Generate_theoretical_modes/Generate_modes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate theoretical modes\n",
    "\n",
    "See Supplementary Information Section 2.1\n",
    "\n",
    "Requires:\n",
    "* Numpy\n",
    "* Matplotlib\n",
    "* pyMMF (our custom made library to simulate multimode fiber modes available [here](https://github.com/wavefrontshaping/pyMMF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyMMF\n",
    "from functions import colorize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NA = 0.2\n",
    "radius = 25 # in microns\n",
    "areaSize = 2.4*radius # calculate the field on an area larger than the diameter of the fiber\n",
    "n_points_modes = 256 # resolution of the window\n",
    "n1 = 1.45 # index of refraction at r=0 (maximum)\n",
    "wl = 1.55 # wavelength in microns\n",
    "curvature = None\n",
    "k0 = 2.*np.pi/wl\n",
    "\n",
    "r_max = 3.2*radius\n",
    "npoints_search = 2**8\n",
    "dh = 2*radius/npoints_search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate a graded index fiber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:24:56,783 - pyMMF.core [DEBUG  ]  Debug mode ON.\n",
      "2021-03-01 21:24:56,799 - pyMMF.solv [INFO   ]  Found 5 radial mode(s) for m=0\n",
      "2021-03-01 21:24:56,799 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:24:56,872 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:56,873 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:56,873 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:24:56,942 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:56,942 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:56,943 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:24:57,020 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,021 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,021 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:24:57,093 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,093 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,094 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:24:57,154 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,155 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,155 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:24:57,214 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,214 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,215 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:24:57,280 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,281 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,281 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:24:57,346 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,346 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,347 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:24:57,410 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,410 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,411 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:24:57,477 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,477 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,478 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:24:57,536 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,536 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,537 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.82a\n",
      "2021-03-01 21:24:57,592 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,593 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,594 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.72a\n",
      "2021-03-01 21:24:57,654 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,655 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,655 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.64a\n",
      "2021-03-01 21:24:57,766 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,766 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,767 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.56a\n",
      "2021-03-01 21:24:57,803 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:24:57,868 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,869 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,870 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:24:57,937 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:57,938 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:57,938 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:24:58,002 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,003 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,003 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:24:58,063 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,063 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,064 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:24:58,126 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,127 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,127 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:24:58,191 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,192 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,192 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:24:58,253 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,254 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,254 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:24:58,318 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,318 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,318 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:24:58,390 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,390 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,391 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:24:58,452 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,453 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,453 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:24:58,520 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,520 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,521 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.82a\n",
