From cafb21a4be806c407bd236c6ba07f248f2091289 Mon Sep 17 00:00:00 2001
From: 11of12 <chandlerparkin@gmail.com>
Date: Thu, 19 Oct 2023 22:09:35 -0600
Subject: [PATCH] Added ray-optic model page

---
 book/_toc.yml                            |  1 +
 book/pages/ray_optic_model.ipynb         | 72 ++++++++++++++++++++++++
 book/pages/waveguides_mode_solvers.ipynb | 53 +++++++++++------
 3 files changed, 108 insertions(+), 18 deletions(-)
 create mode 100644 book/pages/ray_optic_model.ipynb

diff --git a/book/_toc.yml b/book/_toc.yml
index a2eac20..9ba5a1d 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -35,6 +35,7 @@ parts:
     - file: pages/waveguides
       sections:
       - file: pages/waveguides_tir
+      - file: pages/ray_optic_model
       - file: pages/waveguides_polarization
       - file: pages/waveguides_mode_solvers
       - file: pages/waveguides_modelling
diff --git a/book/pages/ray_optic_model.ipynb b/book/pages/ray_optic_model.ipynb
new file mode 100644
index 0000000..1c4c090
--- /dev/null
+++ b/book/pages/ray_optic_model.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Improving the Ray-optic Model\n",
+    "So far we have used a simplified model to describe the behavior of light in photonic components called the ray-optic model. This model is a great starting point for building an intuition of key concepts such as why a high index of refraction material surround by lower index of refraction materials would be necessary to achieve total internal reflection. However, this model is an abstraction built on the flawed assumption that the wavelength of light is much smaller than the size of the photonic components. \n",
+    "\n",
+    "This simplified model could lead to misconceptions about the behavior of light in optical compenents. Consider two challenges our simplified model presents in the context of a slab waveguide:\n",
+    "\n",
+    "One implicit assumption from our model is that light is fully contained within the core of the waveguide waveguide. While the electronic and magnetic fields are mostly contained within the core of the waveguide, portions of the field are partially contained within the cladding. These evanescent fields are crucial to the operation of certain optical components such as directional couplers or tapered fibers and will be discussed in later sections.\n",
+    "\n",
+    "Additionally, our current model implies that any angle greater than the critical angle would support the propagation of light within a waveguide, however, light does not propagate through a waveguide well except at discrete angles. These angles correspond to certain modes supported by the geometry of the waveguide and the frequency of the light being used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Supported Modes\n",
+    "The following animation depicts how light interacts with the waveguide at some anagle. The white beam represents the light which has been sent into the waveguide. The orthoganal blue and red lines represent the wavefronts of the light at intervals of 1/2 a wavelength. The blue represents peaks in the amplitude and the red represents troughs in the amplitude. Pay special attention to how these values are swapped upon reflections. Let the electric field be orthogonal to the white line and the wavefronts, such that we have TE polarized light, or in other words, the electric field is coming straight out of the page. It is also important to clarify that the entire animation represents only a point in time and that the reason the white beam is drawn incrementall is to help visualize the angle of the light and how it relates to the wavefronts.  \n",
+    "\n",
+    "With all this a backdrop, it is clear that there will be considerable destructive interference between the wavefronts and the reflected wavefronts. This is why light does not propagate well through a waveguide at most angles: most of the light will be lost due to destructive interferrence. \n",
+    "<div style=\"text-align: center;width: 400px;margin: auto\">\n",
+    "  <img src=\"https://github.com/BYUCamachoLab/Photonics-Bootcamp/blob/main/book/images/destructive_wg_angle.gif?raw=true\">\n",
+    "</div>\n",
+    "\n",
+    "Ideally, we would send the light through the waveguide in such a way that there is minimal destructive interference. Without changing the wavelength, we can simply choose a more appropriate angle and achieve this. The following animation depicts this. Notice how the wavefronts and the reflected wavefronts are more or less in phase. The \"mode\" represented in the waveguide emerges from the interference patterns of the wavefronts. The mode below is the \"fundamental mode.\" Higher order modes will still constructively interfere, but they are more complicated.\n",
