcQqKeWrv3SRlHKXEKLPL5wyBUiQWhZ/jxDCTQjRG+gDHJdSngQQQrxtOlcJSBfk9OnTvPXWW9TW1gJgb2/P/Pnz6d27t5k9+3WklFSnpFC85R12bNnCE6dPcba+Xh/39PTk3//+N9OnTzeLf6U1pVqi2zQdlVaQ9rNE94KwBXruQiW6fwNlZ5uLxdlkMGqba+HRDwZM0EQjYATS6yp+LKxif2Yx+w4Usf+D/fxYoE1d2VhZEOXnxl3X9mNYHw+iA91xtbc24xv7bfyqgEgpVzU53C6EeBXYhxZdXAm+QFaT42yT7WL24Zd6EiHEYmAx0OmmO7o7mZmZbNq0iTrTrnYODg7Mnz+fXr16mdmzX0ZKScVXX1Gw8t8UpqTw9+Ii3m6ySh5g2rRprFy5Eu922pWv3ljPseJjegltSn4KmWWZQPNE94WqKJXo/g001DVJdpsepaYo08oOfKJh5H1adOE3jFq7HqSdKeVAZjH7dhRx4NSXFFZqN0juDtYMCfRg1lB/Yvq4E+briq1V5/1/uOwciBDiHiAIcAbKfuX0y3rKi9jkL9gvipRyNbAatDLeVvBL0Q6cOHGCt99+m3rTHbujoyOxsbHt9oXbEqSUVHzxBfkrV1KTcYhEJyeeKCnmVBPx8PDw4LXXXmPmzJltmis4V3lOz1mk5KeQUZhBdYOWP/Kw8yDCK4Jb+99KhFcEoT1CVb+o30JlgSYS2SaxyEkEUxEBzj7aYr0R92r5i17hlNYKDpwu0qakdh4nOWs/Nab1F4E9HBg7yJuhfdyJ6eNBfy/HLpVD+i1J9O1oeYnbgb+0wmtnA/5Njv2AM4DNJeyKLsKxY8fYsmULDQ1aktDZ2ZnY2Fiz5wguhTQaKf/8cwpW/puaw4ep8/Xhtf79Wb39v83OmzJlCqtWrWr1CKqqvoqMwgxdLFIKUsg7nwdoK7qDewQzfeB0PdHt49g5t5c1C8YGrfVH1l6tf1TWD1ryG8DCCnpFwJAFmmj4D0e6+JJVVMX+U0Xs21vMgVPfczS3AgArC0GoryvzRgQSE+jOkD7ueDu33T71HYHLSaK/AzwtpTwErBFCrAMOouVFroRtwFJTjmM4UCqlPCuEyAcGCCH6AjnALKBjla4oWszhw4d59913MRq1OzQXFxfi4uLw8PAws2c/RxqNlH/6GQUrV1Jz9Cg2ffpwKi6WpWvWNNuD3d3dnVdeeYU5c+Zc8Rf3hY2RLiS5f7rtqp+THzE9Y/RFempF92+kslATiqbRxYVSWkcv8BsG0XHadJTPYGqFLelnSjlwqpj9B3M5cPow+eVasYeznRXRAe7cGunDkEAPovzdsLfpvNNRLeFyIpCNwBZT2e0BwAkw/tpFQojNwFjAUwiRDTyNloi/kFfZjlbCexytjHehaaxeCLEU2IFWxrtWSpn+296WoiOSnp7OBx98oIuHm5sbcXFxuHWwRXTSaKR8xw4t4jh2DJu+fXFf8Wde2L2bl594gqbdG26++WZWr16Nj49Pi15LT3Tnp5BckExqfqq+MZKjtSPhntq2qxdyFx52HU9oOywN9Vp0kb3XtP5iLxSZhF9YQq8wiJpjyl3EgHtfis/XceBUMQcOFXPgkySSs0v06Sh/D3tGBXkSHejO0D7uDPR27jDltObisluZCCEigCjAAtgupcxrS8dagmpl0nFJTU1l69at+pevh4cHsbGxuHagxXRacvxr8l76O7XHT2DTvz+e995LupsrC+PjOXq0sXOpq6srL7/8MrGxsZcdddQb6zleclxvLphakMqPpT8CWqI7yD2ICM8IfZ+Lvq59VaL7t1CRpwnFBcG4WHThP1T76ROF0cqBkwUVmmCcKmb/qWJO5ps6IJimo2IC3bXpqEB3vF265nRUu7QykVKmACkteRFF9yYpKYn//Oc/+rGnpyexsbH6fhcdgepDh8h94a+c37MHm7598X3p71iPGcPTy5fz97//XY+aACZNmsQbb7zxq+tUCqsK9U2Rfrqi+0Kie3K/yfqKbkfrzrm9rFmor4Xc1MbIInsflJzSxi7kLgbP0yILv6Hg3ofzdQ0kZ5Vy4GQRB75KJ/F0CaVVWgWgq701QwLdmRbtR0ygOxF+3W86qiWoViaKNuXAgQN8/HFjuszLy4vY2FicnDpGVVBdXh75//oXpe9/gKWLCz3/+EfcZ85g38GDLBg6lEOHDunnOjs7849//INFixb9LOq4sDFScl6jYGRXaH2NrIQVV3lcxe0Dbtdbl/s6+apE9+UipdZkMGe/KcLYpy3aM+0joldGDbtLE4vekUgrO3JKqkg8XULi7mIOnPqWjLNlNBi1CDjI24lJob0YEuhOdKA7/Twdu/10VEtQAqJoM/bu3csnn3yiH/fq1Yv58+fj4GD+TYKM1dUUGQwUrH4DWVeHR1wcnvfeQ72dHU88/TQvvPBCs6jj+uuvZ82aNfpao6LqIpLykkjKT9Kii4J0vYzW296bSO9IZg6aSaR3JMEewdhZdc3pjzahthLOHDQlu02iUXFOG7Oyg95RjWLhNxRcfampbyAtp4yDp4o5sCuDxNPF5JZpAmNvbUmUvxv3junPkEB3Bge44eagCg9aAyUgijbh+++/59NPP9WPfXx8mDdvHvb25m3LIKWk7OP/kvfSS9SfPYvzhOvxfughbPr0ITExkbi4ONLS0vTzHR0defFvLzJh5gT25u9l9e7VJOUncapMmy6xsrAixCOE6QOnE+kVSaRXJL0ce6no4nIxGqHgaJPoYj/kpZtamKOt6u43RhMK3yHQMwysbMgtqybxVLEpusgkLaeM2gbtGj93e0b060F0gNaZNri3M1aWnWvnxs6C2g9E0ep88803fPnll/qxn58fc+fOxc7OvHfh5xMPkvvC81Qnp2AbEkzPRx/DcfgwamtrWbFiBc8995y+NgVgYMxAhi4byo9WP+qVUR52HkR6RTLYezBR3lGE9AhR/aJ+C+W5mljkHNDE4sxBqDGtS7Z1Bb8hJrGI0QTDsQe19UYyzpaReKqYg1klJJ4qJqdEyyXZWFkQ4etKdKAmFtGBbl1+7UVro/YDUXQIpJTs3LmTnTt36raAgADmzJmDra35vmTrzp0j768vUrZ9O1ZeXvR+7jlcb5uCsLAgOTmZ2LhYUpIb60MsbCzoeUdPrK+zpsK5ggneE4jyjmKw92ACnANUdHG51J6Hcymmiqj9WvvyCy1ALKy0DZLC79AS3b4x0CMILCwao4uvc0k8fYTUnFJ9Zz0fVzsGB7qz8Jo+DAl0J9SMrcwVSkAUrYSUki+//JLdu3frtr59+zJr1ixsbMwz32ysraVonYGCVavAaMRzyb30uPNOpJ0tB88lsfzZ5Xz0+kfIhsYo3CvEi7uevYuJMROJ9IrE1bbjlBl3aIwN2lRUtim6yNkPuRlgWgCJq78mFMPv1n72jgRre2rqG0g/U8bBIyUknk7i4KlizpSaWvpbWRDu60rcyEAGm6ajermq6KIjoQREccVIKfn000/Zs2ePbuvfvz8zZ87E2to8nUXLv/6a3L/8hbpTp3GecD3ydwv4tOEwe/Y8yq69uzi66ijVp6r1821sbVi+YjmPPPgIFhbqjvZXKTvTRCwOaFNRtVpLD2xdwXcwjHpAm4byHQLOPZFSkl1cReLpYg4mneRgVgkZZ0qpMwm4r5s90YHuxAe4Ex3gRoiPS6duNNgdUAKiuCKklHzyySfs27dPtw0cOJA77rgDK6v2/3jVnjpF7nN/oWLnTmz69oWXnuJlx0Q+3bOIhvoG6r6o4/g7xzHWN1ZYjRw5EoPBwMCBA9vd305BVYkmEDkHtMV5ZxKhXNt0CgtrbUV35OxGsTBNRVXU1JOSXcLB/SUcPJ1FUlYxBRVaV1o7awsi/NxYNKovg/21yqieXXShXldGCYiixUgp+fjjj0lMTNRtwcHBTJs2DUvL9r1zNJ4/T8Gq1ylatw5hbU3VPTN4eWAO3+Y/h1OJExPtJ7LjLztIP9jYFcfW1pYVK1bwwAMPtLu/HZa6aq11eU5iY3RReKxx3KM/9BkNvtFa3qJXOFjb0WCUHMsrJ+nHEg7uTCMpq4SjeeVcqNHp5+nItQO9GBzgzmB/N67qpSqjugJKQBQtwmg08tFHH5GU1Lg5ZVhYGLfddlu7fhlLKSnbvp28v75IfW4uFdfFsOqaSvbWfYBnhSfLIpdxbvs5nn3mWX3HQ4Bhw4ZhMBgIDg5uN187HMYGyD+iRRQXIotzaY0bIzn11CKKyJnaT5/BYO8OQF5ZNQezSkhKzyTpdAkp2SX6Fqyu9tZE+bsxKawXUQFuRPm54e6o1l10RZSAKH4zRqORDz/8kNTUVN0WERHBlClT2jV/UH3kKLnPPsv5vXup6tuLN+/uzTceSfSx78OfYv7EwPqBLI5f3Cw3Y2Njw/Lly/nDH/5glik2syGl1upDn4Y6qK3mvtArysYZfKJg5JLGEloXHxCCypp6UnNKSd5bRFLWSZKzSvREt5WFIMTHhWlD/Ijyd2NwgDt9ejioSrVuQjf6C1K0Bg0NDXzwwQdkZDTuMDx48GBuueWWdhOPhtJS8l95leLNm6mzt+adW5zZFppPuHck/wx7gtE+o3n1lVeZ98Q8qqsbE+XR0dGsX7+esLCwdvHTrJTnNo8schKhqkgbs7TVpp4GzzVFFtF63qK+wcjR3AqSj5SQdDqV5OwSjuaWY+oAQoCHA0P6eLDIz5XBAe6E+rhgZ62m/7orSkAUl019fT3vvfceR44c0W0xMTHcdNNN7XLHKY1GSt5/n/yX/kF9aQnfxjiydmQVUQNGsCZsETE9Yzh+/Djjx43n22+/1a+ztrbmqaee4tFHHzVbVVibUlVsSnJfiCwOQlmONiYswCsYrrpJEwrfIeAdAlY2elVUUlYJKXsPk5xVSmpOKVV12lSUm4M1kX5uTAztRZS/GxF+rvRwUosmFY0oAVFcFvX19bzzzjscO9aYUB0+fDgTJ05sF/GoSk7m3J9XUJ2Wxtn+7rw01QLLgT78a8QfGdprKEajkVdeeYXHHnuMqqoq/bqoqCjWr19PREREm/vYLtRWwtnkJoKR2LiDHmitPwJGaklun2joHQE2WpffwooaUrJLSUrPJDm7hJTsUopMe3XbWFkQ6uPCzKH+RPm7EeXvRqCailL8CkpAFL9KXV0db7/9NidPNn5RXX311Vx//fVt/gVTX1BA3t9fonTrVmrdnVh3mz27Q2u4J+r3xIXEYW1pzcmTJ1m4cCG7du3Sr7OysuKPf/wjjz/+eOeNOuqqITddE4kLglFwpLFPlIuvltgePE8TC58oPcldUVNPanYpKd+fIyW7lOTsErKLNWEVAgZ6O3N9sDcRfppYDOzprFZ0K34zSkAUv0htbS2bN28mMzNTt40ePZpx48a1qXjIujqK3nqLgldfo6G6it3jPHljcDEx/Uazdfjj+Dv7YzQaWblyJY888giVlZX6teHh4axfv57Bgwe3mX+tTkMd5B0yTUGZBCM3o7EiysFTiypCpmii4TMYnHsCUF3XQMbZMlISS0jJPkVKTikn8iv0Elo/d3si/dyIHRlIpJ8bYb6uONqqP33FlaM+RYpLUlNTw6ZNmzh9+rRuGzt2LGPGjGnT16387jvOPfcctcdPcC7Ch+dHVFPvZ8Nzw//B9QFa1JOZmUl8fHyzpo2Wlpb83//9H08++aTZ2qdcFsYGKDjWXCzOpUK9KeFv56oJxNVLTZHFYHD1AyGoazByNLec1EOlJGenkpJdwpFz5dSbstyeTrZE+rkyOcKHCH9XInxV3kLRdrSpgAghJgEvo+1t/qaU8vmfjD8MzG3iSzDgJaUsEkJkAuVAA1Df0m6RipZRXV3Nxo0bycnJ0W3XXXcdo0aNarPXrDn5I3l//SsVX39NXS8PVs9x5ZvAfOYEz2Pp4KU4WjsipWT16tU89NBDVFRU6NeGhISwfv16YmI62MdESi1HcSG5feaglsO40PbD2lGbehp6Z2Nk4dEPhKC+wciJ/EpSjpeQmpNOSnYpGWfL9MaCLnZWRPi5sfjafkT4uRHp70ovFzuVt1C0G23Wzl0IYQkcBSYA2cA+YLaUMuMS508GHpBSjjcdZwIxUsqCy31N1c69daiqqmLDhg2cPXtWt91www2MHDmyTV6vobSUgpUrKXprE9hYs+t6b14flM1VvSJ4csSTBPfQFvtlZWURHx/PZ599pl9rYWHBww8/zPLly83a8RfQxKIsp0k1lOlndak2bmWnlc/6DG6MLDwHgIUlDUbJjwUVpOaUkpJdSmp2KelnyvSKKCdbK8J8XYjwcyPc15UIP1cCPFSSW3HldNR27sOA41LKkwBCiLeBKcBFBQSYDWxuQ38Ul0FlZSUbNmwgNzdXt910000MHTq01V9L1tVRvOUdCl55hYbycnLGBbMi7AS1rmU8Ev1H7hh4B5YWlkgpWbduHQ888ABlZWX69VdddRUGg4Hhw4e3um+XRUV+8wT3mYNQmaeNWVhp5bKhUxsFwzsYLK2bi8UPh0nL0cTivGklt721JWG+LsweFkCEnyvhfq707aG2XFV0PNpSQHyBrCbH2cBF/9KFEA7AJGBpE7MEPhVCSOB1KeXqS1y7GFgM6NuNKlpGRUUFCQkJ5Ofn67bJkycTHR3d+q+1axe5z79A7cmT1A2+ipdHObLX6Qg397uZP8T8AU97TwBycnK46667mm2NK4TgoYce4plnnmm/HQ6ry+BsUvOFeaUXPt4CvAZB0PWm8tnB2s55ph5RF8Qidf8xUnNKmomFnbUFoT6uzIjxJ8zXlXBfV4K8nbBUYqHoBLSlgFzsL+BS82WTgW+llEVNbNdIKc8IIbyBz4QQh6WUu356oUlYVoM2hXWlTndXysrKSEhIoLCwENC+pG+99VaioqJa9XVqjh8n94W/UvnNN1j4+/LZkhjecDlIH9e+vDHiDUb0HgFoPa42bNjAsmXLKC0t1a8fMGAA69at45prrmlVv5pRX6P1hDqT2Nj6o+Ao+sfXLVDbNW/43aa1FpFg66TnLNJySklNPEFajpazaCoWIb1duGOIH+Gmqaj+Xo6qqaCi09KWApIN+Dc59gPOXOLcWfxk+kpKecb02hBskQAAIABJREFUM08IsRVtSuxnAqK4ckpLS1m/fj3FxcWAJh5Tp04lPDy81V6jvqCAgpX/pnjLFiwcHMheOIHlPnupIoP7IpayKGwRNpZa5dTZs2e5++67+eijj/TrhRDcf//9PPvsszg4OLSaX3qSW981b79WEXWhfNbRS1u9HTatsaGgYw/qGowcz9Mii7TkTF0squu0BLe9tSWhPi56ZBHm60KQl5MSC0WXoi0FZB8wQAjRF8hBE4k5Pz1JCOEKjAHmNbE5AhZSynLTv28AnmlDX7stxcXFJCQkUFJSAmhJ6WnTphESEtIqz99QVkbhmrUUJSQga2uRt17P81FZ7K/+ipE9R/LHEX8kwEWbepRSsnnzZpYuXaqLGUC/fv1Yt24d11577ZU7dL7I1Kp8f+OGSBd6RFk7agIxckljjyhXP2oajBw9V0HamVJS086QnnOIQ+fK9WooJ1srQnxcmDMskHA/F8J9XenrqaahFF2fNhMQKWW9EGIpsAOtjHetlDJdCHGPaXyV6dSpwKdSysoml/cEtpoqTKyATVLK/7WVr92VoqIi1q9fryemLS0tueOOOxg0aNAVP7fx/HmKNr5F4ZtvYiwrw/7GG/hwjB1rSz+hh+jBi9e+yMQ+jW1QcnNzuffee9m6dWuz51m6dCnPP/88jo6OLXCiAfIPQ9YPkLVXexSdMA0K8LoKrrq5cU9ur6uoahAcOldGek4pqRlFpOVkcjS3cZ2Fi50VYb6uLLi6D6E+LoT5qgS3ovvSZmW85kCV8V4+BQUFrF+/Xl9LYWlpycyZMxkwYMAVPa+sraX43Xcp+PcqGgoKcBhzLQemDOLvpe9TUVfBjIEzWBa9DGcbZ/2ad955hyVLluj5F4DAwEDWrl3L+PHjL//Fa8q1qCJrryYa2fugxlS15egFfsPAb4gmFj6DqRAOZJwpIy2nlLQzpaTllHI8r0LvPOvhaEOojxZRhPm6Eubjir+HvSqdVXQpOmoZr6KDkpeXR0JCgt7+w8rKitmzZ9OvX78WP6dsaKB020cUvPoqdTk5OMTEcOaPi3i0eis/5n/H8N7DeWToIwx0b9w2Nj///9u787gqq/yB45/Dvm8iLoA7Wu4ahWluvzSXNLXMXTRzSKcxayynzRZnmibScbQ0dxQstzT3JLXMwiDNLc0NAxUwRURWWe/5/XHpCoqKyI0ufN+vFy95nuech3M4L++XZznfk8zzzz/P2rVrS5xrwoQJhIaG4urqeuOPKSk9CeKj4Hy0MWBcPFaUJ0oZX6Ft+RT4B0G9INIc/Dh2oShYxKRzNOkAcZezTOk+fFztaenrTu8WtWlR9DZUHXeZlCfE7UgAqWZ+++03wsPDTRlrbW1tGTFiBA0aNCjX+bTWZOzYQfLsOeSdOYND8+boV0J432oXURf+S323+nz0fx/R1a9riQ/j9evXM2HChBKvDPv7+7NkyRJ69uxZ+g9LvwDx30P8d8av37PQ2rkYn1l0eQX8HyLNqy1Hr2B8dfaXNI7uOMvZlOOm0/h6ONKirhsD2/rSytedFnXd8JH1uIW4axJAqpGkpCQiIiJMiyzZ2dkxcuTIcs2f0YWFpG/fTsqixeSeOIFdo0Z4fPgvlnofZ+3pf+Nk48Qrga8w/L7h2Fpfz4abkpLCpEmTWLmy5JzR8ePHM2PGDNzd3a/vzPitWMD4HlJijfvt3aF+Rwh8lozaQRzO9+fnC0Wvz+5L49yVGNMp/DwdaeVrnGfx+60oL1leVYgKIQGkmkhISGDFihWmdcHt7e0ZPXo0vr6+d3UeQ14eaRs2kLJ4CfnnzmHXqBE+7/2TbU2z+OToTLJTs3m66dP8te1f8XTwLFF306ZNhISElJjl7uvry6JFi+jTp48xfXnsTjgVCWe+gZSitUfs3aDew+S3Dea0Y1t+yK7L4cRMjkRdJT7lEmCc/e3vZQwWwx4qChZ13WUtbiHMSAJINXD27Fk+++wz8vKMiwc5OjoyevRo6tSpU+ZzGLKySF29hivLllFw6RIOLVviPXsWexsX8MnPC4g/EE/Huh15JfAVmng2KVE3NTWVyZMnExERUWL/2LFjmfXuVDySY2DlcPh1N+Rng40jhgaduNTkaQ5Zt2ZPei0OJGRy+lgmhYZM4BR13B1o4+fBkAf9ae3rQUtfNzycJFgI8UeSAFLFxcXFsXLlSvLzjRPjnJycCA4OplatWmWqX5CaSmrECq58+imGtDScgoLw/OdbfOmdSMTx/3Ih6gKN3Bsx99G5dPbtfNND561btxISEkJS0vU5pLV9arDohT70qxELS40zzw1ufiTVH8gP1oFsuNqY/SeukVtgAAy4O16mjb8HPZvXoo2fB6393fFxlWcWQlQ2CSBVWGxsLKtXr6agoAAAFxcXgoODqVmz5h3r5l+8yJWlYaSuXYvOzsbl0UexGv0ka2wPs/bkNDLiM3ig1gO8HvQ6Xfy6YKVKzrBOS0vjpZdeIiwsrMT+ke1cmNMzD8+CLVzOa8v+2s+xPrMVX13yhEsKGytFS19bRgb50LaeB20k66wQf1oSQKqoU6dOsWbNGgoLjXmYXF1dGTNmDDVq1LhtvZxTp7iyNIy0rVvBYMDt8b5kDunJ/JzdbDs9BYM20KNeD8a0GEPrmqWvMx4ZGcn48eNJSEgw7fNxVnz8hAcNH+jK/7Jbsynrfq5ec8XV3ob29T15ub0nD9T3oq2/B4521hX3ixBCmI0EkCro+PHjfP755xgMxlQb7u7uBAcH4+XlVWp5rTXZP+4jZekSsr7dg3JwwGPI05x7vC2zU7YSdezvONo4MqTpEEY1H4W/q3+p50lPT+flFyexKCy8xP7HWvng/38jeNOhCzXy3Ahs4sWUBsaA0ay2q6T8EMJCSQCpYo4ePcr69ev5PcOAp6cnwcHBeHh43FRWFxaSsWMHKUuWkvPzz1h7euI08Vm+D3Jh3aWvOP3zGmo41OCFdi8wpNkQ3O3dbzoHAIX57IqYwbhX/sW5y9mm3U6ODvj2DsGr93A63O/DG/fXoqF3OVKSCCH+lCSAVCFHjhxhw4YNpuBRo0YNgoODcXNzK1HOcO0aV7/4givLlpN/7hw2/n5cen4QqxtdIiolAsNJA628W/Fux3d5vNHj2FvfYqW/zGQu7ZjFm//5iEU/ZpY41OShR3nrP//liQ734+5oW3p9IYRFkwBSRRw8eJBNmzaZtr29vQkODi6RDqQgNZXUTz8j9dNPKUxNpeC+hnw/4UGW1ThOtmEzvjm+jG81nn6N+tHQveGtf1hqPFd3/pf9W5cTsjGDuKvX86m5eXjyyby5DB82TB58C1HFSQCpAvbv38/WrVtN2z4+PgQHB5sy2OZfusSVZctJXbUKnZ3Nxbb+fDZI8YPPOVztrtKnweP0b9yfdj7tbnqbqjj9288kbw/F4dRm3tiVw7wfc0scHzBgAPPnz6d27drm6agQ4k9FAoiFi4mJYfv265nua9euzejRo3FyciI/MZGUJUtI/XwduiCfI23cCH/Amgs+yTzi9wgzG/Wnq3/XW9+iAtCavF+/J2X7B9RJ/o6D8VYEby7k0pXrwcPT05M5c+YwcuRIueoQohqRAGLBoqKi2Llzp2nb19eXkSNHYvXbbyQuWEja5k0Y0OxpZcX6ICtqBNRnVOMB9GnY56Y0IzcxGMj8eQsZOz+kTsYR8vNc6LWnITv2HqH4EgD9+vVjwYIF1K1b11zdFEL8SUkAsVB79uzhm2++MW37+/vz1IMPcn7qSxTs/JZ8G8XOdvBtZw86tRvA3CYDaeZVhoWitCbj6Dayt75JrZxfSTXUZFr6IJZv/I7z8YdNxdzd3Zk9ezbBwcFy1SFENSUBxMJordm9ezd79lxfHt7X04P7vt1KUuiHZNvBVx2sSBnQkd7thvKCX9cS2XBvpyDhIMnrp1Lnyo+k6Nosq/Uq3/2cxLKF80xzSgB69+7NokWL8PPzq/D+CSEshwQQC6K1ZteuXURFRZn2eV9JIWjNWq7ZFhLZwwuPkSN4ts3T+Dj5lP3EV89zaeOb+MRtwF67sNzzrzg36c4Hr0zmxIkTpmKurq7MmjWLcePGyVWHEMK8AUQp1RuYjXFN9MVa6//ccLwbsBGIK9q1Xms9vSx1qxutNZHbthFTbMne2klJeJzdy86hDek6+jUmN3j47j7Yc9LI2BmKw08LcDNAhO2T1Oj5Mse/iODDKX1KXHX06NGDJUuWlGvtECFE1WS2AKKUsgbmAj2BBGCfUmqT1vqXG4p+p7XuV866VZ7Wmuz9+9m2eTO/WBfLEXUtkeiHEnlm2lyGlJIF97YK8sj/cTEFX/8H5/x0NulHuBL0D5rVsOO5Z4dy7NgxU1FnZ2dmzpxJSEiIXHUIIUow5xXIQ0Cs1vpXAKXUKmAAUJYgcC91q4S8hETSNm3k6oaN/FCrFnFNGpuOJbtcosuQHrzR7ElsrO5iCLVGH99E9tZpOGedJaawBd81nMywfr1Z+tEMQt5/35R8EaBbt24sXbqUhg1vM6lQCFFtmTOA+ALni20nAEGllHtYKXUYSAJe1lofu4u6VUphRgYZkZGkbdhI9v79GJTiQN8+xBVLRWLva89/Rn+Ai73L3Z08+STXNryIY+JeEgx+RLi+zeODgumTmcDAnl04cuSIqaiTkxOhoaFMnDgRK6tbTywUQlRv5gwgpd3v0DdsHwDqa60zlVJ9gQ1AQBnrGn+IUiFACGCR9+d1QQFZe/eStmEjGbt2oXNzsWvQAPcXnmcV6WQk55vKNm3RlKFPDr27D/W8LAq//RD2fkSewZ4ZajwNek3kjQf8+TD0A/75z3+a1gsB6Ny5M2FhYTRu3Pg2JxVCCPMGkASgeN5vP4xXGSZa6/Ri329TSs1TSnmXpW6xeguBhQCBgYGlBpk/o5wTJ0jbsJG0LVsovHwZa3d3PJ56CrcBT/CN83kiN39FjfTra3e0a9eO/v37l/05hNZwcht5W17BLjORtQVd2BcwmZeffISL8afp1PFhDh48aCru6OjI+++/z6RJk+SqQwhRJuYMIPuAAKVUQyARGAaMKF5AKVUbuKi11kqphwArIAW4eqe6lijv3DnSIyNJ37KV3JMnwdYW125dcR8wAJcuXTh09Rgvx3yI83Fn6l67PrM7MDCQvn37lj14pMZTuHUq1rGRxBn8+dD2Xwwe/DT/vs+b0NBQ3nnnHdMStwAdO3YkLCyMpk2bVnSXhRBVmNkCiNa6QCn1NyAS46u4S7XWx5RSE4qOzwcGAxOVUgXANWCYNubJKLWuudpqTrlxcWREfkX6V5Hk/nIcAIc2ran11jTc+vTBxtOTc+nneHvvq+yK20W3lG54Xbu+8FNQUBC9evUqW/AoyIWoORj2fEhuoWJm/kiy245n5uOtSIw/TceOT7Bv3z5TcXt7e9577z1efPFFrK1lFUAhxN1RxfMaWbrAwEC9v9g8icqSe+YM6ZGRZGyPJPfUKQAc27bFtVcv3B7ria2vLwBpuWnMPzyfVSdXYY89T2Q8QX6xZx6dOnXi0UcfLVvwOPM1hq0vY3XlDFsKO7DEeTxTnurOw408mTlzJtOmTSMvL89UPCgoiGXLlnHfffdVbOeFEBZFKfWT1jqwPHVlJnoF0FqTe+oUGV/tID1yO3mxZ0ApHNu3p9brr+Hasye2deqYyucV5rHyxEoWHFlAVn4WgxoMwjfWl6Tk6495unTpQrdu3e4cPLIuw5dT4eg6ElUd3sh7lYCOA/j0saacjzvDI4/0Jzo62lTczs6O6dOnM2XKFGxsZPiFEOUnnyDllJ+YSFZ0NFk/RJMVE01h8mVQCqfAQDzfHG4MGrVKphMxaAOR8ZHMOTCHhMwEOvl2YlLLScRsi+H8+etvLXfv3p0uXbrcvgFaw8+fY/hyKoacDObkD2an1zD+9UwgbXzdmD17Nm+88QY5OTmmKoGBgSxbtowWLVpU6O9CCFE9SQApo4LUVLJ/DxjR0eSfOweAtbc3zkFBOD/cAZeuXbGpWfOmugZtYNe5Xcw7NI/Yq7EEeAawoMcC2tdoz4oVK0hMTDSV7dGjB506dbp9Y9KTYMtLcGo7v6gAXs59jce6d+OL7o05F/crXbv2L5Evy9bWlnfeeYepU6fKVYcQosLIp0kpdH4+eWfPkhsby7XDR8iKjib3uPEBuJWzM04PPYTXqJE4deiAfUDALW8zaa3ZfX438w7P48SVEzRwa0Bol1B6NehFzrUcwsPDuXDhgql8r1696NChw20apuFAOPqrN8jPy+OD/FF87/UUM559gBZ1Xfn444959dVXuXbtmqlKu3btWLZsGa1bt66YX44QQhSp1gFEFxaSf/48ubGx5J4+Te7pon/j46HoNVdla4tj+/bUfHEyzh064NCyJeoOf8Vrrfk+8XvmHprLsZRj+Lv68+9H/k3fhn2xtrImKyuLiIgILl68aKrTt29fHnzwwVufNDUeNr0Acd9ywKolf895lt6dH2Zjz6YknT9L9+5PlEjxbmNjw5tvvsnrr7+OrW3Z0rkLIcTdqHYBxJCVxW/Tp5Nz+jR5Z35F515fmtXW1xf7gABcunXFPiAA+yZNsGvcGCv72yz5WozWmugL0cw9NJfDyYfxdfFlesfp9G/c35SzKiMjg4iICJKTk031+vfvT/v27W/R4EL4cSF613TyCuHd/GeJcnucGcHteKCeB/Pnz2fq1KlkZWWZqrRq1Yrly5fTrl27cvyGhBCibKpdAFGOjlw7fARbX1+chwdhH9DEGCwaN8bK2blc59Ras//ifuYemstPF3+illMtpnWYxqAmg0os5pSenk54eDgpKSnGtijFgAEDaNOmTeknTj4Fm/4G52P40SaQF7PH0KNDe7b1uY/kCwn07Pk0X3/9tam4tbU1r732GtOmTcPOzq5cfRFCiLKqfgHEyorG27+skHNl52ezLW4bq0+u5sSVE9R0rMlrD73G4KaDsbMu+QF+9epVwsPDSU1NNbZDKZ588klatmx584kNBoieh941nRzlwBsFf2Wv7aOEjmtD5wBvFi1axJQpU8jMzDRVad68OcuXLycwsFyvcwshxF2rdgGkIsSlxbH65Go2xW4iIz+Dpp5NmdZhGk80fgIHG4ebyqemprJ8+XLS0tIAsLKyYvDgwdx///03nzwtATZMhLg9RNt2YFLGGLq0b05k/xakX/6NXr16sWPHDlNxKysrpk6dyttvv42Dw80/WwghzEUCSBkVGArYfX43q06uIuZCDDZWNjxW/zGG3TeMtjXb3vJNrJSUFMLDw0lPN+aNtLa2ZsiQIaXnnfr5c/TWv1OQn8c7hSF8adWT90e35rHmtQgLC+Oll14ynQegWbNmLF++nKCgKp/pXgjxJyQB5A6Ss5NZd3oda0+t5VL2JWo71+aFdi8wKGAQ3o7et6+bnEx4eLjpVpONjQ1Dhw6lSZMmJQteS4WtL8PRzzljfz/jskNoGNCS7U+3Jj89hccff5wvv7x+200pxZQpU5g+fTqOjo4V3mchhCgLCSA30FoTlxZHVFIUUUlRxCTFUKAL6Fi3I28GvUkXvy5YW9058eDFixcJDw8nOzsbMAaP4cOH06hRo5IF4/bAFxMwZPzGAquhzMl6glf7t2R0h3pEREQwefJk060vgICAAMLCwu482VAIIcxMAgiQnpdOzIUYohKj2Ju0lwtZxsl9Dd0bMqr5KAY3HUx9t/plPt+FCxeIiIgwTeizs7NjxIgR1K9f7BwFubBrOvqHuaTY+/Fsztvk1mrHhmHtcNOZDBw4kM2bN5uKK6WYPHky7733Hk5OThXTcSGEuAfVMoAYtIFfUn7h+8Tv2Zu0lyPJRyjUhbjYutChTgf+0vovdKrbiboude98shskJiayYsUKUw4qe3t7Ro4cib9/sfWxLh6DdX+BS8fYYtubqWlDGPHI/bz8WFPWr13NpEmTTG9rATRq1IiwsLA758cSQog/ULULIGm5afT7oh9Xc6+iUDSv0ZxxLcfxiO8jtKrZClur8s/aPn/+PCtWrDClTXdwcGDUqFH4FqVvR2uI/gS9822uWbnwYsFUDtkGsWBcG5q5Gxgx9Gk2bNhQ4pzPP/88H3zwAc7lnKMihBDmUu0CiLu9OwObDOQ+r/t4uO7DeDl43blSGcTHx/PZZ5+ZVvpzdHQkODiY2rVrGwtkX4GNz8PJbRxw6EDI1TE80Lwp259qzY4tX/Dk88+bJhgCNGjQgKVLl9K9e/cKaZ8QQlS0ahdAAKYETqnQ8/3666+sXLmSgoICAJydnQkODsbHpyid+/l98PkzGDJ+Y6Yay9Ks3rz1ZAsebeDAhGdGsXbt2hLnmzBhAqGhobi6ulZoO4UQoiJVywBSkWJjY1m9erUpeLi4uDBmzBi8vb2Nt6x++Bi98x2u2tRk7LVp5NVuz+bh7Tj8XSQt+00skRPL39+fJUuW0LNnz8rqjhBClJmVOU+ulOqtlDqplIpVSr1ayvGRSqkjRV97lVJtih2LV0r9rJQ6pJSq/HVqS3Hy5ElWrVplCh5ubm6MHTvWGDyyr8DKYfDVm0RbP0jX9Hdp2+FRFg9txlsvhjB48OASwWP8+PEcPXpUgocQwmKY7QpEKWUNzAV6AgnAPqXUJq31L8WKxQFdtdapSqk+wEKg+LTq7lrry+Zq47345ZdfWLduHQaDAQB3d3fGjBmDp6cnnP8R1hpvWX2gx7Iqvy8fjm5DTmwMD7TtUSKNu6+vL4sXL6Z3796V1RUhhCgXc97CegiI1Vr/CqCUWgUMAEwBRGu9t1j5aMDPjO2pMEePHmX9+vVorQHw9PRkzJgxuLu6QtRs9K7pXLGuyTM5b2NXL5DP+jTgg7emEhERUeI8Y8eOZdasWXh4eFRGN4QQ4p6YM4D4AueLbSdQ8uriRs8CxdPkauArpZQGFmitF1Z8E+/e4cOH2bhxoyl41KhRg+DgYNys8423rE5H8p1NR/6W+QzB3drQNPcUPTsOLrHyYJ06dVi4cCH9+vWrrG4IIcQ9M2cAKS27oC61oFLdMQaQR4rt7qS1TlJK+QA7lFIntNZ7SqkbAoQA1KtX795bfRsHDhwoMTu8Zs2aBAcH43L1BHpNMIaMS/yr8Bm22DxO6NONWPPxe7wSFlbiHKNGjWLOnDnGW11CCGHBzBlAEoBi06/xA5JuLKSUag0sBvporU0TIbTWSUX/XlJKfYHxlthNAaToymQhQGBgYKkBqiLs27ePbdu2mbZr1arF6NGjcT6xFr3tFVKUF2Nz3sYrIIhXvC7xlwHdSEhIMJX38fFhwYIFDBw40FxNFEKIP5Q538LaBwQopRoqpeyAYcCm4gWUUvWA9cBorfWpYvudlVKuv38PPAYcNWNbbys6OrpE8KhTpw7BI4bhvOtV2DyZfTSnV/Z0enTujP0Pixg6qH+J4DFs2DCOHTsmwUMIUaWY7QpEa12glPobEAlYA0u11seUUhOKjs8H3gJqAPOK1tMo0FoHArWAL4r22QCfaa23m6uttxMVFcXOnTtN276+vozq3x371U9C4k98YhhIhO1IxjXPJHTiAM6dO2cq6+3tzSeffMLgwYMro+lCCGFW6veHwVVBYGCg3r+/4qaM7Nmzh2+++ca07e/vz8hODbHbOJ7cnGwm5zxHul83HA+uJGxxyWf8Tz31FPPmzbs+G10IIf6ElFI/Ff3hftdkJnoptNbs3r2bPXuuP3JpUL8+wxtnYrvqKc6pOjyb8w+a13Ai+sNniI+PN5Xz8vJi3rx5DBky5JarFAohRFUgAeQGWmt27tzJ3r3Xp6g0alCfYY7fY7trLTv0g7xr+At1Enby0fuLS9QdMGAA8+fPv55AUQghqjAJIMVorYmMjCQmJsa0r0n9ugzOXIR13DFC84fyZU5bEr94nai4X01lPD09+eijjxgxYoRcdQghqg0JIEW01mzbto3iz1Ca+XkxKOk9cvMLGJv1Egln4oja8DeKPzfq168fCxYsoG7du198SgghLJkEEIzBY/PmzRw8eNC0r7mPLYPOv02s9uOZxIEk7IogIf6M6bi7uzuzZ88mODhYrjqEENVStQ8gBoOBTZs2cfjwYdO+lu7ZDLq4gA25D/D6Pm9Of/OeKWkiQO/evVm0aBF+fhaRuksIIcyiWgeQwsJCNmzYwNGj1+cotnK4wMCrq3jxbFdW7jzK5YSvTcdcXV2ZNWsW48aNk6sOIUS1V20DSGFhIevWreP48eOmfW2tT/NIxlc8+E1rDkVvKXHV0aNHD5YsWWL2fFtCCGEpqmUAKSgoYO3atZw6ZcqewgMcgbM/0mSzHVcvfmfa7+zszIwZM3juuefkqkMIIYqpdgEkPz+fNWvWEBsba9r3oGE/a78+yPK9F9DFrjq6d+/OkiVLaNiwYWU0VQgh/tSqVQDJy8tj1apVxMXFmfYFZEYzIfwHzl9KM+1zcnIiNDSUiRMnYmVl1lV/hRDCYlWrAHLt2jWuXLli2jbEfc/oiK9LPOvo3LkzYWFhNG7cuDKaKIQQFqNa/Xnt7u7OiAE9cSjM5GjMN0xfvtMUPBwdHfnf//7H7t27JXgIIUQZVKsrkIKCAv4X9jmzZ84lO/uaaX/Hjh0JCwujadOmldg6IYSwLNXqCiQpKYmPZs0wBQ97e3tmzJjBnj17JHgIIcRdqlYBpF69esyYMQOAoKAgDh06xJQpU7C2tq7klgkhhOWpVrewAEJCQnB1dWXIkCHY2FS77gshRIWpdp+gSilGjBhR2c0QQgiLV61uYQkhhKg4Zg0gSqneSqmTSqlYpdSrpRxXSqk5RcePKKXal7WuEEKIymW2AKKUsgbmAn2A5sBwpVTzG4r1AQKKvkKAT+6irhBCiEpkziuQh4BYrfWvWus8YBUw4IYyA4BwbRQNeCil6pSxrhBCiEpkzofovsD5YtsJQFAZyviWsS4ASqkQjFcvALlKqaOllasCvIHLld0IM5LE09mvAAAEjklEQVT+WTbpn+VqVt6K5gwgpeU+12UsU5a6xp1aLwQWAiil9mutA++mkZaiKvcNpH+WTvpnuZRS+8tb15wBJAHwL7btBySVsYxdGeoKIYSoROZ8BrIPCFBKNVRK2QHDgE03lNkEBBe9jdUBSNNaXyhjXSGEEJXIbFcgWusCpdTfgEjAGliqtT6mlJpQdHw+sA3oC8QC2cAzt6tbhh+7sOJ78qdRlfsG0j9LJ/2zXOXum9K61EcLQgghxG3JTHQhhBDlIgFECCFEuVhcALmX9CiWoAz966aUSlNKHSr6eqsy2lkeSqmlSqlLt5qrUwXG7k79s9ixA1BK+SulvlFKHVdKHVNKTS6ljEWOYRn7ZrHjp5RyUEr9qJQ6XNS/d0spc/djp7W2mC+MD9TPAI0wvup7GGh+Q5m+wJcY55J0AGIqu90V3L9uwJbKbms5+9cFaA8cvcVxix27MvbPYseuqP11gPZF37sCp6rK/78y9s1ix69oPFyKvrcFYoAO9zp2lnYFci/pUSxBlU7horXeA1y5TRFLHruy9M+iaa0vaK0PFH2fARzHmDWiOIscwzL2zWIVjUdm0aZt0deNb1Dd9dhZWgC5VeqTuy3zZ1XWtj9cdCn6pVKqxR/TtD+EJY9dWVWJsVNKNQDaYfxLtjiLH8Pb9A0sePyUUtZKqUPAJWCH1vqex87SFpS6l/QolqAsbT8A1NdaZyql+gIbMGYzrgoseezKokqMnVLKBVgHvKi1Tr/xcClVLGYM79A3ix4/rXUh0FYp5QF8oZRqqbUu/rzursfO0q5A7iU9iiW4Y9u11um/X4pqrbcBtkop7z+uiWZlyWN3R1Vh7JRSthg/YD/VWq8vpYjFjuGd+lYVxg9Aa30V2A30vuHQXY+dpQWQe0mPYgnu2D+lVG2llCr6/iGMY5jyh7fUPCx57O7I0seuqO1LgONa6//eophFjmFZ+mbJ46eUqll05YFSyhHoAZy4odhdj51F3cLS95AexRKUsX+DgYlKqQLgGjBMF71C8WenlFqJ8U0Wb6VUAvA2xod5Fj92UKb+WezYFekEjAZ+LrqXDvA6UA8sfgzL0jdLHr86wHJlXKzPClijtd5yr5+dkspECCFEuVjaLSwhhBB/EhJAhBBClIsEECGEEOUiAUQIIUS5SAARQghRLhJAhBBClIsEECHMQCnVQykVUdntEMKcJIAIYR5tgIOV3QghzEkCiBDm0QY4qJSyV0otU0r9+/c0GEJUFRaVykQIC9IGY9rsSGCx1npFJbdHiAonqUyEqGBFWV0vA2eB57TWP1Ryk4QwC7mFJUTFa44xs3IBUAimbKhhSik/ZVw73bZSWyhEBZAAIkTFawPsxZiOP0wpVUtrnQycA2YCL2it8yuzgUJUBAkgQlS8NsBRrfUp4B/AGqWUK9AIKCi2NrUQFk2egQhhZkopG2Ah8C4wBNintd5dqY0SogJIABFCCFEucgtLCCFEuUgAEUIIUS4SQIQQQpSLBBAhhBDlIgFECCFEuUgAEUIIUS4SQIQQQpSLBBAhhBDlIgFECCFEufw/YluXStO1d74AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(ks[:,0], freqs)\n",
+    "ax.plot(ks[:,0], ks[:,0]/1.45, 'k-', linewidth=3, label='SiO2')\n",
+    "ax.plot(ks[:,0], ks[:,0]/1, '-', color='gray', linewidth=3, label='Air')\n",
+    "ax.set_xlabel('$k_x$')\n",
+    "ax.set_ylabel('$\\omega$')\n",
+    "ax.set_xlim([0,3])\n",
+    "ax.set_ylim([0,2])\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But wait! This doesn't look like above! It seems to be multimode at $\\omega = 1/1.55 \\approx 0.65 \\rightarrow \\lambda = 1.55\\mu m$. What's wrong?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting field profiles\n",
+    "\n",
+    "Let's look at the modes at a given frequency, say $1.55\\mu$m. We can proceed inversely : `ms.run()` computes $\\omega(k)$, while `ms.find_k` computes $k(\\omega)$. Here, we will also pass a mpb function to the `run()` function, which will output h5 files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr output\n",
+    "\n",
+    "f_mode = 1/1.55   # frequency corresponding to 1.55 um                                                                                                             \n",
+    "band_min = 1\n",
+    "band_max = 3\n",
+    "kdir = mp.Vector3(1)\n",
+    "tol = 1e-6\n",
+    "kmag_guess = f_mode*3.45\n",
+    "kmag_min = f_mode*0.1\n",
+    "kmag_max = f_mode*4.0\n",
+    "\n",
+    "ms.find_k(mp.NO_PARITY, f_mode, band_min, band_max, kdir, tol, kmag_guess,\n",
+    "          kmag_min, kmag_max, mpb.output_poynting_x)\n",
+    "output_text = output.stdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Initializing fields to random numbers...',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<2.2258064516129035, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.009303092956542969',\n",
+       " 'solve_kpoint (2.22581,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 21 iterations.',\n",
+       " 'freqs:, 1, 2.22581, 0, 0, 2.22581, 0.897189, 0.92391, 1.0992',\n",
+       " 'elapsed time for k point: 0.0725851058959961',\n",
+       " 'total elapsed time for run: 0.0819387435913086',\n",
+       " 'done',\n",
+       " 'find-k 3 at 2.2258064516129035: 0.4540348722818329',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.21213954322627115, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.009177207946777344',\n",
+       " 'solve_kpoint (0.21214,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 24 iterations.',\n",
+       " 'freqs:, 1, 0.21214, 0, 0, 0.21214, 0.170011, 0.179563, 0.348462',\n",
+       " 'elapsed time for k point: 0.07856035232543945',\n",
+       " 'total elapsed time for run: 0.08778166770935059',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.21213954322627115: -0.2966993363935748',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.9725819623426234, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008322715759277344',\n",
+       " 'solve_kpoint (0.972582,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 27 iterations.',\n",
+       " 'freqs:, 1, 0.972582, 0, 0, 0.972582, 0.630428, 0.643598, 0.742551',\n",
+       " 'elapsed time for k point: 0.087799072265625',\n",
+       " 'total elapsed time for run: 0.09616661071777344',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.9725819623426234: 0.09738967790588693',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.8043880162376597, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008353471755981445',\n",
+       " 'solve_kpoint (0.804388,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 84 iterations.',\n",
+       " 'freqs:, 1, 0.804388, 0, 0, 0.804388, 0.575824, 0.578269, 0.62965',\n",
+       " 'elapsed time for k point: 0.2601139545440674',\n",
+       " 'total elapsed time for run: 0.2685117721557617',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.8043880162376597: -0.015511662568443274',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.8374682198713483, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.00809478759765625',\n",
+       " 'solve_kpoint (0.837468,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 10 iterations.',\n",
+       " 'freqs:, 1, 0.837468, 0, 0, 0.837468, 0.589744, 0.593465, 0.646025',\n",
+       " 'elapsed time for k point: 0.03367042541503906',\n",