      "2021-03-01 21:24:58,527 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:24:58,599 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,599 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,600 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:24:58,668 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,669 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,669 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:24:58,741 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,742 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,742 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:24:58,814 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,815 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,815 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:24:58,885 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,885 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,886 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:24:58,949 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:58,949 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:58,950 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:24:59,017 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,018 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,018 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:24:59,082 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,083 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,083 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:24:59,150 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,150 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,151 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:24:59,157 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 4\n",
      "2021-03-01 21:24:59,224 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,224 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,225 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:24:59,286 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,287 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,288 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:24:59,349 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,350 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,350 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:24:59,416 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,417 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,417 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:24:59,484 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,485 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,486 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:24:59,549 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,550 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,550 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:24:59,558 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 5\n",
      "2021-03-01 21:24:59,575 - pyMMF.solv [INFO   ]  Found 5 radial mode(s) for m=1\n",
      "2021-03-01 21:24:59,576 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:24:59,642 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,643 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,643 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:24:59,717 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,717 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,718 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:24:59,796 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,797 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,798 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:24:59,878 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,879 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,880 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:24:59,951 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:24:59,952 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:24:59,952 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:00,017 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,017 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,018 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:00,087 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,087 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,088 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:00,144 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,145 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,146 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:00,206 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,207 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,207 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:00,272 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,272 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,273 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:25:00,340 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,341 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,342 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.82a\n",
      "2021-03-01 21:25:00,414 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,414 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,415 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.72a\n",
      "2021-03-01 21:25:00,476 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,476 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,477 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.64a\n",
      "2021-03-01 21:25:00,532 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,533 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,533 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.56a\n",