+    "\n",
+    "<div style=\"text-align: center;width: 400px;margin: auto\">\n",
+    "\n",
+    "  <img src=\"https://github.com/BYUCamachoLab/Photonics-Bootcamp/blob/main/book/images/constructive_wg_angle.gif?raw=true\">\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now these animations were created in 2D, but obviously the waveguide is a 3D object. MIT has put together some 3D animations to help further visualize waveguide modes.\n",
+    "\n",
+    "https://s3.amazonaws.com/fip-1/Polarization/index.html\n",
+    "\n",
+    "https://s3.amazonaws.com/fip-0/Full-Explorer/index.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "pyrolab",
+   "language": "python",
+   "name": "pyrolab"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/pages/waveguides_mode_solvers.ipynb b/book/pages/waveguides_mode_solvers.ipynb
index 89aee7d..a1b9cd9 100644
--- a/book/pages/waveguides_mode_solvers.ipynb
+++ b/book/pages/waveguides_mode_solvers.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15,18 +14,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using MPI version 4.0, 1 processes\n",
+      "\u001b[32m2023-09-21 20:04:24.752\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.config\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLogLevel: INFO\u001b[0m\n"
+     ]
+    },
     {
      "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'numpy'",
+     "evalue": "No module named 'nlopt'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmeep\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mmp\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
+      "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmp\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgm\u001b[39;00m\n",
+      "File \u001b[0;32m~/.anaconda3/envs/pyrolab/lib/python3.11/site-packages/gdsfactory/simulation/modes/__init__.py:8\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m coupler, waveguide\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfind_coupling_vs_gap\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      5\u001b[0m     find_coupling_vs_gap,\n\u001b[1;32m      6\u001b[0m     plot_coupling_vs_gap,\n\u001b[1;32m      7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfind_mode_dispersion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m find_mode_dispersion\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfind_modes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     10\u001b[0m     find_modes_coupler,\n\u001b[1;32m     11\u001b[0m     find_modes_waveguide,\n\u001b[1;32m     12\u001b[0m )\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfind_neff_ng_dw_dh\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     14\u001b[0m     find_neff_ng_dw_dh,\n\u001b[1;32m     15\u001b[0m     plot_neff_ng_dw_dh,\n\u001b[1;32m     16\u001b[0m )\n",
+      "File \u001b[0;32m~/.anaconda3/envs/pyrolab/lib/python3.11/site-packages/gdsfactory/simulation/modes/find_mode_dispersion.py:7\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m partial\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mget_material\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_index\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfind_modes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m find_modes_waveguide\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Mode\n",
+      "File \u001b[0;32m~/.anaconda3/envs/pyrolab/lib/python3.11/site-packages/gdsfactory/simulation/gmeep/__init__.py:13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mget_sparameters_path\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_sparameters_data_meep\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mget_simulation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_simulation\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmeep_adjoint_optimization\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     14\u001b[0m     get_meep_adjoint_optimizer,\n\u001b[1;32m     15\u001b[0m     run_meep_adjoint_optimizer,\n\u001b[1;32m     16\u001b[0m )\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwrite_sparameters_grating\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     18\u001b[0m     write_sparameters_grating,\n\u001b[1;32m     19\u001b[0m     write_sparameters_grating_batch,\n\u001b[1;32m     20\u001b[0m     write_sparameters_grating_mpi,\n\u001b[1;32m     21\u001b[0m )\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgdsfactory\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwrite_sparameters_meep\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     23\u001b[0m     write_sparameters_meep,\n\u001b[1;32m     24\u001b[0m     write_sparameters_meep_1x1,\n\u001b[1;32m     25\u001b[0m     write_sparameters_meep_1x1_bend90,\n\u001b[1;32m     26\u001b[0m )\n",