+       " 'total elapsed time for run: 0.041808366775512695',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.8374682198713483: 0.0008633584342160328',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.8358096698465939, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008076906204223633',\n",
+       " 'solve_kpoint (0.83581,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 5 iterations.',\n",
+       " 'freqs:, 1, 0.83581, 0, 0, 0.83581, 0.589096, 0.592733, 0.645163',\n",
+       " 'elapsed time for k point: 0.018621444702148438',\n",
+       " 'total elapsed time for run: 0.02674126625061035',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.8358096698465939: 2.025486580570224e-06',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.8358057603623551, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008080482482910156',\n",
+       " 'solve_kpoint (0.835806,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 1 iterations.',\n",
+       " 'freqs:, 1, 0.835806, 0, 0, 0.835806, 0.589095, 0.592731, 0.645161',\n",
+       " 'elapsed time for k point: 0.006573200225830078',\n",
+       " 'total elapsed time for run: 0.014696836471557617',\n",
+       " 'done',\n",
+       " 'find-k 3 at 0.8358057603623551: -1.0726994847942706e-09',\n",
+       " 'find-k 2 at 0.8358057603623551: -0.052430469959551806 (cached)',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 2 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 2 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Allocating fields...',\n",
+       " 'Solving for band polarization: .',\n",
+       " 'Initializing fields to random numbers...',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.9542668883548844, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.009068489074707031',\n",
+       " 'solve_kpoint (0.954267,0,0):',\n",
+       " 'Solving for bands 1 to 2...',\n",
+       " 'Finished solving for bands 1 to 2 after 20 iterations.',\n",
+       " 'freqs:, 1, 0.954267, 0, 0, 0.954267, 0.625852, 0.637765',\n",
+       " 'elapsed time for k point: 0.04419517517089844',\n",
+       " 'total elapsed time for run: 0.05330491065979004',\n",
+       " 'done',\n",
+       " 'find-k 2 at 0.9542668883548844: -0.007396453669743841',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 2 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 2 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.9770046968663186, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008069992065429688',\n",
+       " 'solve_kpoint (0.977005,0,0):',\n",
+       " 'Solving for bands 1 to 2...',\n",
+       " 'Finished solving for bands 1 to 2 after 9 iterations.',\n",
+       " 'freqs:, 1, 0.977005, 0, 0, 0.977005, 0.631507, 0.644971',\n",
+       " 'elapsed time for k point: 0.02140355110168457',\n",
+       " 'total elapsed time for run: 0.029523611068725586',\n",
+       " 'done',\n",
+       " 'find-k 2 at 0.9770046968663186: -0.00019071591336017324',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 2 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 2 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.9776221246995823, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008052587509155273',\n",
+       " 'solve_kpoint (0.977622,0,0):',\n",
+       " 'Solving for bands 1 to 2...',\n",
+       " 'Finished solving for bands 1 to 2 after 3 iterations.',\n",
+       " 'freqs:, 1, 0.977622, 0, 0, 0.977622, 0.631657, 0.645161',\n",
+       " 'elapsed time for k point: 0.009009599685668945',\n",
+       " 'total elapsed time for run: 0.017103910446166992',\n",
+       " 'done',\n",
+       " 'find-k 2 at 0.9776221246995823: -1.0805440386896237e-07',\n",
+       " 'find-k 1 at 2.2258064516129035: 0.2520273744464778 (cached)',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 1 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 1 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Allocating fields...',\n",
+       " 'Solving for band polarization: .',\n",
+       " 'Initializing fields to random numbers...',\n",
+       " '1 k-points',\n",
+       " '  Vector3<1.1208807925685298, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008707523345947266',\n",
+       " 'solve_kpoint (1.12088,0,0):',\n",
+       " 'Solving for bands 1 to 1...',\n",
+       " 'Finished solving for bands 1 to 1 after 26 iterations.',\n",
+       " 'freqs:, 1, 1.12088, 0, 0, 1.12088, 0.663439',\n",
+       " 'elapsed time for k point: 0.03162527084350586',\n",
+       " 'total elapsed time for run: 0.04037308692932129',\n",
+       " 'done',\n",
+       " 'find-k 1 at 1.1208807925685298: 0.018277694303210357',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 1 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 1 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<1.033340713793474, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008080244064331055',\n",
+       " 'solve_kpoint (1.03334,0,0):',\n",
+       " 'Solving for bands 1 to 1...',\n",
+       " 'Finished solving for bands 1 to 1 after 8 iterations.',\n",
+       " 'freqs:, 1, 1.03334, 0, 0, 1.03334, 0.644602',\n",
+       " 'elapsed time for k point: 0.011093616485595703',\n",
+       " 'total elapsed time for run: 0.019220352172851562',\n",
+       " 'done',\n",
+       " 'find-k 1 at 1.033340713793474: -0.0005593727467398946',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 1 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 1 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<1.0358414573843637, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008075475692749023',\n",
+       " 'solve_kpoint (1.03584,0,0):',\n",
+       " 'Solving for bands 1 to 1...',\n",
+       " 'Finished solving for bands 1 to 1 after 4 iterations.',\n",
+       " 'freqs:, 1, 1.03584, 0, 0, 1.03584, 0.645161',\n",
+       " 'elapsed time for k point: 0.006491184234619141',\n",
+       " 'total elapsed time for run: 0.01460719108581543',\n",
+       " 'done',\n",