      "2021-03-01 21:25:00,549 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:00,616 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,617 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,617 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:00,685 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,685 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:25:00,686 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:00,752 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,752 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,753 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:00,815 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,816 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,816 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:00,878 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,878 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,879 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:00,945 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:00,946 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:00,947 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:01,008 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,008 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,009 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:01,065 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,066 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,066 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:01,123 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,123 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,124 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:01,186 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,187 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,188 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:25:01,203 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:25:01,273 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,273 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,274 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:01,338 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,338 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,339 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:01,401 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,402 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,402 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:01,462 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,462 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,463 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:01,521 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,522 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,522 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:01,582 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,583 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,583 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:01,651 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,651 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,652 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:01,668 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 4\n",
      "2021-03-01 21:25:01,734 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,734 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,737 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:01,806 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,807 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,807 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:01,822 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 5\n",
      "2021-03-01 21:25:01,851 - pyMMF.solv [INFO   ]  Found 4 radial mode(s) for m=2\n",
      "2021-03-01 21:25:01,852 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:01,915 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,915 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,916 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:01,980 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:01,981 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:01,982 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:02,043 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,043 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,044 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:02,106 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,106 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,107 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:02,168 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,168 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,169 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:02,234 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,234 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,235 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:02,295 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,295 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,296 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:02,367 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,368 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,369 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:02,436 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,437 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,437 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:02,494 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,495 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,495 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:25:02,561 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:25:02,561 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,562 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.82a\n",