+      "File \u001b[0;32m~/.anaconda3/envs/pyrolab/lib/python3.11/site-packages/gdsfactory/simulation/gmeep/meep_adjoint_optimization.py:6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LambdaType\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Dict, List, Optional, Tuple, Union\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnlopt\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Block, EigenModeSource, MaterialGrid, Simulation, Vector3, Volume\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nlopt'"
      ]
     }
    ],
@@ -38,7 +49,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -47,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +67,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m modes \u001b[39m=\u001b[39m gm\u001b[39m.\u001b[39mfind_modes_waveguide(\n\u001b[1;32m      2\u001b[0m     parity\u001b[39m=\u001b[39mmp\u001b[39m.\u001b[39mNO_PARITY,\n\u001b[1;32m      3\u001b[0m     wg_width\u001b[39m=\u001b[39m\u001b[39m0.75\u001b[39m,\n\u001b[1;32m      4\u001b[0m     ncore\u001b[39m=\u001b[39m\u001b[39m3.47\u001b[39m,\n\u001b[1;32m      5\u001b[0m     nclad\u001b[39m=\u001b[39m\u001b[39m1.44\u001b[39m,\n\u001b[1;32m      6\u001b[0m     wg_thickness\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m,\n\u001b[1;32m      7\u001b[0m     resolution\u001b[39m=\u001b[39m\u001b[39m128\u001b[39m,\n\u001b[1;32m      8\u001b[0m     sy\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m,\n\u001b[1;32m      9\u001b[0m     sz\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m,\n\u001b[1;32m     10\u001b[0m     nmodes\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m,\n\u001b[1;32m     11\u001b[0m )\n\u001b[1;32m     12\u001b[0m m1 \u001b[39m=\u001b[39m modes[\u001b[39m1\u001b[39m]\n\u001b[1;32m     13\u001b[0m m2 \u001b[39m=\u001b[39m modes[\u001b[39m2\u001b[39m]\n",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m modes \u001b[38;5;241m=\u001b[39m gm\u001b[38;5;241m.\u001b[39mfind_modes_waveguide(\n\u001b[1;32m      2\u001b[0m     parity\u001b[38;5;241m=\u001b[39mmp\u001b[38;5;241m.\u001b[39mNO_PARITY,\n\u001b[1;32m      3\u001b[0m     wg_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.75\u001b[39m,\n\u001b[1;32m      4\u001b[0m     ncore\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3.47\u001b[39m,\n\u001b[1;32m      5\u001b[0m     nclad\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.44\u001b[39m,\n\u001b[1;32m      6\u001b[0m     wg_thickness\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m,\n\u001b[1;32m      7\u001b[0m     resolution\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m,\n\u001b[1;32m      8\u001b[0m     sy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m      9\u001b[0m     sz\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m     10\u001b[0m     nmodes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m     11\u001b[0m )\n\u001b[1;32m     12\u001b[0m m1 \u001b[38;5;241m=\u001b[39m modes[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     13\u001b[0m m2 \u001b[38;5;241m=\u001b[39m modes[\u001b[38;5;241m2\u001b[39m]\n",
       "\u001b[0;31mNameError\u001b[0m: name 'gm' is not defined"
      ]
     }
@@ -81,7 +91,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -90,14 +99,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'm1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, (ax1, ax2) \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m2\u001b[39m, sharex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m----> 2\u001b[0m islice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(m1\u001b[38;5;241m.\u001b[39my)\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39maxes(ax1)\n\u001b[1;32m      5\u001b[0m m1\u001b[38;5;241m.\u001b[39mplot_eps(alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m, show \u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, colorbar \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'm1' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -125,7 +145,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -179,7 +198,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -194,9 +212,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "pyrolab",
    "language": "python",
-   "name": "python3"
+   "name": "pyrolab"
   },
   "language_info": {
    "codemirror_mode": {
@@ -208,9 +226,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.4"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"