+       " 'find-k 1 at 1.0358414573843637: -7.891137594473463e-07',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 1 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 1 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<1.0358449950358957, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008066892623901367',\n",
+       " 'solve_kpoint (1.03584,0,0):',\n",
+       " 'Solving for bands 1 to 1...',\n",
+       " 'Finished solving for bands 1 to 1 after 1 iterations.',\n",
+       " 'freqs:, 1, 1.03584, 0, 0, 1.03584, 0.645161',\n",
+       " 'elapsed time for k point: 0.003102540969848633',\n",
+       " 'total elapsed time for run: 0.01120901107788086',\n",
+       " 'done',\n",
+       " 'find-k 1 at 1.0358449950358957: -4.954723298311592e-09',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 1 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 1 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Solving for band polarization: .',\n",
+       " '1 k-points',\n",
+       " '  Vector3<1.0358450172484495, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.008098363876342773',\n",
+       " 'solve_kpoint (1.03585,0,0):',\n",
+       " 'Solving for bands 1 to 1...',\n",
+       " 'Finished solving for bands 1 to 1 after 1 iterations.',\n",
+       " 'freqs:, 1, 1.03585, 0, 0, 1.03585, 0.645161',\n",
+       " 'elapsed time for k point: 0.0030837059020996094',\n",
+       " 'Outputting fields to flux.v.k01.b01.x.h5...',\n",
+       " 'total elapsed time for run: 0.12282705307006836',\n",
+       " 'done',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 2 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 2 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Allocating fields...',\n",
+       " 'Solving for band polarization: .',\n",
+       " 'Initializing fields to random numbers...',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.9776224749130994, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.00921487808227539',\n",
+       " 'solve_kpoint (0.977622,0,0):',\n",
+       " 'Solving for bands 1 to 2...',\n",
+       " 'Finished solving for bands 1 to 2 after 18 iterations.',\n",
+       " 'freqs:, 1, 0.977622, 0, 0, 0.977622, 0.631657, 0.645161',\n",
+       " 'elapsed time for k point: 0.040230512619018555',\n",
+       " 'Outputting fields to flux.v.k01.b02.x.h5...',\n",
+       " 'total elapsed time for run: 0.16179871559143066',\n",
+       " 'done',\n",
+       " 'Initializing eigensolver data',\n",
+       " 'Computing 3 bands with 1e-07 tolerance',\n",
+       " 'Working in 3 dimensions.',\n",
+       " 'Grid size is 1 x 64 x 64.',\n",
+       " 'Solving for 3 bands at a time.',\n",
+       " 'Creating Maxwell data...',\n",
+       " 'Mesh size is 3.',\n",
+       " 'Lattice vectors:',\n",
+       " '     (1, 0, 0)',\n",
+       " '     (0, 2, 0)',\n",
+       " '     (0, 0, 2)',\n",
+       " 'Cell volume = 4',\n",
+       " 'Reciprocal lattice vectors (/ 2 pi):',\n",
+       " '     (1, -0, 0)',\n",
+       " '     (-0, 0.5, -0)',\n",
+       " '     (0, -0, 0.5)',\n",
+       " 'Geometric objects:',\n",
+       " '     block, center = (0,0,0.5625)',\n",
+       " '          size (1e+20,1e+20,0.875)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " '     block, center = (0,0,0)',\n",
+       " '          size (1e+20,0.3,0.25)',\n",
+       " '          axes (1,0,0), (0,1,0), (0,0,1)',\n",
+       " 'Geometric object tree has depth 2 and 8 object nodes (vs. 2 actual objects)',\n",
+       " 'Initializing epsilon function...',\n",
+       " 'Allocating fields...',\n",
+       " 'Solving for band polarization: .',\n",
+       " 'Initializing fields to random numbers...',\n",
+       " '1 k-points',\n",
+       " '  Vector3<0.8358057624328533, 0.0, 0.0>',\n",
+       " 'elapsed time for initialization: 0.009448528289794922',\n",
+       " 'solve_kpoint (0.835806,0,0):',\n",
+       " 'Solving for bands 1 to 3...',\n",
+       " 'Finished solving for bands 1 to 3 after 30 iterations.',\n",
+       " 'freqs:, 1, 0.835806, 0, 0, 0.835806, 0.589095, 0.592731, 0.645161',\n",
+       " 'elapsed time for k point: 0.09432482719421387',\n",
+       " 'Outputting fields to flux.v.k01.b03.x.h5...',\n",
+       " 'total elapsed time for run: 0.2164139747619629',\n",
+       " 'done',\n",
+       " 'kvals:, 0.6451612903225806, 1, 3, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0358450172484495, 0.9776224749130994, 0.8358057624328533',\n",
+       " '']"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_text = output.stdout\n",
+    "output_text.split('\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the modes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/3-MEEP_simulation.ipynb b/3-MEEP_simulation.ipynb
new file mode 100644
index 0000000..31bafbd
--- /dev/null
+++ b/3-MEEP_simulation.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MEEP simulations\n",
+    "\n",
+    "We will look again at a waveguide, but in the time-domain. This will introduce concepts important when doing time-domain simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import meep as mp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import Video"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simple geometry as before :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resolution = 50 # pixels/μm\n",
+    "cell_size = mp.Vector3(14,14)\n",
+    "\n",
+    "w = 1.0 # width of waveguide\n",
+    "\n",
+    "geometry = [mp.Block(center=mp.Vector3(),\n",
+    "                     size=mp.Vector3(mp.inf,w,mp.inf),\n",
+    "                     material=mp.Medium(epsilon=12))]\n",
+    "\n",
+    "w = 1.0 # width of waveguide\n",
+    "\n",
+    "geometry = [mp.Block(center=mp.Vector3(),\n",
+    "                     size=mp.Vector3(mp.inf,w,mp.inf),\n",