      "2021-03-01 21:25:02,619 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,620 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,620 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.72a\n",
      "2021-03-01 21:25:02,680 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,680 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,681 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.64a\n",
      "2021-03-01 21:25:02,697 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:02,761 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,762 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,762 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:02,826 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,827 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,827 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:02,890 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,891 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,891 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:02,955 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:02,955 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:02,956 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:03,016 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,016 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,017 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:03,092 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,092 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,093 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:03,110 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:25:03,214 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,215 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,215 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:03,276 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,276 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,277 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:03,336 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,337 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,337 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:03,399 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,399 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,400 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:03,459 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,460 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,460 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:03,522 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,523 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,523 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:03,538 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 4\n",
      "2021-03-01 21:25:03,566 - pyMMF.solv [INFO   ]  Found 4 radial mode(s) for m=3\n",
      "2021-03-01 21:25:03,566 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:03,636 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,636 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,637 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:03,710 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,711 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,712 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:03,778 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,778 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,779 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:03,840 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,840 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,841 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:03,905 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,906 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,906 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:03,968 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:03,968 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:03,969 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:04,031 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,031 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,032 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:04,094 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,095 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,095 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:04,156 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,157 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,157 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:04,216 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,217 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,217 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.91a\n",
      "2021-03-01 21:25:04,275 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,275 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,276 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.82a\n",
      "2021-03-01 21:25:04,334 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,335 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,335 - pyMMF.solv [WARNING]  Retrying by changing r_max to 1.72a\n",
      "2021-03-01 21:25:04,353 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:04,421 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,422 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,422 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:25:04,489 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,489 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,490 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:04,554 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,555 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,555 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:04,616 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,616 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,617 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:04,678 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,679 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,679 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:04,738 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,739 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,739 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:04,805 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,805 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,806 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:04,868 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,868 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,869 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:04,886 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:25:04,960 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:04,961 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:04,961 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:05,027 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,028 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,028 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:05,095 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,096 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,096 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:05,112 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 4\n",
      "2021-03-01 21:25:05,142 - pyMMF.solv [INFO   ]  Found 3 radial mode(s) for m=4\n",
      "2021-03-01 21:25:05,143 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:05,208 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,208 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,209 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:05,276 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,277 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,277 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:05,341 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,342 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,342 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:05,402 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,403 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,403 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:05,461 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,462 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,462 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:05,523 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,523 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,524 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:05,591 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,591 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,592 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:05,651 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,652 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,652 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:05,716 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,716 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,717 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:05,732 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:05,798 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,799 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,799 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:05,871 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,872 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,872 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:05,944 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:05,945 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:05,945 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:06,007 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,008 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,008 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:06,070 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,070 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,071 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:06,143 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,144 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,144 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:06,161 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:25:06,191 - pyMMF.solv [INFO   ]  Found 3 radial mode(s) for m=5\n",
      "2021-03-01 21:25:06,192 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:06,258 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,259 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,260 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:06,367 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,367 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,367 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-01 21:25:06,429 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,429 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,429 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:06,490 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,491 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,491 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:06,554 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,555 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,555 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:06,618 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,618 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,619 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:06,691 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,691 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,692 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:06,751 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,752 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,761 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.12a\n",