+    "                     material=mp.Medium(epsilon=12))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important difference from MPB is that we would like to avoid periodic boundary conditions. To do so, we can introduce perfectly-matched layers around the simulation region :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pml_layers = [mp.PML(thickness=2)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the time-domain, we now need to define a way to excite the electromagnetic fields in the system. The most reliable way to do so is with en eigensource (see https://meep.readthedocs.io/en/latest/Python_Tutorials/Eigenmode_Source/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n",
+    "kx = 0.4    # initial guess for wavevector in x-direction of eigenmode\n",
+    "bnum = 1    # band number of eigenmode\n",
+    "\n",
+    "kpoint = mp.Vector3(kx)\n",
+    "\n",
+    "sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n",
+    "                              center=mp.Vector3(),\n",
+    "                              size=mp.Vector3(y=3*w),\n",
+    "                              direction=mp.NO_DIRECTION,\n",
+    "                              eig_kpoint=kpoint,\n",
+    "                              eig_band=bnum,\n",
+    "                              eig_parity=mp.EVEN_Y+mp.ODD_Z,\n",
+    "                              eig_match_freq=True)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the simulation object is similar to ModeSolver :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = mp.Simulation(cell_size=cell_size,\n",
+    "                    resolution=resolution,\n",
+    "                    boundary_layers=pml_layers,\n",
+    "                    sources=sources,\n",
+    "                    geometry=geometry,\n",
+    "                    symmetries=[mp.Mirror(mp.Y)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------\n",
+      "Initializing structure...\n",
+      "Halving computational cell along direction y\n",
+      "time for choose_chunkdivision = 0.000775099 s\n",
+      "Working in 2D dimensions.\n",
+      "Computational cell is 14 x 14 x 0 with resolution 50\n",
+      "     block, center = (0,0,0)\n",
+      "          size (1e+20,1,1e+20)\n",
+      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
+      "          dielectric constant epsilon diagonal = (12,12,12)\n",
+      "time for set_epsilon = 0.380896 s\n",
+      "-----------\n",
+      "MPB solved for omega_1(0.4,0,0) = 0.14249 after 9 iters\n",
+      "MPB solved for omega_1(0.426652,0,0) = 0.149974 after 7 iters\n",
+      "MPB solved for omega_1(0.426745,0,0) = 0.15 after 4 iters\n",
+      "MPB solved for omega_1(0.426745,0,0) = 0.15 after 1 iters\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plt.figure(dpi=100)\n",
+    "sim.plot2D(ax=f.gca())\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meep progress: 26.400000000000002/100.0 = 26.4% done in 4.0s, 11.2s to go\n",
+      "on time step 2640 (time=26.4), 0.00151524 s/step\n",
+      "Meep progress: 53.03/100.0 = 53.0% done in 8.0s, 7.1s to go\n",
+      "on time step 5303 (time=53.03), 0.00150235 s/step\n",
+      "Meep progress: 79.71000000000001/100.0 = 79.7% done in 12.0s, 3.1s to go\n",
+      "on time step 7971 (time=79.71), 0.00149946 s/step\n",
+      "run 0 finished at t = 100.0 (10000 timesteps)\n"
+     ]
+    },
+    {
+     "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": [
+    "sim.run(until=100)\n",
+    "sim.plot2D(output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10)),\n",
+    "           fields=mp.Ez,\n",
+    "           field_parameters={'alpha':0.9})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "on time step 10000 (time=100), 0.0522457 s/step\n",
+      "Meep progress: 12.019999999999996/383.3333435058594 = 3.1% done in 4.0s, 123.6s to go\n",
+      "on time step 12200 (time=122), 0.00181969 s/step\n",
+      "Meep progress: 34.0/383.3333435058594 = 8.9% done in 8.0s, 82.5s to go\n",
+      "on time step 14398 (time=143.98), 0.00182029 s/step\n",
+      "Meep progress: 55.75/383.3333435058594 = 14.5% done in 12.0s, 70.7s to go\n",
+      "on time step 16550 (time=165.5), 0.00185921 s/step\n",
+      "Meep progress: 77.5/383.3333435058594 = 20.2% done in 16.0s, 63.3s to go\n",
+      "on time step 18727 (time=187.27), 0.00183807 s/step\n",
+      "Meep progress: 99.25999999999999/383.3333435058594 = 25.9% done in 20.0s, 57.3s to go\n",
+      "on time step 20903 (time=209.03), 0.00183887 s/step\n",
+      "Meep progress: 121.00999999999999/383.3333435058594 = 31.6% done in 24.0s, 52.1s to go\n",
+      "on time step 23091 (time=230.91), 0.0018288 s/step\n",
+      "Meep progress: 142.88/383.3333435058594 = 37.3% done in 28.0s, 47.2s to go\n",
+      "on time step 25270 (time=252.7), 0.00183615 s/step\n",
+      "Meep progress: 164.62/383.3333435058594 = 42.9% done in 32.0s, 42.6s to go\n",
+      "on time step 27443 (time=274.43), 0.001841 s/step\n",
+      "Meep progress: 186.42000000000002/383.3333435058594 = 48.6% done in 36.0s, 38.1s to go\n",
+      "on time step 29621 (time=296.21), 0.001837 s/step\n",
+      "Meep progress: 208.01999999999998/383.3333435058594 = 54.3% done in 40.0s, 33.7s to go\n",
+      "on time step 31800 (time=318), 0.00184671 s/step\n",
+      "Meep progress: 229.79000000000002/383.3333435058594 = 59.9% done in 44.0s, 29.4s to go\n",
+      "on time step 33981 (time=339.81), 0.00183427 s/step\n",
+      "Meep progress: 251.70999999999998/383.3333435058594 = 65.7% done in 48.0s, 25.1s to go\n",
+      "on time step 36175 (time=361.75), 0.00182335 s/step\n",
+      "Meep progress: 273.6/383.3333435058594 = 71.4% done in 52.0s, 20.9s to go\n",
+      "Normalizing field data...\n",
+      "run 1 finished at t = 383.34000000000003 (38334 timesteps)\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = plt.figure(dpi=100)\n",
+    "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n",
+    "sim.run(mp.at_every(1,animate),until_after_sources=50)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating MP4...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<video src=\"media/oblique-source-normal.mp4\" controls  >\n",
+       "      Your browser does not support the <code>video</code> element.\n",