      "2021-03-01 21:25:06,817 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,817 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,818 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.01a\n",
      "2021-03-01 21:25:06,832 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:06,898 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,899 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,899 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:06,965 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:06,965 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:06,966 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:07,032 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,032 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,033 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:07,096 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,097 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,097 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:07,113 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 3\n",
      "2021-03-01 21:25:07,142 - pyMMF.solv [INFO   ]  Found 2 radial mode(s) for m=6\n",
      "2021-03-01 21:25:07,143 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:07,210 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,211 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,211 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:07,281 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,282 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,282 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:07,351 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,351 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,352 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:07,422 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,423 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,423 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:07,486 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,487 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,487 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:07,549 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,550 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,550 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.35a\n",
      "2021-03-01 21:25:07,611 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,611 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,612 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.23a\n",
      "2021-03-01 21:25:07,629 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:07,660 - pyMMF.solv [INFO   ]  Found 2 radial mode(s) for m=7\n",
      "2021-03-01 21:25:07,661 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:07,728 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,729 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,730 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:07,791 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,792 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,792 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:07,858 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,858 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,859 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.74a\n",
      "2021-03-01 21:25:07,921 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,922 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,922 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.60a\n",
      "2021-03-01 21:25:07,988 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:07,988 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:07,989 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.47a\n",
      "2021-03-01 21:25:08,005 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 2\n",
      "2021-03-01 21:25:08,035 - pyMMF.solv [INFO   ]  Found 1 radial mode(s) for m=8\n",
      "2021-03-01 21:25:08,036 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:08,108 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:08,109 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:08,109 - pyMMF.solv [WARNING]  Retrying by changing r_max to 3.03a\n",
      "2021-03-01 21:25:08,177 - pyMMF.solv [WARNING]  maximum recursion depth exceeded while calling a Python object\n",
      "2021-03-01 21:25:08,178 - pyMMF.solv [WARNING]  Boundary condition could not be met.\n",
      "2021-03-01 21:25:08,178 - pyMMF.solv [WARNING]  Retrying by changing r_max to 2.88a\n",
      "2021-03-01 21:25:08,208 - pyMMF.solv [INFO   ]  Found 1 radial mode(s) for m=9\n",
      "2021-03-01 21:25:08,210 - pyMMF.solv [INFO   ]  Searching propagation constant for |l| = 1\n",
      "2021-03-01 21:25:08,239 - pyMMF.solv [INFO   ]  Found 0 radial mode(s) for m=10\n",
      "2021-03-01 21:25:08,239 - pyMMF.core [DEBUG  ]  Mode data stored in memory.\n"
     ]
    }
   ],
   "source": [
    "def get_modes(npoints):\n",
    "    profile = pyMMF.IndexProfile(npoints = npoints, areaSize = areaSize)\n",
    "    profile.initParabolicGRIN(n1=n1,a=radius,NA=NA)\n",
    "\n",
    "    solver = pyMMF.propagationModeSolver()\n",
    "    solver.setIndexProfile(profile)\n",
    "    solver.setWL(wl)\n",
    "    modes = solver.solve(mode='radial',\n",
    "                        curvature = None,\n",
    "                        r_max = r_max, # max radius to calculate (and first try for large radial boundary condition)\n",
    "                        dh = dh, # radial resolution during the computation\n",
    "                        min_radius_bc = 1.5, # min large radial boundary condition\n",
    "                        change_bc_radius_step = 0.95, #change of the large radial boundary condition if fails \n",
    "                        N_beta_coarse = 1000, # number of steps of the initial coarse scan\n",
    "                        degenerate_mode = 'exp',\n",
    "                        )\n",
    "    return modes\n",
    "\n",
    "modes = get_modes(n_points_modes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show some modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mJKJTO5GE3IS9uxsJGohAfH8O3LRdgKBxfY2H+hZYPCBUcrGeci2D111jcpJqWk0SK5KOE1OSVDtmkVT2Z3KxNP/VzFX+OxA0PD2dNCkjJkI4a9dY1kHUccw7EFQeWYJEeyEm0JGckjQ1TrYHqw9ewpSNWjNpvkqCSfJp2ynCZrTnBCXEjL93dycSxvYEQUci8ro4Ua1gVMbErXnoLqnlJi5CBk3uUwLdyFnTEc8FrGpmSCFPEDPlV6qmq0XC3KJpUQ9BHeasJKW68HYiMeM617bICGnGBx3PdZq3EO31Jrc0nUvRQ2YXNWtzF9bjArqR10nMAIyEtDQmWSaqtjw+5vd5CSpRSsz7+zk57+/nbXEzdiASGebthEBEY4Q3aeIWEFRrZ6zG2N9ab+82LDST0/P0aUHu6Vjjp+ZMlkl5acqmTFgvIRkBw+Pjicx8XSPs0xPCUDcxks7ycceBEXmyRAiMkKRpyWjGDiQNd3en67VIyonKzzeQ0qApOdKOZTV1oj44j1vwttFK3GZytqr/HEoG24JG6IkoC8GmkHhnUtOwHmJywsX1x0fQ4+O4TWy/JG5cpFaezKNOLoLOZGBR2SCIGe7uQFFjRnI9PQH392ffMxKTE1SDRlCpEeW+4V4QI+pYlm3L+9FDIZQ+ELxtxHM39zl7dcRCq1+RrdfrZ8r90se0iCkIB0bGcV1bFLKORNWCQ+pFzoNAJExW4ubs/f1pie1wMkqiamMHjAQNd3dzjc7N2LjtMG973XNpMS3lcvVSWN005xroeZOyJo8mgJaPyTWnRUxOyhcv0sRUiKr6oezhgDD92sJkTlL6mhohOSm15eHhPAb39+nBi+awHL9xcEUwyOFjruE/9sammnNJZ3hJSK05XoelRWv8y5R25OSM69o+Qc7AtScj6GSONV6jQczRrNXI+fAwXdcCUDlicihm7kRmpOaMY+3QnnuRvVw/Wvq5abTWW1aaI956veVU31JZH30jjaCcpBoxB8KFx0fQixdTUmrrnKTcPxWm7SwBYrwoOmtPGQi6v0fgJm0k5ePjaT0S8+Fheo1xPCwzmrU9nhfNYX6Mj20qKSHVBL8njvK8XIms5epcEk3kbI2GeYM9uQitNdjJ9mXQJ7A3SIZfXnYilBpBNc2paEcSRAxxn1yPZOSk5sQP5/zbkaD88gZiTvJmeRSWa8hISm7aRmJq121BZiXxedK4XxB1fOANx4IwcyfHtSat2yuOe4JJuWO5spocbupzAvknUY/IWgo9o7gTWL6lpTE1M1ZqRb48f37WooyYk/WhHh7VHQNEDrN2kgs7kJO4KfvixZSg2oMmF3iaDSwjqdCg6pgz4vUU7qXNXinbvYOXXci5VNRrMaR8S+04L6dpTCsYxAnKlhBJ+fz5SNLw4gUobvNF+qBCe85MTmBKDp5sIP1Mbs5KH5eTEoD5ndxUJtLT0+mYpkG1sbWyiPhxB3oQw1PH0nK/+ZcQdgsrCMQipKSRUvM5pbZkpIwL8e0UQS1yKj5nkpwPD1OtyYmp1Kv+cRIn4uPjPEI8lFFfDNDq6oQeNe1BqSyaIdQDJQ66r8IKramQU82dtQg5bIdIzNTCNausY1gmWUW8T3zMuFkr/Uxpzoo6VXM2KO+LCh9zko0U2356wvjKmKXh+Th30p4erCG7m/qcvS8uZceXnNfWCUFGALMkd4uYCZIS057j8tprM4JONKti2vKoLYl+jmMmplFImzaJBH32bB4JTvixM2JyM/bxcerrxv2MbJN3PKcN1N8yYPJw8tTkDSzWYjOfs+ZCWu146/zqQbW0qNwXRJpcbn5TCwppWjISU/5KckrtKU1bSSZtjlOYtOHFC1DUnPLBYgV/NELK1844GQ2CEh9rS3ta2wbkA71GVvZgynJUk3MJYtae7643Fejh61LYcxozRUolQjsjZFwMM1czbfH4WOZzJkxay4ydD7QS9JHtaPsjMeNvHNOU9rRIWUFWuX9JWeyJ7gEhq+OtF9NrMMyBzUVo+boVtbWmUjRzVhJT055WcMhK5ZsMGCMHT9kbNCfxPubq4vVpGlO+2RIXTkw+Xny7gIBeUuTKLSWLvUnbnZya3d+r0z3qGc/XAkCWBpXbKbNW+ptawoFF0OfPEV577eRzvvZa3rStJacxbaK+fiYJKZcXL5IEHZMgZHApJhtI85YTUwSGvPe+xjUqhSbfvbXpop/G7N3ppnpiYCdXDxNS9QNeCYKOWT1Cc0ZSziK1Ukt+4hNnYg77ZpHbRnKGgZykkJMsUiracvLq2eOjSVDi2pNpzJj1E4ND41gnTNbx3jVEbXvL4pImbjE517C3U220tm+ZtLzeKAAmMTVz9oklpQtykjRtFe3JNea4ziO3mt9ZQs5Ba5J8+yRqsvhA4mNgaUyelxv7Erc1S0LRnCNJY5CIkS0Aasoe3yqRg1h2CbkqOae0jUV9TtkR7+As6S+cGpv7VLOPP/N1j+a0AkJWhpA2fRKJqWnO58/nyQhPT/Pv2wK2KcpzacW5E+FNaUoZWOKRYKlB+VglCDoJDJHxCpnYVyIHpXK1VsAnh2Jy5jpdS6xNBkOS1PI/43bUMk/GJy6dgaGZJmWmbCQt155qUIhr6xJyPj2NSe0ktC5Z50aNqy3x+u7v56+0pTSnHFsrSttgwtZiqdZK672Y9L2mp5kWffSUTwWFDFLO/E4r8V2bMpGalGvYHmat7G9J4EeSUqb9xes2zOZIUunHqx+d9t6jTPm9aMBarEJOOUg1g8YdcL7djJT25Pu4n8S1piCq5Xcm5z5j8vtAxiCJ2YOciQBSGMqTRmhuwnJSxu2oPaOZrJGSaU9pgk/uY2etKWVu1l4D1iD+KuSUF8EDD/J47qJbwuRjoGF2IEx/47qiNdU0Ps3/zE2tKEQl8atmC/Ui56SoYv5aqX48gUFc4+wbR+IBZn4ZXzNrDb8zZKZULBnwEElTItb5a2jkxd/ntMpa5Vs0bJE/LASURytnBJZa0woKGT7nxNS1zFwtoqttp8iZ8jmD/lK2GvgZSBnu708pfhYxlb6M2lMJ/My+IiHHfyDj5K8D4/gP29o95nJSSprUubm2UvX10KyLv8/ZUtZCb5NCvUGp4EWCoNy0C0+nL+iRFORcml+KvA3knL2ULcu8eDGJyJL1WhlrXwaB1ICQYyxHYqJcuNf0K70KoEefdh0Q8mjXLBzBoJRGHfc5lvETmZr/mYrkWmRNTc/EevgDYnJRwzdqn57m35tVSDmas6l+KdZB7jWz5KKMMyU0pHpfGnzSNfzGFqyWW7sWakzs5D7ph/J1abZpgZCUmeshao4sCXJO/vogfjXPisqK/sHTR0bI2f/DWES01gvuUY+ycJZd0krLYbHc2hL0HICSAZ99FCvY/7epBjG0RQZFFLLOIrqSxNa0DCdLymzkqXJ3589Sguh0LiPn6GOyesnoj9qevCaNlN7xJPFX9fEeETW/bliKUUY61onC+nahOUufYK1krjaXLU2QIessi0cKuuanWf4bJ0vGrJ19ljISM9eGtVj9T5mtIUyDbBlt2cWVUVAqP1srGQC4yxfxYwkTIIKMdVkuV59Z1hIaS6iUp39QBHQyfRA1okVUFkhyEcTSzqJMsIhVQMig1G9d78zcl2OYI6qxP3n/lLIclvx46/NAThG2Yjd/O790feaDQ2pCq0zuWDSJgVFAKSjTB44l+2e8kpiRhEP7o685zG+OfyvPtKn136I1/eRTNCTGpGpM+XEln7anEtizzG6SIeQ95oXn/InQpFBSRvsN59fNLDNvqWU2Dok2LWJmScvrs9rRxtE7rrnoaxBzoAaWDkr2fEBY6GrWRpSo9j2HsmeIAhzXEzC1ND+WMus04TYIodUSRJls3al+s/1BqSt5D3NtXyhqXasSFJGztgMtBAwN7a7Z5iWJXMD+rnGpuq16e8hVzfkl5xSRszZjo2WAPOHs3oNstjlMQVBcF+dk+8SyYPj2rHyc6kjti+tGrimJMvw8Gs4hts8DAqYm5dAn3vfZdSvlrbo5et3TlCXgkauU7KaClBY2nUopwZombQCgfitV9sMoA1nG+o3Cyib7xw8si/382OybsN6FtT8RyMx56v93esrzutl4xPEl0afZmHnGNV6DUWYNfy/Zh5WwyVspSyB3w1I3+1RBgpApwVKEUf3yOVvIWHeR5O7uHLCJb5vw3NnYF5ZowLOBah8GWvlZXYkxKRpTfg3aofSZq2CNPiwSENoCnsFK6kNLWFKawNCGUqgDJ4r8vqv2YnNimbxzyX+1+pUyVNie2tfCB0uQY2iMqWbuF91Dfnuc5faMKs25llmRQ+xHaV+s/meviwudtk9b4rd3LEKWEjQmscc+Pyn/RWK1Fb/1U7tY/beWoS8U+8R/teF13Juecpe633uQ8Spypi4odVwr3zIApQQj8Tt+OzXuJ5bbqWlGvp4gY1KraS81WwTin/zgc43sr9/HBANplkvNqf3NvGyf/7luitCybn4OCVNejlliPMe4AL+fRG5SlspTquwoIwV1lsp/DptmCK094Kmy5pNZ86XiukZMS2PFl5clKawPZ4kPZs2+IkB0zq2NmlP2O/qYjrb44uqjICs3t1VzNzee0Amx1L2X7WjbS7efwy7f54yDVG1+8nKa4KbqswTd61sxrTPRQFIbaWSMxBj+YXrybqhGzKg1h4CQNGvHr62LNscvHPDPkPBta1E0v7xG71ip5u2wL3XvtfIeWMTLydqW2CU5c4PkHcSScrObYwmRpS2lkN6dv/M6+29M7ds88pWw+BfwMquGt60logOj5pp8G4hrxkjESEpriWUSZHUFmBxjycd/CaL0kqkixeCs08Lm5PT4iD3qmh0XGnKiYaX2VDQkCWJa/uTMlGWEnPwN3+Pj/D8yLV/SIGd8yACY9pGTM5Lt2bM8OSVBOVHlNaYI6dCck3c4NRI773EpUpZZi5nbo49dyVljhsr9HoJp51n7Zscts1X6blYQg8QUggySaEEU4yt2nJjqd3msIE9cpOacFKV5n4QZG549Aw0kDQ8Pp/4UEDSatBMLQY5Fipgpn9Qwe2uFviRYQ+LXqm9pM3iVgFDJRZSYHy3RsXFwNS05bE/KaOasEgCSf1tAnJRxPZqtxic/Zh/i4n1IkFM1yxPkpGfPTho0kpQvKaIOdZDQpKrvmSIpMLdm5DXIMoX3GMO5a5vJPbBrszYH93kK4ZLnDuWtKZXJVIH0MzW/ciDj5LMjz56lX6bm/eXt8I9x5T5TIvun+ZmSlJGYObJ6g0bSNxdjaab75e5zJhjUQ7O1BiRbsfm/jMW6NsmXTGjNlN85/q2dw+fkBI3EnJDUIhhvU7ZjfUNIXluGnIFrz5deOhORr6fM3hQpc/5n7CP/levathMtspR7gLeY1iXnbq45I9Yg5rmxjN8JnNethRNUarK4HiOuwowlSSw+j4mpOT3Tmvf3dV9818xaqSFfegl46aWzL8q0KEkNmpuCsbRmiqAZf5OjNDmACs5ZVRYTWDR9b49zR2qfBFmjKTnLFrL8Tk1rRpKyecvUl+x4n2Z+LvdltS/2Bfav1PyaNM0pTVuFnDT8ZrVnzqR1RG61rKCx/wmUyBWJ355YUsa7/wVgxBLzVk31RD/SUY6