+       "    </video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Video object>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = 'media/oblique-source-normal.mp4'\n",
+    "animate.to_mp4(10,filename)\n",
+    "Video(filename)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/4-MEEP_S_parameters.ipynb b/4-MEEP_S_parameters.ipynb
new file mode 100644
index 0000000..0531737
--- /dev/null
+++ b/4-MEEP_S_parameters.ipynb
@@ -0,0 +1,32 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/5-complex_geometries.ipynb b/5-complex_geometries.ipynb
new file mode 100644
index 0000000..4e17387
--- /dev/null
+++ b/5-complex_geometries.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GDS geometries and MEEP\n",
+    "\n",
+    "Last week you were introduced to zeropdk, a way to create GDS masks. Today I will use zeropdk to define a complex geometry that we can then import into MEEP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pya\n",
+    "\n",
+    "layout = pya.Layout()\n",
+    "dbu = layout.dbu = 0.001\n",
+    "TOP = layout.create_cell(\"TOP\")\n",
+    "\n",
+    "origin = pya.DPoint(0, 0)\n",
+    "ex = pya.DVector(1, 0)\n",
+    "ey = pya.DVector(0, 1)\n",
+    "\n",
+    "MZI_Broadband_DC(\"test\").place_cell(TOP, origin)\n",
+    "print(\"Wrote to demo2_output.gds\")\n",
+    "layout.write(\"demo2_output.gds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = 50        # pixels/μm\n",
+    "d = 0.12        # branch separation\n",
+    "\n",
+    "gdsII_file = 'coupler.gds'\n",
+    "CELL_LAYER = 0\n",
+    "PORT1_LAYER = 1\n",
+    "PORT2_LAYER = 2\n",
+    "PORT3_LAYER = 3\n",
+    "PORT4_LAYER = 4\n",
+    "SOURCE_LAYER = 5\n",
+    "UPPER_BRANCH_LAYER = 31\n",
+    "LOWER_BRANCH_LAYER = 32\n",
+    "default_d = 0.3\n",
+    "\n",
+    "t_oxide = 1.0\n",
+    "t_Si = 0.22\n",
+    "t_air = 0.78\n",
+    "\n",
+    "dpml = 1\n",
+    "cell_thickness = dpml+t_oxide+t_Si+t_air+dpml\n",
+    "si_zmin = 0\n",
+    "\n",
+    "oxide = mp.Medium(epsilon=2.25)\n",
+    "silicon=mp.Medium(epsilon=12)\n",
+    "\n",
+    "lcen = 1.55\n",
+    "fcen = 1/lcen\n",
+    "df = 0.2*fcen\n",
+    "\n",
+    "# read cell size, volumes for source region and flux monitors,\n",
+    "# and coupler geometry from GDSII file\n",
+    "upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax)\n",
+    "lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax)\n",
+    "\n",
+    "cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax)\n",
+    "p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax)\n",
+    "p2 = mp.GDSII_vol(gdsII_file, PORT2_LAYER, si_zmin, si_zmax)\n",
+    "p3 = mp.GDSII_vol(gdsII_file, PORT3_LAYER, si_zmin, si_zmax)\n",
+    "p4 = mp.GDSII_vol(gdsII_file, PORT4_LAYER, si_zmin, si_zmax)\n",
+    "src_vol = mp.GDSII_vol(gdsII_file, SOURCE_LAYER, si_zmin, si_zmax)\n",
+    "\n",
+    "geometry = upper_branch+lower_branch\n",
+    "\n",
+    "sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df),\n",
+    "                              size=src_vol.size,\n",
+    "                              center=src_vol.center,\n",
+    "                              eig_band=1,\n",
+    "                              eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y+mp.ODD_Z,\n",
+    "                              eig_match_freq=True)]\n",
+    "\n",
+    "# Display simulation object\n",
+    "sim = mp.Simulation(resolution=res,\n",
+    "                    eps_averaging=False,\n",
+    "                    subpixel_maxeval=1,\n",
+    "                    subpixel_tol=1,\n",
+    "                    cell_size=cell.size,\n",
+    "                    boundary_layers=[mp.PML(dpml)],\n",
+    "                    sources=sources,\n",
+    "                    geometry=geometry)\n",
+    "\n",
+    "mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1))\n",
+    "mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2))\n",
+    "mode3 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p3))\n",
+    "mode4 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p4))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/6-shell_simulations.ipynb b/6-shell_simulations.ipynb
new file mode 100644
index 0000000..b4014c5
--- /dev/null
+++ b/6-shell_simulations.ipynb
@@ -0,0 +1,39 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can do everything we just did in a Python script run from the terminal :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMeep",
+   "language": "python",
+   "name": "pymeep"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..a1cf7a4
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,65 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'predefined'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-8d713ca1e58b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpya\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpredefined\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMZI_Broadband_DC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlayout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpya\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdbu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdbu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.001\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'predefined'"
+     ]
+    }
+   ],
+   "source": [
+    "import pya\n",
+    "\n",
+    "layout = pya.Layout()\n",
+    "dbu = layout.dbu = 0.001\n",
+    "TOP = layout.create_cell(\"TOP\")\n",
+    "\n",
+    "origin = pya.DPoint(0, 0)\n",
+    "ex = pya.DVector(1, 0)\n",
+    "ey = pya.DVector(0, 1)\n",
+    "\n",
+    "MZI_Broadband_DC(\"test\").place_cell(TOP, origin)\n",
+    "print(\"Wrote to demo2_output.gds\")\n",
+    "layout.write(\"demo2_output.gds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "layout",
+   "language": "python",
+   "name": "layout"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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