As8+padC706R/eHqaBkG49ozkjMTUpkiUT4CMwiq03ujLRrNY1Kn+zYIMVFnkfHg4a0xp3rLtmfZMac3YLrFECE7KYZzN19By97ISvWWxRCOXYrFPY840QUUdEql6vFMwYz2ceMLPnDzNLYJGoYuvbmkEjcSUBE34mBOtqZmkhuZ0+5yJoBBxf1NqTisolPE9Z0n/cexYHyd/TqT5n8OvNzpfM3deSjBNvnN1lPJgNZ+z5OK1cj0uPD7l9IM0moaqeagQdPy7A20ahRPU0pZKHyxCjSl3MlnBSc7Zv1Ir5AwaQXMkzUVqNXLy/mn3LqEZPZqqhrwlbtpuo7U5lD6xSuptnnYhmpt/w/7R5JWCwUkJjCYt7oaXnuP2/b3+D2TWttYHzZ/lKXfyf1JSdcq6eFbPw8OZ7FxzxnWpPTlBuSnsMW05QSVRtevm/bfuoeuO6+gxdbeUjEvsJlrrQfenlQgEzbbjPvnLzVlOjij8nIyaZvNoTUEmNQe3hJxCc07ya625zZx565nnTGlQTyCowr+sMWv3iIsi52qwtCfXohpBIzHv7s7mbCRNUP48lrcnI7NR2OM/jhkZRfz7uNz3AWX+qdqa87RImorYPjzMHiqzVD6pLeV1NwR5ZrevW01taDV/F51K2RP43KGqLbUAES8jfscopEVQYPprTXVokVnNPzRMWlI0M/H6OUE0TSfmPE2Sct/04eGclshzbe/PCfCcmObHxCQ08mrHLwS8tzWcqSLnZQ3RCcV9zgjPRCsBU/MyEjT+CrLPosGSRDKyqv2jtPbmihZpluSUdQsNOvqdym+Ifqlh1qqvj1nEXEhrtmBJpVNTbxezNnVRnguWZWoGqeicGu0Zy8VfSUzpf97fn4/JangdOdNTJslHchpBpnEMZN1Ec3JaUyvaPKi18Do1P9OK1qbGlv9CPMwWRKkMtsp9Dl3ImeqEp4OyzKLELDkvJUh3d9M/BwJ0Qlvna8S0tKbUmAlyqm1o9fNgk6FF5aKZspNAkEbQVDDIoTUXu7cZ1Mitday2L03k3IvvWfX0IjGtktKeXIDisbhfI2eKpDIwomlOnkMb50wNk5bn5M76a/md/JcTzSKqtu6N0koSaqRMaE0S2xLWPd6DXAJtHLnqaG31DZLElCTm65oGTdVr+YPxVbDHx/OvICf/4Jf67qdFTkb+SaKEFniyiGqR8p59hsWhMSfCqhG0EFuTcEkF1UTOFpt8S2Qjt3J7EJzZ9URiGqRUr18z66JQc0IqfzU/+Wt3+e2g1FQKI8r48TEtKpxaODHZeTNieuc3FS2Z2t6rLOXQ0udmzbnmhC83Fj1150yepEMfzV5GWOvtiTFgIUiqEvPx8bx+N/9C38S8laYs/0ym8DdJtBsJOmqsuzvw189m2jNHVHk81qMQM37hXovSzv4IKY61cR/MsYz3KXFMlsvVVYNUXZvMc3J4BL0FvN7S+l0Dl9KeQnOqPmgUrJQWZVpinPMbTOHJ+5qMlOHpCaSZsk/DZzLl62bCz5VTNbNv+1gmLiehWJ9oW+2BwrVzKvBjac4KremVCa3c0jLbWnc3n7OlI97BX8y0EQSVX4KflANsHzRj5k60SfzubCRqFO5hnYZXzsaPSvOPfgmTVpIzavJRo2uBIU1ja1FXti/7GUzNjJXjp1gfBEV7ZnxQSxZKZGQpM7lXvZsGhEqfMGuZIzMhkVHcFCRBLX+Lac9RK/JzZeBHy6U10gFHS4ObtaSk82la2yKsh5Ap/1LTlo4gUGk0dpWHucBS7ew+WrtaIMBr3qYCRAPGv+kD5nVaJOULJ6eVRC/nN1Mph0ZwSCWosUx8Y4XgVoqe+r8p/HdYt7Sm995r9yJ3bmuigbedWmxOzihSSxPQRfIMQcfPlgwEzT69uSaMJIz1SrJoX0vIkRPQExB4/wVJk+SURJUmsCzL6rBS9DzEnPW5Ar3lx5lOYp7boz+LkTN2sNWZb73IZuecEZQTc/T1SJm7i4SJxOQE4uYsnV/YHhPpU4SUSe6SmEbAakZQhVwzLZgyW6X5yrfjmDBijr6v0rcWQW4lQepcT72WjPd6UFCwnroAkDyYRs3ALW3CurW0dtkKCSbBGI00fOEf4sqRzyKkWAKgB654t6OGt0zqFFE10mrnaT5lDTEzWnMNK2sTuTUSh7tqziWfZD0Qn3L5gjQnqDBxIUzcLAYNOgZwuAbl+0I4a1upecUDYEJMw+ecEJOvWwRNbLvfLmHrFjHHvol9OawhI8CKsY4EFtOce0L1QDs16ESLadpTbisaNbVMXtKWAaDcLdL8vZIlFYGV9fFtrW25ntqXwR7I0w1raE4OPnjWuqcOJMp766q+iU4NCmLZRKJ/o5a16uaa0yClSvq4PmlQ8Tn5tkIi80XoHBGl2WoR0kHMGqL1IGapmdxDpkuwqubULoI30HKB1gB1GbgCDWr+pjSod3+qDQsp0uTMXc/+XBuyH1rfKrHUPU/J5CJEXFtzqn1w7ONPs5KBsMp1eboVaNDiOqOWjNo3pyU1UloE1chhkZStq0Eko6xaZ2o9tc+JeC9z97y0znhu6vw1TelVNKeXGLUEWsqsmNXr1aB8vUSbauupX6tPHDUE1fbVaknNbGXHV7t3nc8rNYmTMDTnIuS0zNelnjqldZf6vUUE5duFZJV5srO82RJSShgkHX1GuV1DRsvXze1j8JqqS8pTDRots/XIuSbWcMznjdoEHZ+oLRo1d47VhxRyZPKS0dCQYVgn5Zjahw7YG0GrsTU5S0jUery0P3UVnMkY/WMA86QAL0m1fezY7F/EGsg50Zb8mFcrJvxJTtLJGHfwMVvQU+a6PxS2JOdaTzhPO7Umt1rGGp4UQeV2qYbkhJV9UqZS+MODxDFZtjiw4zFjlf3V4+04vidZc2MLcu7Z7OghIOeCaZKO9XhJahybPL2lJk11D8b99xJPOebSig5t2fU+bIAufdvarL16pMexbTtVf0m0Nre/VDOmyNfZv7xqHORcCSUk1fZlgk3qXfTMc8aiyGi81ojrxr7lReIgZxmaBCU3bD01Y6qMhygtmrWmvQvA6g+JSwgIXfKT0woYjVFc9aRCLcvbKrw12S/cFWrDyXVVBHz2ik1kdWvN2etilg6rJ8nkqFc9dyBpsk5lqCfn5EzbUgzyYNaXIKsV+e01drUR9Vy52mNaWRSUz+JayNkLS0UJZ1MXk4PO4XSUKxUmV1mvWdpRU+5NLjbB1uS8JjQLlJirBBKEFuW79iWjHWO/POVzOEiYwB7eSgHab1J3k6ICSSK5KjifyX1H0+TK+YvAnMBawjkvzo5Z5dSkhQosRcwepmiLCb00Ds25J1QOd3cBG4iNljoy2AsBdgFDcxqfJl8WORFc+4kg2wvGfmuf55gLROP/jJRoLFPrFbQ7aXs4vyWwkztGYp91D9bC3mQSuBDN6dUMuadxzyDKZuZ16y1ZeS5SGyc5tosEtzLntPShO/bic1roMVi58iX19aqr+03f0US/59o8Wr3nfanFbojKsIlZqyF1w2rMtiVVfknde7jJHD3HZS/XlrumUvnZy3Xthpy9seQAaybSpWAtwVtzTPZCpt7YnJwB0xt5SYIesbZw1IzR2uN6iYSRcri1LG5OTi0quMbALBGd85zT47pqBL9HymNJuZ73bw1ZkG3URqt7YnNyRvCo3hoDUxrI8AhISTrg3q0F2T/v/SDxm6u7pM4e0NqWMrcHrQnsiJxbP6Us9JoykQKeC3iV1FtzLIde/UvVvcncobPMHuRxN+SM2JM/VZMe1lqPt86I1qjjEmPnrXMPBODYg7bk2B05U7mg1r415r5aytb2bylCL409ke6Sp712R07AdtAlPD7eHoS1BK39bUncuMSxqpnjrKlnC+ySnL18Euk7WPXtJWEBSAeiavJPU+d4HoBroeYe9PANN0vDdGCX5ATWdcp7t6MleNecK1GT9lZyTstDakmNv2Rkey/BHw27JWfEJaabtfiKLXmnpbDaqtViS6Fn5HjvU1gcuyfnljm0PbBk5HKpa68l6d7vBbBfLalh9+TMYSkz7RKwdaJGa/kWBLQHcvZO1IshZ8mNWNvJXzPIUpMet0RKXaqdNdrSsno82GtkVsPFkLPkRpTMlXqQEu5U5k9PRKGS6XEliQqEZYXTMw6lD1jvfq/7s+cAkMTFkFMilZRgoTUZoGautRcsoaoxPbcUztIc3dLza+Rir7hIcpYK6iUFNrZ+G6e0XC1q5mxbyl+KtuS4SHJyeG5yj6yZ3sJaYp5tMZ20tjD3CD5ZsnCJxASugJxrzQvWvEJWUl+vsnuE9cBcOqHg0sft4skZoQU6ZPRQO17rpy5541tNvr20EdHiP+b6WXNPLwVXQ05g/jSWT1FNy7ZO/pcIeW1CQqup6zH1S8ahxm+tmWax7pm1LSPnl46rIeeaN6X29bDaPrb60b392JrrWNPcvBaCXg05I7YIZKxtRuXa80wnLD1OW5iW10LKiN18VPqSsbYmyLW35XzsVu1dI65Oc5bgmoIH14pbvj83Tc7SgNBSUdM9CuBS/bqF5IFeuGlylmCpSKD3AdGTKN583KXmIvf4MNojDnI6sfUTvGf7WydAbD2Wl4IkOa/9CVf6DZ4lx6P0LYxUOa9J2tpmDWpyaK9ZDlPXltWc1zw4a75MvHQmEn+FzGsq93zzw1tWy1tOYeu3aJaCh1dus/ZaCcrRI2neqm8tv7JVkEveD/WUL03KuFU501Dkc177wHk1RoDvyVcaCd6DhigN2HjzY71v9+xhDJZEydgWJyHsRYjWgPWVg70GSXq9IrVk8El7u+eW5KkER7Q2gbWEptYi0fw569W2vb5wfivErEEVOa/dvF0bLZ9Pyb0F0/pplgN9UMOZJDmJ7Ft0EFTH2uPiDbisQbY13xG9JKTGIcWxvOYk6vZtnmuE922P1rHyzlu2zG+29uXQtnMkxzpBTMBp1vZ4gfhaIacVLIKUzu+lzk+VaZnfTCE3LaT5trdO1lZuZMm5dt7nJYMwJYiHqJcCi5AyQnyJ17YESueLNbg0Z8ouLunMreFas1uAskykW4OLmA5OuaO1B0EPHCZrHr2ICQAUQrK6+cEQuqjsA3NsKfwH8drg5oROTHVn+Tynk/WHFi2H9FkB3/eAPPCkzR1mah3c98PJnYiqJASvWj4I2o5e3wM6SLcMvDLu5QxHdfoebyzV7EHQA9eKJYkJNH59j4jO4fS073rgwG1hIGSLxdKc+D76Kkeq34EbQi4lr4f/novWHjhwYCMcr4wdOLBTHOQ8cGCnOMh54MBOcZDzwIGd4iDngQM7xUHOAwd2iv8P3meD4uWVF2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i_modes = [0,1,5,10,15,25,35]\n",
    "\n",
    "M0 = modes.getModeMatrix()\n",
    "\n",
    "for i in i_modes:\n",
    "    Mi = M0[...,i]\n",
    "    profile = Mi.reshape([n_points_modes]*2)\n",
    "    plt.figure(figsize = (4,4))\n",
    "    plt.imshow(colorize(profile,'white'))\n",
    "    plt.axis('off')\n",
    "    plt.title(f'Mode {i} (l={modes.l[i]}, m={modes.m[i]})')\n",
    "    # save figure\n",
    "    # plt.savefig(f'mode_{i}.svg')\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}